**Distractor or Noise? The Influence of Different Sounds on Cognitive Performance in Inattentive and Attentive Children**

Göran Söderlund1,2\* and Sverker Sikström3

*1Department of Pedagogics, Sogndal University College, 2School of Psychology, University of Southampton, 3Department of Psychology, Lund University, 1Norway 2England 3Sweden* 

#### **1. Introduction**

232 Current Directions in ADHD and Its Treatment

Steenkamp, D.S. (2001). Attention Deficit Hyperactivity Disorder and Motivational deficits

Stevenson, C.S.; Whitmont, S.; Bornholt, L.; Livesey D. & Stevenson, R.J. (2002). A cognitive

Summerfield, J. (2006). Do we coach or do we counsel? Thoughts on the 'emotional life' of a

Swartz, S.L.; Prevatt, F. & Proctor, B.E. (2005). A coaching intervention for college students

Wallace, I. (1998). *You and Your ADD Child: Practical Strategies for coping with everyday* 

Webb, J.T.; Amend, E.R.; Webb, N.E.; Goerss, J.; Beljan, P. & Olenchek, F.R. (2005).

Young, S. & Amarasinghe, J.M. 2010. Practitioner review: non-pharmacological treatments

Zwart, L.M. & Kallemyn, L.M. (2001). Peer-based coaching for college students with ADHD and learning disabilities. *Journal of Postsecondary Education and Disability,* 15:1-15.

*Australian and New Zealand Journal of Psychiatry,* 36: 610-616.

coaching session. *The Coaching Psychologist,* 2(1):24-27.

*problems*. Sydney: Harper Collins Publishers. Australia.

Elizabeth.

Press, Inc.

133.

in Early Adolescence. Unpublished D Ed Thesis. Port Elizabeth: University of Port

remediation program for adults with attention deficit hyperactivity disorder.

with Attention Deficit/Hyperactivity Disorder. *Psychology in Schools,* 42(6):647-656.

*Misdiagnosis and dual diagnosis of gifted children and adults.* Scotsdale: Great Potential

for ADHD: a lifespan approach. *Journal of Child Psychology and Psychiatry,* 51(2):116-

It is a well known and certified fact that noise under most circumstances interfere with cognitive processing of various kinds, e.g. vigilance (e.g. Broadbent, 1951), arithmetic's (Broadbent, 1958), and response speed (Broadbent, 1957). This effect is assumed to be due to the competition of attentional resources between the target and the distracting stimuli. This finding is often replicated and found valid among different tasks and participant populations (Belleville, Rouleau, Van der Linden, & Collette, 2003; Boman, 2004; Klatte, Meis, Sukowski, & Schick, 2007; Rouleau & Belleville, 1996). Most research since Broadbent's days has dealt with the negative effects of noise and different kinds of auditory distraction. In line with this earlier research has demonstrated that inattentive persons, such as children with ADHD (attention deficit /hyperactivity disorder) are even more susceptible to distraction as compared with their attentive peers. This has been shown in numerous of studies (e.g. Corbett & Stanczak, 1999; Geffner, Lucker, & Koch, 1996; Rickman, 2001).

However, in contrast to the main body of evidence, there have been a few reports of contradictory findings. Specifically, it has been shown that under certain circumstances, children with attentional problems, rather than being distracted, actually benefit from environmental noise presented with the concurrent target task. Until recently, this facilitating effect of non-task related environmental auditory stimulation has been limited to the effects of background music on arithmetic task performance by children with ADHD (Abikoff, Courtney, Szeibel, & Koplewicz, 1996; Gerjets, Graw, Heise, Westermann, & Rothenberger, 2002). In addition, road traffic noise was found to improve episodic memory among children from households with low socio-economic status, a group that is likely to be distinguished by attentional problems and academic under-achievement (Matheson et al., 2010; Stansfeld et al., 2005). However, these studies have not provided a satisfactory theoretical account for why noise, under certain circumstances, can be beneficial for cognitive performance.

Distractor or Noise? The Influence of Different Sounds

action potentials.

on Cognitive Performance in Inattentive and Attentive Children 235

Fig. 1. Stochastic resonance where a weak sinusoidal signal goes undetected as it does not bring the neuron over its activation threshold. With added noise, the same signal results in

