2 63 F Acute pancreatitis - - Treatment of pancreatitis

Seliac disease Surgery of

hydrocephaly Hypertension

In pure CIN, neurogenic pattern including high amplitude and polyphasic motor unit action potentials (MUAPs) with reduced recruitment are observed in needle electromyography [5]. However, patients are sometimes unable to perform motor units recruitment and nothing could be detected due to patients' poor effort. In these situations, the only observable sign in the needle EMG would be presence of denervation potentials (fib/psw) at the rest phase of needle exam.

Electrodiagnostic criteria of the present study were based on these principals and all of above-mentioned characteristics were applied to our patients diagnosed with CIN as well.

Some points should be highlighted regarding the EDX findings in CIN: *firstly,* if SNAPs were normal or mildly low amplitude associated with diminished CMAPs, other diagnosis rather than CIN would be investigated i.e. CIM (critical illness myopathy) or AMAN (motor axonal neuropathy). At this point, needle EMG differentiates these two entities from each other. *Secondly,* another fact to be kept in mind is that low amplitude SNAPs could be related to previous underlying disease such as diabetes or renal failure and subsequent diabetic or uremic neuropathy rather than CIN. *Thirdly,* in cases with mixed CIN and CIM, in spite of the fact that SNAPs are abnormal, myogenic pattern in needle EMG demonstrates muscle weakness may be more related to the superimposed myopathy.

#### **4.2. Phrenic nerve electrodiagnostic study**

One of the major reasons contributing to unsuccessful weaning trials from the ventilator in intubated patients is unilateral or bilateral phrenic neuropathy which could be idiopathic, due to autoimmune or post-infection etiologies such as Bell's palsy and secondary to iatrogenic causes after coronary artery bypass grafting or other thoracic surgeries [2].

Phrenic nerve could also be affected as an accompanying disorder observed with polyneuropathies such as GBS or CIN. To obtain phrenic nerve CMAP, recording electrode is secured a few centimeters proximal to the xyphoid process while the reference electrode being placed 16 cm lower along with the ribcage on both sides corresponding to the phrenic nerve excited. Stimulation electrode is placed 3 cm superior to the clavicle parallel to the cricoid cartilage [6]. The important issue is that normal amplitude for phrenic CMAP is 300- 500 µv which in fact could be associated with unclear responses due to the electrical noise existing in the ICU.

Neuromuscular Disorders in Critically-Ill Patients –

Approaches to Electrophysiologic Changes in Critical Illness Neuropathy and Myopathy 113

112 Advances in Clinical Neurophysiology

the end of this section.

needle exam.

well.

myopathy.

surgeries [2].

existing in the ICU.

**4.2. Phrenic nerve electrodiagnostic study**

Compound muscle action potentials (CMAPs) are profoundly reduced in amplitude or absent. Motor conduction velocities are normal or slightly reduced and distal latencies are normal or slightly prolonged, while sensory nerve action potentials (SNAPs) should be significantly diminished in amplitude or unobtainable. Phrenic nerve study also reveals absence or reduction of obtained CMAP from diaphragm muscle, which will be explained in

In pure CIN, neurogenic pattern including high amplitude and polyphasic motor unit action potentials (MUAPs) with reduced recruitment are observed in needle electromyography [5]. However, patients are sometimes unable to perform motor units recruitment and nothing could be detected due to patients' poor effort. In these situations, the only observable sign in the needle EMG would be presence of denervation potentials (fib/psw) at the rest phase of

Electrodiagnostic criteria of the present study were based on these principals and all of above-mentioned characteristics were applied to our patients diagnosed with CIN as

Some points should be highlighted regarding the EDX findings in CIN: *firstly,* if SNAPs were normal or mildly low amplitude associated with diminished CMAPs, other diagnosis rather than CIN would be investigated i.e. CIM (critical illness myopathy) or AMAN (motor axonal neuropathy). At this point, needle EMG differentiates these two entities from each other. *Secondly,* another fact to be kept in mind is that low amplitude SNAPs could be related to previous underlying disease such as diabetes or renal failure and subsequent diabetic or uremic neuropathy rather than CIN. *Thirdly,* in cases with mixed CIN and CIM, in spite of the fact that SNAPs are abnormal, myogenic pattern in needle EMG demonstrates muscle weakness may be more related to the superimposed

One of the major reasons contributing to unsuccessful weaning trials from the ventilator in intubated patients is unilateral or bilateral phrenic neuropathy which could be idiopathic, due to autoimmune or post-infection etiologies such as Bell's palsy and secondary to iatrogenic causes after coronary artery bypass grafting or other thoracic

Phrenic nerve could also be affected as an accompanying disorder observed with polyneuropathies such as GBS or CIN. To obtain phrenic nerve CMAP, recording electrode is secured a few centimeters proximal to the xyphoid process while the reference electrode being placed 16 cm lower along with the ribcage on both sides corresponding to the phrenic nerve excited. Stimulation electrode is placed 3 cm superior to the clavicle parallel to the cricoid cartilage [6]. The important issue is that normal amplitude for phrenic CMAP is 300- 500 µv which in fact could be associated with unclear responses due to the electrical noise


a Critical illness neuropathy bAcute tubular necrosis CCritical illness myopathy dIntracranial hemorrhage e Chronic obstructive pulmonary disease f combined critical illness neuropathy and myopathy (neuromyopathic syndrome) gLower motor neuron hUpper motor neuron I Acute motor axonal neuropathy J Acute motor and sensory axonal neuropathy

**Table 1.** Characteristics of ill patients with profound weakness, who were referred for electrodiagnosis

Needle EMG of the diaphragm muscle is performed in cases with phrenic nerve involvement accompanying CIN as well as CIM. For this purpose, needle is inserted between the two Anterior Axillary and Medial clavicular lines, through 7th or 8th intercostal spaces, exactly over the rib. Neurogenic or myogenic patterns with or without fib/psw could be detected in inspiration phase of respiration [6].

Neuromuscular Disorders in Critically-Ill Patients –

Approaches to Electrophysiologic Changes in Critical Illness Neuropathy and Myopathy 115

Therefore, the following three factors contributing to reduction in muscle membrane

Furthermore, released cytokines throughout sepsis could be associated with a catabolic state

Nerve conduction studies demonstrate diminished amplitudes of CMAPs with normal distal latencies and conduction velocities. In contrast, SNAPs are normal or mildly reduced (greater than 80% of lower limit of normal) [1]. Repetitive stimulation test (RST) is usually normal, but in some studies decreasing response after high rate and low rate RST have also been reported [8]. Electromyography frequently demonstrates prominent fibrillation potentials and positive sharp waves (fib/psw), which is rarely accompanied by myotoinc discharges. Short duration, small –amplitude and polyphasic MUAPs that recruit early are evident. In severe cases, it may be difficult to recruit and activate any MUAPs [1]. Of course, this process is reversed and small MUAPs will be appeared again during the recovery period.

Direct muscle stimulation technique is utilized for differentiation CIM from critical illness neuropathy. This method has been reported by Rich and colleagues for first time [7, 9]. In fact, direct muscle stimulation bypasses distal motor nerve and neuromuscular junction area. This technique is performed by placing a monopolar needle stimulating electrode as cathode in the distal third of muscle using 0.1 msec stimulus duration with gradually increasing current from 10 to 100 mA until a clear twitch is seen. A subdermal needle electrode as active recording electrode is placed 1-3 cm from the stimulation electrode. The stimulation intensity is increased until a maximal response or direct muscle action potential (dm CMAP) is obtained. Next, using the same recording montage, nerve to the muscle is stimulated in the usual manner to obtain a nerve- evoked compound muscle action potential (ne CMAP) [2]. As a result muscle membrane should retain its excitability, direct muscle stimulation CMAP (dm CMAP) should be near normal despite a low or absent nerve stimulation evoked CMAP (ne CMAP). In contrast, if the muscle membrane excitability is reduced, both the ne CMAP and dm CMAP should be very low [1]. Therefore, in CIM as well as normal people, the ne CMAP/ dm CMAP ratio is close to one (1:1), because both amplitudes is proportionally reduced or normal, respectively. In CIN or neuromuscular junction disorder, the ratio is much lower and approaches zero because of disproportionally lower ne CMAP compared with the dm CMAP. In conclusion, absent or low amplitude of dm CMAP with ne CMAP/ dm CMAP greater than 0.9 is demonstrated in majority of patients with CIM, While ne CMAP/ dm CMAP ratio

excitability are introduced [7]:

2. Reduced membrane resistance, 3. Decreased sodium currents

**6. Electrophysiologic features in CIM**

**6.1. Direct muscle stimulation technique** 

is 0.5 or less in patients with severe CIN [1].

1. Partial depolarization of the resting membrane potential,

in muscles and results in breakdown of structural proteins [1,7].

Phrenic nerve EDX was requested for 4 of 14 patients in the present study. In two patients diagnosed with CIN and mixed neuromyopathic syndrome, there was no phrenic nerve involvement. In one patient secondary to AMAN, phrenic neuropathy was detected as bilateral asymmetric low amplitude CMAPs associated with neurogenic pattern in the diaphragm muscle and in one patient secondary to CIM, phrenic nerve involvement was revealed as bilateral absent CMAPs along with fib/psw and myogenic pattern in diaphragm muscle.

It should however be borne in mind that phrenic nerve EDX is mostly helpful whenever bilateral normal or unilateral abnormal responses are detected. In other words, if responses would be absent bilaterally it is difficult to distinguish from technical reasons [2].

**Treatment:** There is no specific treatment for CIN and supportive measures should be taken to avoid multi organ failure occurrence.

#### **5. Critical Illness Myopathy (CIM)**

The most common muscular disorder leading to weakness in ICU is critical illness myopathy. CIM is also known as acute quadriplegic myopathy, thick filament myopathy and ICU myopathy [1,2].

CIM is mostly seen concomitant to the administration of intravenous steroids and NMBAs; this complication is frequent in patients with asthma, COPD and administration of highdose intravenous methyl-prednisolone [1].

Occurrence of one case of CIM and one case of mixed CIM/CIN in our patients' series with the history of hospitalization due to COPD exacerbation or corpulmonale confirms this fact (Table 1). This disorder however could also be seen in critically ill patients with sepsis or multiorgan failure without history of receiving corticosteroids or NMBAs. Prolonged mechanical ventilation per se even with absence of mentioned medications usage could cause CIM as well. In some cases, CIM is associated with increase in serum CPK levels (up to 10 times the upper limit of normal) [1]. High mortality rates with CIM; even a rate of 30% have been reported [4]. Of course, it should be noticed that the mortality cause has mostly been due to the sepsis or multiorgan failure rather than the myopathy itself. In present study, one case with pure CIM and one case with CIN were expired (Table 1). Survived and treated patients would acquire ambulation ability within the following 3-4 months [1].

From the histopathologic aspect, type II muscle fiber atrophy is observed more frequently than type I. In muscle biopsy, specific signs of myosin loss is often detected which solely could not explain the inexcitability of the muscle membrane that occurs with this myopathy. Therefore, the following three factors contributing to reduction in muscle membrane excitability are introduced [7]:


114 Advances in Clinical Neurophysiology

muscle.

be detected in inspiration phase of respiration [6].

to avoid multi organ failure occurrence.

and ICU myopathy [1,2].

**5. Critical Illness Myopathy (CIM)** 

dose intravenous methyl-prednisolone [1].

Needle EMG of the diaphragm muscle is performed in cases with phrenic nerve involvement accompanying CIN as well as CIM. For this purpose, needle is inserted between the two Anterior Axillary and Medial clavicular lines, through 7th or 8th intercostal spaces, exactly over the rib. Neurogenic or myogenic patterns with or without fib/psw could

Phrenic nerve EDX was requested for 4 of 14 patients in the present study. In two patients diagnosed with CIN and mixed neuromyopathic syndrome, there was no phrenic nerve involvement. In one patient secondary to AMAN, phrenic neuropathy was detected as bilateral asymmetric low amplitude CMAPs associated with neurogenic pattern in the diaphragm muscle and in one patient secondary to CIM, phrenic nerve involvement was revealed as bilateral absent CMAPs along with fib/psw and myogenic pattern in diaphragm

It should however be borne in mind that phrenic nerve EDX is mostly helpful whenever bilateral normal or unilateral abnormal responses are detected. In other words, if responses

**Treatment:** There is no specific treatment for CIN and supportive measures should be taken

The most common muscular disorder leading to weakness in ICU is critical illness myopathy. CIM is also known as acute quadriplegic myopathy, thick filament myopathy

CIM is mostly seen concomitant to the administration of intravenous steroids and NMBAs; this complication is frequent in patients with asthma, COPD and administration of high-

Occurrence of one case of CIM and one case of mixed CIM/CIN in our patients' series with the history of hospitalization due to COPD exacerbation or corpulmonale confirms this fact (Table 1). This disorder however could also be seen in critically ill patients with sepsis or multiorgan failure without history of receiving corticosteroids or NMBAs. Prolonged mechanical ventilation per se even with absence of mentioned medications usage could cause CIM as well. In some cases, CIM is associated with increase in serum CPK levels (up to 10 times the upper limit of normal) [1]. High mortality rates with CIM; even a rate of 30% have been reported [4]. Of course, it should be noticed that the mortality cause has mostly been due to the sepsis or multiorgan failure rather than the myopathy itself. In present study, one case with pure CIM and one case with CIN were expired (Table 1). Survived and treated patients would acquire ambulation ability within the following 3-4 months [1].

From the histopathologic aspect, type II muscle fiber atrophy is observed more frequently than type I. In muscle biopsy, specific signs of myosin loss is often detected which solely could not explain the inexcitability of the muscle membrane that occurs with this myopathy.

would be absent bilaterally it is difficult to distinguish from technical reasons [2].

Furthermore, released cytokines throughout sepsis could be associated with a catabolic state in muscles and results in breakdown of structural proteins [1,7].

#### **6. Electrophysiologic features in CIM**

Nerve conduction studies demonstrate diminished amplitudes of CMAPs with normal distal latencies and conduction velocities. In contrast, SNAPs are normal or mildly reduced (greater than 80% of lower limit of normal) [1]. Repetitive stimulation test (RST) is usually normal, but in some studies decreasing response after high rate and low rate RST have also been reported [8].

Electromyography frequently demonstrates prominent fibrillation potentials and positive sharp waves (fib/psw), which is rarely accompanied by myotoinc discharges. Short duration, small –amplitude and polyphasic MUAPs that recruit early are evident. In severe cases, it may be difficult to recruit and activate any MUAPs [1]. Of course, this process is reversed and small MUAPs will be appeared again during the recovery period.

#### **6.1. Direct muscle stimulation technique**

Direct muscle stimulation technique is utilized for differentiation CIM from critical illness neuropathy. This method has been reported by Rich and colleagues for first time [7, 9]. In fact, direct muscle stimulation bypasses distal motor nerve and neuromuscular junction area. This technique is performed by placing a monopolar needle stimulating electrode as cathode in the distal third of muscle using 0.1 msec stimulus duration with gradually increasing current from 10 to 100 mA until a clear twitch is seen. A subdermal needle electrode as active recording electrode is placed 1-3 cm from the stimulation electrode. The stimulation intensity is increased until a maximal response or direct muscle action potential (dm CMAP) is obtained. Next, using the same recording montage, nerve to the muscle is stimulated in the usual manner to obtain a nerve- evoked compound muscle action potential (ne CMAP) [2]. As a result muscle membrane should retain its excitability, direct muscle stimulation CMAP (dm CMAP) should be near normal despite a low or absent nerve stimulation evoked CMAP (ne CMAP). In contrast, if the muscle membrane excitability is reduced, both the ne CMAP and dm CMAP should be very low [1]. Therefore, in CIM as well as normal people, the ne CMAP/ dm CMAP ratio is close to one (1:1), because both amplitudes is proportionally reduced or normal, respectively. In CIN or neuromuscular junction disorder, the ratio is much lower and approaches zero because of disproportionally lower ne CMAP compared with the dm CMAP.

In conclusion, absent or low amplitude of dm CMAP with ne CMAP/ dm CMAP greater than 0.9 is demonstrated in majority of patients with CIM, While ne CMAP/ dm CMAP ratio is 0.5 or less in patients with severe CIN [1].

#### **6.2. Treatment**

There is no medical therapy other than supportive care and treating underlying systemic abnormalities (e.g., antibiotics in sepsis). If patients are still receiving high doses of corticosteroids or non- depolarizing neuromuscular blockers, the medications should be stopped.

Neuromuscular Disorders in Critically-Ill Patients –

Approaches to Electrophysiologic Changes in Critical Illness Neuropathy and Myopathy 117

Neuromuscular junction block (NMJ block) occurs primarily in diseases like Myastenia gravis and Lambert-Eaton myastenic syndrome without simultaneous myopathy or neuropathy. However, this complication is frequently observed in association with CIN or

The definite diagnosis is achieved by performing RST or RNS (Repetitive Nerve Stimulation Test) and appearance of decreasing responses and fatigue following repetitive stimulation of low rates at 2-3 Hz in Myastenia gravis or NMJ block in combination with CIM. In contrast, increment responses and facilitation are observed following stimulation of high rates at 25-

It should be mentioned that pure NMJ block are rarely seen in these patients. In the present study, RST was not performed to confirm the simultaneous NMJ block for referred patients, who were critically ill and poorly cooperative with definite diagnosis in each category of CIN, CIM, AMAN, etc., because of this fact that RST tests are time consuming, painful and somewhat unnecessary, since it does not add extra benefit concerning the final therapeutic

In the previous sections, approaches to critically ill patients suspected of having neuropathy or myopathy were reviewed, now we turn our focus to the other neuropathies ,which are in the differential diagnostic list of profound weakness in the inpatient or ICU setting. These

The most well known acute neuropathy that results in marked weakness and respiratory compromise is Guillain-Barre syndrome (GBS) [2]. GBS is an acquired motor and sensory

Demylinating is prescribed with motor nerve conduction velocity less than 60-70% of LLN (lower limit of normal),prolonged distal motor latencies and F-waves greater than 25-50% of ULN( upper limit of normal), absent F-waves and presence of conduction block or temporal dispersion in one or more motor nerves in electrodiagnostic studies. These criteria have been defined and reported by Cornblath et al, and are used as research criteria for diagnosis

In our patients' series, there was no case of GBS and as previously mentioned most of them were hospitalized due to primary non-neurologic medical reasons and CIN or CIM was added later. Other cases that were presented with weakness in upper and lower extremities with or without other medical problems were included in axonal variants of GBS, which is discussed as

CIM in critically ill patients who secondarily affected by neuromuscular disorders.

**8. Prolonged Neuromuscular Junction Blockage:** 

process as well as prognosis in this spectrum of patients.

**9. Acute inflammatory demylinating polyradiculoneuropathy (AIDP)** 

polyradiculneuopathy that is usually demylinating.

**9.1. Electrophysiologic features in GBS** 

of GBS since 1990 [11,12].

follows.

disorders are often amenable to treat, so correct diagnosis is important.

50 Hz in Lambert-Eaton syndrome [10].

### **7. Critical illness neuropathy in combination with myopathy (Neuromyopathic syndrome)**

Patients occasionally present with combination of CIN and CIM symptoms, which make it complicated to distinguish or detect from each other [1].This condition is named as Neuromyopathic Syndrome.

In our patients' series, Neuromyopathic Syndrome was observed in 4 from 14 patients, two of whom had a combination of CIN and CIM. Third case admitted with chronic severe motor and sensory neuropathy symptoms; later presence of myogenic pattern in needle EMG revealed additional CIM. Forth patient suffered from FUO (Fever unknown origin) and demonstrated CIM which had been superimposed on previously diagnosed AMAN. (Table1). One explanation is partly related to this fact, that preexisting neuropathic aspects in these patients were completed parallel to disease progression and subsequently myogenic process and thick filament myopathy initiated following intubation and administration of NMBAs and corticosteroids. We categorized these cases in "Neuromyopathic Syndrome" or "combination of critical illness neuropathy and myopathy" group.

#### **7.1. Electrophysiologic features in combined CIN/CIM**

NCS shows CMAPs and SNAPs both are low amplitude or unobtainable. NCVs are reduced but within axonal range (greater than 60-70% of lower limit of normal). Despite of these neuropathic features, myogenic process with low amplitude, short duration and polyphasic MUAPs are mostly seen, which is occasionally associated with patchy neurogenic changes in needle electromyography examination.

Table 2 illustrates the electrodiagnostic changes in differential diagnosis among critically ill patients.


aLower Limit of Normal, bFibrillation Potentials, c Critical Illness Neuropathy, dCritical Illness Myopathy, e Gullian Barre Syndrome, f Acute Motor Axonal Neuropathy, gAcute Motor and Sensory Axonal Neuropathy, hcombination of CIN and CIM (Neuromyopathic syndrome)

**Table 2.** Electrodiagnostic changes in various neuromuscular disorders among critically ill patients

#### **8. Prolonged Neuromuscular Junction Blockage:**

116 Advances in Clinical Neurophysiology

**(Neuromyopathic syndrome)**

Neuromyopathic Syndrome.

There is no medical therapy other than supportive care and treating underlying systemic abnormalities (e.g., antibiotics in sepsis). If patients are still receiving high doses of corticosteroids

Patients occasionally present with combination of CIN and CIM symptoms, which make it complicated to distinguish or detect from each other [1].This condition is named as

In our patients' series, Neuromyopathic Syndrome was observed in 4 from 14 patients, two of whom had a combination of CIN and CIM. Third case admitted with chronic severe motor and sensory neuropathy symptoms; later presence of myogenic pattern in needle EMG revealed additional CIM. Forth patient suffered from FUO (Fever unknown origin) and demonstrated CIM which had been superimposed on previously diagnosed AMAN. (Table1). One explanation is partly related to this fact, that preexisting neuropathic aspects in these patients were completed parallel to disease progression and subsequently myogenic process and thick filament myopathy initiated following intubation and administration of NMBAs and corticosteroids. We categorized these cases in "Neuromyopathic Syndrome" or

NCS shows CMAPs and SNAPs both are low amplitude or unobtainable. NCVs are reduced but within axonal range (greater than 60-70% of lower limit of normal). Despite of these neuropathic features, myogenic process with low amplitude, short duration and polyphasic MUAPs are mostly seen, which is occasionally associated with patchy neurogenic changes

Table 2 illustrates the electrodiagnostic changes in differential diagnosis among critically ill

**CINc** ++ ++ - ++ - ++ **CIMd** - ++ - +++ ++ - **GBSe** +/- +/- ++ - - + **AMANf** - + - ++ - ++ **AMSANg** + + - + - + **CIN/CIMh** + + - ++ + +/-

**Table 2.** Electrodiagnostic changes in various neuromuscular disorders among critically ill patients

**Reduced NCV > 60-70% of LLNa**

Acute Motor Axonal Neuropathy, gAcute Motor and Sensory Axonal Neuropathy, hcombination of CIN

**fib/pswb Myogenic** 

Critical Illness Neuropathy, dCritical Illness Myopathy, e

**Pattern**

**Neurogenic Pattern**

Gullian Barre

or non- depolarizing neuromuscular blockers, the medications should be stopped.

**7. Critical illness neuropathy in combination with myopathy** 

"combination of critical illness neuropathy and myopathy" group.

**Low amplitude or absent CMAP**

**7.1. Electrophysiologic features in combined CIN/CIM** 

in needle electromyography examination.

**Low amplitude or absent SNAP**

aLower Limit of Normal, bFibrillation Potentials, c

and CIM (Neuromyopathic syndrome)

patients.

Syndrome, f

**6.2. Treatment** 

Neuromuscular junction block (NMJ block) occurs primarily in diseases like Myastenia gravis and Lambert-Eaton myastenic syndrome without simultaneous myopathy or neuropathy. However, this complication is frequently observed in association with CIN or CIM in critically ill patients who secondarily affected by neuromuscular disorders.

The definite diagnosis is achieved by performing RST or RNS (Repetitive Nerve Stimulation Test) and appearance of decreasing responses and fatigue following repetitive stimulation of low rates at 2-3 Hz in Myastenia gravis or NMJ block in combination with CIM. In contrast, increment responses and facilitation are observed following stimulation of high rates at 25- 50 Hz in Lambert-Eaton syndrome [10].

It should be mentioned that pure NMJ block are rarely seen in these patients. In the present study, RST was not performed to confirm the simultaneous NMJ block for referred patients, who were critically ill and poorly cooperative with definite diagnosis in each category of CIN, CIM, AMAN, etc., because of this fact that RST tests are time consuming, painful and somewhat unnecessary, since it does not add extra benefit concerning the final therapeutic process as well as prognosis in this spectrum of patients.

In the previous sections, approaches to critically ill patients suspected of having neuropathy or myopathy were reviewed, now we turn our focus to the other neuropathies ,which are in the differential diagnostic list of profound weakness in the inpatient or ICU setting. These disorders are often amenable to treat, so correct diagnosis is important.

### **9. Acute inflammatory demylinating polyradiculoneuropathy (AIDP)**

The most well known acute neuropathy that results in marked weakness and respiratory compromise is Guillain-Barre syndrome (GBS) [2]. GBS is an acquired motor and sensory polyradiculneuopathy that is usually demylinating.

#### **9.1. Electrophysiologic features in GBS**

Demylinating is prescribed with motor nerve conduction velocity less than 60-70% of LLN (lower limit of normal),prolonged distal motor latencies and F-waves greater than 25-50% of ULN( upper limit of normal), absent F-waves and presence of conduction block or temporal dispersion in one or more motor nerves in electrodiagnostic studies. These criteria have been defined and reported by Cornblath et al, and are used as research criteria for diagnosis of GBS since 1990 [11,12].

In our patients' series, there was no case of GBS and as previously mentioned most of them were hospitalized due to primary non-neurologic medical reasons and CIN or CIM was added later. Other cases that were presented with weakness in upper and lower extremities with or without other medical problems were included in axonal variants of GBS, which is discussed as follows.

#### **10. Acute motor and sensory axonal neuropathy (AMSAN)**

Feasby and colleagues initially reported this axonal variant of GBS in 1986 [13]. Clinical and electrodiagnostic features in AMSAN are indistinguishable from those with AIDP initially [5]. Patients with AMSAN refer with a rapidly progressive and generalized weakness which progresses within days unlike the AIDP which progresses within weeks. Ophthalmoparesia, swallowing disorders, and facial muscles weakness are prominent in these patients. Other accompanying symptoms include complete areflexia, sensory loss and autonomic disorders such as arrhythmia. Most of these patients would require ventilator and their prognosis are often poorer compared with AIDP patients.

Neuromuscular Disorders in Critically-Ill Patients –

Approaches to Electrophysiologic Changes in Critical Illness Neuropathy and Myopathy 119

respiratory disturbances. There was also a case without respiratory involvement with an

It is suggested to treat AMAN patients with IVIG 2mg/kg over 5 days or plasma exchange as an alternative. One of large studies reported no significant difference in outcome regardless

**In conclusion**, CIN and CIM have no cure, so in this conditions, underlying disease and drugs dose should be treated and adjusted, respectively; despite of this management, muscle power weakness lasts several months to recover. AIDP, AMAN and AMSAN,

*Tabriz University of Medical Sciences, Physical medicine & Rehabilitation Research Center, Tabriz,* 

[1] Dumitru D, Amato AA (2002) Acquired myopathies. In: Dumitru D, Amato AA, Zwarts MJ, editors. Electrodiagnostic medicine. 2nd ed. Philadelphia, PA: Hanley & Belfus Inc;

[2] Preston DC, Shapiro BE (2005) Approach to Electrodiagnostic Studies in the Intensive Care Unit. In: Preston DC, Shapiro BE, eds. Electromyography and Neuromuscular

[3] Op de Coul AAW, Lambregts PC, Koeman J,et al (1985) Neuromuscular complications in patients given Pavulon during artificial ventilation. Clin Neurol Neurosurg . 87:17-20. [4] Lacomis D, Petrella JT, Giuliani MJ (1998) Causes of neuromuscular weakness in the intensive care unit: a study of ninety-two patients. Muscle Nerve. 21(5):610-7 [5] Dumitru D, Amato AA (2002) Acquired neuropathies In: Dumitru D, Amato AA, Zwarts MJ, editors. Electrodiagnostic medicine. 2nd ed. Philadelphia, PA: Hanley & Belfus Inc;

[6] Dumitru D, Zwarts MJ (2002) Focal cranial neuropathies. In: Dumitru D, Amato AA, Zwarts MJ, editors. Electrodiagnostic medicine. 2nd ed. Philadelphia, PA: Hanley &

[7] Rich MM, Teener JW, Raps EC, Schotland DL, Bird SJ (1996) Muscle is electrically

[8] Road J, Mackie G, Jiang TX, Stewart H, Eisen A (1997) Reversible paralysis with status asthmaticus, steroids, and pancuronium: clinical electrophysiological correlates. Muscle

unexcitable in acute quadriplegic myopathy. Neurology. 46(3):731-6

of therapy (IVIG,PE,..) between AIDP and AMAN among 300 patients[15].

however, are curable and therefore distinguish them together is important.

disorders. 2nd ed. Philadelphia, Elsevier. pp.615-625

appropriate response to IVIG (Table 1).

Fariba Eslamian and Mohammad Rahbar

**11.2. Treatment** 

**Author details** 

**12. References** 

pp. 1402-1404.

pp.937-989.

Belfus Inc; pp.688-690.

Nerve. 20(12):1587-90

*Iran* 

#### **10.1. Electrophysiologic features in AMSAN**

NCS reveal markedly diminished amplitudes or absent CMAPs. SNAPs are also profoundly low amplitude or absent. But distal latencies of CMAPs and NCVs, when obtainable, should be normal or only mildly affected [5] .In other words, this abnormality would be within axonal range(greater than 60-70% of LLN). Neurogenic pattern with large MUAPs with or without denervation potentials are seen in needle EMG exam (Table 2).

In the present study, there was one case with AMSAN had started with above-mentioned clinical picture followed by COPD exacerbation and diagnosis was confirmed by EDX criteria.

#### **10.2. Treatment**

Because it is difficult to distinguish AIDP from AMSAN clinically or electrophysiologically, at least initially, treatment with plasma exchange or IVIG is warranted [5].

#### **11. Acute Motor Axonal Neuropathy (AMAN)**

In northern china, AMAN is the most common variant of GBS [14].Clinical manifestation is similar to that of AMSAN and GBS, however distal muscles are affected more severely than proximal muscles. Respiratory failure and mechanical ventilation requirement is observed in one third of the patients. Unlike AMSAN and GBS no sensory sings are noted. Furthermore, Anti-GM1 antibody for Compilobacter jejuni is more frequently detected in patients with AMAN especially in Children [15].

#### **11.1. Electrophysiologic features in AMAN**

Low amplitude or absent CMAPs with normal SNAPs and mildly reduced NCVs, are the characteristic features of nerve conduction studies in AMAN. Other EDx evidences are similar to AMSAN as above noted.

In our patients' series, there were two cases with AMAN from whom one case was associated with phrenic nerve involvement and diaphragm muscle paralysis with respiratory disturbances. There was also a case without respiratory involvement with an appropriate response to IVIG (Table 1).

#### **11.2. Treatment**

118 Advances in Clinical Neurophysiology

criteria.

**10.2. Treatment** 

often poorer compared with AIDP patients.

**10.1. Electrophysiologic features in AMSAN** 

**10. Acute motor and sensory axonal neuropathy (AMSAN)**

without denervation potentials are seen in needle EMG exam (Table 2).

at least initially, treatment with plasma exchange or IVIG is warranted [5].

**11. Acute Motor Axonal Neuropathy (AMAN)**

patients with AMAN especially in Children [15].

**11.1. Electrophysiologic features in AMAN**

similar to AMSAN as above noted.

Feasby and colleagues initially reported this axonal variant of GBS in 1986 [13]. Clinical and electrodiagnostic features in AMSAN are indistinguishable from those with AIDP initially [5]. Patients with AMSAN refer with a rapidly progressive and generalized weakness which progresses within days unlike the AIDP which progresses within weeks. Ophthalmoparesia, swallowing disorders, and facial muscles weakness are prominent in these patients. Other accompanying symptoms include complete areflexia, sensory loss and autonomic disorders such as arrhythmia. Most of these patients would require ventilator and their prognosis are

NCS reveal markedly diminished amplitudes or absent CMAPs. SNAPs are also profoundly low amplitude or absent. But distal latencies of CMAPs and NCVs, when obtainable, should be normal or only mildly affected [5] .In other words, this abnormality would be within axonal range(greater than 60-70% of LLN). Neurogenic pattern with large MUAPs with or

In the present study, there was one case with AMSAN had started with above-mentioned clinical picture followed by COPD exacerbation and diagnosis was confirmed by EDX

Because it is difficult to distinguish AIDP from AMSAN clinically or electrophysiologically,

In northern china, AMAN is the most common variant of GBS [14].Clinical manifestation is similar to that of AMSAN and GBS, however distal muscles are affected more severely than proximal muscles. Respiratory failure and mechanical ventilation requirement is observed in one third of the patients. Unlike AMSAN and GBS no sensory sings are noted. Furthermore, Anti-GM1 antibody for Compilobacter jejuni is more frequently detected in

Low amplitude or absent CMAPs with normal SNAPs and mildly reduced NCVs, are the characteristic features of nerve conduction studies in AMAN. Other EDx evidences are

In our patients' series, there were two cases with AMAN from whom one case was associated with phrenic nerve involvement and diaphragm muscle paralysis with It is suggested to treat AMAN patients with IVIG 2mg/kg over 5 days or plasma exchange as an alternative. One of large studies reported no significant difference in outcome regardless of therapy (IVIG,PE,..) between AIDP and AMAN among 300 patients[15].