In humans SR has been found in different modalities: in touch, where tactile random stimulation made skin receptors more sensitive (Wells, Ward, Chua, & Timothy Inglis, 2005); in audition, where white noise improves auditory detection in a group with normal hearing (Zeng, Fu, & Morse, 2000), and in participants with cochlear implants (Behnam & Zeng, 2003); in vision, where visual (flickering) noise improved detection of weak signals (Simonotto et al., 1999). Interestingly, cross modal SR has been found, where weak visual signals became detectable when participants where exposed to loud auditory white noise (Manjarrez, Mendez, Martinez, Flores, & Mirasso, 2007). SR can improve motor control and balance as well. Elderly, diabetics, and Parkinson patients' performance was enhanced through stochastic noise transmitted by vibrating soles (Novak & Novak, 2006; Priplata, Niemi, Harry, Lipsitz, & Collins, 2003; Priplata et al., 2006). In neurodegenerative disorders galvanic stimulation of the vestibular organs improved motor control considerably (Pan, Soma, Kwak, & Yamamoto, 2008; Yamamoto, Struzik, Soma, Ohashi, & Kwak, 2005). To sum up, SR is present in the entire nervous system in all modalities, and it seems that the nervous system can take advantage of noise both in sensory discrimination and motor control. SR is usually quantified by plotting detection of a weak signal, or cognitive performance, as a function of noise intensity. This relation exhibits an inverted U-curve, where performance peaks at a moderate noise level. That is, moderate noise is beneficial for

performance, whereas too much, or too little noise attenuates performance.

While less known, empirical evidence also suggest that SR improves central processing in the brain and thus improves cognitive performance. For example a facilitating effect of cognitive SR has been found where auditory noise improved the speed of arithmetic computations in a normal group of school pupils (Usher & Feingold, 2000). In a visual task, face recognition, response times got shorter when the vestibular organs where stimulated by a weak stochastic galvanic current (Wilkinson, Nicholls, Pattenden, Kilduff, & Milberg, 2008) finally, figure copying became more accurate when exposed to galvanic stimulation

There are some early studies that provide a theoretical account for noise enhancement. In these studies, hyperactive children improved their performance in demanding attention tasks where noise was introduced by visual stimulation (Zentall, 1986; Zentall & Dwyer, 1989; Zentall, Falkenberg, & Smith, 1985), or auditory stimulation (Zentall & Shaw, 1980). In these experiments the positive effect was attributed to a general increase of arousal, formulated in a theoretical framework named "the optimal stimulation theory" (Zentall & Zentall, 1983). However, this optimal stimulation theory has not been explored or developed further.

The aim with the present chapter is to present a plausible theoretical explanation as to why, when, and how noise can improve executive functions and cognitive performance in various tasks. Our research has recently extended these findings and for the first time will here be suggested a theoretical framework for understanding which conditions are necessary for noise induced cognitive enhancement to occur. We have shown that auditory noise has different effects on the memory performance of children with an ADHD diagnosis compared to normally developed children (Söderlund, Sikstrom, & Smart, 2007). These effects have been replicated, and found valid in further studies comprising sub-clinical, inattentive participants (Söderlund, Marklund, & Lacerda, 2009; Söderlund, Sikström, Loftesnes, & Sonuga-Barke, 2010). In the following section we introduce a model and findings that demonstrate a link between noise stimulation and cognitive performance. This has been named the Moderate Brain Arousal (MBA) model (Sikström & Söderlund, 2007), which suggests a link between attention, dopamine transmission, and external auditory noise (white noise) stimulation.

### **2. The phenomenon of Stochastic Resonance**

Perceptual stochastic resonance (SR) is the counterintuitive phenomenon by which weak sensory signals that cannot be detected because they are presented below the detection threshold, become detectable when additional random (stochastic) noise is added (Moss, Ward, & Sannita, 2004). Signaling in the brain is characterized by noisy inputs and outputs. The crucial task of the central nervous system is to distinguish between the signal, the information-carrying component, and noise that constitute meaningless neural inputs. The paradox is that the brain can actually use noise to differentiate the signal in the targeted stimuli from noise, so noise actually improves or increases the signal-to-noise ratio. The requirement for this phenomenon to occur is the introduction of non-linearity in the response, for example through a threshold function. This is shown in Figure 1, where the noise and the signal interact. The noise adds to the signal and brings the neuron over the activation threshold, and elicits a neural response (action potential), giving the auditory system a representation of the signal (a sinus tone).

SR is well established across a range of settings, and exists in any threshold-based system. The concept of SR was originally introduced to explain climate changes (Benzi, Parisi, Sutera, & Vulpiani, 1982), but has been identified in a number of naturally occurring phenomena, some examples are: in bi-stable optical systems (Gammaitoni, Hänggi, Jung, & Marchesoni, 1998); in mechanoreceptors of the crayfish (Douglass, Wilkens, Pantazelou, & Moss, 1993); and in the feeding behavior of the paddlefish (Russell, Wilkens, & Moss, 1999). SR is in particular found in the nervous system, distinguished by its all-or-none nature of action potentials.