**In conclusion**, CIN and CIM have no cure, so in this conditions, underlying disease and drugs dose should be treated and adjusted, respectively; despite of this management, muscle power weakness lasts several months to recover. AIDP, AMAN and AMSAN, however, are curable and therefore distinguish them together is important.

#### **Author details**

Fariba Eslamian and Mohammad Rahbar

*Tabriz University of Medical Sciences, Physical medicine & Rehabilitation Research Center, Tabriz, Iran* 

#### **12. References**


[9] Rich MM, Bird SJ, Raps EC, McCluskey LF, Teener JW (1997) Direct muscle stimulation in acute quadriplegic myopathy. Muscle Nerve. 20(6):665-673.

**Chapter 6** 

© 2012 Peña-Ortega, licensee InTech. This is an open access chapter distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

© 2012 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution,

For the purpose of this chapter, pacemakers are defined as neurons that can generate oscillatory bursts of action potentials independently of the network, i.e. in the absence of any synaptic input [Fig. 1; upper trace] [1,9]. They do so because they have a mixture of ionic conductances that allow them to produce rhythmic excursions of the membrane

and reproduction in any medium, provided the original work is properly cited.

**Pacemaker Neurons and Neuronal Networks in** 

Neural network activity provides the operational basis for diverse neural circuits to determine temporal windows during which multiple, coherent neuronal assemblies engaged in the generation of specific behaviors can be recruited [1-3]. Neural network activity emerges from the combination of intrinsic neural properties and the synaptic interactions among them [1-5]. However, the relative contributions of intrinsic and synaptic properties to circuit activity are diverse and change, depending on the state of the network, mainly through the action of neuromodulators [6]. On top of this diversity, the intrinsic properties of neurons are also heterogeneous, ranging from silent "linear" neurons (also called followers or non-pacemakers; Fig 1 bottom trace) to "non-linear" intrinsic bursters (also called pacemakers; Fig. 1 upper trace) [7]. The presence of pacemaker neurons and their pivotal role in network activity generation is an accepted fact for invertebrate networks [8]. In the case of mammalian circuits, accumulating evidence supports the presence and participation of these pacemakers in generating network rhythmic activity by several circuits throughout the brain in normal and abnormal conditions [1,4,5, 9-11]. In mammalian networks, bursting has been related to neural network generation [1], induction of synaptic plasticity, [12] as well as to the transition of abnormal neural network states [13,14]. Here, I will review just some examples of neural networks that contain pacemaker neurons, the main ionic mechanisms involved in their bursting generation, and the participation of these pacemakers in generating neural network function under normal and pathological

**Health and Disease** 

Additional information is available at the end of the chapter

Fernando Peña-Ortega

http://dx.doi.org/10.5772/50264

**1. Introduction** 

conditions.


### **Pacemaker Neurons and Neuronal Networks in Health and Disease**

Fernando Peña-Ortega

120 Advances in Clinical Neurophysiology

1126.

Hanley & Belfus Inc. pp.1148-1177.

and prognostic value. Ann Neurol. 23(4):354-359.

Trial Group. Ann Neurol. 44(5):780-788.

[9] Rich MM, Bird SJ, Raps EC, McCluskey LF, Teener JW (1997) Direct muscle stimulation

[10] Dumitru D, Amato AA (2002) Neuromuscular junction disorders. In: Dumitru D, Amato AA, Zwarts MJ, eds. Electrodiagnostic medicine. 2nd ed. Philadelphia, PA:

[11] Cornblath DR, Asbury AK, Albers JW (1991) Research criteria for diagnosis of chronic inflammatory demyelinating polyneuropathy (CIDP). Neurology. 41:617-618. [12] Cornblath DR, Mellits ED, Griffin JW, McKhann GM, Albers JW, Miller RG, Feasby TE, Quaskey SA (1988) Motor conduction studies in Guillain-Barré syndrome: description

[13] Feasby TE, Gilbert JJ, Brown WF, Bolton CF, Hahn AF, Koopman WF, Zochodne DW (1986) An acute axonal form of Guillain-Barré polyneuropathy. Brain. 109 (Pt 6):1115-

[14] McKhann GM, Cornblath DR, Ho T, Li CY, Bai AY, Wu HS, Yei QF, Zhang WC, Zhaori 11 Z, Jiang Z, et al. (1991) Clinical and electrophysiological aspects of acute paralytic disease of children and young adults in northern China. Lancet. 7; 338(8767):593-597. [15] Hadden RD, Cornblath DR, Hughes RA, Zielasek J, Hartung HP, Toyka KV, Swan AV (1998) Electrophysiological classification of Guillain-Barré syndrome: clinical associations and outcome. Plasma Exchange/Sandoglobulin Guillain-Barré Syndrome

in acute quadriplegic myopathy. Muscle Nerve. 20(6):665-673.

Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/50264

#### **1. Introduction**

Neural network activity provides the operational basis for diverse neural circuits to determine temporal windows during which multiple, coherent neuronal assemblies engaged in the generation of specific behaviors can be recruited [1-3]. Neural network activity emerges from the combination of intrinsic neural properties and the synaptic interactions among them [1-5]. However, the relative contributions of intrinsic and synaptic properties to circuit activity are diverse and change, depending on the state of the network, mainly through the action of neuromodulators [6]. On top of this diversity, the intrinsic properties of neurons are also heterogeneous, ranging from silent "linear" neurons (also called followers or non-pacemakers; Fig 1 bottom trace) to "non-linear" intrinsic bursters (also called pacemakers; Fig. 1 upper trace) [7]. The presence of pacemaker neurons and their pivotal role in network activity generation is an accepted fact for invertebrate networks [8]. In the case of mammalian circuits, accumulating evidence supports the presence and participation of these pacemakers in generating network rhythmic activity by several circuits throughout the brain in normal and abnormal conditions [1,4,5, 9-11]. In mammalian networks, bursting has been related to neural network generation [1], induction of synaptic plasticity, [12] as well as to the transition of abnormal neural network states [13,14]. Here, I will review just some examples of neural networks that contain pacemaker neurons, the main ionic mechanisms involved in their bursting generation, and the participation of these pacemakers in generating neural network function under normal and pathological conditions.

For the purpose of this chapter, pacemakers are defined as neurons that can generate oscillatory bursts of action potentials independently of the network, i.e. in the absence of any synaptic input [Fig. 1; upper trace] [1,9]. They do so because they have a mixture of ionic conductances that allow them to produce rhythmic excursions of the membrane

© 2012 Peña-Ortega, licensee InTech. This is an open access chapter distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. © 2012 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

potential on top of which barrages of action potentials are generated [3; 11; Fig. 1; upper trace]. In networks that contain them, pacemaker neurons may act as true pacemakers or as resonators that respond preferentially to specific firing frequencies [1,9]. Non-pacemaker neurons change their firing rate gradually in almost strict correspondence to their synaptic input [1]. In contrast, the nonlinearity of bursting activity enables pacemaker neurons to modulate more abruptly their firing [1]. Moreover, bursting neurons amplify synaptic input and transmit their information more reliably through synaptic contacts [15-17]. As a consequence of these properties, pacemaker neurons can facilitate the onset of excitatory states or synchronize neuronal ensembles involved in diverse functional roles, such as movement control, sleep-wakefulness cycling, perception, attention, etc. [1,9]. The ability of these neurons to generate bursts of action potentials lies in voltage-sensitive ion fluxes, which act in specific voltage- and time-windows and whose activity is regulated by the metabolic state of the neurons, by neuromodulators, and by activity-dependent mechanisms [1, 18-20]. Next, I will describe some examples of mammalian neural networks containing pacemaker neurons.

Pacemaker Neurons and Neuronal Networks in Health and Disease 123

**Figure 1. Identification of pacemaker and non-pacemaker neurons.** Recordings from two neurons in the preBötzinger Complex are shown in conditions where fast synaptic transmission has been blocked using a cocktail of glutamate, GABA, and glycine receptor antagonists (synaptic isolation). Whereas both neurons were originally identified as rhythmic inspiratory neurons, in synaptic isolation pacemakers can be identified by their ability to continue the generation of oscillatory bursts of action potentials independently of the network. In contrast, non-pacemaker neurons become either silent or

fire tonically in a non-rhythmic fashion.

One of the more popular examples of a mammalian pacemaker neuron is, perhaps, the reticular thalamic neuron (RTN) [22,23]. RTNs are able to generate bursts of action potentials depending on two major inward currents: the low-threshold (T-type) Ca2+ channels [22,23] and the hyperpolarization-activated and cyclic nucleotide-gated nonselective cation channel (HCN) [24]. Interestingly, these neurons can switch from the "bursting mode" to a "tonic mode" depending on their membrane potential [25,26]. The transitions between firing modes are determined by the action of several neuromodulators as well as by GABAergic phasic inhibition [25,26]. It has been proposed that the "bursting mode" of these RTNs dominates the generation of slow-wave activity during non-REM sleep, whereas the transition to the tonic firing mode is related to the generation of faster rhythms produced during wakefulness [25,26]. Therefore, it has been proposed that pacemaker RTN neurons are key elements of the cortico-thalamic neural network that gates the transitions among different states of consciousness [i.e. sleep/awakening] [22,23]. From a clinical point of view, it has been reported that an increase during wakefulness of the bursting mode of RTN neurons is related to the generation of absence seizures [27,28]. Accordingly, absence seizures are successfully treated with T-type Ca2+ channel blockers such as ethosuximide, which reduces the bursting mode of RTNs [28,29].

Intrinsic bursting neurons have been identified in the neocortex [30-34]. These pacemakers correspond to a subgroup of pyramidal neurons and to a subset of Martinotti-interneuron cells [30-33]. As expected, pacemaker pyramidal cells are functionally and anatomical different from regular spiking (RS) pyramidal neurons. For example, intrinsic bursters have specific morphological features that differentiate them from the typical pyramidal RS neurons [31]. Intrinsic bursters are larger than RS neurons; they have a triangular soma rather than the more rounded soma of RS pyramidal neurons and a more complex dendritic tree [31]. Regarding their projection, intrinsic bursters send collaterals that are limited to layers 5/6, whereas axonal collaterals from RS pyramidal neurons are more pronounced in

pacemaker neurons.

potential on top of which barrages of action potentials are generated [3; 11; Fig. 1; upper trace]. In networks that contain them, pacemaker neurons may act as true pacemakers or as resonators that respond preferentially to specific firing frequencies [1,9]. Non-pacemaker neurons change their firing rate gradually in almost strict correspondence to their synaptic input [1]. In contrast, the nonlinearity of bursting activity enables pacemaker neurons to modulate more abruptly their firing [1]. Moreover, bursting neurons amplify synaptic input and transmit their information more reliably through synaptic contacts [15-17]. As a consequence of these properties, pacemaker neurons can facilitate the onset of excitatory states or synchronize neuronal ensembles involved in diverse functional roles, such as movement control, sleep-wakefulness cycling, perception, attention, etc. [1,9]. The ability of these neurons to generate bursts of action potentials lies in voltage-sensitive ion fluxes, which act in specific voltage- and time-windows and whose activity is regulated by the metabolic state of the neurons, by neuromodulators, and by activity-dependent mechanisms [1, 18-20]. Next, I will describe some examples of mammalian neural networks containing

One of the more popular examples of a mammalian pacemaker neuron is, perhaps, the reticular thalamic neuron (RTN) [22,23]. RTNs are able to generate bursts of action potentials depending on two major inward currents: the low-threshold (T-type) Ca2+ channels [22,23] and the hyperpolarization-activated and cyclic nucleotide-gated nonselective cation channel (HCN) [24]. Interestingly, these neurons can switch from the "bursting mode" to a "tonic mode" depending on their membrane potential [25,26]. The transitions between firing modes are determined by the action of several neuromodulators as well as by GABAergic phasic inhibition [25,26]. It has been proposed that the "bursting mode" of these RTNs dominates the generation of slow-wave activity during non-REM sleep, whereas the transition to the tonic firing mode is related to the generation of faster rhythms produced during wakefulness [25,26]. Therefore, it has been proposed that pacemaker RTN neurons are key elements of the cortico-thalamic neural network that gates the transitions among different states of consciousness [i.e. sleep/awakening] [22,23]. From a clinical point of view, it has been reported that an increase during wakefulness of the bursting mode of RTN neurons is related to the generation of absence seizures [27,28]. Accordingly, absence seizures are successfully treated with T-type Ca2+ channel blockers

such as ethosuximide, which reduces the bursting mode of RTNs [28,29].

Intrinsic bursting neurons have been identified in the neocortex [30-34]. These pacemakers correspond to a subgroup of pyramidal neurons and to a subset of Martinotti-interneuron cells [30-33]. As expected, pacemaker pyramidal cells are functionally and anatomical different from regular spiking (RS) pyramidal neurons. For example, intrinsic bursters have specific morphological features that differentiate them from the typical pyramidal RS neurons [31]. Intrinsic bursters are larger than RS neurons; they have a triangular soma rather than the more rounded soma of RS pyramidal neurons and a more complex dendritic tree [31]. Regarding their projection, intrinsic bursters send collaterals that are limited to layers 5/6, whereas axonal collaterals from RS pyramidal neurons are more pronounced in

**Figure 1. Identification of pacemaker and non-pacemaker neurons.** Recordings from two neurons in the preBötzinger Complex are shown in conditions where fast synaptic transmission has been blocked using a cocktail of glutamate, GABA, and glycine receptor antagonists (synaptic isolation). Whereas both neurons were originally identified as rhythmic inspiratory neurons, in synaptic isolation pacemakers can be identified by their ability to continue the generation of oscillatory bursts of action potentials independently of the network. In contrast, non-pacemaker neurons become either silent or fire tonically in a non-rhythmic fashion.

the supragranular layers [31, 34, 35]. Moreover, the intracortical circuits for intrinsic bursters are different from those of RS neurons [35]. For instance, intrinsic bursters receive intracolumnar excitatory innervations from all layers, whereas RS neurons receive intracolumnar inhibitory and excitatory inputs from layers 2/3 and 5 [35]. Finally, the extracortical projections of these two types of pyramidal cells differ; for instance, intrinsic bursters project to the thalamus, pons, and colliculus while RS neurons project to cortical and striatal targets [36,37]. Cortical pacemakers have been hypothesized to play a major role in the generation of spontaneous activity [4, 33]. For instance, Cunningham et al. [4] have described a group of intrinsic pacemakers that produce bursts of action potentials in the gamma range, relying on the persistent sodium current (INap), and that their blockade abolishes gamma generation in the auditory cortex. Similarly, other types of intrinsic bursters that also rely on the INap, but that fire their bursts at lower frequencies, have been implicated in the generation of population activity in the somatosensory cortex [38,39]. Based on this and other evidence, it has been proposed that the cortex may act as a central pattern generator [2]. From a pathological point of view, cortical pacemaker neurons play a role in the generation of epileptic network activity [13; 14]. For example, we have found that human cortical epileptic foci have an increased number of cells with INap-dependent pacemaker properties [13,14], which may explain why reducing the INap has an antiepileptic effect [40-42].

Pacemaker Neurons and Neuronal Networks in Health and Disease 125

the activity of the two principal output structures of the network: the internal pallidal segment and the substantia nigra pars reticulata [58, 55, 56]. Interestingly, pacemaker activity of subthalamic neurons has been associated with Parkinson´s disease [59]. For instance, an increase in subthalamic burst firing has been found in animal models of parkinsonism [60,61] and in parkinsonian patients [62,63]. Also noteworthy is that highfrequency stimulation of the subthalamic nucleus, which reduces subthalamic busrstiness [62,64], produces a reduction in motor impairments associated with parkinsonism and is currently used in the treatment of parkinsonian patients [63,65]. Moreover, modulation of

the T-type calcium channel in subthalamic busrters also reduces parkinsonisms [66].

**2. Role of pacemakers can be state-dependent: An example of the** 

Respiratory rhythm commands are generated by two, interacting oscillators, one controlling inspiration (preBötC) and other, located in the parafacial respiratory group (pFRG), possibly controlling active expiration [76-79]. Neurons with pacemaker properties have been identified in the preBötC [5,80,81; Fig. 1]. However, a rather complex picture has emerged regarding their intrinsic properties. PreBötC pacemakers have been found to show considerable variability in the range of interburst and intraburst frequencies, the amplitude of the plateau potential underlying bursting firing, and the voltage trajectory of this plateau [5, 80-86]. A biophysical and pharmacological characterization of their intrinsic properties have shown us that preBötC pacemakers can be grouped into two major groups: those that rely on the INap and those that rely on a Ca2+-activated non-specific cationic current [ICAN] [5, 82, 84, 86]. Interestingly, the participation of these pacemakers in respiratory rhythm generation is state dependent. We found that blocking either the pacemakers that rely on the INap or the pacemakers that rely on the ICAN is not sufficient to abolish respiratory rhythm generation by the preBötC [5, 87]. However, when both of the two pacemaker populations are blocked, the preBötC ceases the generation of its rhythmic activity [5], and the animals die [87]. This evidence suggests that breathing generation relies on the activity of two distinct pacemaker neurons [5,87]. However, this is not the case when

preBötzinger Complex (preBötC).

**inspiratory rhythm generator** 

Pacemaker neurons have also been identified in the spinal cord, where they seem to play a major role in its central pattern generators [67-69]. For instance, in the central pattern generator for locomotion, some interneurons exhibit intrinsic bursting activity [67-69] that relies on the INap [67-69]. This mechanism is essential for the activity of the locomotion central pattern generator, since blockade of INap abolishes bursting activity and fictive locomotion [67-69]. Also in the spinal cord, it was recently reported that an increase in pacemaker activity is observed in the dorsal horn of animals that suffer from chronic pain [70]. These intrinsic bursters exhibit an increase in the density of the INap and the HCN [70], which may offer therapeutic targets to treat chronic pain [71,72]. In fact, several blockers of the INap have shown very promising effects against acute and chronic pain [73-75]. Finally, I will review the role of pacemaker neurons in the activity of a vital network: the

Similarly to gamma rhythm, pacemakers involved in theta rhythm generation have been identified in the septohippocampal network [10,43]. These putative theta pacemaker neurons are GABAergic cells that are localized in the medial septum and express parvalbumin and the HCN [44-46]. Interestingly, alterations in the activity of these theta pacemaker neurons might be involved in the pathophysiology of Alzheimer disease (AD), which progresses with a reduction in evoked-theta oscillations [47]. Accordingly, application of the AD-related amyloid-beta peptide reduces the activity of theta-pacemaker neurons and reduces theta rhythm in rats [43, 48-50].

Pacemaker neurons have been reported in the hypothalamic arcuate nucleus, which is responsible for the control of the satiety-hunger cycle [51]. These neurons, which contain neuropeptide Y [NPY], are conditional pacemakers that are activated by orexigens (ghrelin and orexin) and inhibited by the anorexigens (leptin) [51]. The busting properties of these neurons do not depend on the INap, because their membrane potential oscillations persist in the presence of tetrodotoxin, but are inhibited by blocking the T-type calcium channel [51]. Since these arcuate pacemakers can contribute to balanced food consumption, an alteration in their activity can be associated with eating disorders and obesity [52-54].

Subthalamic neurons can exhibit bursting properties, depending on the state of the network. As RTNs, subthalamic pacemakers can shift from a regular, single-spike mode to a burstfiring mode depending on their depolarization level [55,56]. The bursting mode relies on the L-type and the T-type Ca2+ channels, and it is insensitive to tetrodotoxin [55,57]. The subthalamic nucleus is composed of glutamatergic neurons, whose normal transition between tonic and bursting modes controls the circuitry of the basal ganglia by modulating the activity of the two principal output structures of the network: the internal pallidal segment and the substantia nigra pars reticulata [58, 55, 56]. Interestingly, pacemaker activity of subthalamic neurons has been associated with Parkinson´s disease [59]. For instance, an increase in subthalamic burst firing has been found in animal models of parkinsonism [60,61] and in parkinsonian patients [62,63]. Also noteworthy is that highfrequency stimulation of the subthalamic nucleus, which reduces subthalamic busrstiness [62,64], produces a reduction in motor impairments associated with parkinsonism and is currently used in the treatment of parkinsonian patients [63,65]. Moreover, modulation of the T-type calcium channel in subthalamic busrters also reduces parkinsonisms [66].

124 Advances in Clinical Neurophysiology

antiepileptic effect [40-42].

neurons and reduces theta rhythm in rats [43, 48-50].

the supragranular layers [31, 34, 35]. Moreover, the intracortical circuits for intrinsic bursters are different from those of RS neurons [35]. For instance, intrinsic bursters receive intracolumnar excitatory innervations from all layers, whereas RS neurons receive intracolumnar inhibitory and excitatory inputs from layers 2/3 and 5 [35]. Finally, the extracortical projections of these two types of pyramidal cells differ; for instance, intrinsic bursters project to the thalamus, pons, and colliculus while RS neurons project to cortical and striatal targets [36,37]. Cortical pacemakers have been hypothesized to play a major role in the generation of spontaneous activity [4, 33]. For instance, Cunningham et al. [4] have described a group of intrinsic pacemakers that produce bursts of action potentials in the gamma range, relying on the persistent sodium current (INap), and that their blockade abolishes gamma generation in the auditory cortex. Similarly, other types of intrinsic bursters that also rely on the INap, but that fire their bursts at lower frequencies, have been implicated in the generation of population activity in the somatosensory cortex [38,39]. Based on this and other evidence, it has been proposed that the cortex may act as a central pattern generator [2]. From a pathological point of view, cortical pacemaker neurons play a role in the generation of epileptic network activity [13; 14]. For example, we have found that human cortical epileptic foci have an increased number of cells with INap-dependent pacemaker properties [13,14], which may explain why reducing the INap has an

Similarly to gamma rhythm, pacemakers involved in theta rhythm generation have been identified in the septohippocampal network [10,43]. These putative theta pacemaker neurons are GABAergic cells that are localized in the medial septum and express parvalbumin and the HCN [44-46]. Interestingly, alterations in the activity of these theta pacemaker neurons might be involved in the pathophysiology of Alzheimer disease (AD), which progresses with a reduction in evoked-theta oscillations [47]. Accordingly, application of the AD-related amyloid-beta peptide reduces the activity of theta-pacemaker

Pacemaker neurons have been reported in the hypothalamic arcuate nucleus, which is responsible for the control of the satiety-hunger cycle [51]. These neurons, which contain neuropeptide Y [NPY], are conditional pacemakers that are activated by orexigens (ghrelin and orexin) and inhibited by the anorexigens (leptin) [51]. The busting properties of these neurons do not depend on the INap, because their membrane potential oscillations persist in the presence of tetrodotoxin, but are inhibited by blocking the T-type calcium channel [51]. Since these arcuate pacemakers can contribute to balanced food consumption, an alteration

Subthalamic neurons can exhibit bursting properties, depending on the state of the network. As RTNs, subthalamic pacemakers can shift from a regular, single-spike mode to a burstfiring mode depending on their depolarization level [55,56]. The bursting mode relies on the L-type and the T-type Ca2+ channels, and it is insensitive to tetrodotoxin [55,57]. The subthalamic nucleus is composed of glutamatergic neurons, whose normal transition between tonic and bursting modes controls the circuitry of the basal ganglia by modulating

in their activity can be associated with eating disorders and obesity [52-54].

Pacemaker neurons have also been identified in the spinal cord, where they seem to play a major role in its central pattern generators [67-69]. For instance, in the central pattern generator for locomotion, some interneurons exhibit intrinsic bursting activity [67-69] that relies on the INap [67-69]. This mechanism is essential for the activity of the locomotion central pattern generator, since blockade of INap abolishes bursting activity and fictive locomotion [67-69]. Also in the spinal cord, it was recently reported that an increase in pacemaker activity is observed in the dorsal horn of animals that suffer from chronic pain [70]. These intrinsic bursters exhibit an increase in the density of the INap and the HCN [70], which may offer therapeutic targets to treat chronic pain [71,72]. In fact, several blockers of the INap have shown very promising effects against acute and chronic pain [73-75]. Finally, I will review the role of pacemaker neurons in the activity of a vital network: the preBötzinger Complex (preBötC).

#### **2. Role of pacemakers can be state-dependent: An example of the inspiratory rhythm generator**

Respiratory rhythm commands are generated by two, interacting oscillators, one controlling inspiration (preBötC) and other, located in the parafacial respiratory group (pFRG), possibly controlling active expiration [76-79]. Neurons with pacemaker properties have been identified in the preBötC [5,80,81; Fig. 1]. However, a rather complex picture has emerged regarding their intrinsic properties. PreBötC pacemakers have been found to show considerable variability in the range of interburst and intraburst frequencies, the amplitude of the plateau potential underlying bursting firing, and the voltage trajectory of this plateau [5, 80-86]. A biophysical and pharmacological characterization of their intrinsic properties have shown us that preBötC pacemakers can be grouped into two major groups: those that rely on the INap and those that rely on a Ca2+-activated non-specific cationic current [ICAN] [5, 82, 84, 86]. Interestingly, the participation of these pacemakers in respiratory rhythm generation is state dependent. We found that blocking either the pacemakers that rely on the INap or the pacemakers that rely on the ICAN is not sufficient to abolish respiratory rhythm generation by the preBötC [5, 87]. However, when both of the two pacemaker populations are blocked, the preBötC ceases the generation of its rhythmic activity [5], and the animals die [87]. This evidence suggests that breathing generation relies on the activity of two distinct pacemaker neurons [5,87]. However, this is not the case when the preBötC is challenged with hypoxic conditions. During hypoxia, the respiratory network is reconfigured and generates a "last-resort" respiratory rhythm called gasping [79]. Under these conditions the pacemaker neurons relying on the ICAN cease to fire, and the respiratory network relies only on the INap-dependent pacemaker neuron, whose blockade abolishes gasping generation [5,87]. These findings may have clinical relevance since gasping is an important autoresuscitation mechanism that seems to fail in victims of sudden infant death syndrome [SIDS, 88,89]. SIDS victims breathe normally during normoxia, when the respiratory rhythm can be generated by either of the two types of pacemaker, but they do not gasp efficiently in hypoxia [88,89], when the respiratory network relies exclusively on one type of pacemaker neuron.

Pacemaker Neurons and Neuronal Networks in Health and Disease 127

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behaving rat. Neuron. 21:179-189.

Curr. Opin. Neurobiol. 12:646-651.

#### **3. Conclusion**

In conclusion, pacemaker neurons are important components of several mammalian neural networks. The presence of pacemaker neurons allows these networks to produce different types of network activities, both in normal and in pathological conditions. Moreover, the contribution of pacemaker neurons to neural network dynamics is not fixed but depends on the action of neuromodulators or the state of the network. I believe that pacemaker neurons provide neural networks with the ability to coordinate population activity and to adjust it in response to several physiological demands. Unfortunately, changes in pacemaker activity can also lead to pathological states associated with several neurological diseases. The study of pacemaker properties, which is a very interesting topic itself, may also identify molecular targets to correct abnormal network activity.

#### **Author details**

Fernando Peña-Ortega *Instituto de Neurobiología, UNAM, México* 

#### **Acknowledgement**

I would like to thank Dorothy Pless for reviewing the English version of this paper. The research in my lab has been sponsored by grants from DGAPA IB200212, CONACyT 151261, and from the Alzheimer's Association NIRG-11-205443.

#### **4. References**


[3] Peña-Ortega F (2011) Possible role or respiratory pacemaker neurons in the generation of different breathing patterns. En: Pacemakers, Theory and Applications. Transworld Research Network., pp. Intech Open Acces Publisher

126 Advances in Clinical Neurophysiology

one type of pacemaker neuron.

targets to correct abnormal network activity.

*Instituto de Neurobiología, UNAM, México* 

151261, and from the Alzheimer's Association NIRG-11-205443.

integrative view. Curr. Opin. Neurobiol. 14:665-674.

generator. Nat. Rev. Neurosci. 6:477-483.

**3. Conclusion** 

**Author details** 

Fernando Peña-Ortega

**Acknowledgement** 

**4. References** 

the preBötC is challenged with hypoxic conditions. During hypoxia, the respiratory network is reconfigured and generates a "last-resort" respiratory rhythm called gasping [79]. Under these conditions the pacemaker neurons relying on the ICAN cease to fire, and the respiratory network relies only on the INap-dependent pacemaker neuron, whose blockade abolishes gasping generation [5,87]. These findings may have clinical relevance since gasping is an important autoresuscitation mechanism that seems to fail in victims of sudden infant death syndrome [SIDS, 88,89]. SIDS victims breathe normally during normoxia, when the respiratory rhythm can be generated by either of the two types of pacemaker, but they do not gasp efficiently in hypoxia [88,89], when the respiratory network relies exclusively on

In conclusion, pacemaker neurons are important components of several mammalian neural networks. The presence of pacemaker neurons allows these networks to produce different types of network activities, both in normal and in pathological conditions. Moreover, the contribution of pacemaker neurons to neural network dynamics is not fixed but depends on the action of neuromodulators or the state of the network. I believe that pacemaker neurons provide neural networks with the ability to coordinate population activity and to adjust it in response to several physiological demands. Unfortunately, changes in pacemaker activity can also lead to pathological states associated with several neurological diseases. The study of pacemaker properties, which is a very interesting topic itself, may also identify molecular

I would like to thank Dorothy Pless for reviewing the English version of this paper. The research in my lab has been sponsored by grants from DGAPA IB200212, CONACyT

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**Chapter 7** 

© 2012 Rodríguez-Carreño et al., licensee InTech. This is an open access chapter distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is

© 2012 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution,

properly cited.

**Motor Unit Action Potential Duration:** 

Ignacio Rodríguez-Carreño, Luis Gila-Useros and Armando Malanda-Trigueros

The quantification of the bioelectric phenomena originating in nervous and muscular tissues is an essential task in diagnosis within the field of Electromedicine. Clinical electromyography is the part of Clinical Neurophysiology focused on the neuromuscular system, and includes the study of the electrical activity of peripheral nerves (electroneurography), striated muscles (electromyography, in its strict sense) and a number

The background to all these scientific areas is based on the parameterization of the bioelectrical functions of the neuromuscular structures. The definition and formulation of such parameters represents the theoretical and practical basis which enables the analysis of the function of muscles and nerves in normal and pathological conditions. The quantification of bioelectrical parameters makes possible to delimit their normal ranges. The presence of parameter values beyond normal ranges, as measured by neurophysiologic techniques, is used in the diagnosis of diseases of nerves and muscles, which is the main

A basic concept in electromyography is the so called motor unit (MU), which represents the anatomical and functional element of the neuromuscular system. The MU is formed by the alpha spinal motorneuron and its innervated set of muscular cells. The electrical changes generated by activity of the MU can be acquired and amplified by electrodes located in muscle mass and these changes can be recorded and edited using electromyographic (EMG) devices. The representation of the changes generated by a MU is the so called motor unit action potential (MUAP). A MUAP waveform can be characterized by a number of parameters related to certain aspects of the structure and physiology of the MU (Figure 1). Therefore, the quantitative measurement of such parameters is a basic issue in electromyography, and the duration of the MUAP is a key measure as it defines the

and reproduction in any medium, provided the original work is properly cited.

**Measurement and Significance** 

Additional information is available at the end of the chapter

of reflex circuits (reflexology), among others [1].

goal of clinical electromyography [2].

http://dx.doi.org/10.5772/50265

**1. Introduction** 


### **Motor Unit Action Potential Duration: Measurement and Significance**

Ignacio Rodríguez-Carreño, Luis Gila-Useros and Armando Malanda-Trigueros

Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/50265

#### **1. Introduction**

132 Advances in Clinical Neurophysiology

J. Neurosci. 31:1219-1232.

Pulmonol. 36:113-122.

[82] Del Negro CA, Morgado-Valle C, Hayes JA, Mackay DD, Pace RW, Crowder EA, Feldman JL (2005) Sodium and calcium current-mediated pacemaker neurons and

[83] Viemari JC, Ramirez JM (2006) Norepinephrine differentially modulates different types of respiratory pacemaker and nonpacemaker neurons. J. Neurophysiol. 95:2070-82. [84] Tryba AK, Peña F, Ramirez JM (2006) Gasping activity in vitro: a rhythm dependent on

[85] Mellen NM, Mishra D (2010) Functional anatomical evidence for respiratory rhythmogenic function of endogenous bursters in rat medulla. J. Neurosci. 30:8383-8392. [86] Ben-Mabrouk F, Tryba AK (2010) Substance P modulation of TRPC3/7 channels improves respiratory rhythm regularity and ICAN-dependent pacemaker activity. Eur.

[87] Peña F, Aguileta MA (2007) Effects of riluzole and flufenamic acid on eupnea and

[88] Poets CF, Meny RG, Chobanian MR, Bonofiglo RE (1999) Gasping and other cardiorespiratory patterns during sudden infant deaths. Pediatr. Res. 45:350-354. [89] Sridhar R, Thach BT, Kelly DH, Henslee JA (2003) Characterization of successful and failed autoresuscitation in human infants, including those dying of SIDS. Pediatr.

respiratory rhythm generation. J. Neurosci. 25(2):446-453.

gasping of neonatal mice in vivo. Neurosci. Lett. 415:288-293.

5-HT2A receptors. J. Neurosci. 26:2623-2634.

The quantification of the bioelectric phenomena originating in nervous and muscular tissues is an essential task in diagnosis within the field of Electromedicine. Clinical electromyography is the part of Clinical Neurophysiology focused on the neuromuscular system, and includes the study of the electrical activity of peripheral nerves (electroneurography), striated muscles (electromyography, in its strict sense) and a number of reflex circuits (reflexology), among others [1].

The background to all these scientific areas is based on the parameterization of the bioelectrical functions of the neuromuscular structures. The definition and formulation of such parameters represents the theoretical and practical basis which enables the analysis of the function of muscles and nerves in normal and pathological conditions. The quantification of bioelectrical parameters makes possible to delimit their normal ranges. The presence of parameter values beyond normal ranges, as measured by neurophysiologic techniques, is used in the diagnosis of diseases of nerves and muscles, which is the main goal of clinical electromyography [2].

A basic concept in electromyography is the so called motor unit (MU), which represents the anatomical and functional element of the neuromuscular system. The MU is formed by the alpha spinal motorneuron and its innervated set of muscular cells. The electrical changes generated by activity of the MU can be acquired and amplified by electrodes located in muscle mass and these changes can be recorded and edited using electromyographic (EMG) devices. The representation of the changes generated by a MU is the so called motor unit action potential (MUAP). A MUAP waveform can be characterized by a number of parameters related to certain aspects of the structure and physiology of the MU (Figure 1). Therefore, the quantitative measurement of such parameters is a basic issue in electromyography, and the duration of the MUAP is a key measure as it defines the

© 2012 Rodríguez-Carreño et al., licensee InTech. This is an open access chapter distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. © 2012 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

boundaries of the MUAP waveform and the rest of the MUAP parameters are measured within the time span defined by the MUAP duration [3].

Motor Unit Action Potential Duration: Measurement and Significance 135

**2. Quantitative characterization of MUAP** 

**2.1. Anatomical and physiological description of MU and MUAP** 

which restores the basal conditions of the membrane at rest. [4].

of other MUs in the muscle concerned.

their superposition.

Striated or skeletal muscles are the effectors of voluntary movements. Striated muscle cells or muscle fibers (MF) have an elongated form, and their contraction is brought about by the sliding of contractile protein filaments contained in their cytoplasm (sarcoplasm). As in any living cell, across the membrane of MFs there exists a difference of electric potential of approximately 90 mV (the inside of the cell being negative with respect to the outside) because there is a difference in the amount of electrical charge in intra- and extra-cellular fluids. A basic property of MFs and neurons is the ability to change the membrane potential and transiently convert their inside into a positive potential in specific conditions. This inversion of potential or depolarization is called an action potential (AP), and it arises by a brief opening of membrane sodium channels, with the consequent rise in the membrane permeability to this ion. The changes of ionic fluxes related to the AP are transmitted towards neighbouring points of the membrane, being conducted along the MF at a velocity of 3-5 m/s. After the depolarization begins, the repolarization phase proceeds, in which there is further passive and active (by the action of the Na-K pump) transmembrane flux of ions,

The nervous system controls the degree of contraction of the MFs by means of the frequency of the nervous impulses of the alpha motor neurons, whose central cellular components are located in the anterior horns of the spinal cord. These nervous impulses are APs of the motor neurons; they travel along the axons and are transmitted to MFs at neuromuscular junctions. As previously described, the system formed by an alpha motor neuron and its set of innervated MFs forms a MU, which represents the anatomical and functional unit of skeletal muscle. The number of MFs innervated by the MU varies according to the muscle. The number is small in the eye muscles, that need very precise adjustments; the large muscles of the lower extremities have several hundred MFs [5]. During a slight voluntary contraction, only a few MUs are activated, and they discharge APs at low frequencies (around 5 per second). To increase the strength of contraction, the nervous system drives a progressive increase in the discharge frequency and a progressive activation or recruitment

The recording and analysis of the electrical activity of MFs and MUs (myoelectrical activity) is the subject of electromyography. Conventional EMG studies are performed with needle electrodes that capture the activity of MFs within a hemisphere of 2.5 mm radius from the tip of the needle electrode. To study the MUAPs of a certain muscle, a needle electrode is inserted into the muscle mass, which the subject is asked to maintain under slight contraction. In this way, a low number of MUs are activated and the successive discharges of the corresponding MUAPs can be collected. If the degree of contraction is excessive, too many MUs are discharging and the recorded waveforms of their MUAPs are distorted by

The main parameters defined to characterize the MUAP waveform are reviewed in this chapter, which also covers the particular issues related to the significance and measurement of the MUAP duration.

**Figure 1.** Schematic representation of a motor unit with n muscle fibers. The algebraic summation of the action potentials (AP) of all the single fibers present in the recording uptake area of the electrode (AP1+AP2+...+APn) generates the motor unit action potential (MUAP). The main parameters of the MUAP waveform are indicated: amp = amplitude; dur = duration; p = phase; t = turn. BL = baseline.

#### **2. Quantitative characterization of MUAP**

134 Advances in Clinical Neurophysiology

of the MUAP duration.

within the time span defined by the MUAP duration [3].

boundaries of the MUAP waveform and the rest of the MUAP parameters are measured

The main parameters defined to characterize the MUAP waveform are reviewed in this chapter, which also covers the particular issues related to the significance and measurement

**Figure 1.** Schematic representation of a motor unit with n muscle fibers. The algebraic summation of the action potentials (AP) of all the single fibers present in the recording uptake area of the electrode (AP1+AP2+...+APn) generates the motor unit action potential (MUAP). The main parameters of the MUAP waveform are indicated: amp = amplitude; dur = duration; p = phase; t = turn. BL = baseline.

#### **2.1. Anatomical and physiological description of MU and MUAP**

Striated or skeletal muscles are the effectors of voluntary movements. Striated muscle cells or muscle fibers (MF) have an elongated form, and their contraction is brought about by the sliding of contractile protein filaments contained in their cytoplasm (sarcoplasm). As in any living cell, across the membrane of MFs there exists a difference of electric potential of approximately 90 mV (the inside of the cell being negative with respect to the outside) because there is a difference in the amount of electrical charge in intra- and extra-cellular fluids. A basic property of MFs and neurons is the ability to change the membrane potential and transiently convert their inside into a positive potential in specific conditions. This inversion of potential or depolarization is called an action potential (AP), and it arises by a brief opening of membrane sodium channels, with the consequent rise in the membrane permeability to this ion. The changes of ionic fluxes related to the AP are transmitted towards neighbouring points of the membrane, being conducted along the MF at a velocity of 3-5 m/s. After the depolarization begins, the repolarization phase proceeds, in which there is further passive and active (by the action of the Na-K pump) transmembrane flux of ions, which restores the basal conditions of the membrane at rest. [4].

The nervous system controls the degree of contraction of the MFs by means of the frequency of the nervous impulses of the alpha motor neurons, whose central cellular components are located in the anterior horns of the spinal cord. These nervous impulses are APs of the motor neurons; they travel along the axons and are transmitted to MFs at neuromuscular junctions. As previously described, the system formed by an alpha motor neuron and its set of innervated MFs forms a MU, which represents the anatomical and functional unit of skeletal muscle. The number of MFs innervated by the MU varies according to the muscle. The number is small in the eye muscles, that need very precise adjustments; the large muscles of the lower extremities have several hundred MFs [5]. During a slight voluntary contraction, only a few MUs are activated, and they discharge APs at low frequencies (around 5 per second). To increase the strength of contraction, the nervous system drives a progressive increase in the discharge frequency and a progressive activation or recruitment of other MUs in the muscle concerned.

The recording and analysis of the electrical activity of MFs and MUs (myoelectrical activity) is the subject of electromyography. Conventional EMG studies are performed with needle electrodes that capture the activity of MFs within a hemisphere of 2.5 mm radius from the tip of the needle electrode. To study the MUAPs of a certain muscle, a needle electrode is inserted into the muscle mass, which the subject is asked to maintain under slight contraction. In this way, a low number of MUs are activated and the successive discharges of the corresponding MUAPs can be collected. If the degree of contraction is excessive, too many MUs are discharging and the recorded waveforms of their MUAPs are distorted by their superposition.

In Europe, the needle electrodes currently used are concentric, which have a core of platinum or stainless steel embedded in insulating material located inside a stainless steel cannula. The core is the active electrode, and the cannula is the reference electrode. A MUAP is a recording of the changes produced by the discharge of the MFs of a MU (Figure 1). In general, normal MUAPs show mean peak-to-peak amplitudes of around 0.5 mV and a duration from 8 to 14 ms, depending on the size of the MUs. The size and shape of MUAPs is determined by certain structural and functional aspects of MUs. Pathologic processes of the peripheral nervous system (neurogenic processes) and of muscles (myopathic pathologies) can alter these aspects, leading to abnormal deviations in MUAP parameters; i.e., the EMG signal captures pathologic remodelling of the MUs caused by neuromuscular diseases. Once other neurophysiologic data and the clinical context of the patient have been taken into account, a deviation with respect to the normal pattern for a given muscle constitutes the basis of an EMG diagnosis.

Motor Unit Action Potential Duration: Measurement and Significance 137

multivariate analysis was used to find the optimal separation from normal MUAPs; in this way, the size index was formulated as [2 x log10 (amplitude) + (area/amplitude)]. However, this index is not significantly better than other parameters for the detection of abnormality

2. MUAP waveform shape parameters transcribe the temporal synchrony / dispersion of the activation times of the MFs and their conduction velocities. These parameters include the number of phases, the number of turns, and indices such as the coefficient

A phase is the part of a MUAP that falls between two baseline (BL) crossings. A turn is a peak (i.e. a point of directional change) in a MUAP waveform. The number of phases is counted within the MUAP duration. Various amplitude and duration criteria are used in computerized measurements to exclude from the count brief BL crossings or small peaks, which may be due to noise [11]. Normal MUAPs have simple shapes between two and four phases. Polyphasic MUAPs have more than four phases, and those with more than five turns are called polyturn or complex MUAPs. These terms all reflect the same feature: increased temporal dispersion of MFs potentials, but polyphasia indicates more

To enhance the sensitivity and precision of measurement of MF synchronicity, other estimators have been proposed, such as the coefficient of irregularity [12]. This is defined as the total amplitude change (over the MUAP length) divided by the peak-to-peak amplitude. The minimum value that MUAP irregularity can have is 2. As the complexity of a waveform increases, the value of this index increases too. Significant differences have been found between pathologies (neurogenic as well as myopathic) of both slow and quick progression [13]; but in general, and in spite of its theoretical background, the coefficient of irregularity

3. Stability parameters or jiggle parameters have been defined to quantify the degree of variability in MUAP shape at consecutive discharges [14]. These parameters are the consecutive amplitude differences (CAD) and cross-correlational coefficients of consecutive discharges (CCC). The efficiency of CAD and CCC has been proved mainly in simulated signals. There are very few studies with real EMG recordings [15], but the presence of noise has been found to significantly affect quantification of the jiggle using these parameters, and consequently the estimation of jiggle still requires subjective

MUAP duration is defined as the time from the start of activation of MU fibers until the end of their repolarization phase, i.e., the time in which the bioelectric changes produced by a

has not shown better performance than conventional parameters.

verification by visual assessment.

**3. MUAP duration** 

discharge of a MU take place.

**3.1. Definition** 

in myopathic conditions [10].

of irregularity.

pronounced changes.

#### **2.2. Parameters of the MUAP and their physiological significance**

To characterize a MUAP waveform quantitatively, a number of parameters have been defined (Figure 1). These parameters are related to certain anatomical and physiological aspects of MFs and MUs. There are three groups of MUAP parameters to characterize the size, shape and stability, respectively, of the MUAP. These parameters, which provide information about certain spatial and temporal characteristics of MF and MU activity, are described below:

1. Size parameters are related to the size (diameter), number and density of generators of a MUAP (i.e. the MFs of the MU). These parameters include duration, amplitude, area and indices such as the size index and thickness index. Since duration will be treated extensively later in this chapter, it will not be described in this section, where a brief description of the other parameters is given.

The amplitude is the voltage difference from minimum to maximum peaks. Computer simulations of MUAPs show that the amplitude is determined by the few MFs (less than eight) located within a semicircular uptake area of 0.5 mm radius from the electrode [6]. Consequently, amplitude can vary considerably within the MU territory (the space within which the MFs of a MU are randomly scattered).

Area can be calculated automatically by integrating the rectified MUAP within the duration. It depends on the MFs present within 1.5 mm from the core of the concentric electrode [7]. Relatively small movements of the recording electrode affect the amplitude and area parameters considerably because the amplitude of the APs of the MFs decays quickly with distance to the electrode [8].

In the quest for more stable estimators of the magnitude of MU generators, new parameters have been defined, the most relevant being the thickness and size indices. The thickness index is computed as the area-to-amplitude ratio, and is a sensitive detector of myopathic abnormalities [9], but not of neurogenic ones. To improve detection of neurogenic MUAPs, multivariate analysis was used to find the optimal separation from normal MUAPs; in this way, the size index was formulated as [2 x log10 (amplitude) + (area/amplitude)]. However, this index is not significantly better than other parameters for the detection of abnormality in myopathic conditions [10].

2. MUAP waveform shape parameters transcribe the temporal synchrony / dispersion of the activation times of the MFs and their conduction velocities. These parameters include the number of phases, the number of turns, and indices such as the coefficient of irregularity.

A phase is the part of a MUAP that falls between two baseline (BL) crossings. A turn is a peak (i.e. a point of directional change) in a MUAP waveform. The number of phases is counted within the MUAP duration. Various amplitude and duration criteria are used in computerized measurements to exclude from the count brief BL crossings or small peaks, which may be due to noise [11]. Normal MUAPs have simple shapes between two and four phases. Polyphasic MUAPs have more than four phases, and those with more than five turns are called polyturn or complex MUAPs. These terms all reflect the same feature: increased temporal dispersion of MFs potentials, but polyphasia indicates more pronounced changes.

To enhance the sensitivity and precision of measurement of MF synchronicity, other estimators have been proposed, such as the coefficient of irregularity [12]. This is defined as the total amplitude change (over the MUAP length) divided by the peak-to-peak amplitude. The minimum value that MUAP irregularity can have is 2. As the complexity of a waveform increases, the value of this index increases too. Significant differences have been found between pathologies (neurogenic as well as myopathic) of both slow and quick progression [13]; but in general, and in spite of its theoretical background, the coefficient of irregularity has not shown better performance than conventional parameters.

3. Stability parameters or jiggle parameters have been defined to quantify the degree of variability in MUAP shape at consecutive discharges [14]. These parameters are the consecutive amplitude differences (CAD) and cross-correlational coefficients of consecutive discharges (CCC). The efficiency of CAD and CCC has been proved mainly in simulated signals. There are very few studies with real EMG recordings [15], but the presence of noise has been found to significantly affect quantification of the jiggle using these parameters, and consequently the estimation of jiggle still requires subjective verification by visual assessment.

#### **3. MUAP duration**

#### **3.1. Definition**

136 Advances in Clinical Neurophysiology

constitutes the basis of an EMG diagnosis.

description of the other parameters is given.

which the MFs of a MU are randomly scattered).

distance to the electrode [8].

described below:

**2.2. Parameters of the MUAP and their physiological significance** 

To characterize a MUAP waveform quantitatively, a number of parameters have been defined (Figure 1). These parameters are related to certain anatomical and physiological aspects of MFs and MUs. There are three groups of MUAP parameters to characterize the size, shape and stability, respectively, of the MUAP. These parameters, which provide information about certain spatial and temporal characteristics of MF and MU activity, are

1. Size parameters are related to the size (diameter), number and density of generators of a MUAP (i.e. the MFs of the MU). These parameters include duration, amplitude, area and indices such as the size index and thickness index. Since duration will be treated extensively later in this chapter, it will not be described in this section, where a brief

The amplitude is the voltage difference from minimum to maximum peaks. Computer simulations of MUAPs show that the amplitude is determined by the few MFs (less than eight) located within a semicircular uptake area of 0.5 mm radius from the electrode [6]. Consequently, amplitude can vary considerably within the MU territory (the space within

Area can be calculated automatically by integrating the rectified MUAP within the duration. It depends on the MFs present within 1.5 mm from the core of the concentric electrode [7]. Relatively small movements of the recording electrode affect the amplitude and area parameters considerably because the amplitude of the APs of the MFs decays quickly with

In the quest for more stable estimators of the magnitude of MU generators, new parameters have been defined, the most relevant being the thickness and size indices. The thickness index is computed as the area-to-amplitude ratio, and is a sensitive detector of myopathic abnormalities [9], but not of neurogenic ones. To improve detection of neurogenic MUAPs,

In Europe, the needle electrodes currently used are concentric, which have a core of platinum or stainless steel embedded in insulating material located inside a stainless steel cannula. The core is the active electrode, and the cannula is the reference electrode. A MUAP is a recording of the changes produced by the discharge of the MFs of a MU (Figure 1). In general, normal MUAPs show mean peak-to-peak amplitudes of around 0.5 mV and a duration from 8 to 14 ms, depending on the size of the MUs. The size and shape of MUAPs is determined by certain structural and functional aspects of MUs. Pathologic processes of the peripheral nervous system (neurogenic processes) and of muscles (myopathic pathologies) can alter these aspects, leading to abnormal deviations in MUAP parameters; i.e., the EMG signal captures pathologic remodelling of the MUs caused by neuromuscular diseases. Once other neurophysiologic data and the clinical context of the patient have been taken into account, a deviation with respect to the normal pattern for a given muscle

> MUAP duration is defined as the time from the start of activation of MU fibers until the end of their repolarization phase, i.e., the time in which the bioelectric changes produced by a discharge of a MU take place.

Motor Unit Action Potential Duration: Measurement and Significance 139

total duration, but this does not need necessarily be so if a satellite is present. Satellites, which usually follow the terminal part (but exceptionally precede the initial part), are included in the measurement of spike duration and thus, spike duration may exceed the

**Figure 3.** Example of recording a MUAP at different distances from the end-plate zone (position 1). As the distance from the end plate increases (1 to 4), the initial positive part of the MUAP becomes longer.

3. The terminal part: from the last positive turn until the endpoint, where the signal reaches the BL. The terminal part is longer than the previous parts because the approach to the BL is gradual. The terminal part is generated by the volley of APs

leaving the electrode and it includes the main part of the repolarization phase. 4. Small positive afterwave: these are not usually seen in recordings with concentric electrodes, but can be observed within the terminal part of MUAPs in recordings performed with monopolar electrodes (which are more commonly used in the United States than Europe). The small positive afterwave reflects the arrival of MF depolarization at the muscle-tendon union with the tendon [21]. When a small positive wave is present, usually superimposed on the terminal part, it is included in

Below, a schematic presentation of the recording positions with respect to the end-plate zone.

total duration of the MUAP [20].

the MUAP duration.

**Figure 2.** Parts of the MUAP. MUAP parameters: duration, spike duration, turns, phases.

#### **3.2. Parts of the MUAP**

Over the duration of a MUAP, several parts of the MUAP waveform can be delimited (Figure 2), each one having specific structural and functional significance [16]:


total duration, but this does not need necessarily be so if a satellite is present. Satellites, which usually follow the terminal part (but exceptionally precede the initial part), are included in the measurement of spike duration and thus, spike duration may exceed the total duration of the MUAP [20].

138 Advances in Clinical Neurophysiology

**3.2. Parts of the MUAP** 

**Figure 2.** Parts of the MUAP. MUAP parameters: duration, spike duration, turns, phases.

(Figure 2), each one having specific structural and functional significance [16]:

Over the duration of a MUAP, several parts of the MUAP waveform can be delimited

1. The initial part: from the start of MF activation to the first positive turn. Graphically this is a positive deflection whose charactersitics depend on the distance of the motor end-plate region until the situation of the recording electrode in the length of the fiber. If the electrode is close to the end-plate zone, the initial positive part in the MUAP hardly exists, and the MUAP waveform begins with an initial upward defection (Figure 3). As the distance between the end-plate region and the tip of the electrode increases, the initial part becomes more and more evident and its duration increases as well, being maximal when

the electrode is located near the extreme of the MFs near the tendon. [3, 16-18].

2. The spike part: between the first and the last positive turns. The spike part mainly depends on the temporal dispersion of the MF potentials as they pass in the vicinity of the recording electrode. It is thought likely that only less than 15 fibers contribute to the spike part in normal MUs [19]. The spike usually has one negative peak, called the main spike, but may have several positive peaks. Note that a MUAP may contain spike components other than the main spike. Such parts are called satellites. Spike duration is measured between the first and the last positive peak of the MUAP (Figure 2). If the MUAP is recorded in the end-plate region, the start of the MUAP and of the spike part coincide, because there is no initial part. The spike duration is usually shorter than the

**Figure 3.** Example of recording a MUAP at different distances from the end-plate zone (position 1). As the distance from the end plate increases (1 to 4), the initial positive part of the MUAP becomes longer. Below, a schematic presentation of the recording positions with respect to the end-plate zone.


5. Negative afterwave: this is an artifactual wave that arises due to the effect of the highpass filter of the amplifier (Figure 4), mainly when the MUAP has a dominating positive phase, which is counterbalanced by a negative afterwave [22, 23]. A negative afterwave usually has low amplitude (less than 10 microvolts), but, in any case, it is an artifact and should be excluded from duration measurements.

Motor Unit Action Potential Duration: Measurement and Significance 141

over samples of normal subjects for each muscle and age range. [24, 25]. Reference values from healthy subjects show little correlation to gender, height and weight. Within the age range of 15 to 65 years the effect of age is negligible [26], but an increase of duration has

*Increased amplitude* Muscle fibers grouping (reinervation, regeneration)

*Increased duration* Increase in the number of muscle fibers (collateral growing) *Increased spike duration* Variation in the diameter of the muscle fibers

fibers

*Increase in the jiggle* Abnormal neuromuscular transmission

population for the same muscle and age group as the subject under study [28].

neuromuscular transmission (such as in botulism or myasthenia gravis).

For the EMG examination of a muscle, a sample of 20 MUAPs must be extracted [18]. The mean values of the different MUAP parameters are matched up with their respective reference values. Deviations from normality may be defined as a value of mean duration plus/minus 2 standard deviations above or below that for samples from the normal

Abnormally high duration values result from an abnormal increase in the number of MFs in the MUs in neurogenic processes due to collateral reinnervation and focal grouping. The neurogenic MUAPs can have simple or complex shapes and can be stable or instable (normal or increased jiggle) depending on the nature of the pathology and its temporal course (acute, subacute or chronic). With regard to abnormally low duration values, a low duration reflects loss of MFs in myopathic processes, myophatic atrophy of MFs or neurogenic lesions at early stages of reinnervation (nascent MUAPs), or severe blocking of

MUAP duration is a basic parameter of the MUAP due to its physiopathologic significance and also due to the fact that the duration markers (the established start and end points)

**Table 1.** Relation between MUAP alterations and abnormality reflected.

Increasement of connective tissue Excessive jitter and blocking

Serious MUAPs blocking in endplate

Increase in the width of the endplate

Increase in the variability of the diameter of muscle

Decrease in the force generated by individual MUs

Slow conduction in terminal axons Increase in the width of the endplate

Muscle fibers hypertrophia

Loss of muscle fibers

**MUAP abnormality Anatomical phenomena related**

*Decreased amplitude* Muscle fibers' atrophia

*Decreased duration* Muscle fibers' atrophia

been reported for subjects of older ages [27].

*Increase in the number of turns and* 

*Increase in the firing rate* Loss of MUs

*phases* 

**Figure 4.** Effects of the high pass filter in the MUAP waveform. The cut-off frequencies of the filters 8 applied in (a) and (b) are 10 and 50 Hz, respectively.

#### **3.3. Physiopathological significance of MUAP duration**

Computer simulations of the MUAP indicate that the duration reflects the current generated by the MFs within 2.5 mm of the active recording surface of the electrode [6]. The total current is determined by the number of MFs and their cross-sectional area. Duration is not affected by slight changes in the electrode position, in comparison to amplitude and area, both of which are sensitive to this change.

The total MUAP duration comprises the slow initial and terminal phases of the MUAP signal. These parts represent the time when the APs of the MFs are at some distance from the electrode and the APs are still relatively equidistant from the recording surface and contribute to a similar extent. Therefore, the duration of the normal MUAP is not so much dependent on the temporal dispersion of the individual MF APs but more on the number of MFs within the recording area [3]. Although the degree of temporal dispersion of the APs of MFs is specifically expressed by the spike duration and shape parameters, temporal dispersion also influences the magnitude of the total MUAP duration, as can be seen in pathologic MUAPs. When there is large variability in MF diameters, an enlarged end-plate region or a mixture of slow- and fast-conducting terminal axons, the temporal dispersion of MF potentials is pronounced, resulting in MUAPs with long durations and more or less complex waveforms (sometimes extremely complex).

The physiopathological correlations underlying the magnitude of total MUAP duration, makes the duration measurement clinically useful (Table 1). The duration is a parameter currently used in clinical electromyography and its normative values have been established over samples of normal subjects for each muscle and age range. [24, 25]. Reference values from healthy subjects show little correlation to gender, height and weight. Within the age range of 15 to 65 years the effect of age is negligible [26], but an increase of duration has been reported for subjects of older ages [27].

140 Advances in Clinical Neurophysiology

5. Negative afterwave: this is an artifactual wave that arises due to the effect of the highpass filter of the amplifier (Figure 4), mainly when the MUAP has a dominating positive phase, which is counterbalanced by a negative afterwave [22, 23]. A negative afterwave usually has low amplitude (less than 10 microvolts), but, in any case, it is an artifact and

**Figure 4.** Effects of the high pass filter in the MUAP waveform. The cut-off frequencies of the filters 8

Computer simulations of the MUAP indicate that the duration reflects the current generated by the MFs within 2.5 mm of the active recording surface of the electrode [6]. The total current is determined by the number of MFs and their cross-sectional area. Duration is not affected by slight changes in the electrode position, in comparison to amplitude and area,

The total MUAP duration comprises the slow initial and terminal phases of the MUAP signal. These parts represent the time when the APs of the MFs are at some distance from the electrode and the APs are still relatively equidistant from the recording surface and contribute to a similar extent. Therefore, the duration of the normal MUAP is not so much dependent on the temporal dispersion of the individual MF APs but more on the number of MFs within the recording area [3]. Although the degree of temporal dispersion of the APs of MFs is specifically expressed by the spike duration and shape parameters, temporal dispersion also influences the magnitude of the total MUAP duration, as can be seen in pathologic MUAPs. When there is large variability in MF diameters, an enlarged end-plate region or a mixture of slow- and fast-conducting terminal axons, the temporal dispersion of MF potentials is pronounced, resulting in MUAPs with long durations and more or less

The physiopathological correlations underlying the magnitude of total MUAP duration, makes the duration measurement clinically useful (Table 1). The duration is a parameter currently used in clinical electromyography and its normative values have been established

should be excluded from duration measurements.

applied in (a) and (b) are 10 and 50 Hz, respectively.

both of which are sensitive to this change.

complex waveforms (sometimes extremely complex).

**3.3. Physiopathological significance of MUAP duration** 


**Table 1.** Relation between MUAP alterations and abnormality reflected.

For the EMG examination of a muscle, a sample of 20 MUAPs must be extracted [18]. The mean values of the different MUAP parameters are matched up with their respective reference values. Deviations from normality may be defined as a value of mean duration plus/minus 2 standard deviations above or below that for samples from the normal population for the same muscle and age group as the subject under study [28].

Abnormally high duration values result from an abnormal increase in the number of MFs in the MUs in neurogenic processes due to collateral reinnervation and focal grouping. The neurogenic MUAPs can have simple or complex shapes and can be stable or instable (normal or increased jiggle) depending on the nature of the pathology and its temporal course (acute, subacute or chronic). With regard to abnormally low duration values, a low duration reflects loss of MFs in myopathic processes, myophatic atrophy of MFs or neurogenic lesions at early stages of reinnervation (nascent MUAPs), or severe blocking of neuromuscular transmission (such as in botulism or myasthenia gravis).

MUAP duration is a basic parameter of the MUAP due to its physiopathologic significance and also due to the fact that the duration markers (the established start and end points) define the boundaries of the MUAP waveform and thereby separate those parts of the recorded signal which will be analyzed from other parts, such as BL or background activity. All MUAP parameters and features are measured within the MUAP duration or, in the event of the presence of satellites, with respect to it; consequently, duration is the first parameter that must be determined.

Motor Unit Action Potential Duration: Measurement and Significance 143

above) and the "clinical" [29, 30]. The above considerations are indicative of the operational difficulties encountered with the simple physiologic definition of MUAP duration. The concept of clinical duration is that applied generally in diagnostic applications and will be used in the rest of this text. As with physiologic duration, there are difficulties in the

measurement of MUAP clinical duration. These difficulties are discussed below.

**Figure 5.** The same MUAP displayed at different gains. As the MUAP is amplified, its duration is measured longer due to the visual effect. Continuous, short dashed, and dashed lines represent the

Clinical MUAP duration is defined as the time between the start and end points of the MUAP, when observed at a sensitivity of 100 μV/cm and a sweep screen of 10 ms/cm [3, 16, 29]. At higher gains, duration measurements tend to be longer because more of the slight initial or terminal slopes are visible before they merge with the random noise of background activity [23], see Figure 5. The gain of 100 μ V/cm was arbitrarily chosen to standardize the visual resolution at which duration markers should be manually placed. In this way, duration can be conceived of as a morphological feature, operationally defined in accordance with a specified magnitude of display resolution at which the recorded signal is

When making manual measurements, electromyographists measure MUAP duration by visual inspection at the standardized settings stated above. Manual measurements can be made for an isolated discharge, over the averaged potential resulting from a set of MUAP

duration markers at 500, 100 and 50 μV/cm, respectively.

represented.

**4.3. Manual measurement of clinical MUAP duration** 

#### **4. Measurement of MUAP duration**

#### **4.1. A challenge for quantitative electromyography**

Technical improvements implemented on recent EMG machines have made many aspects of EMG examinations easier. Examples of such improvements are facilities for extraction of MUAP signals; edition, storage, automatic measurement of parameters; calculation of mean values; and the process of matching normative ranges. However, in clinical electromyography, diagnostic judgment, i.e., the final diagnostic conclusions built upon the collected data, is still mainly dependent on the knowledge and experience of the electromyographist who performs the study. Quantitative methods try to overcome subjective considerations by means of precise measurements of physiopathologically significant features. The performance of such methodologies is in general satisfactory when the conditions of the study are favorable: a collaborating patient, a fully developed pathology, and low levels of noise. But, working circumstances are seldom so perfect, and there are still important limitations mainly due to two disrupting factors that currently can only be partially controlled: variability and noise. In this respect, the measurement of MUAP duration can serve as a paradigmatic example of a fundamental challenge facing clinical neurophysiology: how to extract objective and consistent parameter estimates. The nature of the challenge is shown in the following considerations.

#### **4.2. Clinical and physiologic duration**

The definition of MUAP duration is, as stated above, simply the time between the beginning and the end of the bio-electrical activity of the MUs detected by the recording electrode. Often, the "duration onset" can be easily determined because the takeoff of the MUAP waveform, which is associated with the depolarization of MFs at the end-plates, is so abrupt that the waveform appears clearly deflected from the BL. This occurs especially if the recording has been made close to the end-plate zone and if the MUAP does not have an initial negative part. However, the "duration end", which is not associated with any clearly identified physiological event, is more difficult to determine because the terminal part of the waveform approaches the BL gradually. With real recordings and in simulation studies, it has been demonstrated that the extinction of APs continues for over 20 ms after the main spike of the MUAP [29-31]. In real recordings, a very stable BL and a large number of averaged discharges are needed in order to observe such a slow return to the BL in the terminal part of the MUAP. Routine recordings, however, almost invariably have slow BL fluctuations and other noisy interference that obscure the full extension of the terminal part. Thus, two meanings of "duration" should be considered: the "physiologic" (as defined above) and the "clinical" [29, 30]. The above considerations are indicative of the operational difficulties encountered with the simple physiologic definition of MUAP duration. The concept of clinical duration is that applied generally in diagnostic applications and will be used in the rest of this text. As with physiologic duration, there are difficulties in the measurement of MUAP clinical duration. These difficulties are discussed below.