There are some early studies that provide a theoretical account for noise enhancement. In these studies, hyperactive children improved their performance in demanding attention tasks where noise was introduced by visual stimulation (Zentall, 1986; Zentall & Dwyer, 1989; Zentall, Falkenberg, & Smith, 1985), or auditory stimulation (Zentall & Shaw, 1980). In these experiments the positive effect was attributed to a general increase of arousal, formulated in a theoretical framework named "the optimal stimulation theory" (Zentall & Zentall, 1983). However, this optimal stimulation theory has not been explored or developed

The aim with the present chapter is to present a plausible theoretical explanation as to why, when, and how noise can improve executive functions and cognitive performance in various tasks. Our research has recently extended these findings and for the first time will here be suggested a theoretical framework for understanding which conditions are necessary for noise induced cognitive enhancement to occur. We have shown that auditory noise has different effects on the memory performance of children with an ADHD diagnosis compared to normally developed children (Söderlund, Sikstrom, & Smart, 2007). These effects have been replicated, and found valid in further studies comprising sub-clinical, inattentive participants (Söderlund, Marklund, & Lacerda, 2009; Söderlund, Sikström, Loftesnes, & Sonuga-Barke, 2010). In the following section we introduce a model and findings that demonstrate a link between noise stimulation and cognitive performance. This has been named the Moderate Brain Arousal (MBA) model (Sikström & Söderlund, 2007), which suggests a link between attention, dopamine transmission, and external auditory

Perceptual stochastic resonance (SR) is the counterintuitive phenomenon by which weak sensory signals that cannot be detected because they are presented below the detection threshold, become detectable when additional random (stochastic) noise is added (Moss, Ward, & Sannita, 2004). Signaling in the brain is characterized by noisy inputs and outputs. The crucial task of the central nervous system is to distinguish between the signal, the information-carrying component, and noise that constitute meaningless neural inputs. The paradox is that the brain can actually use noise to differentiate the signal in the targeted stimuli from noise, so noise actually improves or increases the signal-to-noise ratio. The requirement for this phenomenon to occur is the introduction of non-linearity in the response, for example through a threshold function. This is shown in Figure 1, where the noise and the signal interact. The noise adds to the signal and brings the neuron over the activation threshold, and elicits a neural response (action potential), giving the auditory

SR is well established across a range of settings, and exists in any threshold-based system. The concept of SR was originally introduced to explain climate changes (Benzi, Parisi, Sutera, & Vulpiani, 1982), but has been identified in a number of naturally occurring phenomena, some examples are: in bi-stable optical systems (Gammaitoni, Hänggi, Jung, & Marchesoni, 1998); in mechanoreceptors of the crayfish (Douglass, Wilkens, Pantazelou, & Moss, 1993); and in the feeding behavior of the paddlefish (Russell, Wilkens, & Moss, 1999). SR is in particular found in the nervous system, distinguished by its all-or-none nature of

further.

noise (white noise) stimulation.

action potentials.

**2. The phenomenon of Stochastic Resonance** 

system a representation of the signal (a sinus tone).

Fig. 1. Stochastic resonance where a weak sinusoidal signal goes undetected as it does not bring the neuron over its activation threshold. With added noise, the same signal results in action potentials.

In humans SR has been found in different modalities: in touch, where tactile random stimulation made skin receptors more sensitive (Wells, Ward, Chua, & Timothy Inglis, 2005); in audition, where white noise improves auditory detection in a group with normal hearing (Zeng, Fu, & Morse, 2000), and in participants with cochlear implants (Behnam & Zeng, 2003); in vision, where visual (flickering) noise improved detection of weak signals (Simonotto et al., 1999). Interestingly, cross modal SR has been found, where weak visual signals became detectable when participants where exposed to loud auditory white noise (Manjarrez, Mendez, Martinez, Flores, & Mirasso, 2007). SR can improve motor control and balance as well. Elderly, diabetics, and Parkinson patients' performance was enhanced through stochastic noise transmitted by vibrating soles (Novak & Novak, 2006; Priplata, Niemi, Harry, Lipsitz, & Collins, 2003; Priplata et al., 2006). In neurodegenerative disorders galvanic stimulation of the vestibular organs improved motor control considerably (Pan, Soma, Kwak, & Yamamoto, 2008; Yamamoto, Struzik, Soma, Ohashi, & Kwak, 2005). To sum up, SR is present in the entire nervous system in all modalities, and it seems that the nervous system can take advantage of noise both in sensory discrimination and motor control. SR is usually quantified by plotting detection of a weak signal, or cognitive performance, as a function of noise intensity. This relation exhibits an inverted U-curve, where performance peaks at a moderate noise level. That is, moderate noise is beneficial for performance, whereas too much, or too little noise attenuates performance.