142 Advances in Clinical Neurophysiology

parameter that must be determined.

**4. Measurement of MUAP duration** 

**4.1. A challenge for quantitative electromyography** 

the challenge is shown in the following considerations.

**4.2. Clinical and physiologic duration** 

define the boundaries of the MUAP waveform and thereby separate those parts of the recorded signal which will be analyzed from other parts, such as BL or background activity. All MUAP parameters and features are measured within the MUAP duration or, in the event of the presence of satellites, with respect to it; consequently, duration is the first

Technical improvements implemented on recent EMG machines have made many aspects of EMG examinations easier. Examples of such improvements are facilities for extraction of MUAP signals; edition, storage, automatic measurement of parameters; calculation of mean values; and the process of matching normative ranges. However, in clinical electromyography, diagnostic judgment, i.e., the final diagnostic conclusions built upon the collected data, is still mainly dependent on the knowledge and experience of the electromyographist who performs the study. Quantitative methods try to overcome subjective considerations by means of precise measurements of physiopathologically significant features. The performance of such methodologies is in general satisfactory when the conditions of the study are favorable: a collaborating patient, a fully developed pathology, and low levels of noise. But, working circumstances are seldom so perfect, and there are still important limitations mainly due to two disrupting factors that currently can only be partially controlled: variability and noise. In this respect, the measurement of MUAP duration can serve as a paradigmatic example of a fundamental challenge facing clinical neurophysiology: how to extract objective and consistent parameter estimates. The nature of

The definition of MUAP duration is, as stated above, simply the time between the beginning and the end of the bio-electrical activity of the MUs detected by the recording electrode. Often, the "duration onset" can be easily determined because the takeoff of the MUAP waveform, which is associated with the depolarization of MFs at the end-plates, is so abrupt that the waveform appears clearly deflected from the BL. This occurs especially if the recording has been made close to the end-plate zone and if the MUAP does not have an initial negative part. However, the "duration end", which is not associated with any clearly identified physiological event, is more difficult to determine because the terminal part of the waveform approaches the BL gradually. With real recordings and in simulation studies, it has been demonstrated that the extinction of APs continues for over 20 ms after the main spike of the MUAP [29-31]. In real recordings, a very stable BL and a large number of averaged discharges are needed in order to observe such a slow return to the BL in the terminal part of the MUAP. Routine recordings, however, almost invariably have slow BL fluctuations and other noisy interference that obscure the full extension of the terminal part. Thus, two meanings of "duration" should be considered: the "physiologic" (as defined

**Figure 5.** The same MUAP displayed at different gains. As the MUAP is amplified, its duration is measured longer due to the visual effect. Continuous, short dashed, and dashed lines represent the duration markers at 500, 100 and 50 μV/cm, respectively.

#### **4.3. Manual measurement of clinical MUAP duration**

Clinical MUAP duration is defined as the time between the start and end points of the MUAP, when observed at a sensitivity of 100 μV/cm and a sweep screen of 10 ms/cm [3, 16, 29]. At higher gains, duration measurements tend to be longer because more of the slight initial or terminal slopes are visible before they merge with the random noise of background activity [23], see Figure 5. The gain of 100 μ V/cm was arbitrarily chosen to standardize the visual resolution at which duration markers should be manually placed. In this way, duration can be conceived of as a morphological feature, operationally defined in accordance with a specified magnitude of display resolution at which the recorded signal is represented.

When making manual measurements, electromyographists measure MUAP duration by visual inspection at the standardized settings stated above. Manual measurements can be made for an isolated discharge, over the averaged potential resulting from a set of MUAP discharges or by visual inspection of a set of discharges in superimposed and/or raster modes.

Motor Unit Action Potential Duration: Measurement and Significance 145

variance of repeated measurements of a given feature, and it is currently applied in industrial quality control studies. It was designed to assess both the variability in product magnitudes caused by the production process itself (part-to-part variability) and the variability attributable to the measurement system (the gage). The latter component of variability includes that attributable to the measurement device (the repeatability or intraoperator variability), assessed by repeated measurements by the same operator, and that attributable to the operator (reproducibility or interoperator variability), assessed by comparison of the measurements made by different operators. In the context of MUAP duration measurement, the part-to-part variability is related to the intrinsic variability of MUAP duration present in each sample of MUAPs extracted from a given muscle. This intrinsic variability of MUAP duration (i.e. variability of the object being measured as opposed to the process of measurement) is due to differences in size and structure of a

The Gage R&R method was applied to six independent duration measurements performed by two electromyographists (three measurements separated in time by each electromyographist) on a set of 240 MUAPs from two muscles without pathology: the tibialis anterior and the first dorsal interosseous. The MUAPs accepted for analysis had well-defined waveforms and were free of superposition, gross BL fluctuations and distortions of other sources. In order to make manual measurements, an interactive software tool displaying the averaged MUAP and the set of the extracted discharges in raster and superimposed modes was provided. The time base and sensitivity could be changed by the operators, but the sensitivity and sweep speed for placing duration markers was fixed at the

In spite of the favourable conditions (the clean and well-defined MUAP waveforms and the good-quality visualization and measurement software), a high degree of variability in duration measurements was observed. Of the six evaluations of start marker position, the biggest difference for a MUAP was 6.6 ms. Broader ranges, up to 11.2 ms, were observed for end marker positions. The biggest ranges were observed in end marker positions for MUAPs with a long and gradually-sloped terminal part to their waveforms (Figure 6a). This particular feature of MUAP waveforms was found to be the major cause of difficulty in the manual procedure, since other confounding factors, such as the presence of noise, BL fluctuations and secondary MUAPs in the recordings, were minimised at the time of selecting samples of MUAPs for the study. Examples of other difficulties encountered in

The reproducibility and repeatability analysis by the Gage R&R method decomposes the total variability of the measurements into that intrinsic to the sampled MUAPs (the part-topart variability, i.e., the variability in the measured parameter *per se*) and the variability attributable to the electromyographists. The latter component accounted for over 30% of total variability and was mainly due to variability in repeated measurements by the same examiner (intraoperator variability). In industrial contexts, where the Gage R&R method is

muscle's MUs and to differences in electrode positioning within the muscle.

standard values of 100 μV/cm and 10 ms/cm, respectively.

manual placement of duration markers are given in Figure 6.

#### **4.4. Automatic measurement of MUAP duration**

A number of algorithmic methods for automatic measurement of duration have been designed and implemented on commercial equipment. Such algorithms aim to reproduce the manual procedure, and those used in computer-aided methods include BL calculation and use quantitative amplitude or slope criterion or a combination of both to look for the limit points between the MUAP waveform and the BL [3, 16]. Quantitative definitions applied to the analysis of morphologic features of the MUAP are similar to the automatic counting of turns and phases [32, 33]. Usually these algorithms are applied to the averaged MUAP waveform obtained from the discharges that have been recorded and extracted with automatic assistance [34, 35].

One might expect these algorithms to be more reliable than manual measurement, but in fact they suffer from several limitations when dealing with real signals. High variability has been observed in automatic as well as manual measurements. In addition, automatic measurements are often inaccurate, always require visual supervision, and frequently require manual correction of duration marker positions.

#### **5. Variability of manual measurements**

Duration has long been recognized as the most difficult MUAP parameter to define and measure in an unequivocal way, and exact positioning of the endpoint is recognized to be somewhat arbitrary [16]. It is therefore likely that the inter- and intra-examiner variability of manual duration measurements is greater than that for the other MUAP parameters. An important consequence of this variability is that the normal limits of MUAP duration for a given muscle and age range have broad margins, which drastically reduce the diagnostic sensitivity of the parameter [36]. Thus, whilst large deviations from normality are easily identified, the intepretation of the significance of smaller deviations depends considerably on the examiner.

Several studies have investigated the variability of repeated manual duration measurements. In one study, a set of 25 nearly-normal MUAPs recorded from the brachial biceps muscle were manually analyzed three times on different days by the same single electromyographist. In the three repeated manual measurements, the mean durations ranged from 14.9 to 15.7 ms, and the largest difference between durations of MUAPs from the same MU was 8 ms [16]. Similar observations of such low degrees of reliability of manual duration readings have been reported by other authors [37-39].

In another study, for a systematic quantitative estimation of the intra- and inter-examiner variability in MUAP duration measurements, the Gage Reproducibility and Repeatability (Gage R&R) method [40, 41] was applied [42]. This method is based on the analysis of the variance of repeated measurements of a given feature, and it is currently applied in industrial quality control studies. It was designed to assess both the variability in product magnitudes caused by the production process itself (part-to-part variability) and the variability attributable to the measurement system (the gage). The latter component of variability includes that attributable to the measurement device (the repeatability or intraoperator variability), assessed by repeated measurements by the same operator, and that attributable to the operator (reproducibility or interoperator variability), assessed by comparison of the measurements made by different operators. In the context of MUAP duration measurement, the part-to-part variability is related to the intrinsic variability of MUAP duration present in each sample of MUAPs extracted from a given muscle. This intrinsic variability of MUAP duration (i.e. variability of the object being measured as opposed to the process of measurement) is due to differences in size and structure of a muscle's MUs and to differences in electrode positioning within the muscle.

144 Advances in Clinical Neurophysiology

automatic assistance [34, 35].

on the examiner.

**4.4. Automatic measurement of MUAP duration** 

require manual correction of duration marker positions.

**5. Variability of manual measurements** 

modes.

discharges or by visual inspection of a set of discharges in superimposed and/or raster

A number of algorithmic methods for automatic measurement of duration have been designed and implemented on commercial equipment. Such algorithms aim to reproduce the manual procedure, and those used in computer-aided methods include BL calculation and use quantitative amplitude or slope criterion or a combination of both to look for the limit points between the MUAP waveform and the BL [3, 16]. Quantitative definitions applied to the analysis of morphologic features of the MUAP are similar to the automatic counting of turns and phases [32, 33]. Usually these algorithms are applied to the averaged MUAP waveform obtained from the discharges that have been recorded and extracted with

One might expect these algorithms to be more reliable than manual measurement, but in fact they suffer from several limitations when dealing with real signals. High variability has been observed in automatic as well as manual measurements. In addition, automatic measurements are often inaccurate, always require visual supervision, and frequently

Duration has long been recognized as the most difficult MUAP parameter to define and measure in an unequivocal way, and exact positioning of the endpoint is recognized to be somewhat arbitrary [16]. It is therefore likely that the inter- and intra-examiner variability of manual duration measurements is greater than that for the other MUAP parameters. An important consequence of this variability is that the normal limits of MUAP duration for a given muscle and age range have broad margins, which drastically reduce the diagnostic sensitivity of the parameter [36]. Thus, whilst large deviations from normality are easily identified, the intepretation of the significance of smaller deviations depends considerably

Several studies have investigated the variability of repeated manual duration measurements. In one study, a set of 25 nearly-normal MUAPs recorded from the brachial biceps muscle were manually analyzed three times on different days by the same single electromyographist. In the three repeated manual measurements, the mean durations ranged from 14.9 to 15.7 ms, and the largest difference between durations of MUAPs from the same MU was 8 ms [16]. Similar observations of such low degrees of reliability of

In another study, for a systematic quantitative estimation of the intra- and inter-examiner variability in MUAP duration measurements, the Gage Reproducibility and Repeatability (Gage R&R) method [40, 41] was applied [42]. This method is based on the analysis of the

manual duration readings have been reported by other authors [37-39].

The Gage R&R method was applied to six independent duration measurements performed by two electromyographists (three measurements separated in time by each electromyographist) on a set of 240 MUAPs from two muscles without pathology: the tibialis anterior and the first dorsal interosseous. The MUAPs accepted for analysis had well-defined waveforms and were free of superposition, gross BL fluctuations and distortions of other sources. In order to make manual measurements, an interactive software tool displaying the averaged MUAP and the set of the extracted discharges in raster and superimposed modes was provided. The time base and sensitivity could be changed by the operators, but the sensitivity and sweep speed for placing duration markers was fixed at the standard values of 100 μV/cm and 10 ms/cm, respectively.

In spite of the favourable conditions (the clean and well-defined MUAP waveforms and the good-quality visualization and measurement software), a high degree of variability in duration measurements was observed. Of the six evaluations of start marker position, the biggest difference for a MUAP was 6.6 ms. Broader ranges, up to 11.2 ms, were observed for end marker positions. The biggest ranges were observed in end marker positions for MUAPs with a long and gradually-sloped terminal part to their waveforms (Figure 6a). This particular feature of MUAP waveforms was found to be the major cause of difficulty in the manual procedure, since other confounding factors, such as the presence of noise, BL fluctuations and secondary MUAPs in the recordings, were minimised at the time of selecting samples of MUAPs for the study. Examples of other difficulties encountered in manual placement of duration markers are given in Figure 6.

The reproducibility and repeatability analysis by the Gage R&R method decomposes the total variability of the measurements into that intrinsic to the sampled MUAPs (the part-topart variability, i.e., the variability in the measured parameter *per se*) and the variability attributable to the electromyographists. The latter component accounted for over 30% of total variability and was mainly due to variability in repeated measurements by the same examiner (intraoperator variability). In industrial contexts, where the Gage R&R method is frequently used, degrees of operator variability greater than 10% are considered as poor, and greater than 30% as unacceptable [43].

Motor Unit Action Potential Duration: Measurement and Significance 147

above, no single manual measurement can be accepted as the true and exact one, a probabilistic approach to the definition of the GSP has been proposed [42]. For the start or end point of a given MUAP, the GSP was calculated from a set of six marker positions obtained from the repeated marker placements made by two examiners. Specifically, the GSP was calculated as the mean of the three marker positions that were closest together.

**Figure 7.** Determination of the gold standard of the GSP in an example of six manual marker positions

As illustrated in the example in Figure 7, the six markers were ordered by their respective time values from lesser to greater (1 to 6). The five differences between the six position values were obtained (*d1* to *d5*) and the means between two consecutive differences were calculated ( <sup>1</sup> *x* to 4 *x* ). The smallest of the four mean values was selected ( <sup>1</sup> *x* in this example) and the GSP (marked with a cross in the figure) was obtained as the mean of the three manual markers with lowest mean difference (markers 1, 2 and 3 in this example) [42]. By means of this approximation, although a position cannot be assumed to be "true" or even "the best", it can be regarded as a "most likely" position. Thus, such a position can be adopted as a GSP on the basis that it is better in a probabilistic sense than any single position

**7. Description of conventional methods for automatic measurement of** 

The use of computer-aided measurements can theoretically resolve the problem of intraand inter-examiner variability. The execution of any algorithm on the same signal will always give the same results, without any variability in repeated measurements. In view of this, several automatic methods were developed to try to reproduce the manual procedure used by electromyographists, using amplitude and/or slope criteria to look for the limit

of the end point.

made by manual placement.

**MUAP duration** 

**Figure 6.** Variability in the manual placement of the duration markers. For different electromyographists, there are usually small differences in the manual positions of the start markers (a, d). Great dispersion in the position of the start or end marker can be seen occasionally in the initial part of the MUAP when it has a low slope (a, c). Superimposed discharges of other MUAPs over the initial or terminal portions of the MUAP waveform (b) and the presence of two different slopes separated by an inflexion point at the final portion of the MUAP (d) can be other sources of greta variability in the position of end marker.

#### **6. Proposal of a "gold standard" for MUAP duration measurement**

As can be concluded from the above discussion, a manual procedure does not guarantee consistent and reliable measurements of MUAP duration. Therefore, if duration markers are automatically placed by a modern EMG device and an error is detected by visual inspection, manual correction does not ensure an accurate estimate of MUAP duration.

In order to assess the effectiveness of a given automatic method of MUAP duration measurement, it is necessary to have available a "gold standard" of duration marker positions (GSP), that is, the marker positions which the automatic method should be finding automatically. Since, as a result of the conceptual and operational limitations exposed above, no single manual measurement can be accepted as the true and exact one, a probabilistic approach to the definition of the GSP has been proposed [42]. For the start or end point of a given MUAP, the GSP was calculated from a set of six marker positions obtained from the repeated marker placements made by two examiners. Specifically, the GSP was calculated as the mean of the three marker positions that were closest together.

146 Advances in Clinical Neurophysiology

position of end marker.

and greater than 30% as unacceptable [43].

frequently used, degrees of operator variability greater than 10% are considered as poor,

**Figure 6.** Variability in the manual placement of the duration markers. For different

electromyographists, there are usually small differences in the manual positions of the start markers (a, d). Great dispersion in the position of the start or end marker can be seen occasionally in the initial part of the MUAP when it has a low slope (a, c). Superimposed discharges of other MUAPs over the initial or terminal portions of the MUAP waveform (b) and the presence of two different slopes separated by an inflexion point at the final portion of the MUAP (d) can be other sources of greta variability in the

**6. Proposal of a "gold standard" for MUAP duration measurement** 

manual correction does not ensure an accurate estimate of MUAP duration.

As can be concluded from the above discussion, a manual procedure does not guarantee consistent and reliable measurements of MUAP duration. Therefore, if duration markers are automatically placed by a modern EMG device and an error is detected by visual inspection,

In order to assess the effectiveness of a given automatic method of MUAP duration measurement, it is necessary to have available a "gold standard" of duration marker positions (GSP), that is, the marker positions which the automatic method should be finding automatically. Since, as a result of the conceptual and operational limitations exposed

**Figure 7.** Determination of the gold standard of the GSP in an example of six manual marker positions of the end point.

As illustrated in the example in Figure 7, the six markers were ordered by their respective time values from lesser to greater (1 to 6). The five differences between the six position values were obtained (*d1* to *d5*) and the means between two consecutive differences were calculated ( <sup>1</sup> *x* to 4 *x* ). The smallest of the four mean values was selected ( <sup>1</sup> *x* in this example) and the GSP (marked with a cross in the figure) was obtained as the mean of the three manual markers with lowest mean difference (markers 1, 2 and 3 in this example) [42]. By means of this approximation, although a position cannot be assumed to be "true" or even "the best", it can be regarded as a "most likely" position. Thus, such a position can be adopted as a GSP on the basis that it is better in a probabilistic sense than any single position made by manual placement.

#### **7. Description of conventional methods for automatic measurement of MUAP duration**

The use of computer-aided measurements can theoretically resolve the problem of intraand inter-examiner variability. The execution of any algorithm on the same signal will always give the same results, without any variability in repeated measurements. In view of this, several automatic methods were developed to try to reproduce the manual procedure used by electromyographists, using amplitude and/or slope criteria to look for the limit points of the MUAP waveform with respect to the BL. Among the reported methods there are differences in several aspects, as described in detail in [16] and [35]. To illustrate these computer-aided techniques, a brief description of several conventional automatic methods (CAMs) is given below. Descriptions include a consideration of differences in the extraction procedure of the MUAP waveform, the definition of the BL and the criteria applied to find the MUAP start and end points (the duration markers positions). The five methods reviewed are the Turku method 1 (T1), the Turku method 2 (T2), the Uppsala method 2 (U2), the Aalborg method (AM) [16], and the Nandedkar's method (NM) [35].

Motor Unit Action Potential Duration: Measurement and Significance 149

And it has been reported by various authors that manual correction of automatic placements

**Figure 8.** Description T2 and NM. In T2 (a), the MUAP onset is determined from the trigger to a point with slope < 0.8 μV/ms over a 1 ms. If there is a point before with amplitude > 20 μV, a new point fulfilling the slope criterion is looked for. In NM (b), the peak with maximum deviation from the BL is identified. The area of the MUAP from the first sample to the peak is calculated. Then the sample point with 90% of this area to the peak is obtained. If the absolute amplitude at this point is greater than 20 μV, a sample with 10 μV amplitude towards the beginning of the window will be the MUAP onset. Otherwise a point toward the peak with 20 μV amplitude is reached. The MUAP onset then will be the

The accuracy of CAMs has been systematically assessed in normal and pathological MUAPs [42] [45]. Comparing the GSPs (determined by means of the probabilistic method referred above) with the marker positions obtained with CAMs (Figure 9), mean differences of up to 8.5 ms were found, with the T1 CAM. Absolute differences of more than 5 ms between the GSP and an automatic marker position (considered gross errors) were found in many cases:

In pathological MUAPs, the worst CAM results were observed with chronic neurogenic MUAPs, which have unusually long duration and are highly polyphasic (Figure 10c and 10d). The results were slightly better with myopathic (Figure 10a) and subacute neurogenic MUAPs (Figure 10b). Analysis of the mean and standard deviation of differences to the GSP (bias and precision, respectively) of the CAMs, showed that some methods, particularly the NM method, provided relatively good results with some pathologic MUAP groups. However, rates of gross errors (differences greater than 5 ms) were seen in around 40% of

In general, end marker placement presents higher levels of error than start marker placement. As in the manual procedure, errors in end marker placement are more pronounced for MUAPs with long-tailed terminal parts. (Figure 10c). Other important sources of error that reduce the performance of CAMs are the presence of several kinds of noise in the recordings, such as the superposition of secondary MUAPs over the analyzed

is required for 20-50% of MUAPs [26, 34, 38, 44].

point with amplitude 10 μV toward the first sample.

estimates for several pathologic groups.

MUAP or BL, and BL fluctuations (Figure 9a. 9b, 9c).

from 15.0% for AM end markers to 49.6% for U2 end markers.

The methods calculate the MUAP duration within a 40, 50 or 100 ms long analysis window. MUAP waveform extraction procedure differ in the following ways:


With respect to definition of the BL and the MUAP start and end markers, the different criteria used by the five automatic methods are outlined below:


#### **8. Accuracy of conventional automatic methods**

Automatic measurements are free of the intra- and inter-examiner variability present in manual measurements. On ideal EMG signals with well-defined waveforms and without noise, the algorithms may perform satisfactorily. But on real recordings, the available methods for automatic measurement of MUAP duration demonstrate poor agreement and low stability [32]. Thus, visual inspection is always necessary and manual cursor adjustments are frequently required. This is the everyday experience in clinical practice; And it has been reported by various authors that manual correction of automatic placements is required for 20-50% of MUAPs [26, 34, 38, 44].

148 Advances in Clinical Neurophysiology

100 discharges are averaged.

BL as the electrical zero.

averaging between 20 and 200 discharges.

begins its search from the maximum peak.

**8. Accuracy of conventional automatic methods** 

waveform is obtained using median averaging.

criteria used by the five automatic methods are outlined below:

points of the MUAP waveform with respect to the BL. Among the reported methods there are differences in several aspects, as described in detail in [16] and [35]. To illustrate these computer-aided techniques, a brief description of several conventional automatic methods (CAMs) is given below. Descriptions include a consideration of differences in the extraction procedure of the MUAP waveform, the definition of the BL and the criteria applied to find the MUAP start and end points (the duration markers positions). The five methods reviewed are the Turku method 1 (T1), the Turku method 2 (T2), the Uppsala method 2 (U2),

The methods calculate the MUAP duration within a 40, 50 or 100 ms long analysis window.




With respect to definition of the BL and the MUAP start and end markers, the different




Automatic measurements are free of the intra- and inter-examiner variability present in manual measurements. On ideal EMG signals with well-defined waveforms and without noise, the algorithms may perform satisfactorily. But on real recordings, the available methods for automatic measurement of MUAP duration demonstrate poor agreement and low stability [32]. Thus, visual inspection is always necessary and manual cursor adjustments are frequently required. This is the everyday experience in clinical practice;

under the MUAP and to the amplitude sample values (Figure 8b).

the Aalborg method (AM) [16], and the Nandedkar's method (NM) [35].

MUAP waveform extraction procedure differ in the following ways:

**Figure 8.** Description T2 and NM. In T2 (a), the MUAP onset is determined from the trigger to a point with slope < 0.8 μV/ms over a 1 ms. If there is a point before with amplitude > 20 μV, a new point fulfilling the slope criterion is looked for. In NM (b), the peak with maximum deviation from the BL is identified. The area of the MUAP from the first sample to the peak is calculated. Then the sample point with 90% of this area to the peak is obtained. If the absolute amplitude at this point is greater than 20 μV, a sample with 10 μV amplitude towards the beginning of the window will be the MUAP onset. Otherwise a point toward the peak with 20 μV amplitude is reached. The MUAP onset then will be the point with amplitude 10 μV toward the first sample.

The accuracy of CAMs has been systematically assessed in normal and pathological MUAPs [42] [45]. Comparing the GSPs (determined by means of the probabilistic method referred above) with the marker positions obtained with CAMs (Figure 9), mean differences of up to 8.5 ms were found, with the T1 CAM. Absolute differences of more than 5 ms between the GSP and an automatic marker position (considered gross errors) were found in many cases: from 15.0% for AM end markers to 49.6% for U2 end markers.

In pathological MUAPs, the worst CAM results were observed with chronic neurogenic MUAPs, which have unusually long duration and are highly polyphasic (Figure 10c and 10d). The results were slightly better with myopathic (Figure 10a) and subacute neurogenic MUAPs (Figure 10b). Analysis of the mean and standard deviation of differences to the GSP (bias and precision, respectively) of the CAMs, showed that some methods, particularly the NM method, provided relatively good results with some pathologic MUAP groups. However, rates of gross errors (differences greater than 5 ms) were seen in around 40% of estimates for several pathologic groups.

In general, end marker placement presents higher levels of error than start marker placement. As in the manual procedure, errors in end marker placement are more pronounced for MUAPs with long-tailed terminal parts. (Figure 10c). Other important sources of error that reduce the performance of CAMs are the presence of several kinds of noise in the recordings, such as the superposition of secondary MUAPs over the analyzed MUAP or BL, and BL fluctuations (Figure 9a. 9b, 9c).

Motor Unit Action Potential Duration: Measurement and Significance 151

purpose, standard methods as adaptive filters have been found unsatisfactory. The sequential application of several techniques of signal processing was necessary, including: 1) wavelet transforms for identifying segments of the EMG signal free of MUAP discharges, 2) averaging of the samples of each of these segments, 3) reconstruction of curves through the averaged points using cubic splines, 4) frequency analysis of this reconstructed BL, and 5) specific filtering based on autoregressive (AR) modeling. In spite of the sophisticated cancellation of BL fluctuation demonstrated by this method, the MUAP duration results of the five CAMs evaluated were not significantly improved when they were provided with signals that had been submitted to it [47]. To optimize automatic duration measurement,

**Figure 10.** Performance of the CAMs with best results (T2 and NM) and the new duration method based on the wavelet transform (WTM) in pathological MUAPs: myopathic (a), subacute neurogenic (b),

The computational capacity of new computer systems enables the design and implementation of more complex algorithms for the automatic processing of the EMG recordings. Signal processing techniques such as the wavelet transform have been applied in the research and

**9. New techniques of automatic measurement of MUAP duration** 

development of alternative automatic algorithms for measurement of MUAP duration.

and chronic neurogenic MUAPs (c and d).

strategies other than, or in addition to, BL treatment are required.

**Figure 9.** CAMs Errors in normal MUAPs. The presence of discharges of secondary MUAPs upon the BL before or after the analysed MUAP waveform induces gross errors in U2 and T1 (a and b). Distortions of the MUAP waveform may cause errors in automatic placements (start markers in c). Poor agreement can be seen among the automatic end marker placements in MUAPs with relatively slow terminal slope (c). Misplacements can also result from an inadequate estimation of the BL, calculated as a constant value, electrical zero in Aalborg method (d).

An attempt to improve the performance of CAMs was carried out by means a signal process to accommodate BL fluctuation [46]. Conceptually, the EMG signal may be considered as an isoelectric BL (zero value) in which the discharges of the active MUs are superimposed. But in real recordings, the BL always shows slow fluctuations due to the activity of distant MUs and movements of the needle electrode. Two problems arise: on the one hand, the high-pass filter does not fully clean all the slow fluctuation and, on the other hand, if the high-pass filter's cut-off frequency is too high, the MUAP waveform can be distorted by creation of a more or less prominent negative afterwave, as previously described. The conventional approaches for dealing with the BL, are either to regard the BL as a straight line [23] of zero value (used by the U2 and AM methods) or to regard the BL as the average of the samples in initial and final segments of the analysis window (used by T1, T2 and NM methods) [16, 35]. An alternative approach for cancelling the BL fluctuation is to reconstruct the course of the BL followed by specific filtering designed not to distort the MUAP waveform [46]. For this purpose, standard methods as adaptive filters have been found unsatisfactory. The sequential application of several techniques of signal processing was necessary, including: 1) wavelet transforms for identifying segments of the EMG signal free of MUAP discharges, 2) averaging of the samples of each of these segments, 3) reconstruction of curves through the averaged points using cubic splines, 4) frequency analysis of this reconstructed BL, and 5) specific filtering based on autoregressive (AR) modeling. In spite of the sophisticated cancellation of BL fluctuation demonstrated by this method, the MUAP duration results of the five CAMs evaluated were not significantly improved when they were provided with signals that had been submitted to it [47]. To optimize automatic duration measurement, strategies other than, or in addition to, BL treatment are required.

150 Advances in Clinical Neurophysiology

**Figure 9.** CAMs Errors in normal MUAPs. The presence of discharges of secondary MUAPs upon the BL before or after the analysed MUAP waveform induces gross errors in U2 and T1 (a and b).

Distortions of the MUAP waveform may cause errors in automatic placements (start markers in c). Poor agreement can be seen among the automatic end marker placements in MUAPs with relatively slow terminal slope (c). Misplacements can also result from an inadequate estimation of the BL, calculated as

An attempt to improve the performance of CAMs was carried out by means a signal process to accommodate BL fluctuation [46]. Conceptually, the EMG signal may be considered as an isoelectric BL (zero value) in which the discharges of the active MUs are superimposed. But in real recordings, the BL always shows slow fluctuations due to the activity of distant MUs and movements of the needle electrode. Two problems arise: on the one hand, the high-pass filter does not fully clean all the slow fluctuation and, on the other hand, if the high-pass filter's cut-off frequency is too high, the MUAP waveform can be distorted by creation of a more or less prominent negative afterwave, as previously described. The conventional approaches for dealing with the BL, are either to regard the BL as a straight line [23] of zero value (used by the U2 and AM methods) or to regard the BL as the average of the samples in initial and final segments of the analysis window (used by T1, T2 and NM methods) [16, 35]. An alternative approach for cancelling the BL fluctuation is to reconstruct the course of the BL followed by specific filtering designed not to distort the MUAP waveform [46]. For this

a constant value, electrical zero in Aalborg method (d).

**Figure 10.** Performance of the CAMs with best results (T2 and NM) and the new duration method based on the wavelet transform (WTM) in pathological MUAPs: myopathic (a), subacute neurogenic (b), and chronic neurogenic MUAPs (c and d).

#### **9. New techniques of automatic measurement of MUAP duration**

The computational capacity of new computer systems enables the design and implementation of more complex algorithms for the automatic processing of the EMG recordings. Signal processing techniques such as the wavelet transform have been applied in the research and development of alternative automatic algorithms for measurement of MUAP duration.

The discrete wavelet transform (DWT) is a technique that simultaneously obtains a time and a scale representation of signals and has been successfully applied for detecting biological events [48]. This technique has provided promising results in the analysis of various electrophysiological signals such as blink reflex [49], EMG and electrocardiographic recordings [50-52], electroencephalographic signals for analysis of epileptic activity [53], and event-related potentials [54]. By regarding transformed EMG signals at a suitable scale in the DWT domain, it is possible to evade high frequency noise and low frequency BL fluctuations. Thus the DWT provides a useful way to detect the boundaries between the MUAP waveform and the BL, that is, for measuring MUAP duration.

Motor Unit Action Potential Duration: Measurement and Significance 153

**Figure 11.** New method based on the DWT. (a) Original MUAP. (b) The MUAP (I) and the DWT at scales 4 (II), 5 (III) and 6 (IV). (c) MUAP and selected wavelet scale (thick continuous line) for finding start and end points. Maxima and minima related to the MUAP for the start and the end (thick crosses). (d) MUAP duration. Onset and offset (vertical lines) are shown and also the GSP (crosses) for this

**Figure 12.** Errors in WTM start position in MUAPs with a small turn in their final (a) or initial part (b). Error in WTM in the end position in the low-slope tail of a MUAP in its terminal part (b). The waveform of the MUAPs (thick black line) and their selected scales of the DWT (thin grey line) are shown. GSP are

MUAP.

in crosses.

A method based on the DWT was applied for measuring the MUAP duration [45, 55]. A schematic description of this method is given in Figure 11. The MUAP waveform consists of a set of peaks (Figure 11a) and the method makes use of the DWT with the non-orthogonal quadratic spline wavelet to detect not only the MUAP but also the start and end points of these peaks. The method selects two intermediate scales (one to find the start and another to find the end marker) from the DWT (Figure 11b) that represents the MUAP signal in terms of energy (thereby evading noise and BL fluctuation). In these DWT scales the peaks related to MUAP peaks are identified (Figure 11c) and amplitude and slope thresholds are used to determine MUAP start and end points (Figure. 11d). For finding MUAP start and end markers, this wavelet transform method (WTM) makes use of 10 parameters, which include the amplitude and slope thresholds. In the study, a genetic algorithm was applied to a sample of normal MUAPs in order to calculate the values of the WTM parameters [56].

This DWT-based automatic method was compared to other available algorithms and found to perform excellently, achieving accurate results for both normal and pathological MUAPs. Duration marker positions were significantly better than those of the other CAMs tested: the DWT-based method was the least biased and the most precise method as evidenced by the fact that it demonstrated the lowest mean and the lowest standard deviation of differences to the GSP. These improvements were observed with both normal and pathologic MUAPs, including myopathic, subacute and chronic neurogenic MUAPs, and also with fibrillation potentials [45, 55] (Figure 10).

The DWT-based method deals better with problems such as the presence of secondary MUAPs, BL fluctuations or high-frequency noise, performing equally well on signals recorded by various different commercial EMG hardware with varying amounts of technical noise. The rate of gross aberrant errors in start marker placement is low: 2.9, 0.8 or 0.0% for normal MUAPs, myopathic MUAPs and fibrillations, respectively. For the end marker, gross errors were more frequent: up to 27.6% for chronic neurogenic MUAPs, and around 10% for other kinds of pathologic MUAPs and for normal MUAPs. Although having less influence in the DWT-based method, the sources of error are the same as those for the other CAMs tested: long and high polyphasic waveforms (such as in chronic neurogenic MUAPs), the presence of consecutive peaks with low amplitude variation in initial or terminal parts (Figure 12a), and a low-sloped tail in the terminal part (Figure 12b). The latter is not detected by the DWT method because there is no corresponding maximum-minimum pair in the DWT.

potentials [45, 55] (Figure 10).

The discrete wavelet transform (DWT) is a technique that simultaneously obtains a time and a scale representation of signals and has been successfully applied for detecting biological events [48]. This technique has provided promising results in the analysis of various electrophysiological signals such as blink reflex [49], EMG and electrocardiographic recordings [50-52], electroencephalographic signals for analysis of epileptic activity [53], and event-related potentials [54]. By regarding transformed EMG signals at a suitable scale in the DWT domain, it is possible to evade high frequency noise and low frequency BL fluctuations. Thus the DWT provides a useful way to detect the boundaries between the

A method based on the DWT was applied for measuring the MUAP duration [45, 55]. A schematic description of this method is given in Figure 11. The MUAP waveform consists of a set of peaks (Figure 11a) and the method makes use of the DWT with the non-orthogonal quadratic spline wavelet to detect not only the MUAP but also the start and end points of these peaks. The method selects two intermediate scales (one to find the start and another to find the end marker) from the DWT (Figure 11b) that represents the MUAP signal in terms of energy (thereby evading noise and BL fluctuation). In these DWT scales the peaks related to MUAP peaks are identified (Figure 11c) and amplitude and slope thresholds are used to determine MUAP start and end points (Figure. 11d). For finding MUAP start and end markers, this wavelet transform method (WTM) makes use of 10 parameters, which include the amplitude and slope thresholds. In the study, a genetic algorithm was applied to a sample of normal MUAPs in order to calculate the values of the WTM parameters [56].

This DWT-based automatic method was compared to other available algorithms and found to perform excellently, achieving accurate results for both normal and pathological MUAPs. Duration marker positions were significantly better than those of the other CAMs tested: the DWT-based method was the least biased and the most precise method as evidenced by the fact that it demonstrated the lowest mean and the lowest standard deviation of differences to the GSP. These improvements were observed with both normal and pathologic MUAPs, including myopathic, subacute and chronic neurogenic MUAPs, and also with fibrillation

The DWT-based method deals better with problems such as the presence of secondary MUAPs, BL fluctuations or high-frequency noise, performing equally well on signals recorded by various different commercial EMG hardware with varying amounts of technical noise. The rate of gross aberrant errors in start marker placement is low: 2.9, 0.8 or 0.0% for normal MUAPs, myopathic MUAPs and fibrillations, respectively. For the end marker, gross errors were more frequent: up to 27.6% for chronic neurogenic MUAPs, and around 10% for other kinds of pathologic MUAPs and for normal MUAPs. Although having less influence in the DWT-based method, the sources of error are the same as those for the other CAMs tested: long and high polyphasic waveforms (such as in chronic neurogenic MUAPs), the presence of consecutive peaks with low amplitude variation in initial or terminal parts (Figure 12a), and a low-sloped tail in the terminal part (Figure 12b). The latter is not detected by the DWT method

because there is no corresponding maximum-minimum pair in the DWT.

MUAP waveform and the BL, that is, for measuring MUAP duration.

**Figure 11.** New method based on the DWT. (a) Original MUAP. (b) The MUAP (I) and the DWT at scales 4 (II), 5 (III) and 6 (IV). (c) MUAP and selected wavelet scale (thick continuous line) for finding start and end points. Maxima and minima related to the MUAP for the start and the end (thick crosses). (d) MUAP duration. Onset and offset (vertical lines) are shown and also the GSP (crosses) for this MUAP.

**Figure 12.** Errors in WTM start position in MUAPs with a small turn in their final (a) or initial part (b). Error in WTM in the end position in the low-slope tail of a MUAP in its terminal part (b). The waveform of the MUAPs (thick black line) and their selected scales of the DWT (thin grey line) are shown. GSP are in crosses.

DWT-based automatic duration marker positions were compared with the corresponding manual positions for a small set of repeatedly recorded MUAPs. While no significant differences were found for the start point, the dispersion of automatic endpoint placements was lower than the dispersion of the corresponding manual placements. This points at the possibility of reaching more consistent estimates of this parameter with automatic procedures than with manual measurements.

Motor Unit Action Potential Duration: Measurement and Significance 155

about the MU, it also delimits the MUAP waveform within which other MUAP parameters are measured. Thus, measurement of MUAP duration is an essential issue in EMG examinations, and it is of necessity the first task that must be accomplished before

Since we must have a measurement of MUAP duration, there is a strong requirement for a method which can provide "acceptable" estimations. By "acceptable" we recognize that there is not a unique true value of clinical duration. As has been discussed above, manual measurement does not ensure consistent estimates but there is reason to hope that an automatic method could be consistent enough. An automatic method might be considered good if it never makes gross misplacements, demonstrates low variability, works in real time and can deal with the relatively noisy signals found in daily clinical practice. An automatic method will show maximum repeatability because it will always give the same positions markers on re-analysis of a given MUAP input signal. If an effective automatic method suffers from any bias in marker positioning, it will be systematic and homogeneous in trend and magnitude, not arbitrary as occurs with subjective manual placements. Thus, the ideal method for attaining satisfactory consistency in MUAP duration measurement is an automatic method, which will overcome the inherent variability of human assessment.

The new, DWT-based computational strategy described above has demonstrated clear improvement in performance relative to conventional algorithms. There is, however, still significant room for betterment. More important than the results *per se* is the indication that some of the seemingly intractable difficulties in the management of bioelectrical recordings can be successfully overcome by new technologies of signal processing. The relevance of this conclusion extends beyond the area of EMG studies: the problems related to noise and variability in MU recording and measurement procedures are present in all the modalities of neurophysiologic studies and in Electromedicine in general. The measurement of MUAP duration is illustrative of the problematic nature of the analysis of bioelectric signals, but can be approached and managed with the latest signal processing techniques. Indeed, these techniques are being applied to other EMG features [58], such as the study of muscular fatigue [59], decomposition of surface EMG recordings [60, 61] and noise reduction for

With respect to MUAP duration, further theoretical and empirical research is needed to develop automatic methods to provide robust and objective measurements, so that the MUAP duration measurement ceases to be an arbitrary task. Accurate and reliable automatic measurement of MUAP duration running on commercial equipment will serve to reduce the requirement for manual intervention in duration marker placement thereby helping the electromyographist. Together with multi-MUAP systems, automatic measurement methods could also contribute to a reduction in patient discomfort by shortening the examination time. Moreover, the availability of robust duration measurements would provide data of sufficient consistency and comparability for input into expert systems for diagnostic purposes, a natural

determination of other MUAP features.

MUAPs extraction [62].

goal of medical technology in the 21st Century.

#### **10. Conclusions and future perspectives**

The measurement of MUAP duration is a matter of particular difficulty. Especially difficult is placement of a MUAP's endpoint marker, and this is reflected in the high degree of variability observed in manual measurements of MUAP duration. Neither is the accuracy of automatic measurement of MUAP duration good, and thus continuous supervision and frequent manual revision of duration marker position are necessary. (Figure 13). Such manual adjustments are time consuming and tedious and still do not guarantee accuracy.

**Figure 13.** MUAPs automatically extracted by commercial equipment. MUAP durations are erroneously calculated and therefore manual corrections are needed.

Given the intrinsic difficulties, the measurement of MUAP duration has been described, quite correctly, as "an arbitrary task" [57]. However, the measurement of MUAP duration cannot be bypassed or avoided: not only does duration provide physiological information about the MU, it also delimits the MUAP waveform within which other MUAP parameters are measured. Thus, measurement of MUAP duration is an essential issue in EMG examinations, and it is of necessity the first task that must be accomplished before determination of other MUAP features.

154 Advances in Clinical Neurophysiology

procedures than with manual measurements.

**10. Conclusions and future perspectives** 

DWT-based automatic duration marker positions were compared with the corresponding manual positions for a small set of repeatedly recorded MUAPs. While no significant differences were found for the start point, the dispersion of automatic endpoint placements was lower than the dispersion of the corresponding manual placements. This points at the possibility of reaching more consistent estimates of this parameter with automatic

The measurement of MUAP duration is a matter of particular difficulty. Especially difficult is placement of a MUAP's endpoint marker, and this is reflected in the high degree of variability observed in manual measurements of MUAP duration. Neither is the accuracy of automatic measurement of MUAP duration good, and thus continuous supervision and frequent manual revision of duration marker position are necessary. (Figure 13). Such manual adjustments are time consuming and tedious and still do not guarantee accuracy.

**Figure 13.** MUAPs automatically extracted by commercial equipment. MUAP durations are

Given the intrinsic difficulties, the measurement of MUAP duration has been described, quite correctly, as "an arbitrary task" [57]. However, the measurement of MUAP duration cannot be bypassed or avoided: not only does duration provide physiological information

erroneously calculated and therefore manual corrections are needed.

Since we must have a measurement of MUAP duration, there is a strong requirement for a method which can provide "acceptable" estimations. By "acceptable" we recognize that there is not a unique true value of clinical duration. As has been discussed above, manual measurement does not ensure consistent estimates but there is reason to hope that an automatic method could be consistent enough. An automatic method might be considered good if it never makes gross misplacements, demonstrates low variability, works in real time and can deal with the relatively noisy signals found in daily clinical practice. An automatic method will show maximum repeatability because it will always give the same positions markers on re-analysis of a given MUAP input signal. If an effective automatic method suffers from any bias in marker positioning, it will be systematic and homogeneous in trend and magnitude, not arbitrary as occurs with subjective manual placements. Thus, the ideal method for attaining satisfactory consistency in MUAP duration measurement is an automatic method, which will overcome the inherent variability of human assessment.

The new, DWT-based computational strategy described above has demonstrated clear improvement in performance relative to conventional algorithms. There is, however, still significant room for betterment. More important than the results *per se* is the indication that some of the seemingly intractable difficulties in the management of bioelectrical recordings can be successfully overcome by new technologies of signal processing. The relevance of this conclusion extends beyond the area of EMG studies: the problems related to noise and variability in MU recording and measurement procedures are present in all the modalities of neurophysiologic studies and in Electromedicine in general. The measurement of MUAP duration is illustrative of the problematic nature of the analysis of bioelectric signals, but can be approached and managed with the latest signal processing techniques. Indeed, these techniques are being applied to other EMG features [58], such as the study of muscular fatigue [59], decomposition of surface EMG recordings [60, 61] and noise reduction for MUAPs extraction [62].

With respect to MUAP duration, further theoretical and empirical research is needed to develop automatic methods to provide robust and objective measurements, so that the MUAP duration measurement ceases to be an arbitrary task. Accurate and reliable automatic measurement of MUAP duration running on commercial equipment will serve to reduce the requirement for manual intervention in duration marker placement thereby helping the electromyographist. Together with multi-MUAP systems, automatic measurement methods could also contribute to a reduction in patient discomfort by shortening the examination time. Moreover, the availability of robust duration measurements would provide data of sufficient consistency and comparability for input into expert systems for diagnostic purposes, a natural goal of medical technology in the 21st Century.

#### **Author details**

#### Ignacio Rodríguez-Carreño

*Universidad de Navarra, Department of Quantitative Methods in Economics, Pamplona, Spain* 

Motor Unit Action Potential Duration: Measurement and Significance 157

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#### Armando Malanda-Trigueros

*Universidad Pública de Navarra, Department of Electrical and Electronic Engineering, Pamplona, Spain* 

#### **11. References**


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*Universidad Pública de Navarra, Department of Electrical and Electronic Engineering,* 

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	- [62] Ren X, Yan Z, Wang Z, Hu X (2006). Noise reduction based on ICA decomposition and wavelet transform for the extraction of motor unit action potentials. J Neurosci Methods; 158: 313-322.

**Chapter 8** 

© 2012 Rusnáková and Rektor, licensee InTech. This is an open access chapter distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

© 2012 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution,

**The Neurocognitive Networks** 

**of the Executive Functions** 

Additional information is available at the end of the chapter

Executive functions are associated with complex mental operations, such as planning, internal ordering, time perception, working memory, inhibition, self-monitoring, selfregulation, motor control, regulation of emotion, motivation (Norman & Shallice, 1986; Luu

• **Planning**: organizational process of creating and maintaining a plan and the psychological process of thinking about the activities required to create a desired goal

• **Internal ordering:** A condition of logical or comprehensible arrangement among the

• **Time perception:** timing of sensory information from multiple sensory streams is

• **Working memory**: is a system for temporarily storing and managing the information required to carry out complex cognitive tasks such as learning, reasoning, and comprehension. Working memory is involved in the selection, initiation, and termination of information-processing functions such as encoding, storing, and

retrieving data; that is the ability to hold information in mind and manipulate it. • **Inhibition**: that is the ability to concentrate to execute task and to ignore distraction;

• **Self-regulation:** self-directed action that serves to alter the probability of a subsequent

and reproduction in any medium, provided the original work is properly cited.

**2. Definition of particular components of the executive functions:** 

essential for many aspects of human perception and action

response so as to alter the likelihood of a future consequence.

function needed for goal-directed behaviour

Štefania Rusnáková and Ivan Rektor

http://dx.doi.org/10.5772/51602

**1. Introduction** 

&Tucker; 2000).

on some scale

separate elements of a group

• **Self-monitoring:** self-discipline

**Chapter 8** 

### **The Neurocognitive Networks of the Executive Functions**

Štefania Rusnáková and Ivan Rektor

Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/51602

**1. Introduction** 

160 Advances in Clinical Neurophysiology

Methods; 158: 313-322.

[62] Ren X, Yan Z, Wang Z, Hu X (2006). Noise reduction based on ICA decomposition and wavelet transform for the extraction of motor unit action potentials. J Neurosci

> Executive functions are associated with complex mental operations, such as planning, internal ordering, time perception, working memory, inhibition, self-monitoring, selfregulation, motor control, regulation of emotion, motivation (Norman & Shallice, 1986; Luu &Tucker; 2000).

#### **2. Definition of particular components of the executive functions:**


© 2012 Rusnáková and Rektor, licensee InTech. This is an open access chapter distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. © 2012 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Actions are executive if they involve the "when" or "whether" aspects of behaviour, whereas nonexecutive functions involve the "what" and "how."

The Neurocognitive Networks of the Executive Functions 163

2006). Nevertheless, preschool children do not have fully mature executive functions and continue to make errors related to these emerging abilities - often not due to the absence of the abilities, but rather because they lack the awareness to know when and how to use particular strategies in particular contexts (Espy 2004). In the human brain, dendrites of pyramidal neurons in layer III of dorsolateral prefrontal cortex undergo their most dramatic expansion between the ages of 71/2 and 12 months. Pyramidal neurons in dorsolateral prefrontal cortex have relatively short dendritic extents at 71/2 months, but reach their full mature extent by 12 months (Koenderink, Ulyings and Mrzljiak; 1994). The level of glucose metabolism in in dorsolateral prefrontal cortex increases during this period as well, approximating adult levels by 1 year of age (Chugani, Phelps and Mazziotta, 1987). One particularly important developmental change during this period might be increased levels of dopamine in dorsolateral prefrontal cortex. Dopamine is important neurotransmitter in prefrontal cortex and reducing dopamine in prefrontal cortex impairs performance on executive function task (Brozoski, Brownm Resvold and Goldman, 1979;

Preadolescent children continue to exhibit certain growth spurts in executive functions. During preadolescence, children display major increases in verbal working memory, response inhibition, selective attention, goal-directed behavior and strategic planning (Brocki 2004; Anderson 2001; Klimkeit 2004). Between the ages of 8 to 10, cognitive flexibility in particular begins to match adult levels (Lucca 2003; Luciana 2002). However, similar to patterns in childhood development, executive functioning in preadolescents is limited because they do not reliably apply these executive functions across multiple contexts as a

During adolescence different brain systems become better integrated. At this time, youth implement executive functions, such as inhibitory control improve. Just as inhibitory control emerges in childhood and improves over time, planning and goal-directed behavior also demonstrate an extended time course with ongoing growth over adolescence. Likewise, functions such as attentional control, with a potential spurt at age 15, along with working

The major change that occurs in the brain in adulthood is the constant myelination of neurons in the prefrontal cortex. At age 20-29, executive functioning skills are at their peak, which allows people of this age to participate in some of the most challenging mental tasks. These skills begin to decline in later adulthood. Working memory and spatial span are areas

result of ongoing development of inhibitory control (de Lucca 2008).

memory, continue developing at this stage (Anderson et al, 2001).

where decline is most readily noted (de Lucca et al, 2008).

Diamond, 2001).

**5. Preadolescence** 

**6. Adolescence** 

**7. Adulthood** 

The term *executive functions* seem to incorporate (Barkley, 1997):


#### **3. Developmental aspects of executive functions**

Mature cognition is characterized by abilities that include being able:


These abilities are referees to respectively as working memory, inhibition, and cognitive flexibility. Together they are key components of both "cognitive control" and "executive functions" (Davidson MC et al; 2006).

When studying executive functions, a developmental framework is helpful because these abilities mature at different rates over time. Some abilities peak in late childhood or adolescence while others progress into early adulthood. Furthermore, executive functioning development corresponds to the neurophysiological developments of the growing brain; as the processing capacity of the frontal lobes and other interconnected regions increases the core executive functions emerge (Lucca & Leventer 2008; Anderson 2002).

#### **4. Childhood**

Inhibitory control and working memory are among the earliest executive functions to appear, with initial signs observed in infants, 7 to 12-months old. Then in the preschool years, children display a spurt in performance on tasks of inhibition and working memory, usually between the ages of 3 to 5 years. Also during this time, cognitive flexibility, goaldirected behavior, and planning begin to develop (Lucca & Leventer 2008; Anderson 2002). Also between 8 and 12 months, infants are able to hold in mind for progressive longer period where a desired objects has been hidden, and are able to control their behavior so that they do not repeat a previously correct search that would not be wrong (Diamond 2006). Nevertheless, preschool children do not have fully mature executive functions and continue to make errors related to these emerging abilities - often not due to the absence of the abilities, but rather because they lack the awareness to know when and how to use particular strategies in particular contexts (Espy 2004). In the human brain, dendrites of pyramidal neurons in layer III of dorsolateral prefrontal cortex undergo their most dramatic expansion between the ages of 71/2 and 12 months. Pyramidal neurons in dorsolateral prefrontal cortex have relatively short dendritic extents at 71/2 months, but reach their full mature extent by 12 months (Koenderink, Ulyings and Mrzljiak; 1994). The level of glucose metabolism in in dorsolateral prefrontal cortex increases during this period as well, approximating adult levels by 1 year of age (Chugani, Phelps and Mazziotta, 1987). One particularly important developmental change during this period might be increased levels of dopamine in dorsolateral prefrontal cortex. Dopamine is important neurotransmitter in prefrontal cortex and reducing dopamine in prefrontal cortex impairs performance on executive function task (Brozoski, Brownm Resvold and Goldman, 1979; Diamond, 2001).

#### **5. Preadolescence**

162 Advances in Clinical Neurophysiology

attaining a goal • Self-awareness across time

• **Regulation of emotions:** self-regulation of emotions

• Inhibition and resistance to distraction

functions" (Davidson MC et al; 2006).

**4. Childhood** 

whereas nonexecutive functions involve the "what" and "how." The term *executive functions* seem to incorporate (Barkley, 1997):

**3. Developmental aspects of executive functions** 

Mature cognition is characterized by abilities that include being able:

inappropriate behaviors and responding appropriately • to quickly and flexibly adapt behavior to changing situations

mentally manipulate that information, and to act on the basis of it

core executive functions emerge (Lucca & Leventer 2008; Anderson 2002).

• Volition, planning, and purposive, goal-directed, or intentional action

• Problem-solving and strategy development, selection, and monitoring

• **Motivation:** refers to a process that elicits, controls, and sustains certain behaviours

Actions are executive if they involve the "when" or "whether" aspects of behaviour,

• Flexible shifting of actions to meet task demands. Maintenance of persistence toward

• to hold information in mind, including complicated representional structures to

• to act on the basis of choice rather than impulse, exercising self-control by resisting

These abilities are referees to respectively as working memory, inhibition, and cognitive flexibility. Together they are key components of both "cognitive control" and "executive

When studying executive functions, a developmental framework is helpful because these abilities mature at different rates over time. Some abilities peak in late childhood or adolescence while others progress into early adulthood. Furthermore, executive functioning development corresponds to the neurophysiological developments of the growing brain; as the processing capacity of the frontal lobes and other interconnected regions increases the

Inhibitory control and working memory are among the earliest executive functions to appear, with initial signs observed in infants, 7 to 12-months old. Then in the preschool years, children display a spurt in performance on tasks of inhibition and working memory, usually between the ages of 3 to 5 years. Also during this time, cognitive flexibility, goaldirected behavior, and planning begin to develop (Lucca & Leventer 2008; Anderson 2002). Also between 8 and 12 months, infants are able to hold in mind for progressive longer period where a desired objects has been hidden, and are able to control their behavior so that they do not repeat a previously correct search that would not be wrong (Diamond Preadolescent children continue to exhibit certain growth spurts in executive functions. During preadolescence, children display major increases in verbal working memory, response inhibition, selective attention, goal-directed behavior and strategic planning (Brocki 2004; Anderson 2001; Klimkeit 2004). Between the ages of 8 to 10, cognitive flexibility in particular begins to match adult levels (Lucca 2003; Luciana 2002). However, similar to patterns in childhood development, executive functioning in preadolescents is limited because they do not reliably apply these executive functions across multiple contexts as a result of ongoing development of inhibitory control (de Lucca 2008).

#### **6. Adolescence**

During adolescence different brain systems become better integrated. At this time, youth implement executive functions, such as inhibitory control improve. Just as inhibitory control emerges in childhood and improves over time, planning and goal-directed behavior also demonstrate an extended time course with ongoing growth over adolescence. Likewise, functions such as attentional control, with a potential spurt at age 15, along with working memory, continue developing at this stage (Anderson et al, 2001).

#### **7. Adulthood**

The major change that occurs in the brain in adulthood is the constant myelination of neurons in the prefrontal cortex. At age 20-29, executive functioning skills are at their peak, which allows people of this age to participate in some of the most challenging mental tasks. These skills begin to decline in later adulthood. Working memory and spatial span are areas where decline is most readily noted (de Lucca et al, 2008).

#### **8. The neurocognitive networks of the executive functions**

Cognitive models typically describe executive functions as higher-level processes that exert control over elementary mental operations (Norman and Shallice, 1986; Luu and Tucker, 2002). A central position of the prefrontal cortex (PFC) and its cortical and sub-cortical connections in processing the executive functions have been suggested (Stuss and Benson, 1986; Badgaiyan, 2000). Ventromedial PFC is involved in decision-making processes, the dorsolateral portion has a role in working memory, planning and sequencing of behaviour. The caudal PFC is reported to be involved in attentional mechanism (Goldberg and Bruce, 1985). This theory was reviewed by Parkin (Parkin, 1998) who criticized the concept of the central position of the PFC in the executive functions. He suggested instead a pattern of extensive heterogenity with different executive tasks associated with different neural substrates. In fact, several studies have documented the diversity of executive functions and related anatomy (Godefroy, 2003).

The Neurocognitive Networks of the Executive Functions 165

were implanted in 590 cortical sites. We focused on local sources of P3-like potentials. Only the "phase reversal" and "steep voltage change" were considered to be generators of the studied potentials, because of their significance as the accepted signs of proximity to

In the two tasks, the P3 like potential sources were displayed in the mesial temporal structures; the lateral temporal neocortex; the anterior and posterior cingulate; the orbitofrontal cortex and dorsolateral prefrontal cortex. The P3 like potentials occurred more frequently with the incongruent than with congruent stimuli in all these areas. This more frequent occurrence of P3 sources elicited by the incongruent task appeared significant in

Event- related synchronization and desynchronization (ERD/S) represents a quantitative nonlinear EEG signal analysis method that enables to evaluate the changes of the background activity in any frequency ranges. These changes are related to an external or internal stimulus and are linked to the brain activation. It is widely used in the neuroscience

**9.2. Event-related synchronization and desynchronization (ERD/S)** 

temporal lateral neocortex and orbitofrontal cortex.

generating structure (Vaughan et al., 1986; Halgren et al., 1995a, b).

**Figure 1.**

Recent findings show that executive functions and cognition are associated with a lot of other structures.

#### **9. Methods of neurocognitive network research**

#### **9.1. Cognitive ERP**

Endogenous event-related potentials (ERPs) are thought to reflect the neurophysiologic correlates of cognitive processes. The P3 component of ERPs, which is a target detection response, has been one most studied. This long-latency waveform (300 milliseconds range) may represented various functions, such as closure of sensory analysis, cognitive closure of the recognition processing, the attentional and decisional processes and the update of working memory (Roesler et al, 1986; Verleger et al, 1994, 2005; Comerchero and Polich, 1999).

The main ERP components were identified by visual inspection and quantified by latency and amplitude measures. P3-like waves were identified in the 250-600 milliseconds latency range.

In our study (Rusnáková et al, 2011) the occurrence of the local generators of P3 like potentials, elicited by a noise-compatibility flanker test was used in order to study the processing of executive functions, particularly in the frontal and temporal cortices.

The test performed with arrows comprised a simpler congruent and a more difficult incongruent task. The two tasks activated the attention and several particular executive functions i.e. working memory, time perception, initiation and motor control of executed task. The incongruent task increased demand on executive functions, and beside the functions common for both tasks an inhibition of automatic responses, the reversal of incorrect response tendency, the internal ordering of the correct response and the initiation of the target-induced correct response was involved. In seven epilepsy surgery candidates (4 males and 3 females), ranging in age from 26 to 38 years, multi-contact depth electrodes were implanted in 590 cortical sites. We focused on local sources of P3-like potentials. Only the "phase reversal" and "steep voltage change" were considered to be generators of the studied potentials, because of their significance as the accepted signs of proximity to generating structure (Vaughan et al., 1986; Halgren et al., 1995a, b).

#### **Figure 1.**

164 Advances in Clinical Neurophysiology

related anatomy (Godefroy, 2003).

**9. Methods of neurocognitive network research** 

other structures.

**9.1. Cognitive ERP** 

1999).

range.

**8. The neurocognitive networks of the executive functions** 

Cognitive models typically describe executive functions as higher-level processes that exert control over elementary mental operations (Norman and Shallice, 1986; Luu and Tucker, 2002). A central position of the prefrontal cortex (PFC) and its cortical and sub-cortical connections in processing the executive functions have been suggested (Stuss and Benson, 1986; Badgaiyan, 2000). Ventromedial PFC is involved in decision-making processes, the dorsolateral portion has a role in working memory, planning and sequencing of behaviour. The caudal PFC is reported to be involved in attentional mechanism (Goldberg and Bruce, 1985). This theory was reviewed by Parkin (Parkin, 1998) who criticized the concept of the central position of the PFC in the executive functions. He suggested instead a pattern of extensive heterogenity with different executive tasks associated with different neural substrates. In fact, several studies have documented the diversity of executive functions and

Recent findings show that executive functions and cognition are associated with a lot of

Endogenous event-related potentials (ERPs) are thought to reflect the neurophysiologic correlates of cognitive processes. The P3 component of ERPs, which is a target detection response, has been one most studied. This long-latency waveform (300 milliseconds range) may represented various functions, such as closure of sensory analysis, cognitive closure of the recognition processing, the attentional and decisional processes and the update of working memory (Roesler et al, 1986; Verleger et al, 1994, 2005; Comerchero and Polich,

The main ERP components were identified by visual inspection and quantified by latency and amplitude measures. P3-like waves were identified in the 250-600 milliseconds latency

In our study (Rusnáková et al, 2011) the occurrence of the local generators of P3 like potentials, elicited by a noise-compatibility flanker test was used in order to study the

The test performed with arrows comprised a simpler congruent and a more difficult incongruent task. The two tasks activated the attention and several particular executive functions i.e. working memory, time perception, initiation and motor control of executed task. The incongruent task increased demand on executive functions, and beside the functions common for both tasks an inhibition of automatic responses, the reversal of incorrect response tendency, the internal ordering of the correct response and the initiation of the target-induced correct response was involved. In seven epilepsy surgery candidates (4 males and 3 females), ranging in age from 26 to 38 years, multi-contact depth electrodes

processing of executive functions, particularly in the frontal and temporal cortices.

In the two tasks, the P3 like potential sources were displayed in the mesial temporal structures; the lateral temporal neocortex; the anterior and posterior cingulate; the orbitofrontal cortex and dorsolateral prefrontal cortex. The P3 like potentials occurred more frequently with the incongruent than with congruent stimuli in all these areas. This more frequent occurrence of P3 sources elicited by the incongruent task appeared significant in temporal lateral neocortex and orbitofrontal cortex.

#### **9.2. Event-related synchronization and desynchronization (ERD/S)**

Event- related synchronization and desynchronization (ERD/S) represents a quantitative nonlinear EEG signal analysis method that enables to evaluate the changes of the background activity in any frequency ranges. These changes are related to an external or internal stimulus and are linked to the brain activation. It is widely used in the neuroscience research as a form of functional brain mapping. Especially the intracerebral recording data analysis have a big importance.

The Neurocognitive Networks of the Executive Functions 167

further areas, e.g. rostral cingulum. Thus although the contribution of efMRI to recognition of the neuroanatomical correlate of mental processes is extremely high, it is unable to provide alone a complete map of activated cerebral areas in the course of cognitive operations. The reason is most probably the inability to reflect fully transient short-term mentary method and

Intracranial and neuroimaging studies demonstrated a widespread distribution of cognitive ERPs in multiple cortical and subcortical regions in the human brain. The participation of the frontal, temporal and parietal cortices, in addition to the cingulate and mesial temporal regions, the basal ganglia and thalamus, has been shown with visual, auditory and somatosensory stimuli (Halgren et al., 1995 a,b, 1998; Clarke et al., 1999, 2003; Smith et al., 1990; Baudena et al., 1995; Lamarche et al., 1995; Brázdil et al., 1999, 2003; Rektor et al., 2001

Based on other studies (Baláž et al, 2008; Rektor et al, 2009; Bočková et al.), even subthalamic nucleus (STN) is a part of widespread neurocognitive network. Cognitive activities in the STN could be explained by existence of hyperdirect cortico-STN pathway. Certain effect of deep brain stimulation (DBS) on cognitive performance is possibly caused by a direct

In conclusion, reviewed studies, confirm theory of widespread and complex neurocognitive

*Department of Neurology, Masaryk University, St. Anne´s Hospital, Brno, Czech Republic* 

*Department of Neurology, Masaryk University, St. Anne´s Hospital, Brno, Czech Republic* 

it´s results must be evaluated with maximum caution (Brázdil et al; 2003).

a,b, 2004, 2007; Bočkova et al 2007; Rusnáková et al. 2011).

influence on ´cognitive´ parts of STN (Rektor et al, 2009).

ERD/ERS: Event Related Desynchronization/Synchronization

efMRI: event-related functional magnetic resonance imaging

*Clinic of Child Neurology, University Hospital Brno, Czech Republic* 

network of the executive functions.

EEG: electroencephalography

ERPs: event-related potentials

fMRI: functional magnetic resonance imaging

**Abbreviations** 

FT: Flanker test

**Author details** 

Štefania Rusnáková \*

Corresponding Author

Ivan Rektor

 \*

**10. Conclusion** 

In a previous intracerebral depth electrodes study (Bočková et al., 2007) the neurocognitive network in the frontal and lateral temporal cortices was investigated by a visual-motor tasks of writing of single letters. The first task consisted of copying letters appearing on a monitor. In the second task, the patients were requested to write any other letter. The cognitive load of the second task was increased mainly by larger involvement of the executive functions. The task-related Event Related Desynchronization/Synchronization (ERD/ERS) of the alpha, beta and gamma rhythms was studied. The alpha and beta ERD/ERS linked specifically to the increased cognitive load was present in the PFC, the orbitofrontal cortex and surprisingly also the temporal neocortex. Particularly the TLC was activated by the increased cognitive load. It was suggested that the TLC together with frontal areas forms a cognitive network processing executive functions. The test used in Bočková's study consisted from an original and rather complex task, with involvement of several executive and non-executive processes. In consequence, the interpretation was rather complex. In order to confirm the suggested involvement of the TLC in the central executive we decided to perform the present study with a test that has been commonly used for studying executive functions

In conclusion, in Bočková et al. cognitive intracerebral studies was documented using ERD/S methodology the involvement of the lateral temporal neocortex in the neurocognitive network of executive functions.

#### **9.3. Functional magnetic resonance (fMRI)**

During the last decade occurred brisk development of the method of functional MRI which maps of regional changes of cerebral perfusion and indirectly assesses also the neuronal activation in the examined parts of the brain. It´s contribution to investigations of cognitive functions is not quite unequivocal so far. In the study of Brázdil et al. (2003) auditory "oddball" task examination was performed in 10 healthy volunteers using the method of "event-related" functional MRI (efMRI). The authors compared the assembled results with the results of previous efMRI and intracerebral ERP studies with the objective to evaluate the extent of agreement between areas with haemodynamically significantly different response to rare target stimuli and known intracerebral generator of the P3 potential. Both methods proved the activation of several areas in particular the parietal and frontal lobe (lobulus parietalis superior, inferior, gyrus supramarginalis, gyrus cinguli, of the lateral prefrontal cortex, gyrus temporalis superior and of the thalamus). Consistent with the assumed significant role of the neurocognitive network for directed attention in the course of detection of target stimuli in the majority of these structures a more marked haemodynamic response was observed on the right side. Against expectation in the presented experiment nor in any previous efMRI studies a significant haemodynamic response to target stimuli was not proved at the side of the most marked P3 generator in the amygdalohippocampal complex. Different results were also obtained on examination of further areas, e.g. rostral cingulum. Thus although the contribution of efMRI to recognition of the neuroanatomical correlate of mental processes is extremely high, it is unable to provide alone a complete map of activated cerebral areas in the course of cognitive operations. The reason is most probably the inability to reflect fully transient short-term mentary method and it´s results must be evaluated with maximum caution (Brázdil et al; 2003).

#### **10. Conclusion**

166 Advances in Clinical Neurophysiology

executive functions

network of executive functions.

**9.3. Functional magnetic resonance (fMRI)** 

analysis have a big importance.

research as a form of functional brain mapping. Especially the intracerebral recording data

In a previous intracerebral depth electrodes study (Bočková et al., 2007) the neurocognitive network in the frontal and lateral temporal cortices was investigated by a visual-motor tasks of writing of single letters. The first task consisted of copying letters appearing on a monitor. In the second task, the patients were requested to write any other letter. The cognitive load of the second task was increased mainly by larger involvement of the executive functions. The task-related Event Related Desynchronization/Synchronization (ERD/ERS) of the alpha, beta and gamma rhythms was studied. The alpha and beta ERD/ERS linked specifically to the increased cognitive load was present in the PFC, the orbitofrontal cortex and surprisingly also the temporal neocortex. Particularly the TLC was activated by the increased cognitive load. It was suggested that the TLC together with frontal areas forms a cognitive network processing executive functions. The test used in Bočková's study consisted from an original and rather complex task, with involvement of several executive and non-executive processes. In consequence, the interpretation was rather complex. In order to confirm the suggested involvement of the TLC in the central executive we decided to perform the present study with a test that has been commonly used for studying

In conclusion, in Bočková et al. cognitive intracerebral studies was documented using ERD/S methodology the involvement of the lateral temporal neocortex in the neurocognitive

During the last decade occurred brisk development of the method of functional MRI which maps of regional changes of cerebral perfusion and indirectly assesses also the neuronal activation in the examined parts of the brain. It´s contribution to investigations of cognitive functions is not quite unequivocal so far. In the study of Brázdil et al. (2003) auditory "oddball" task examination was performed in 10 healthy volunteers using the method of "event-related" functional MRI (efMRI). The authors compared the assembled results with the results of previous efMRI and intracerebral ERP studies with the objective to evaluate the extent of agreement between areas with haemodynamically significantly different response to rare target stimuli and known intracerebral generator of the P3 potential. Both methods proved the activation of several areas in particular the parietal and frontal lobe (lobulus parietalis superior, inferior, gyrus supramarginalis, gyrus cinguli, of the lateral prefrontal cortex, gyrus temporalis superior and of the thalamus). Consistent with the assumed significant role of the neurocognitive network for directed attention in the course of detection of target stimuli in the majority of these structures a more marked haemodynamic response was observed on the right side. Against expectation in the presented experiment nor in any previous efMRI studies a significant haemodynamic response to target stimuli was not proved at the side of the most marked P3 generator in the amygdalohippocampal complex. Different results were also obtained on examination of Intracranial and neuroimaging studies demonstrated a widespread distribution of cognitive ERPs in multiple cortical and subcortical regions in the human brain. The participation of the frontal, temporal and parietal cortices, in addition to the cingulate and mesial temporal regions, the basal ganglia and thalamus, has been shown with visual, auditory and somatosensory stimuli (Halgren et al., 1995 a,b, 1998; Clarke et al., 1999, 2003; Smith et al., 1990; Baudena et al., 1995; Lamarche et al., 1995; Brázdil et al., 1999, 2003; Rektor et al., 2001 a,b, 2004, 2007; Bočkova et al 2007; Rusnáková et al. 2011).

Based on other studies (Baláž et al, 2008; Rektor et al, 2009; Bočková et al.), even subthalamic nucleus (STN) is a part of widespread neurocognitive network. Cognitive activities in the STN could be explained by existence of hyperdirect cortico-STN pathway. Certain effect of deep brain stimulation (DBS) on cognitive performance is possibly caused by a direct influence on ´cognitive´ parts of STN (Rektor et al, 2009).

In conclusion, reviewed studies, confirm theory of widespread and complex neurocognitive network of the executive functions.

#### **Abbreviations**

EEG: electroencephalography ERD/ERS: Event Related Desynchronization/Synchronization ERPs: event-related potentials fMRI: functional magnetic resonance imaging efMRI: event-related functional magnetic resonance imaging FT: Flanker test

#### **Author details**

Štefania Rusnáková \* *Department of Neurology, Masaryk University, St. Anne´s Hospital, Brno, Czech Republic* 

*Clinic of Child Neurology, University Hospital Brno, Czech Republic* 

#### Ivan Rektor

*Department of Neurology, Masaryk University, St. Anne´s Hospital, Brno, Czech Republic* 

\* Corresponding Author

#### **Acknowledgement**

This study was supported by a grant from PharmAround project number CZ.1.07/2.4.00/17.0034.

The Neurocognitive Networks of the Executive Functions 169

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*Developmental Neuropsychology* 2001; 20 (1): 385–406.

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Rektor I, Brazdil M, Nestrasil I, Bares M, Daniel P. Modifications of cognitive and motor tasks affect the occurrence of event-related potentials in the human cortex. *Eur J Neurosci* 2007, 26:1371-1380.

**Chapter 9** 

© 2012 Moretti et al., licensee InTech. This is an open access chapter distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

© 2012 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution,

**Mild Cognitive Impairment** 

Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/47881

**1. Introduction** 

frequency bands.

changes, better explaining MCI state.

**and Quantitative EEG Markers:** 

D. V. Moretti, G. B. Frisoni, G. Binetti and O. Zanetti

**Degenerative Versus Vascular Brain Damage** 

We evaluated the changes induced by cerebrovascular damage (CVD and ) and amigdalohippocampal atrophy (AHC) on brain rhythmicity as revelaled by scalp electroencephalography (EEG) in a cohort of subjects with mild cognitive impairment (MCI).

All MCI subjects (Mini-Mental State Examination [MMSE] mean score 26.6). All subjects underwent EEG recording and magnetic resonance imaging (MRI). EEGs were recorded at rest. Relative power was separately computed for delta, theta, alpha1, alpha2, and alpha3

In the spectral bandpower the severity of cerebrovascular damage (CVD) was associated with increased delta power and decreased alpha2 power. No association of vascular damage was observed with alpha3 power. Moreover, the theta/alpha 1 ratio could be a reliable index for the estimation of the individual extent of CV damage. On the other side, the group with moderate hippocampal atrophy showed the highest increase of alpha2 and alpha3 power. Moreover, when the amygdalar and hippocampal volume (AHC) are separately considered, within AHC, the increase of theta/gamma ratio is best associated with amygdalar atrophy

CVD and AHC are associated with specific EEG markers. So far, these EEG markers could have a prospective value in differential diagnosis between vascular and degenerative MCI. Moreover, EEG markers could be expression of different global network pathological

Mild cognitive impairment (MCI) is a clinical state intermediate between elderly normal cognition and dementia which affects a significant amount of the elderly population,

and reproduction in any medium, provided the original work is properly cited.

whereas alpha3/alpha2 ratio is best associated with hippocampal atrophy.


## **Mild Cognitive Impairment and Quantitative EEG Markers: Degenerative Versus Vascular Brain Damage**

D. V. Moretti, G. B. Frisoni, G. Binetti and O. Zanetti

Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/47881

#### **1. Introduction**

170 Advances in Clinical Neurophysiology

235-248.

*Neurosci* 2007, 26:1371-1380.

Rektor I, Brazdil M, Nestrasil I, Bares M, Daniel P. Modifications of cognitive and motor tasks affect the occurrence of event-related potentials in the human cortex. *Eur J* 

Rektor I, Baláž M, Bočková M. Cognitive activities in the subthalamic nukleus. Invasive

Rosler F, Sutton S, Johnson R, et al. Endogenous ERP components and cognitive constructs.

Rusnáková Š, Daniel P, Chládek J, Jurák P, Rektor I. The Executive Functions in Frontal and Temporal Lobes: A Flanker Task Intracerebral Recording Study. Journal of Clinical

Smith ME, Halgren E, Sokolik M, et al. The intracranial topography of the P3 event-related potential elicited during auditory oddball. *Electroencephalogr Clin Neurophysiol* 1990; 76:

Verleger R, Heide W, Butt C, Kompf D. Reduction of P3b in patients with temporo-parietal

Verleger R, Gorgen S, Jaskowski P. An ERP indicator of processing relevant gestalts in

studies. Parkinsonism and related disorders 2009; S82-S86.

Neurophysiology, Volume 28 - Issue 1 - pp 30-35; 2011.

lesions. Brain Res Cogn Brain Res 1994�2:103-116.

masked priming. Psychophysiology 2005� 42:677-690.

A review. Electroencephalogr Clin Neurophysiol Suppl 1986; 38:51-92.

We evaluated the changes induced by cerebrovascular damage (CVD and ) and amigdalohippocampal atrophy (AHC) on brain rhythmicity as revelaled by scalp electroencephalography (EEG) in a cohort of subjects with mild cognitive impairment (MCI).

All MCI subjects (Mini-Mental State Examination [MMSE] mean score 26.6). All subjects underwent EEG recording and magnetic resonance imaging (MRI). EEGs were recorded at rest. Relative power was separately computed for delta, theta, alpha1, alpha2, and alpha3 frequency bands.

In the spectral bandpower the severity of cerebrovascular damage (CVD) was associated with increased delta power and decreased alpha2 power. No association of vascular damage was observed with alpha3 power. Moreover, the theta/alpha 1 ratio could be a reliable index for the estimation of the individual extent of CV damage. On the other side, the group with moderate hippocampal atrophy showed the highest increase of alpha2 and alpha3 power. Moreover, when the amygdalar and hippocampal volume (AHC) are separately considered, within AHC, the increase of theta/gamma ratio is best associated with amygdalar atrophy whereas alpha3/alpha2 ratio is best associated with hippocampal atrophy.

CVD and AHC are associated with specific EEG markers. So far, these EEG markers could have a prospective value in differential diagnosis between vascular and degenerative MCI. Moreover, EEG markers could be expression of different global network pathological changes, better explaining MCI state.

Mild cognitive impairment (MCI) is a clinical state intermediate between elderly normal cognition and dementia which affects a significant amount of the elderly population,

© 2012 Moretti et al., licensee InTech. This is an open access chapter distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. © 2012 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

featuring memory complaints and cognitive impairment on neuropsychological testing, but no dementia (Flicker et al., 1991; Petersen et al., 1995, 2001).

Mild Cognitive Impairment and Quantitative EEG Markers: Degenerative Versus Vascular Brain Damage 173

2008), as well as with memory deficits, that are a major risk for the development of AD in MCI subjects (Moretti et al., 2009). Based on the tertiles values of decreasing AHC volume, three groups of AHC growing atrophy were obtained. AHC atrophy is associated with memory deficits as well as with increase of theta/gamma and alpha3/alpha2 ratio. Moreover, when the amygdalar and hippocampal volume are separately considered, within AHC, the increase of theta/gamma ratio is best associated with amygdalar atrophy whereas

The role of cerebrovascular (CV) disease and ischemic brain damage in cognitive decline remains controversial. Although not all patients with mild cognitive impairment due to CV damage develop a clinically defined dementia, all such patients are at risk and could develop dementia in the 5 years following the detection of cognitive decline. Cognitive impairment due to subcortical CV damages is thought to be caused by focal or multifocal lesions involving strategic brain areas. These lesions in basal ganglia, thalamus or connecting white matter induce interruption of thalamocortical and striatocortical pathways. As a consequence, deafferentation of frontal and limbic cortical structures is produced. The pattern of cognitive impairment is consistent with models of impaired cortical and subcortical neuronal pathways (Kramer et al., 2002). Even when CV pathology appears to be the main underlying process, the effects of the damaged brain parenchyma are variable and, therefore, the clinical, radiological and pathological appearances may be heterogeneous. A neurophysiological approach could be helpful in differentiating structural from functional CV damage (Moretti et al., 2007b). The quantitative analysis of electroencephalographic (EEG) rhythms in resting subjects is a low-cost but still powerful

alpha3/alpha2 ratio is best associated with hippocampal atrophy.

approach to the study of elderly subjects in normal aging, MCI and dementia.

For the present study, 99 subjects with MCI were recruited. All experimental protocols had been approved by the local Ethics Committee. Informed consent was obtained from all participants or their caregivers, according to the Code of Ethics of the World Medical

GROUP 1 GROUP 2 GROUP 3 GROUP 4

Association (Declaration of Helsinki). Table 1 shows the main features of this group.

SUBJECTS (F/M) 27 (18/9) 41 (31/10) 19 (10/9) 12 (9/3)

**Table 1.** Mean values ± standard error of demographic characteristics, neuropsychological and ARWMC scores of the MCI subgroups. F/m, female/male. Age and education are expressed in years

AGE 70.1 (±1.7) 69.9 (±1.1) 69.7 (±1.9) 70.5 (±2.4) EDUCATION 7.1 (±0.7) 7 (±0.6) 7 (±0.9) 10 (±1.6) MMSE 26.7 (±0.4) 26.5 (±0.4) 27 (±0.4) 26.1 (±0.7) ARWMC scale 0 1-5 6-10 11-15 Group 1, no vascular damage; group 2, mild vascular damage; group 3 moderate vascular damage; group 4, severe

**2. Materials and methods** 

*2.1.1. Cerebrovascular impairment* 

**2.1. Subjects** 

vascular damage.

The hippocampus is one of the first and most affected brain regions impacted by both Alzheimer's disease and mild cognitive impairment (MCI; Arnold et al., 1991; Bobinski et al., 1995; Price and Morris, 1999; Schonheit et al., 2004; Bennett et al., 2005, Frisoni et al., 2009). In mild-to-moderate Alzheimer's disease patients, it has been shown that hippocampal volumes are 27% smaller than in normal elderly controls (Callen et al., 2001; Du et al., 2001), whereas patients with MCI show a volume reduction of 11% (Du et al., 2001). So far, from a neuropathological point of view, the progression of disease from early or very early MCI to later stages seems to follow a linear course. Nevertheless, there is some evidence from functional (Gold et al., 2000; Della Maggiore et al., 2002; Hamalaainen et al., 2006) and biochemical studies (Lavenex and Amaral, 2000) that the process of conversion from nondemented to clinically-evident demented state is not so linear. Recent fMRI studies have suggested increased medial temporal lobe (MTL) activations in MCI subjects vs controls, during the performance of memory tasks (Dickerson et al., 2004, 2005). Nonetheless, fMRI findings in MCI are discrepant, as MTL hypoactivation similar to that seen in AD patients (Pariente et al., 2005) has also been reported (Machulda et al., 2003). Recent postmortem data from subjects – who had been prospectively followed and clinically characterized up to immediately before their death – indicate that hippocampal choline acetyltransferase levels are reduced in Alzheimer's dementia, but in fact they are upregulated in MCI (Lavenex and Amaral, 2000), presumably because of reactive upregulations of the enzyme activity in the unaffected hippocampal cholinergic axons. Previous EEG studies (Jelic et al. 2000, 1996; Ferreri et al., 2003,Pijnenburg et al., 2004; Jiang et al., 2005, 2006, Zheng et al., 2007) have shown a decrease – ranging from 8 to 10.5 Hz (low alpha) – of the alpha frequency power band in MCI subjects, when compared to normal elderly controls (Zappoli et al., 1995; Huang et al., 2000; Jelic et al., 2000; Koenig et al., 2005; Babiloni et al., 2006). However, a recent study has shown an increase – ranging from 10.5 to 13 Hz (high alpha) – of the alpha frequency power band, on the occipital region in MCI subjects, when compared to normal elderly and AD patients (Babiloni et al., 2006). These somewhat contradictory findings may be explained by the possibility that MCI subjects have different patterns of plastic organization during the disease, and that the activation (or hypoactivation) of different cerebral areas is based on various degrees of hippocampal atrophy. If this hypothesis is true, then EEG changes of rhythmicity have to occur non-proportionally to the hippocampal atrophy, as previously demonstrated in a study of auditory evoked potentials (Golob et al, 2007).

In a recent study (Moretti et al., 2007a), the results confirm the hypothesis that the relationship between hippocampal volume and EEG rhythmicity is not proportional to the hippocampal atrophy, as revealed by the analyses of both the relative band powers and the individual alpha markers. Such a pattern seems to emerge because, rather than a classification based on clinical parameters, discrete hippocampal volume differences (about 1 cm3) are analyzed. Indeed, the group with moderate hippocampal atrophy showed the highest increase in the theta power on frontal regions, and of the alpha2 and alpha3 powers on frontal and temporo-parietal areas.

Recently, two specific EEG markers, theta/gamma and alpha3/alpha2 frequency ratio have been reliable associated to the atrophy of amygdalo-hippocampal complex (Moretti et al. 2008), as well as with memory deficits, that are a major risk for the development of AD in MCI subjects (Moretti et al., 2009). Based on the tertiles values of decreasing AHC volume, three groups of AHC growing atrophy were obtained. AHC atrophy is associated with memory deficits as well as with increase of theta/gamma and alpha3/alpha2 ratio. Moreover, when the amygdalar and hippocampal volume are separately considered, within AHC, the increase of theta/gamma ratio is best associated with amygdalar atrophy whereas alpha3/alpha2 ratio is best associated with hippocampal atrophy.

The role of cerebrovascular (CV) disease and ischemic brain damage in cognitive decline remains controversial. Although not all patients with mild cognitive impairment due to CV damage develop a clinically defined dementia, all such patients are at risk and could develop dementia in the 5 years following the detection of cognitive decline. Cognitive impairment due to subcortical CV damages is thought to be caused by focal or multifocal lesions involving strategic brain areas. These lesions in basal ganglia, thalamus or connecting white matter induce interruption of thalamocortical and striatocortical pathways. As a consequence, deafferentation of frontal and limbic cortical structures is produced. The pattern of cognitive impairment is consistent with models of impaired cortical and subcortical neuronal pathways (Kramer et al., 2002). Even when CV pathology appears to be the main underlying process, the effects of the damaged brain parenchyma are variable and, therefore, the clinical, radiological and pathological appearances may be heterogeneous. A neurophysiological approach could be helpful in differentiating structural from functional CV damage (Moretti et al., 2007b). The quantitative analysis of electroencephalographic (EEG) rhythms in resting subjects is a low-cost but still powerful approach to the study of elderly subjects in normal aging, MCI and dementia.

#### **2. Materials and methods**

#### **2.1. Subjects**

172 Advances in Clinical Neurophysiology

featuring memory complaints and cognitive impairment on neuropsychological testing, but

The hippocampus is one of the first and most affected brain regions impacted by both Alzheimer's disease and mild cognitive impairment (MCI; Arnold et al., 1991; Bobinski et al., 1995; Price and Morris, 1999; Schonheit et al., 2004; Bennett et al., 2005, Frisoni et al., 2009). In mild-to-moderate Alzheimer's disease patients, it has been shown that hippocampal volumes are 27% smaller than in normal elderly controls (Callen et al., 2001; Du et al., 2001), whereas patients with MCI show a volume reduction of 11% (Du et al., 2001). So far, from a neuropathological point of view, the progression of disease from early or very early MCI to later stages seems to follow a linear course. Nevertheless, there is some evidence from functional (Gold et al., 2000; Della Maggiore et al., 2002; Hamalaainen et al., 2006) and biochemical studies (Lavenex and Amaral, 2000) that the process of conversion from nondemented to clinically-evident demented state is not so linear. Recent fMRI studies have suggested increased medial temporal lobe (MTL) activations in MCI subjects vs controls, during the performance of memory tasks (Dickerson et al., 2004, 2005). Nonetheless, fMRI findings in MCI are discrepant, as MTL hypoactivation similar to that seen in AD patients (Pariente et al., 2005) has also been reported (Machulda et al., 2003). Recent postmortem data from subjects – who had been prospectively followed and clinically characterized up to immediately before their death – indicate that hippocampal choline acetyltransferase levels are reduced in Alzheimer's dementia, but in fact they are upregulated in MCI (Lavenex and Amaral, 2000), presumably because of reactive upregulations of the enzyme activity in the unaffected hippocampal cholinergic axons. Previous EEG studies (Jelic et al. 2000, 1996; Ferreri et al., 2003,Pijnenburg et al., 2004; Jiang et al., 2005, 2006, Zheng et al., 2007) have shown a decrease – ranging from 8 to 10.5 Hz (low alpha) – of the alpha frequency power band in MCI subjects, when compared to normal elderly controls (Zappoli et al., 1995; Huang et al., 2000; Jelic et al., 2000; Koenig et al., 2005; Babiloni et al., 2006). However, a recent study has shown an increase – ranging from 10.5 to 13 Hz (high alpha) – of the alpha frequency power band, on the occipital region in MCI subjects, when compared to normal elderly and AD patients (Babiloni et al., 2006). These somewhat contradictory findings may be explained by the possibility that MCI subjects have different patterns of plastic organization during the disease, and that the activation (or hypoactivation) of different cerebral areas is based on various degrees of hippocampal atrophy. If this hypothesis is true, then EEG changes of rhythmicity have to occur non-proportionally to the hippocampal atrophy, as previously demonstrated in

no dementia (Flicker et al., 1991; Petersen et al., 1995, 2001).

a study of auditory evoked potentials (Golob et al, 2007).

In a recent study (Moretti et al., 2007a), the results confirm the hypothesis that the relationship between hippocampal volume and EEG rhythmicity is not proportional to the hippocampal atrophy, as revealed by the analyses of both the relative band powers and the individual alpha markers. Such a pattern seems to emerge because, rather than a classification based on clinical parameters, discrete hippocampal volume differences (about 1 cm3) are analyzed. Indeed, the group with moderate hippocampal atrophy showed the highest increase in the theta power on frontal regions, and of the alpha2 and alpha3 powers on frontal and temporo-parietal areas.

Recently, two specific EEG markers, theta/gamma and alpha3/alpha2 frequency ratio have been reliable associated to the atrophy of amygdalo-hippocampal complex (Moretti et al.

#### *2.1.1. Cerebrovascular impairment*

For the present study, 99 subjects with MCI were recruited. All experimental protocols had been approved by the local Ethics Committee. Informed consent was obtained from all participants or their caregivers, according to the Code of Ethics of the World Medical Association (Declaration of Helsinki). Table 1 shows the main features of this group.


Group 1, no vascular damage; group 2, mild vascular damage; group 3 moderate vascular damage; group 4, severe vascular damage.

**Table 1.** Mean values ± standard error of demographic characteristics, neuropsychological and ARWMC scores of the MCI subgroups. F/m, female/male. Age and education are expressed in years

#### **2.2. Degenerative impairment**

#### *2.2.1. Subjects*

For the present study, 79 subjects with MCI were recruited from the memory Clinic of the Scientific Institute for Research and Care (IRCCS) of Alzheimer's and psychiatric diseases 'Fatebenefratelli' in Brescia, Italy. All experimental protocols had been approved by the local Ethics Committee. Informed consent was obtained from all participants or their caregivers, according to the Code of Ethics of the World Medical Association (Declaration of Helsinki). Table 2 shows the main characteristic of the group.

Mild Cognitive Impairment and Quantitative EEG Markers: Degenerative Versus Vascular Brain Damage 175

A digital FFT-based power spectrum analysis (Welch technique, Hanning windowing function, no phase shift) computed – ranging from 2 to 45 Hz – the power density of EEG rhythms with a 0.5 Hz frequency resolution. Methods are exposed in detail elsewhere (Moretti et al., 2004, 2007a,b]. Briefly, two anchor frequencies were selected according to literature guidelines (Klimesch et al., 1999), that is, the theta/alpha transition frequency (TF) and the individual alpha frequency (IAF) peak. Based on TF and IAF, we estimated the following frequency band range for each subject: delta, theta, low alpha band (alpha1 and alpha2), and high alpha band (alpha3). Moreover, individual beta and gamma frequencies were computed. Three frequency peaks were detected in the frequency range from the individual alpha 3 frequency band and 45 Hz. These peaks were named beta1 peak (IBF 1), beta2 peak (IBF 2) and gamma peak (IGF). Based on peaks, the frequency ranges were determined. Beta1 ranges from alpha 3 to the lower spectral power value between beta1 and beta2 peak; beta2 frequency ranges from beta 1 to the lower spectral power value between beta2 and gamma peak; gamma frequency ranges from beta 2 to 45 Hz, which is the end of the range considered. The mean frequency range computed in MCI subjects considered as a whole are: delta 2.9-4.9 Hz; theta 4.9-6.9 Hz; alpha1 6.9-8.9 Hz; alpha2 8.9-10.9 Hz; alpha3 10.9-12-9 Hz; beta1 12,9-19,2 Hz; beta2 19.2-32.4; gamma 32.4-45. In the frequency bands determined in this way, the relative power spectra for each subject were computed. The relative power density for each frequency band was computed as the ratio between the absolute power and the mean power spectra from 2 to 45 Hz. Th9 relative band power at each band was defined as the mean of the relative band power for each frequency bin within that band. Finally, the theta/gamma and alpha3/alpha2 relative power ratio were computed

and analyzed. The analysis of other frequencies was not in the scope of this study.

In this study we enrolled subjects afferents to the scientific institute of research and cure Fatebenefratelli in Brescia, Italy. Patients were taken from a prospective project on clinical progression of MCI. The project was aimed to study the natural history of non demented persons with apparently primary cognitive deficits, not caused by psychic (anxiety, depression, etc.) or physical (uncontrolled heart disease, uncontrolled diabetes, etc.) conditions. Patients were rated with a series of standardized diagnostic tests, including the Mini-Mental State Examination (MMSE; Folstein et al., 1975), the Clinical Dementia Rating Scale (CDRS; Hughes et al., 1982), the Hachinski Ischemic Scale (HIS; Rosen et al., 1980), and the Instrumental and Basic Activities of Daily Living (IADL, BADL, Lawton and Brodie, 1969). In addition, patients were subjected to diagnostic neuroimaging procedures (magnetic resonance imaging, MRI) and laboratory blood analysis to rule out other causes of

The present inclusion and exclusion criteria for MCI were based on previous seminal studies (Albert et al., 1991; Petersen et al., 1995, 1997, 2001; Portet et al., 2006; Geroldi et al., 2006). Inclusion criteria in the study were all of the following: (i) complaint by the patient or report

*2.3.2. Analysis of individual frequency bands* 

*2.3.3. Diagnostic criteria* 

cognitive impairment.


Group 1, no vascular damage; group 2, mild vascular damage; group 3 moderate vascular damage; group 4, severe vascular damage.

**Table 2.** Mean values ± standard error of theta/alpha1, alpha1/alpha2, alpha2/alpha3 ratios in the MCI subgroups.

#### **2.3. Shared procedures**

#### *2.3.1. EEG recordings*

All recordings were obtained in the morning with subjects resting comfortably. Vigilance was continuously monitored in order to avoid drowsiness.

The EEG activity was recorded continuously from 19 sites by using electrodes set in an elastic cap (Electro-Cap International, Inc.) and positioned according to the 10-20 International system (Fp1, Fp2, F7, F3, Fz, F4, F8, T3, C3, Cz, C4, T4, T5, P3, Pz, P4, T6, O1, O2). The ground electrode was placed in front of Fz. The left and right mastoids served as reference for all electrodes. The recordings were used off-line to re-reference the scalp recordings to the common average. Data were recorded with a band-pass filter of 0.3-70 Hz, and digitized at a sampling rate of 250 Hz (BrainAmp, BrainProducts, Germany). Electrodes-skin impedance was set below 5 kW. Horizontal and vertical eye movements were detected by recording the electrooculogram (EOG). The recording lasted 5 minutes, with subjects with closed eyes. Longer recordings would have reduced the variability of the data, but they would also have increased the possibility of slowing of EEG oscillations due to reduced vigilance and arousal. EEG data were then analyzed and fragmented off-line in consecutive epochs of 2 seconds, with a frequency resolution of 0.5 Hz. The average number of epochs analyzed was 140 ranging from 130 to150. The EEG epochs with ocular, muscular and other types of artifacts were discarded.

#### *2.3.2. Analysis of individual frequency bands*

174 Advances in Clinical Neurophysiology

*2.2.1. Subjects* 

vascular damage.

**2.3. Shared procedures** 

*2.3.1. EEG recordings* 

subgroups.

**2.2. Degenerative impairment** 

Table 2 shows the main characteristic of the group.

For the present study, 79 subjects with MCI were recruited from the memory Clinic of the Scientific Institute for Research and Care (IRCCS) of Alzheimer's and psychiatric diseases 'Fatebenefratelli' in Brescia, Italy. All experimental protocols had been approved by the local Ethics Committee. Informed consent was obtained from all participants or their caregivers, according to the Code of Ethics of the World Medical Association (Declaration of Helsinki).

t/a1 0,7 (±0.05) 0,77 (±0.05) 1,17 (±0.05) 1,39 (±0.14)

Group 1, no vascular damage; group 2, mild vascular damage; group 3 moderate vascular damage; group 4, severe

**Table 2.** Mean values ± standard error of theta/alpha1, alpha1/alpha2, alpha2/alpha3 ratios in the MCI

All recordings were obtained in the morning with subjects resting comfortably. Vigilance

The EEG activity was recorded continuously from 19 sites by using electrodes set in an elastic cap (Electro-Cap International, Inc.) and positioned according to the 10-20 International system (Fp1, Fp2, F7, F3, Fz, F4, F8, T3, C3, Cz, C4, T4, T5, P3, Pz, P4, T6, O1, O2). The ground electrode was placed in front of Fz. The left and right mastoids served as reference for all electrodes. The recordings were used off-line to re-reference the scalp recordings to the common average. Data were recorded with a band-pass filter of 0.3-70 Hz, and digitized at a sampling rate of 250 Hz (BrainAmp, BrainProducts, Germany). Electrodes-skin impedance was set below 5 kW. Horizontal and vertical eye movements were detected by recording the electrooculogram (EOG). The recording lasted 5 minutes, with subjects with closed eyes. Longer recordings would have reduced the variability of the data, but they would also have increased the possibility of slowing of EEG oscillations due to reduced vigilance and arousal. EEG data were then analyzed and fragmented off-line in consecutive epochs of 2 seconds, with a frequency resolution of 0.5 Hz. The average number of epochs analyzed was 140 ranging from 130 to150. The EEG epochs with ocular, muscular

a1/a2 0,46 (±0.03) 0,5 (±0.03) 0,53 (±0.05) a2/a3 1,27 (±0.12) 1,16 (±0.1) 0,85 (±0.05)

was continuously monitored in order to avoid drowsiness.

and other types of artifacts were discarded.

GROUP 1 GROUP 2 GROUP 3 GROUP 4

0,47 (±0.04) 0,79 (±0.07) A digital FFT-based power spectrum analysis (Welch technique, Hanning windowing function, no phase shift) computed – ranging from 2 to 45 Hz – the power density of EEG rhythms with a 0.5 Hz frequency resolution. Methods are exposed in detail elsewhere (Moretti et al., 2004, 2007a,b]. Briefly, two anchor frequencies were selected according to literature guidelines (Klimesch et al., 1999), that is, the theta/alpha transition frequency (TF) and the individual alpha frequency (IAF) peak. Based on TF and IAF, we estimated the following frequency band range for each subject: delta, theta, low alpha band (alpha1 and alpha2), and high alpha band (alpha3). Moreover, individual beta and gamma frequencies were computed. Three frequency peaks were detected in the frequency range from the individual alpha 3 frequency band and 45 Hz. These peaks were named beta1 peak (IBF 1), beta2 peak (IBF 2) and gamma peak (IGF). Based on peaks, the frequency ranges were determined. Beta1 ranges from alpha 3 to the lower spectral power value between beta1 and beta2 peak; beta2 frequency ranges from beta 1 to the lower spectral power value between beta2 and gamma peak; gamma frequency ranges from beta 2 to 45 Hz, which is the end of the range considered. The mean frequency range computed in MCI subjects considered as a whole are: delta 2.9-4.9 Hz; theta 4.9-6.9 Hz; alpha1 6.9-8.9 Hz; alpha2 8.9-10.9 Hz; alpha3 10.9-12-9 Hz; beta1 12,9-19,2 Hz; beta2 19.2-32.4; gamma 32.4-45. In the frequency bands determined in this way, the relative power spectra for each subject were computed. The relative power density for each frequency band was computed as the ratio between the absolute power and the mean power spectra from 2 to 45 Hz. Th9 relative band power at each band was defined as the mean of the relative band power for each frequency bin within that band. Finally, the theta/gamma and alpha3/alpha2 relative power ratio were computed and analyzed. The analysis of other frequencies was not in the scope of this study.

#### *2.3.3. Diagnostic criteria*

In this study we enrolled subjects afferents to the scientific institute of research and cure Fatebenefratelli in Brescia, Italy. Patients were taken from a prospective project on clinical progression of MCI. The project was aimed to study the natural history of non demented persons with apparently primary cognitive deficits, not caused by psychic (anxiety, depression, etc.) or physical (uncontrolled heart disease, uncontrolled diabetes, etc.) conditions. Patients were rated with a series of standardized diagnostic tests, including the Mini-Mental State Examination (MMSE; Folstein et al., 1975), the Clinical Dementia Rating Scale (CDRS; Hughes et al., 1982), the Hachinski Ischemic Scale (HIS; Rosen et al., 1980), and the Instrumental and Basic Activities of Daily Living (IADL, BADL, Lawton and Brodie, 1969). In addition, patients were subjected to diagnostic neuroimaging procedures (magnetic resonance imaging, MRI) and laboratory blood analysis to rule out other causes of cognitive impairment.

The present inclusion and exclusion criteria for MCI were based on previous seminal studies (Albert et al., 1991; Petersen et al., 1995, 1997, 2001; Portet et al., 2006; Geroldi et al., 2006). Inclusion criteria in the study were all of the following: (i) complaint by the patient or report

by a relative or the general practitioner of memory or other cognitive disturbances; (ii) mini mental state examination (MMSE; Folstein et al., 1975) score of 24 to 27/30 or MMSE of 28 and higher plus low performance (score of 2/6 or higher) on the clock drawing test (Shulman, 2000); (iii) sparing of instrumental and basic activities of daily living or functional impairment stably due to causes other than cognitive impairment, such as physical impairments, sensory loss, gait or balance disturbances, etc. Exclusion criteria were any one of the following: (i) age of 90 years and older; (ii) history of depression or psychosis of juvenile onset; (iii) history or neurological signs of major stroke; (iv) other psychiatric diseases, epilepsy, drug addiction, alcohol dependence; (v) use of psychoactive drugs including acetylcholinesterase inhibitors or other drugs enhancing brain cognitive functions; and (vi) current or previous uncontrolled or complicated systemic diseases (including diabetes mellitus) or traumatic brain injuries.

Mild Cognitive Impairment and Quantitative EEG Markers: Degenerative Versus Vascular Brain Damage 177

were assigned in basal ganglia for: no WMC, 1 focal lesion, more than 1 focal lesion and confluent lesions, respectively. Total score was the sum of subscores for each area in the left and right hemisphere, ranging from 0 to 30. As regards the ARWMC scale, the interrater reliability, as calculated with weighted k value, was 0.67, indicative of moderate agreement (Wahlund et al., 2001). We assessed test-retest reliability on a random sample of 20 subjects. The intraclass correlation coefficient was 0.98, values above 0.80 being considered indicative

Based on increasing subcortical CV damage, the 99 MCI subjects were subsequently divided in 4 sub-groups along the range between the minimum and maximum ARWMC score (respectively 0 and 15). In order to have the higher sensibility to the CV damage, the first group was composed by subjects with score = 0. The other groups were composed according to equal range ARWMC scores. As a consequence, we obtained the following groups: group 1 (G1) no vascular damage, CV score 0; group 1 (G2), mild vascular damage, CV score 1-5; group 3 (G3), moderate vascular damage, CV score 6-10; group 1 (G4), severe vascular

Table 1 reports the mean values of demographic and clinical characteristics of the 4 subgroups.

Preliminarly, any significant difference between groups in demographic variables, age, education and gender as well as MMSE score was taken into account. Only education showed a significant differences between groups (p<0.03). For avoiding confounding effect, subsequent statistical analyses of variance (ANOVA) were carried out using age, education, gender and MMSE score as covariates. Duncan's test was used for post hoc comparisons.

A second session of ANOVA was performed on EEG relative power data. In this analysis, Group factor was the independent variable and frequency band power (delta, theta, alpha1,

As successive step, to evaluate the presence of EEG indexes that correlate specifically with vascular damage, we performed statistical analyses to evaluate the specificity of the following ratios: theta/alpha1, using as covariate also TF; alpha2/alpha3 using as covariate also IAF and alpha1/alpha2 with both TF and IAF as covariate. Moreover, we performed correlations (Pearson's moment correlation) between CV damage score and frequency markers (TF and IAF), spectral power, and MMSE. Finally, we performed a control statistical analysis with 4 frequency bands, considering alpha1 and alpha2 as single band (low alpha). This analysis had the aim to verify if the low alpha, when considered as a

Figure 1 displays the results for ANOVA analysis of these data showing a significant interaction between Group and Band factors [F (12.380)= 2.60); p < 0.002] . Interestingly,

of good agreement.

damage, CV score 11-15.

**2.5. Statistical analysis** 

For all statistical test the significance was set to p<0.05.

alpha2, alpha3) the dependent variable.

whole, has the same behavior.

**3. Results** 

All patients underwent: (i) semi-structured interview with the patient and – whenever possible – with another informant (usually the patient's spouse or a child) by a geriatrician or neurologist; (ii) physical and neurological examinations; (iii) performance-based tests of physical function, gait and balance; (iv) neuropsychological assessment evaluating verbal and non-verbal memory, attention and executive functions (Trail Making Test B-A; Clock Drawing Test; Amodio et al., 2000; Shulman, 2000), abstract thinking (Raven matrices; Basso et al., 1987), frontal functions (Inverted Motor Learning; Spinnler and Tognoni, 1987); language (Phonological and Semantic fluency; Token test; Carlesimo et al., 1996), and apraxia and visuo-constructional abilities (Rey figure copy; Caffarra et al., 2002); (v) assessment of depressive symptoms with the Center for Epidemiologic Studies Depression Scale (CES-D; Radloff, 1977). Given the aim of the study to evaluate the impact of vascular damage on EEG rhythms, in this study we did not consider the clinical subtype of MCI, i.e. amnesic, non amnesic or multiple domain.

#### **2.4. Magnetic resonance imaging (MRI) and CV damage evaluation**

Magnetic Resonance (MR) images were acquired using a 1.0 Tesla Philips Gyroscan. Axial T2 weighted, proton density (DP) and fluid attenuated inversion recovery (FLAIR) images were acquired with the following acquisition parameters: TR = 2000 ms, TE = 8.8/110 ms, flip angle = 90°, field of view = 230 mm, acquisition matrix 256x256, slice thickness 5 mm for T2/DP sequences and TR = 5000 ms, TE = 100 ms, flip angle = 90°, field of view = 230 mm, acquisition matrix 256x256, slice thickness 5 mm for FLAIR images.

Subcortical cerebrovascular disease (sCVD) was assessed using the rating scale for agerelated white matter changes (ARWMC) on T2-weighted and FLAIR MR images. White matter changes (WMC) was rated by a single observer (R.R.) in the right and left hemispheres separately in frontal, parieto-occipital, temporal, infratentorial areas and basal ganglia on a 4 point scale. The observer of white matter changes was blind to the clinical information of the subjects. Subscores of 0, 1, 2, and 3 were assigned in frontal, parietooccipital, temporal, infratentorial areas for: no WMC, focal lesions, beginning confluence of lesions, and diffuse involvement of the entire region, respectively. Subscores of 0, 1, 2, and 3 were assigned in basal ganglia for: no WMC, 1 focal lesion, more than 1 focal lesion and confluent lesions, respectively. Total score was the sum of subscores for each area in the left and right hemisphere, ranging from 0 to 30. As regards the ARWMC scale, the interrater reliability, as calculated with weighted k value, was 0.67, indicative of moderate agreement (Wahlund et al., 2001). We assessed test-retest reliability on a random sample of 20 subjects. The intraclass correlation coefficient was 0.98, values above 0.80 being considered indicative of good agreement.

Based on increasing subcortical CV damage, the 99 MCI subjects were subsequently divided in 4 sub-groups along the range between the minimum and maximum ARWMC score (respectively 0 and 15). In order to have the higher sensibility to the CV damage, the first group was composed by subjects with score = 0. The other groups were composed according to equal range ARWMC scores. As a consequence, we obtained the following groups: group 1 (G1) no vascular damage, CV score 0; group 1 (G2), mild vascular damage, CV score 1-5; group 3 (G3), moderate vascular damage, CV score 6-10; group 1 (G4), severe vascular damage, CV score 11-15.

Table 1 reports the mean values of demographic and clinical characteristics of the 4 subgroups.

#### **2.5. Statistical analysis**

176 Advances in Clinical Neurophysiology

diabetes mellitus) or traumatic brain injuries.

amnesic, non amnesic or multiple domain.

by a relative or the general practitioner of memory or other cognitive disturbances; (ii) mini mental state examination (MMSE; Folstein et al., 1975) score of 24 to 27/30 or MMSE of 28 and higher plus low performance (score of 2/6 or higher) on the clock drawing test (Shulman, 2000); (iii) sparing of instrumental and basic activities of daily living or functional impairment stably due to causes other than cognitive impairment, such as physical impairments, sensory loss, gait or balance disturbances, etc. Exclusion criteria were any one of the following: (i) age of 90 years and older; (ii) history of depression or psychosis of juvenile onset; (iii) history or neurological signs of major stroke; (iv) other psychiatric diseases, epilepsy, drug addiction, alcohol dependence; (v) use of psychoactive drugs including acetylcholinesterase inhibitors or other drugs enhancing brain cognitive functions; and (vi) current or previous uncontrolled or complicated systemic diseases (including

All patients underwent: (i) semi-structured interview with the patient and – whenever possible – with another informant (usually the patient's spouse or a child) by a geriatrician or neurologist; (ii) physical and neurological examinations; (iii) performance-based tests of physical function, gait and balance; (iv) neuropsychological assessment evaluating verbal and non-verbal memory, attention and executive functions (Trail Making Test B-A; Clock Drawing Test; Amodio et al., 2000; Shulman, 2000), abstract thinking (Raven matrices; Basso et al., 1987), frontal functions (Inverted Motor Learning; Spinnler and Tognoni, 1987); language (Phonological and Semantic fluency; Token test; Carlesimo et al., 1996), and apraxia and visuo-constructional abilities (Rey figure copy; Caffarra et al., 2002); (v) assessment of depressive symptoms with the Center for Epidemiologic Studies Depression Scale (CES-D; Radloff, 1977). Given the aim of the study to evaluate the impact of vascular damage on EEG rhythms, in this study we did not consider the clinical subtype of MCI, i.e.

**2.4. Magnetic resonance imaging (MRI) and CV damage evaluation** 

acquisition matrix 256x256, slice thickness 5 mm for FLAIR images.

Magnetic Resonance (MR) images were acquired using a 1.0 Tesla Philips Gyroscan. Axial T2 weighted, proton density (DP) and fluid attenuated inversion recovery (FLAIR) images were acquired with the following acquisition parameters: TR = 2000 ms, TE = 8.8/110 ms, flip angle = 90°, field of view = 230 mm, acquisition matrix 256x256, slice thickness 5 mm for T2/DP sequences and TR = 5000 ms, TE = 100 ms, flip angle = 90°, field of view = 230 mm,

Subcortical cerebrovascular disease (sCVD) was assessed using the rating scale for agerelated white matter changes (ARWMC) on T2-weighted and FLAIR MR images. White matter changes (WMC) was rated by a single observer (R.R.) in the right and left hemispheres separately in frontal, parieto-occipital, temporal, infratentorial areas and basal ganglia on a 4 point scale. The observer of white matter changes was blind to the clinical information of the subjects. Subscores of 0, 1, 2, and 3 were assigned in frontal, parietooccipital, temporal, infratentorial areas for: no WMC, focal lesions, beginning confluence of lesions, and diffuse involvement of the entire region, respectively. Subscores of 0, 1, 2, and 3 Preliminarly, any significant difference between groups in demographic variables, age, education and gender as well as MMSE score was taken into account. Only education showed a significant differences between groups (p<0.03). For avoiding confounding effect, subsequent statistical analyses of variance (ANOVA) were carried out using age, education, gender and MMSE score as covariates. Duncan's test was used for post hoc comparisons. For all statistical test the significance was set to p<0.05.

A second session of ANOVA was performed on EEG relative power data. In this analysis, Group factor was the independent variable and frequency band power (delta, theta, alpha1, alpha2, alpha3) the dependent variable.

As successive step, to evaluate the presence of EEG indexes that correlate specifically with vascular damage, we performed statistical analyses to evaluate the specificity of the following ratios: theta/alpha1, using as covariate also TF; alpha2/alpha3 using as covariate also IAF and alpha1/alpha2 with both TF and IAF as covariate. Moreover, we performed correlations (Pearson's moment correlation) between CV damage score and frequency markers (TF and IAF), spectral power, and MMSE. Finally, we performed a control statistical analysis with 4 frequency bands, considering alpha1 and alpha2 as single band (low alpha). This analysis had the aim to verify if the low alpha, when considered as a whole, has the same behavior.

#### **3. Results**

Figure 1 displays the results for ANOVA analysis of these data showing a significant interaction between Group and Band factors [F (12.380)= 2.60); p < 0.002] . Interestingly,

Duncan post hoc showed a significant decrease of delta power in G1 compared to G4 (p < 0.050) and a significant increase in alpha2 power in G1 compared to G3 and G4 (p < 0.000). On the contrary, no differences were found in theta, alpha1 and alpha 3 band power. Moreover, a closer look at the data, in respect to the alpha1 frequency, showed a decrease proportional to the degree of CV damage very similar to alpha2 band, although not significant. On the contrary, in the alpha3 band power this trend was not present, suggesting that vascular damage had no impact on this frequency band.

Mild Cognitive Impairment and Quantitative EEG Markers: Degenerative Versus Vascular Brain Damage 179

**cohort Group 1 Group 2 Group 3 p value** 

7082.7±26 6.9

4935.6±38 0.9

2147.1±30 1.3

4969.4±25 7.6

2079.2±12 2.8

5661.8±72

3967.9±65

1693.9±28

3890.1±55

1621.6±18

0.4 0.00001

0.3 0.00001

8.5 0.0001

1.4 0.00001

5.2 0.0001

**(ANOVA)** 

showed a main effect of Group [F (3.91)=4.60; p < 0.005]. Duncan post hoc testing showed a significant decrease of the ratio between G1 and G3 (p < 0.02), G1 and G4 (p < 0.010) and between G2 and G4 (p < 0.05). The statistical analysis of the alpha1/alpha2 ratio did not

**Age (years)** 69.2±2.3 66.8±6.8 69.4±8.7 71.5±6.9 0.1

**MMSE** 27.1±0.4 27.5±1.5 27.4±1.5 26.6±1.8 0.1

8151.2±43 6.4

5771.6±36 1.1

2379.6±32 1.3

5809.6±31 4.2

2514.4±25 9.5

**hyperintensities (mm3)** 3.8±0.5 3.2±2.8 4.2±3.8 4.1±3.6 0.7

**Table 3.** Mean values ± standard deviation of sociodemographic characteristics, MMSE scores, white matter hyperintensities, hippocampal and amygdalar volume measurements. Hippocampal and amygdalar volumes are referred to the whole amygdalo-hippocampal complex (AHC) and singularly

MRI scans were acquired with a 1.0 Tesla Philips Gyroscan at the Neuroradiology Unit of the Città di Brescia hospital, Brescia. The following sequences were used to measure hippocampal and amygdalar volumes: a high-resolution gradient echo T1-weighted sagittal

**Education (years)** 7.7±0.8 8.3±4.5 6.7±3.1 8.2±4.6 0.2

**MCI** 

48.8

4891.7±90 2.6

2073.5±34 8.7

4889.8±96 2.4

2071.7±44 6.4

considered (individual). The t-test refers to AHC vs individual volume in each group.

**3.1. MRI scans and amygdalo-hippocampal atrophy evaluation** 

**Number of subjects (f/m)** 79 (42/37) 27 (14/13) 27 (15/12) 25 (13/12)

show the main effect of Group (p < 0.2).

 **AHC-hippocampal volume (mm3)** 

 **AHC-amygdalar volume (mm3)** 

 **individual hippocampal volume (mm3)** 

 **individual amygdalar volume (mm3)** 

> **White matter**

**Total AHC volume** 6965.3±12

**Figure 1.** Statistical ANOVA interaction among groups, factor and relative band power (delta, theta, alpha1, alpha2, alpha3). In the diagram the difference in delta and alpha2 power among groups is also indicated, based on Duncan's post-hoc testing. (G1, group 1) no vascular damage; (G2, group 2) mild vascular damage; (G3, group 3) moderate vascular damage; (G4, group 4) severe vascular damage (Moretti et al., 2007).

The correlation analysis between CV score and spectral band power showed a significant positive correlation with delta power (r= 0.221; p < 0.03), a significant negative correlation with alpha1 (r= -0.312; p < 0.002) and alpha2 power (r -0.363; p < 0.0003). The correlations between CV score with theta power (r= 0.183; p = 0.07) and alpha3 power (r= -0.002; p = 0.93) were not significant as well as the correlation between CV score and MMSE (r= -0.07; p= 0.4).

Table 3 displays the values of the theta/alpha1 and alpha2/alpha3 power ratio. The statistical analysis of the theta/alpha1 ratio showed a main effect of Group [F (3.91)=15.51; p < 0.000]. Duncan post hoc testing showed a significant increase of the theta/alpha1 ratio between G1 and G2 respect to G3 and G4 (p < 0.000). Moreover, the increase of this ratio was significant also between G3 and G4 (p<0.04). The statistical analysis of the alpha2/alpha3 power ratio showed a main effect of Group [F (3.91)=4.60; p < 0.005]. Duncan post hoc testing showed a significant decrease of the ratio between G1 and G3 (p < 0.02), G1 and G4 (p < 0.010) and between G2 and G4 (p < 0.05). The statistical analysis of the alpha1/alpha2 ratio did not show the main effect of Group (p < 0.2).

178 Advances in Clinical Neurophysiology

(Moretti et al., 2007).

Duncan post hoc showed a significant decrease of delta power in G1 compared to G4 (p < 0.050) and a significant increase in alpha2 power in G1 compared to G3 and G4 (p < 0.000). On the contrary, no differences were found in theta, alpha1 and alpha 3 band power. Moreover, a closer look at the data, in respect to the alpha1 frequency, showed a decrease proportional to the degree of CV damage very similar to alpha2 band, although not significant. On the contrary, in the alpha3 band power this trend was not present,

**Figure 1.** Statistical ANOVA interaction among groups, factor and relative band power (delta, theta, alpha1, alpha2, alpha3). In the diagram the difference in delta and alpha2 power among groups is also indicated, based on Duncan's post-hoc testing. (G1, group 1) no vascular damage; (G2, group 2) mild vascular damage; (G3, group 3) moderate vascular damage; (G4, group 4) severe vascular damage

The correlation analysis between CV score and spectral band power showed a significant positive correlation with delta power (r= 0.221; p < 0.03), a significant negative correlation with alpha1 (r= -0.312; p < 0.002) and alpha2 power (r -0.363; p < 0.0003). The correlations between CV score with theta power (r= 0.183; p = 0.07) and alpha3 power (r= -0.002; p = 0.93) were not significant as well as the correlation between CV score and MMSE (r= -0.07; p= 0.4).

Table 3 displays the values of the theta/alpha1 and alpha2/alpha3 power ratio. The statistical analysis of the theta/alpha1 ratio showed a main effect of Group [F (3.91)=15.51; p < 0.000]. Duncan post hoc testing showed a significant increase of the theta/alpha1 ratio between G1 and G2 respect to G3 and G4 (p < 0.000). Moreover, the increase of this ratio was significant also between G3 and G4 (p<0.04). The statistical analysis of the alpha2/alpha3 power ratio

suggesting that vascular damage had no impact on this frequency band.


**Table 3.** Mean values ± standard deviation of sociodemographic characteristics, MMSE scores, white matter hyperintensities, hippocampal and amygdalar volume measurements. Hippocampal and amygdalar volumes are referred to the whole amygdalo-hippocampal complex (AHC) and singularly considered (individual). The t-test refers to AHC vs individual volume in each group.

#### **3.1. MRI scans and amygdalo-hippocampal atrophy evaluation**

MRI scans were acquired with a 1.0 Tesla Philips Gyroscan at the Neuroradiology Unit of the Città di Brescia hospital, Brescia. The following sequences were used to measure hippocampal and amygdalar volumes: a high-resolution gradient echo T1-weighted sagittal 3D sequence (TR = 20 ms, TE = 5 ms, flip angle = 30°, field of view = 220 mm, acquisition matrix = 256 x 256, slice thickness = 1.3 mm), and a fluid-attenuated inversion recovery (FLAIR) sequence (TR = 5000 ms, TE = 100 ms, flip angle = 90°, field of view = 230 mm, acquisition matrix = 256 x 256, slice thickness = 5 mm). Hippocampal, amygdalar and white matter hyperintensities (WMHs) volumes were obtained for each subject. The hippocampal and amygdalar boundaries were manually traced on each hemisphere by a single tracer with the software program DISPLAY (McGill University, Montreal, Canada) on contiguous 1.5 mm slices in the coronal plane. The amygdala is an olive-shaped mass of gray matter located in the superomedial part of the temporal lobe, partly superior and anterior to the hippocampus. The starting point for amygdala tracing was at the level where it is separated from the entorhinal cortex by intrarhinal sulcus, or tentorial indentation, which forms a marked indent at the site of the inferior border of the amygdala. The uncinate fasciculus, at the level of basolateral nuclei groups, was considered as the anterior-lateral border. The amygdalo-striatal transition area, which is located between lateral amygdaloid nucleus and ventral putamen, was considered as the posterior-lateral border. The posterior end of amygdaloid nucleus was defined as the point where gray matter starts to appear superior to the alveolus and laterally to the hippocampus. If the alveolus was not visible, the inferior horn of the lateral ventricle was employed as border (Moretti et al., 2008). The starting point for hippocampus tracing was defined as the hippocampal head when it first appears below the amygdala, the alveus defining the superior and anterior border of the hippocampus. The fimbria was included in the hippocampal body, while the grey matter rostral to the fimbria was excluded. The hippocampal tail was traced until it was visible as an oval shape located caudally and medially to the trigone of the lateral ventricles (Moretti et al., 2007a,b). The intraclass correlation coefficients were 0.95 for the hippocampus and 0.83 for the amygdala.

Mild Cognitive Impairment and Quantitative EEG Markers: Degenerative Versus Vascular Brain Damage 181

Left and right hippocampal as well as amygdalar volumes were estimated and summed to obtain a total volume (individual) of both anatomical structures. In turn, total amygdalar and hippocampal volume were summed obtaining the whole AHC volume. AHC (whole) volume has been divided in tertiles obtaining three groups. In each group hippocampal and amygdalar volume (within AHC) has been computed. The last volumes were compared with the previous obtained individual (hippocampal and amygdalar)

The analysis of variance (ANOVA) has been applied as statistical tool. At first, any significant differences among groups in demographic variables, i.e., age, education, MMSE score, and morphostructural characteristics, i.e., AHC, hippocampal, amygdalar and white matter hyperintensities (WMHs) volume were evaluated (table 1). Greenhouse-Geisser correction and Mauchley's sphericity test were applied to all ANOVAs. In order to avoid a confounding effect, ANOVAs were carried out using age, education, MMSE score, and WMHs as covariates. Duncan's test was used for post-hoc comparisons. For all statistical

At first, we choose to focus the changes of brain rhythmicity induced from hippocampal atrophy alone. ,Subjects were subdivided in four groups based on hippocampal volume of a normal, control sample matched for age, sex and education as compared to the whole MCI group. In the normal group the ratio female/male was 93/46, mean age was 68.9 (SD ±10.3) mean education years 8.9 (SD ±9.4). The mean and standard deviation of the hippocampal volume in the normal old population of 139 subjects were 5.72 ± 1.1 cm3 . In this way, 4 groups were obtained: the no atrophy group with hippocampal volume equal or superior to the normal mean (total hippocampal volume from 6.79 cm3 to 5.75 cm3; G1); the mild atrophy group which has hippocampal volume within 1.5 SD below the mean of hippocampal normal control value (total hippocampal volume from 5.70 to 4.70 cm3; G2); the moderate atrophy group which has hippocampal volume between 1.5 and 3 SD below the mean of normal hippocampus (total hippocampal volume from 4.65 to 3.5 cm3; G3); and the severe atrophy group which has hippocampal volume between 3 and 4.5 SD below the mean of hippocampal normal control volume (total hippocampal volume from 3.4 to 2.53 cm3; G4). The rationale for the selection of 1,5 SD was to obtain reasonably pathological groups based on hippocampal volume. A SD below 1.5 could recollect still normal population based on hippocampal volume. On the other side, a SD over 1.5 could not allow an adequate size of all subgroups in

Subsequently, ANOVA was performed in order to verify 1) the difference of AHC volume among groups; 2) the difference of hippocampal and amygdalar volume within AHC among groups; 3) the difference of hippocampal and amygdalar volume individually

considered among groups; 4) NPS impairment based on ACH atrophy.

volumes.

study.

**3.2. Statistical analysis and data management** 

tests the significance level was set at p<0.05.

White matter hyperintensities (WMHs) were automatically segmented on the FLAIR sequences by using previously described algorithms (Moretti et al., 2007a,b). Briefly, the procedure includes (i) filtering of FLAIR images to exclude radiofrequency inhomogeneities, (ii) segmentation of brain tissue from cerebrospinal fluid, (iii) modelling of brain intensity histogram as a gaussian distribution and (iv) classification of the voxels whose intensities were ≥3.5 SDs above the mean as WMHs (Moretti et al., 2007a,b) Total WMHs volume was computed by counting the number of voxels segmented as WMHs and multiplying by the voxel size (5 mm3). To correct for individual differences in head size, hippocampal, amygdalar and WMHs volumes were normalized to the total intracranial volume (TIV), obtained by manually tracing with DISPLAY the entire intracranial cavity on 7 mm thick coronal slices of the T1 weighted images. Both manual and automated methods user here have advantages and disadvantages. Manual segmentation of the hippocampus and amygdala is currently considered the gold standard technique for the measurement of such complex structures. The main disadvantages of manual tracing are that it is operator dependent and time consuming. Conversely, automated techniques are more reliable and less time-consuming, but may be less accurate when dealing with structures without clearly identifiable borders. This however is not the case for WMHs which appear as hyperintense on FLAIR sequences.

Left and right hippocampal as well as amygdalar volumes were estimated and summed to obtain a total volume (individual) of both anatomical structures. In turn, total amygdalar and hippocampal volume were summed obtaining the whole AHC volume. AHC (whole) volume has been divided in tertiles obtaining three groups. In each group hippocampal and amygdalar volume (within AHC) has been computed. The last volumes were compared with the previous obtained individual (hippocampal and amygdalar) volumes.

#### **3.2. Statistical analysis and data management**

180 Advances in Clinical Neurophysiology

on FLAIR sequences.

3D sequence (TR = 20 ms, TE = 5 ms, flip angle = 30°, field of view = 220 mm, acquisition matrix = 256 x 256, slice thickness = 1.3 mm), and a fluid-attenuated inversion recovery (FLAIR) sequence (TR = 5000 ms, TE = 100 ms, flip angle = 90°, field of view = 230 mm, acquisition matrix = 256 x 256, slice thickness = 5 mm). Hippocampal, amygdalar and white matter hyperintensities (WMHs) volumes were obtained for each subject. The hippocampal and amygdalar boundaries were manually traced on each hemisphere by a single tracer with the software program DISPLAY (McGill University, Montreal, Canada) on contiguous 1.5 mm slices in the coronal plane. The amygdala is an olive-shaped mass of gray matter located in the superomedial part of the temporal lobe, partly superior and anterior to the hippocampus. The starting point for amygdala tracing was at the level where it is separated from the entorhinal cortex by intrarhinal sulcus, or tentorial indentation, which forms a marked indent at the site of the inferior border of the amygdala. The uncinate fasciculus, at the level of basolateral nuclei groups, was considered as the anterior-lateral border. The amygdalo-striatal transition area, which is located between lateral amygdaloid nucleus and ventral putamen, was considered as the posterior-lateral border. The posterior end of amygdaloid nucleus was defined as the point where gray matter starts to appear superior to the alveolus and laterally to the hippocampus. If the alveolus was not visible, the inferior horn of the lateral ventricle was employed as border (Moretti et al., 2008). The starting point for hippocampus tracing was defined as the hippocampal head when it first appears below the amygdala, the alveus defining the superior and anterior border of the hippocampus. The fimbria was included in the hippocampal body, while the grey matter rostral to the fimbria was excluded. The hippocampal tail was traced until it was visible as an oval shape located caudally and medially to the trigone of the lateral ventricles (Moretti et al., 2007a,b). The intraclass correlation coefficients were 0.95 for the hippocampus and 0.83 for the amygdala. White matter hyperintensities (WMHs) were automatically segmented on the FLAIR sequences by using previously described algorithms (Moretti et al., 2007a,b). Briefly, the procedure includes (i) filtering of FLAIR images to exclude radiofrequency inhomogeneities, (ii) segmentation of brain tissue from cerebrospinal fluid, (iii) modelling of brain intensity histogram as a gaussian distribution and (iv) classification of the voxels whose intensities were ≥3.5 SDs above the mean as WMHs (Moretti et al., 2007a,b) Total WMHs volume was computed by counting the number of voxels segmented as WMHs and multiplying by the voxel size (5 mm3). To correct for individual differences in head size, hippocampal, amygdalar and WMHs volumes were normalized to the total intracranial volume (TIV), obtained by manually tracing with DISPLAY the entire intracranial cavity on 7 mm thick coronal slices of the T1 weighted images. Both manual and automated methods user here have advantages and disadvantages. Manual segmentation of the hippocampus and amygdala is currently considered the gold standard technique for the measurement of such complex structures. The main disadvantages of manual tracing are that it is operator dependent and time consuming. Conversely, automated techniques are more reliable and less time-consuming, but may be less accurate when dealing with structures without clearly identifiable borders. This however is not the case for WMHs which appear as hyperintense

The analysis of variance (ANOVA) has been applied as statistical tool. At first, any significant differences among groups in demographic variables, i.e., age, education, MMSE score, and morphostructural characteristics, i.e., AHC, hippocampal, amygdalar and white matter hyperintensities (WMHs) volume were evaluated (table 1). Greenhouse-Geisser correction and Mauchley's sphericity test were applied to all ANOVAs. In order to avoid a confounding effect, ANOVAs were carried out using age, education, MMSE score, and WMHs as covariates. Duncan's test was used for post-hoc comparisons. For all statistical tests the significance level was set at p<0.05.

At first, we choose to focus the changes of brain rhythmicity induced from hippocampal atrophy alone. ,Subjects were subdivided in four groups based on hippocampal volume of a normal, control sample matched for age, sex and education as compared to the whole MCI group. In the normal group the ratio female/male was 93/46, mean age was 68.9 (SD ±10.3) mean education years 8.9 (SD ±9.4). The mean and standard deviation of the hippocampal volume in the normal old population of 139 subjects were 5.72 ± 1.1 cm3 . In this way, 4 groups were obtained: the no atrophy group with hippocampal volume equal or superior to the normal mean (total hippocampal volume from 6.79 cm3 to 5.75 cm3; G1); the mild atrophy group which has hippocampal volume within 1.5 SD below the mean of hippocampal normal control value (total hippocampal volume from 5.70 to 4.70 cm3; G2); the moderate atrophy group which has hippocampal volume between 1.5 and 3 SD below the mean of normal hippocampus (total hippocampal volume from 4.65 to 3.5 cm3; G3); and the severe atrophy group which has hippocampal volume between 3 and 4.5 SD below the mean of hippocampal normal control volume (total hippocampal volume from 3.4 to 2.53 cm3; G4). The rationale for the selection of 1,5 SD was to obtain reasonably pathological groups based on hippocampal volume. A SD below 1.5 could recollect still normal population based on hippocampal volume. On the other side, a SD over 1.5 could not allow an adequate size of all subgroups in study.

Subsequently, ANOVA was performed in order to verify 1) the difference of AHC volume among groups; 2) the difference of hippocampal and amygdalar volume within AHC among groups; 3) the difference of hippocampal and amygdalar volume individually considered among groups; 4) NPS impairment based on ACH atrophy.

Moreover, as a control analysis, in order to detect if difference in EEG markers was linked to significant difference in volume measurements, the volume of hippocampus within AHC was compared with the hippocampal volume individually considered, as well as the amygdalar volume within AHC was compared with the amygdalar volume individually considered. This control analysis was performed through a paired t-test.

Mild Cognitive Impairment and Quantitative EEG Markers: Degenerative Versus Vascular Brain Damage 183

**Figure 2.** Statistical ANOVA interaction among Group factors, and relative band powers (delta, theta, alpha1, alpha2, alpha3), on the full scalp region. The groups are based on mean and standard deviations in a normal elderly sample. Group 1, no hippocampal atrophy; Group 2, mild hippocampal atrophy; Group 3, moderate hippocampal atrophy; Group 4 severe hippocampal atrophy. Post-hoc results are

Table 3 and 4 shows the results of theta/gamma and alpha3/alpha2 ratio in the groups based on the decrease of whole AHC volume as well as, within the same group, the decrease of hippocampal and amygdalar volumes separately considered. ANOVA shows results towards significance when amygdalo-ippocampal volume is considered globally both in theta/gamma (F2,76 =2.77; p<0.06) and alpha3/alpha2 ratio (F2,76 =2.71; p<0.07). When amygdalar and hippocampal volumes were considered separately, ANOVA results revealed significant main effect Group, respectively, in theta/gamma ratio analysis (F2,76 =3.46; p<0.03) for amygdalar and alpha3/alpha2 ratio for hippocampal (F2,76=3.38; p<0.03) decreasing volume. The ANOVA did not show significant results in theta/gamma ratio when considering hippocampal volume (F2,76=0.3; p<0.7) and in alpha3/alpha2 ratio when considering amygdalar volume (F2,76=1.46; p<0.2). The control analysis (individual volumes) did not show any significant result neither for hippocampal (theta/gamma, F2,76=0.3; p<0.7; alpha3/alpha2, F2,76=2.15; p<0.1) nor for amygdalar volume (theta/gamma, F2,76=0.76; p<0.4;

indicated in the diagram (see Moretti et al., 2007).

alpha3/alpha2, F2,76=2.15; p<0.1).

Subsequently, ANOVA was performed in order to check differences in theta/gamma and alpha3/alpha2 relative power ratio in the three groups ordered by decreasing tertile values of the whole AHC volume. In each ANOVA, group was the independent variable, the frequency ratios was the dependent variable and age, education, MMSE score, and WMHs was used as covariates. Duncan's test was used for post-hoc comparisons. For all statistical tests the significance level was set at p<0.05.

In order to check closer association with EEG markers, hippocampal volume, and amygdalar volume within AHC were analyzed separately. A control analysis was carried out also on the individual hippocampal and amygdalar volumes based on decreasing tertile values for homogeneity with the main analysis.

#### **4. Results**

Figure 2 displays the results for ANOVA analysis performed on 4 groups of MCI considering growing values of hippocampal atrphy. The results show a significant interaction between Group and Band power [F (12,336)= 2,36); p < 0.007]. Duncan post hoc showed that G3 group has the highest alpha2 and alpha3 power statistically significant with respect to all other groups (p<0.05; p< 0.006 respectively). The same trend was present in the subsidiary ANOVA. These results show that the relationship between hippocampal atrophy and EEG relative power is not proportional to the hippocampal atrophy and highlight that the group with a moderate hippocampal volume had a particular pattern of EEG activity as compared to all other groups.

Table 2 summarizes the ANOVA results of demographic variables, i.e., age, education, MMSE score, and morphostructural characteristics, i.e., hippocampal, amygdalar and white matter hyperintensities volume in the whole MCI cohort as well as in the three subgroups in study. Hippocampal and aymgdalar volumes are considered as parts of the whole AHC volume as well as individually considered. Significant statistical results were found in hippocampal and amygdalar volume both within the AHC (respectively, F2,76=92.74; p<0.00001 and F2,76=33.82; p<0.00001) and individually considered (respectively, F2,76=157.27; p<0.00001 and F2,76=132.5; p<0.00001). The global AHC volume also showed significant results (F2,76=159.27; p<0.00001). Duncan's post-hoc test showed a significant increase (p< 0.01) in all comparisons. The paired t-test showed significant difference between the volume of ACH-amygdala vs amygdalar volume individually considered in the first group (p <0.03). The amygdalar volume difference in the other groups (respectively p=0.2 and 0.1) as well as the difference in the volume of AHC-hippocampus vs individual hippocampus (p= 0.4 in the first, p=0.5 in the second and p=0.1 in the third group) was not statistically significant.

**4. Results** 

compared to all other groups.

tests the significance level was set at p<0.05.

values for homogeneity with the main analysis.

Moreover, as a control analysis, in order to detect if difference in EEG markers was linked to significant difference in volume measurements, the volume of hippocampus within AHC was compared with the hippocampal volume individually considered, as well as the amygdalar volume within AHC was compared with the amygdalar volume individually

Subsequently, ANOVA was performed in order to check differences in theta/gamma and alpha3/alpha2 relative power ratio in the three groups ordered by decreasing tertile values of the whole AHC volume. In each ANOVA, group was the independent variable, the frequency ratios was the dependent variable and age, education, MMSE score, and WMHs was used as covariates. Duncan's test was used for post-hoc comparisons. For all statistical

In order to check closer association with EEG markers, hippocampal volume, and amygdalar volume within AHC were analyzed separately. A control analysis was carried out also on the individual hippocampal and amygdalar volumes based on decreasing tertile

Figure 2 displays the results for ANOVA analysis performed on 4 groups of MCI considering growing values of hippocampal atrphy. The results show a significant interaction between Group and Band power [F (12,336)= 2,36); p < 0.007]. Duncan post hoc showed that G3 group has the highest alpha2 and alpha3 power statistically significant with respect to all other groups (p<0.05; p< 0.006 respectively). The same trend was present in the subsidiary ANOVA. These results show that the relationship between hippocampal atrophy and EEG relative power is not proportional to the hippocampal atrophy and highlight that the group with a moderate hippocampal volume had a particular pattern of EEG activity as

Table 2 summarizes the ANOVA results of demographic variables, i.e., age, education, MMSE score, and morphostructural characteristics, i.e., hippocampal, amygdalar and white matter hyperintensities volume in the whole MCI cohort as well as in the three subgroups in study. Hippocampal and aymgdalar volumes are considered as parts of the whole AHC volume as well as individually considered. Significant statistical results were found in hippocampal and amygdalar volume both within the AHC (respectively, F2,76=92.74; p<0.00001 and F2,76=33.82; p<0.00001) and individually considered (respectively, F2,76=157.27; p<0.00001 and F2,76=132.5; p<0.00001). The global AHC volume also showed significant results (F2,76=159.27; p<0.00001). Duncan's post-hoc test showed a significant increase (p< 0.01) in all comparisons. The paired t-test showed significant difference between the volume of ACH-amygdala vs amygdalar volume individually considered in the first group (p <0.03). The amygdalar volume difference in the other groups (respectively p=0.2 and 0.1) as well as the difference in the volume of AHC-hippocampus vs individual hippocampus (p= 0.4 in the

first, p=0.5 in the second and p=0.1 in the third group) was not statistically significant.

considered. This control analysis was performed through a paired t-test.

**Figure 2.** Statistical ANOVA interaction among Group factors, and relative band powers (delta, theta, alpha1, alpha2, alpha3), on the full scalp region. The groups are based on mean and standard deviations in a normal elderly sample. Group 1, no hippocampal atrophy; Group 2, mild hippocampal atrophy; Group 3, moderate hippocampal atrophy; Group 4 severe hippocampal atrophy. Post-hoc results are indicated in the diagram (see Moretti et al., 2007).

Table 3 and 4 shows the results of theta/gamma and alpha3/alpha2 ratio in the groups based on the decrease of whole AHC volume as well as, within the same group, the decrease of hippocampal and amygdalar volumes separately considered. ANOVA shows results towards significance when amygdalo-ippocampal volume is considered globally both in theta/gamma (F2,76 =2.77; p<0.06) and alpha3/alpha2 ratio (F2,76 =2.71; p<0.07). When amygdalar and hippocampal volumes were considered separately, ANOVA results revealed significant main effect Group, respectively, in theta/gamma ratio analysis (F2,76 =3.46; p<0.03) for amygdalar and alpha3/alpha2 ratio for hippocampal (F2,76=3.38; p<0.03) decreasing volume. The ANOVA did not show significant results in theta/gamma ratio when considering hippocampal volume (F2,76=0.3; p<0.7) and in alpha3/alpha2 ratio when considering amygdalar volume (F2,76=1.46; p<0.2). The control analysis (individual volumes) did not show any significant result neither for hippocampal (theta/gamma, F2,76=0.3; p<0.7; alpha3/alpha2, F2,76=2.15; p<0.1) nor for amygdalar volume (theta/gamma, F2,76=0.76; p<0.4; alpha3/alpha2, F2,76=2.15; p<0.1).


Mild Cognitive Impairment and Quantitative EEG Markers: Degenerative Versus Vascular Brain Damage 185

the neural communication during memory retrieval (Narayanan et al., 2007). On the other hand, the retrieval of hippocampus-dipendent memory is provided by the integrity of CA3- CA1 interplay coordinated by gamma oscillations (Montgomery and Buzsaki, 2007). Our results confirm and extends all previous findings. The atrophy of AHC determines increasing memory deficits. The brain oscillatory activity of this MCI state is characterized by an increase of theta/gamma ratio and alpha3/alpha2 relative power ratio, confirming the overall reliability of these EEG markers in cognitive decline. Our results suggest that theta synchronization is mainly due to the amygdala activation or as a subsequent final net effect within the AHC functioning driven by the amygdala excitation. The increase in theta activities in AHC, representing an increase in neuronal communication apt to promote or stabilize synaptic plasticity in relation to the effort to retention of associative memories (Sauseng et al., 2004), could be active also during an ongoing degenerative process. The excitation mechanism could be facilitated by the loss of GABA inhibitory process, determining the decrease of gamma rhythm generation (Bragin et al., 1995; Montgomery

As regards the CV damage, our results showed that the CV damage affected both delta and low alpha band power (alpha1 and alpha2). In the delta band we observed a power increase proportional to the CV damage, with a significant increase in the group with severe CV damage, as compared to the no-CV-damage group. The impact of the CV damage on the delta power was confirmed by the significant positive correlation between CV damage score

The increase in the delta band power could be explained as a progressive cortical disconnection due to the slowing of the conduction along cortico-subcortical connecting

This result confirmed the increase in the delta band power we had observed in CV patients, as compared to normal elderly subjects (Moretti et al., 2004). It is to be noted that the increase in the delta band power reflects a global state of cortical deafferentation, due to various anatomofunctional substrates, such as stages of sleep, metabolic encephalopathy or cortico-thalamocortical dysrhythmia (Llinas et al., 1999). In the low a band power, we observed a significant decrease in the a2 band power for the groups with moderate and severe CV damage, as compared to the no-CV-damage group. In the a1 frequency band, there was a similar decrease although it did not reach statistically significant values. These results were confirmed by a correlation analysis which showed a significant negative correlation between CV damage score and a1 and a2 band powers. In our results, the CV damage did not show any impact on the a3 (or high a) power. This is a confirmation of what we found in the previous study, where no differences between VaD patients and normal

elderly (but not in AD vs normal elderly) subjects were detected in the a3 power.

Together, these results could suggest different generators for low a and high a frequency bands. In particular, the low a band power could affect cortico-subcortical mechanisms, such as cortico-thalamic, cortico-striatal and cortico-basal ones. This could explain the sensitivity

and Buzsaki, 2007).

and delta power itself.

pathways.

**Table 4.** Relative power band ratios in amygdalo-hippocampal complex (AHC), hippocampal and amygdalar atrophy. Hippocampal and amygdalar volumes are referred to the whole amygdalohippocampal complex (AHC) and singularly considered (individual).

#### **4.1. Clinical and neurophsyiological remarks**

#### *4.1.1. MCI and EEG markers: degenerative versus vascular impairment*

A large body of literature has previously demonstrated that in subjects with cognitive decline is present an increase of theta relative power (Moretti et al 2007a,b, 2008), a decrease of gamma relative power (Stam et al., 2003, Moretti et al., 2007a,b 2008) as well as an increase of high alpha as compared to low alpha band (Moretti et al., 2008). On the whole theta/gamma ratio and alpha3/alpha2 ratio could be considered reliable EEG markers of cognitive decline.

The amygdalo-hippocampal network is a key structure in the generation of theta rhythm. More specifically, theta synchronization is increased between LA and CA1 region of hippocampus during long-term memory retrieval, but not during short-term or remote memory retrieval (Seidenbecher et al., 2003; Narayanan et al., 2007). In particular, the AHC is critically involved in the formation and retention of fear memories (Narayanan et al., 2007). Theta synchronization in AHC appears to be a neural correlate of fear, apt to improve

**Hippocampal + amygdalar volume** 

**AHC-hippocampal volume**

**AHC-amygdalar volume**

**individual hippocampal volume**

**individual amygdalar volume**

hippocampal complex (AHC) and singularly considered (individual).

*4.1.1. MCI and EEG markers: degenerative versus vascular impairment* 

**4.1. Clinical and neurophsyiological remarks** 

cognitive decline.

**theta/gamma ratio (µv2)** 

Group2 1.43±0.35 1.11±0.14 Group3 1.47±0.44 1.12±0.16

Group2 1.48±0.45 1.11±0.15 Group3 1.43±0.41 1.12±0.14

Group2 1.44±0.36 1.12±0.16 Group3 1.49±0.39 1.09±0.11

Group2 1.48±0.45 1.07±0.15 Group3 1.43±0.40 1.10±0.14

Group2 1.43±0.36 1.12±0.16 Group3 1.46±0.39 1.09±0.11 **Table 4.** Relative power band ratios in amygdalo-hippocampal complex (AHC), hippocampal and amygdalar atrophy. Hippocampal and amygdalar volumes are referred to the whole amygdalo-

A large body of literature has previously demonstrated that in subjects with cognitive decline is present an increase of theta relative power (Moretti et al 2007a,b, 2008), a decrease of gamma relative power (Stam et al., 2003, Moretti et al., 2007a,b 2008) as well as an increase of high alpha as compared to low alpha band (Moretti et al., 2008). On the whole theta/gamma ratio and alpha3/alpha2 ratio could be considered reliable EEG markers of

The amygdalo-hippocampal network is a key structure in the generation of theta rhythm. More specifically, theta synchronization is increased between LA and CA1 region of hippocampus during long-term memory retrieval, but not during short-term or remote memory retrieval (Seidenbecher et al., 2003; Narayanan et al., 2007). In particular, the AHC is critically involved in the formation and retention of fear memories (Narayanan et al., 2007). Theta synchronization in AHC appears to be a neural correlate of fear, apt to improve

Group1 1.40±0.35 0.06 1.05±0.11 0.07

Group1 1.39±0.27 0.7 1.04±0.11 0.03

Group1 1.36±0.37 0.03 1.04±0.13 0.2

Group1 1.39±0.27 0.7 1.04±0.11 0.1

Group1 1.39±0.37 0.1 1.04±0.13 0.4

**p value** **alpha3/alpha2 ratio (µv2)** 

**p value**  the neural communication during memory retrieval (Narayanan et al., 2007). On the other hand, the retrieval of hippocampus-dipendent memory is provided by the integrity of CA3- CA1 interplay coordinated by gamma oscillations (Montgomery and Buzsaki, 2007). Our results confirm and extends all previous findings. The atrophy of AHC determines increasing memory deficits. The brain oscillatory activity of this MCI state is characterized by an increase of theta/gamma ratio and alpha3/alpha2 relative power ratio, confirming the overall reliability of these EEG markers in cognitive decline. Our results suggest that theta synchronization is mainly due to the amygdala activation or as a subsequent final net effect within the AHC functioning driven by the amygdala excitation. The increase in theta activities in AHC, representing an increase in neuronal communication apt to promote or stabilize synaptic plasticity in relation to the effort to retention of associative memories (Sauseng et al., 2004), could be active also during an ongoing degenerative process. The excitation mechanism could be facilitated by the loss of GABA inhibitory process, determining the decrease of gamma rhythm generation (Bragin et al., 1995; Montgomery and Buzsaki, 2007).

As regards the CV damage, our results showed that the CV damage affected both delta and low alpha band power (alpha1 and alpha2). In the delta band we observed a power increase proportional to the CV damage, with a significant increase in the group with severe CV damage, as compared to the no-CV-damage group. The impact of the CV damage on the delta power was confirmed by the significant positive correlation between CV damage score and delta power itself.

The increase in the delta band power could be explained as a progressive cortical disconnection due to the slowing of the conduction along cortico-subcortical connecting pathways.

This result confirmed the increase in the delta band power we had observed in CV patients, as compared to normal elderly subjects (Moretti et al., 2004). It is to be noted that the increase in the delta band power reflects a global state of cortical deafferentation, due to various anatomofunctional substrates, such as stages of sleep, metabolic encephalopathy or cortico-thalamocortical dysrhythmia (Llinas et al., 1999). In the low a band power, we observed a significant decrease in the a2 band power for the groups with moderate and severe CV damage, as compared to the no-CV-damage group. In the a1 frequency band, there was a similar decrease although it did not reach statistically significant values. These results were confirmed by a correlation analysis which showed a significant negative correlation between CV damage score and a1 and a2 band powers. In our results, the CV damage did not show any impact on the a3 (or high a) power. This is a confirmation of what we found in the previous study, where no differences between VaD patients and normal elderly (but not in AD vs normal elderly) subjects were detected in the a3 power.

Together, these results could suggest different generators for low a and high a frequency bands. In particular, the low a band power could affect cortico-subcortical mechanisms, such as cortico-thalamic, cortico-striatal and cortico-basal ones. This could explain the sensitivity of the low a frequency band to subcortical vascular damage. On the contrary, the a3 band power could affect to a greater extent those cortico-cortical interactions based on synaptic efficiency prone to degenerative rather than CV damages (Klimesch, 1999; Klimesch et al., 2007). In order to find reliable indices of CV damage, we checked the theta/a1 band power ratio. Previous studies have shown the reliability of this kind of approach in quantitative EEG in demented patients (Jelic et al., 1997). The importance of this ratio lies in the presence of such frequency bands on the opposite side of the TF, that is, the EEG frequency index most significantly affected by the CV damage. So, the theta/a1 band power ratio could represent the most sensitive EEG marker of CV damage. The results showed a significant increase of the theta/a1 band power ratio in moderate and severe CV damage groups, as compared to mild and no-CV-damage groups. This ratio increase establishes a proportional increase of the theta band power relative to the a1 band power with respect to the CV damage, even though a significant increase in the h band power per se (or a decrease in the a1 band power per se) is not present. This could suggest a reliable specificity for the theta/a1 band power ratio in focusing the presence of a subcortical CV damage.

Mild Cognitive Impairment and Quantitative EEG Markers: Degenerative Versus Vascular Brain Damage 187

The increase of alpha3/alpha2 ratio in our results support the concomitance of anterior attentive mechanism impairment in subject with MCI, even though there are not overt clinical deficits. The mayor association of the increase of alpha3/alpha2 ratio with the hippocampal formation within the AHC, suggest that this filter activity is carried out by hippocampus and its input-output connections along anterior attentive circuit and AHC. Interestingly, a recent work has demonstrated that the mossy fiber (MF) pathway of the hippocampus connects the dentate gyrus to the auto-associative CA3 network, and the information it carries is controlled by a feedforward circuit combining disynaptic inhibition with monosynaptic excitation. Analysis of the MF associated circuit revealed that this circuit

The natural history of a group of subjects at very high-risk for developing dementia due to subcortical vascular damage [subcortical vascular MCI (svMCI)] has recently been described (Frisoni et al., 2002; Galluzzi et al., 2005). In such study, MCI patients with CV etiology developed a distinctive clinical phenotype characterized by poor performance on frontal tests, and neurological features of parkinsonism without tremor (impairment of balance and gait).These clinical features could be explained by our results. In CV patients, we observed a slowing of the a frequency in the two groups with greater CV damage, as compared to the groups with lesser CV damage. This is in line with a previous study (Moretti et al., 2004) showing that the major effect of the CV damage, in patients with vascular dementia (VaD) vs normal elderly and Alzheimer's patients. A reasonable (although speculative) explanation of the present results is that the CV damage-induced slowing of the a frequency start point could be mainly attributed to the lowering of the conduction time of synaptic action potentials throughout cortico-subcortical fibers, such as cortico-basal or corticothalamic pathways (Steriade and Llinas, 1988). In fact, experimental models have previously shown that the EEG frequency is due to axonal delay and synaptic time of corticosubcortical interactions (Lopes da Silva et al., 1976; Nunez et al., 2001; Doiron et al., 2003). Most interestingly, other studies have demonstrated that fiber myelination affects the speed propagation along cortical fibers, and that this parameter is strictly correlated to the frequency range recorded on the scalp. In fact, a theoretical model considering a mean speed propagation in white matter fibers of 7.5 m/s (together with other parameters) is associated with a fundamental mode frequency of 9 Hz (Nunez and Srinivasan, 2006), that is, the typical mode of scalp-recorded EEG. It is to be noted that a correlation between white matter damage and widespread slowing of EEG rhythmicity was found in other studies, following the presence of cognitive decline (Szelies et al., 1999), multiple sclerosis (Leocani et al., 2000), or cerebral tumors (Goldensohn, 1979). In order to find reliable indices of CV damage, we checked the theta/a1 band power ratio. Previous studies have shown the reliability of this kind of approach in quantitative EEG in demented patients (Jelic et al., 1997). The importance of this ratio lies in the presence of such frequency bands on the opposite side of the TF, that is, the EEG frequency index most significantly affected by the CV damage. So, the theta/a1 band power ratio could represent the most sensitive EEG marker of CV damage. The results showed a significant increase of the theta/a1 band power ratio in moderate and severe CV damage groups, as compared to mild and no-CV-damage groups. This ratio increase establishes a proportional increase of the theta band power

could act as a highpass filter (Zalay and Bardakjian, 2006).

#### *4.1.2. MCI , cognitive deficits (memory and attention) and EEG activity*

The vulnerability and damage of the connections of hippocampus with amygdala could affect reconsolidation of long-term memory and give rise to memory deficits and behavioural symptoms. Several experiments shows that amygdala activity is prominent during period of intense arousal, e.g. the anticipation of a noxious stimulus (Parè et al., 2002) or the maintenance of vigilance to negative stimuli (Garolera et al., 2007). So far, the theta synchronization induced by the amygdala is deeply involved in endogenous attentional mechanism. Interestingly, the increase of high alpha synchronization has been found in internally-cued mechanisms of attention, associated with inhibitory top-down processes (Klimesch, 2007). Of note, the amygdala is intimately involved in the anatomo-physiological anterior pathways of attention through its connections with anterior cingulated cortex, anteroventral, anteromedial and pulvinar thalamic nuclei (Young et al., 2007). The particular role of amygdala in negative human emotions could indicate that AHC atrophy is associated with excessive level of subcortical inputs not adequately filtered by attentive processing, determining fear and anxiety and generating cognitive interference in memory performance. Of note, an altered emotional response is very frequent in MCI patients (Ellison et al., 2008, Rozzini et al., 2008). In a feed-back process, this alteration could determine a general state of "hyperattention" during which top-down internal processes prevail on the bottom-up phase, altering attention mechanism and preventing a correct processing of sensory stimuli. Focused attention has been found impaired in MCI patients in particular when they have to benefit from a cue stimulus (Johanssen et al., 1999; Berardi et al., 2005 ; Levinoff et al., 2005 Tales et al. 2005a, 2005b). This particular state could be useful for maintain a relatively spared global cognitive performance, whereas it could fail when a detailed analysis of a sensory stimulus is required. This "hyperattentive" state could represent the attempt to recollect memory and/or spatial traces from hippocampus and to combine them within associative areas connected with hippocampus itself.

The increase of alpha3/alpha2 ratio in our results support the concomitance of anterior attentive mechanism impairment in subject with MCI, even though there are not overt clinical deficits. The mayor association of the increase of alpha3/alpha2 ratio with the hippocampal formation within the AHC, suggest that this filter activity is carried out by hippocampus and its input-output connections along anterior attentive circuit and AHC. Interestingly, a recent work has demonstrated that the mossy fiber (MF) pathway of the hippocampus connects the dentate gyrus to the auto-associative CA3 network, and the information it carries is controlled by a feedforward circuit combining disynaptic inhibition with monosynaptic excitation. Analysis of the MF associated circuit revealed that this circuit could act as a highpass filter (Zalay and Bardakjian, 2006).

186 Advances in Clinical Neurophysiology

of the low a frequency band to subcortical vascular damage. On the contrary, the a3 band power could affect to a greater extent those cortico-cortical interactions based on synaptic efficiency prone to degenerative rather than CV damages (Klimesch, 1999; Klimesch et al., 2007). In order to find reliable indices of CV damage, we checked the theta/a1 band power ratio. Previous studies have shown the reliability of this kind of approach in quantitative EEG in demented patients (Jelic et al., 1997). The importance of this ratio lies in the presence of such frequency bands on the opposite side of the TF, that is, the EEG frequency index most significantly affected by the CV damage. So, the theta/a1 band power ratio could represent the most sensitive EEG marker of CV damage. The results showed a significant increase of the theta/a1 band power ratio in moderate and severe CV damage groups, as compared to mild and no-CV-damage groups. This ratio increase establishes a proportional increase of the theta band power relative to the a1 band power with respect to the CV damage, even though a significant increase in the h band power per se (or a decrease in the a1 band power per se) is not present. This could suggest a reliable specificity for the theta/a1

band power ratio in focusing the presence of a subcortical CV damage.

*4.1.2. MCI , cognitive deficits (memory and attention) and EEG activity* 

combine them within associative areas connected with hippocampus itself.

The vulnerability and damage of the connections of hippocampus with amygdala could affect reconsolidation of long-term memory and give rise to memory deficits and behavioural symptoms. Several experiments shows that amygdala activity is prominent during period of intense arousal, e.g. the anticipation of a noxious stimulus (Parè et al., 2002) or the maintenance of vigilance to negative stimuli (Garolera et al., 2007). So far, the theta synchronization induced by the amygdala is deeply involved in endogenous attentional mechanism. Interestingly, the increase of high alpha synchronization has been found in internally-cued mechanisms of attention, associated with inhibitory top-down processes (Klimesch, 2007). Of note, the amygdala is intimately involved in the anatomo-physiological anterior pathways of attention through its connections with anterior cingulated cortex, anteroventral, anteromedial and pulvinar thalamic nuclei (Young et al., 2007). The particular role of amygdala in negative human emotions could indicate that AHC atrophy is associated with excessive level of subcortical inputs not adequately filtered by attentive processing, determining fear and anxiety and generating cognitive interference in memory performance. Of note, an altered emotional response is very frequent in MCI patients (Ellison et al., 2008, Rozzini et al., 2008). In a feed-back process, this alteration could determine a general state of "hyperattention" during which top-down internal processes prevail on the bottom-up phase, altering attention mechanism and preventing a correct processing of sensory stimuli. Focused attention has been found impaired in MCI patients in particular when they have to benefit from a cue stimulus (Johanssen et al., 1999; Berardi et al., 2005 ; Levinoff et al., 2005 Tales et al. 2005a, 2005b). This particular state could be useful for maintain a relatively spared global cognitive performance, whereas it could fail when a detailed analysis of a sensory stimulus is required. This "hyperattentive" state could represent the attempt to recollect memory and/or spatial traces from hippocampus and to The natural history of a group of subjects at very high-risk for developing dementia due to subcortical vascular damage [subcortical vascular MCI (svMCI)] has recently been described (Frisoni et al., 2002; Galluzzi et al., 2005). In such study, MCI patients with CV etiology developed a distinctive clinical phenotype characterized by poor performance on frontal tests, and neurological features of parkinsonism without tremor (impairment of balance and gait).These clinical features could be explained by our results. In CV patients, we observed a slowing of the a frequency in the two groups with greater CV damage, as compared to the groups with lesser CV damage. This is in line with a previous study (Moretti et al., 2004) showing that the major effect of the CV damage, in patients with vascular dementia (VaD) vs normal elderly and Alzheimer's patients. A reasonable (although speculative) explanation of the present results is that the CV damage-induced slowing of the a frequency start point could be mainly attributed to the lowering of the conduction time of synaptic action potentials throughout cortico-subcortical fibers, such as cortico-basal or corticothalamic pathways (Steriade and Llinas, 1988). In fact, experimental models have previously shown that the EEG frequency is due to axonal delay and synaptic time of corticosubcortical interactions (Lopes da Silva et al., 1976; Nunez et al., 2001; Doiron et al., 2003). Most interestingly, other studies have demonstrated that fiber myelination affects the speed propagation along cortical fibers, and that this parameter is strictly correlated to the frequency range recorded on the scalp. In fact, a theoretical model considering a mean speed propagation in white matter fibers of 7.5 m/s (together with other parameters) is associated with a fundamental mode frequency of 9 Hz (Nunez and Srinivasan, 2006), that is, the typical mode of scalp-recorded EEG. It is to be noted that a correlation between white matter damage and widespread slowing of EEG rhythmicity was found in other studies, following the presence of cognitive decline (Szelies et al., 1999), multiple sclerosis (Leocani et al., 2000), or cerebral tumors (Goldensohn, 1979). In order to find reliable indices of CV damage, we checked the theta/a1 band power ratio. Previous studies have shown the reliability of this kind of approach in quantitative EEG in demented patients (Jelic et al., 1997). The importance of this ratio lies in the presence of such frequency bands on the opposite side of the TF, that is, the EEG frequency index most significantly affected by the CV damage. So, the theta/a1 band power ratio could represent the most sensitive EEG marker of CV damage. The results showed a significant increase of the theta/a1 band power ratio in moderate and severe CV damage groups, as compared to mild and no-CV-damage groups. This ratio increase establishes a proportional increase of the theta band power relative to the a1 band power with respect to the CV damage, even though a significant increase in the theta band power per se (or a decrease in the a1 band power per se) is not present. This could suggest a reliable specificity for the theta/alpha1 band power ratio in focusing the presence of a subcortical CV damage.

Mild Cognitive Impairment and Quantitative EEG Markers: Degenerative Versus Vascular Brain Damage 189

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### *Edited by Ihsan M. Ajeena*

Including some of the newest advances in the field of neurophysiology, this book can be considered as one of the treasures that interested scientists would like to collect. It discusses many disciplines of clinical neurophysiology that are, currently, crucial in the practice as they explain methods and findings of techniques that help to improve diagnosis and to ensure better treatment. While trying to rely on evidence-based facts, this book presents some new ideas to be applied and tested in the clinical practice. Advances in Clinical Neurophysiology is important not only for the neurophysiologists but also for clinicians interested or working in wide range of specialties such as neurology, neurosurgery, intensive care units, pediatrics and so on. Generally, this book is written and designed to all those involved in, interpreting or requesting neurophysiologic tests.

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