While less known, empirical evidence also suggest that SR improves central processing in the brain and thus improves cognitive performance. For example a facilitating effect of cognitive SR has been found where auditory noise improved the speed of arithmetic computations in a normal group of school pupils (Usher & Feingold, 2000). In a visual task, face recognition, response times got shorter when the vestibular organs where stimulated by a weak stochastic galvanic current (Wilkinson, Nicholls, Pattenden, Kilduff, & Milberg, 2008) finally, figure copying became more accurate when exposed to galvanic stimulation

Distractor or Noise? The Influence of Different Sounds

according to the MBA model.

Sörqvist, Ljungberg, & Ljung, 2010).

**4. The difference between distractors and noise** 

on Cognitive Performance in Inattentive and Attentive Children 237

showing significant effects of noise on three different cognitive tasks in parity with, or even

Fig. 2. The relationship between noise levels, attention ability, and cognitive performance. Sub-attentive participants (e.g. ADHD) require more noise for maximal performance

As shown in numerous experiments, environmental auditory noise exerts a negative influence on schoolwork and on cognitive performance in general. Comparisons have been made with various sounds that are proposed to have a negative impact on different kinds of intellectual work. Both irrelevant meaningful speech and road traffic noise have been found ta have a detrimental effect on both semantic and episodic memory recall in adults (Hygge, Boman, & Enmarker, 2003). Also school children were susceptible to these kinds of distractors when performing mathematical computations (Ljung, Sörqvist, & Hygge, 2009). Aircraft noise seems to be detrimental during most kinds of work that require attention (Hygge, Evans, & Bullinger, 2002; Matheson, et al., 2010; Stansfeld, et al., 2005), even the day after the noise exposure (Stansfeld, Hygge, Clark, & Alfred, 2010). Semantically meaningful irrelevant information is found to be distracting, but does also interact with working memory capacity; persons that possess a high working memory capacity were less distracted by irrelevant speech than peers with lower capacity (Sörqvist, 2010a, 2010b;

In the present study we further investigated different environmental soundscapes that have ecological relevance out of a school perspective, and their impact on a demanding working memory task. For this purpose we created four different background noises or soundscapes that could occur in a classroom setting: 1) speech or classroom noise; 2) white noise; 3) a mix of speech + white noise; and finally 4) a silent condition. We posed the question whether pure noise is the best way of introducing cognitive enhancement in inattentive children, or whether ecologically valid soundscapes could produce similar cognitive enhancement.

We predicted that auditory environmental stimuli would have a positive effect on inattentive persons and be detrimental to the attentive persons. In particular, based on previous data, we

larger, than the effects of stimulant medication (Söderlund et al., in progress).

(Wilkinson, Zubko, Degutis, Milberg, & Potter, 2009). This indicates that also higher cognitive processing is susceptible for SR.

#### **3. Individual differences in SR and the Moderate Brain Arousal Model (MBA)**

Most of the above-referred references of the SR-effect are made with normal populations, and the revealed effects of noise are found to be valid for the entire population. Our research group has focused on cognitive effects of SR in particular groups with attentional problems, like in ADHD, where we have found differential effects of noise on cognitive performance. Some groups of participants improve their performance, whereas the performance other groups deteriorate when exposed to noise. The question is how these differentiations can be explained. We propose the Moderate Brain Arousal model (MBA) which is developed to address and explain these differences (Sikström & Söderlund, 2007). The MBA model was developed to respond to the limitation of standard psychophysical models in explaining the noise facilitating effect in children with attention problems. The model is based on established facts concerning SR; *first*, that the SR phenomenon is highly sensitive to the intensity of the signal and; *second*, the intensity of the noise, where the cognitive or perceptual performance shows an inverted U-shaped curve when plotted against noise intensity (e.g. Moss, et al., 2004). Thus, a moderate level of noise is beneficial for performance. Too little noise does not add sufficient input to bring the signal over the activation threshold, and too much noise overpowers the signal – in both cases leading to deterioration in attention and performance. The crucial and innovative insight of the MBA model is that there are individual differences in the benefit of noise; some people need just a small amount of noise and some need a lot of noise to achieve optimal performance (see Figure 2). This is because individuals differ in internal levels of background noise and signal levels in their neural systems. That is, where noise levels are low, external noise has to be added to reach an optimal performance, and to achieve a moderate brain arousal level. Furthermore, required noise levels are linked to neurotransmitter function and in particular to dopamine. A hypo-functioning dopamine system is linked to inattention, and recent research suggests that ADHD possess low levels of extracellular dopamine (Solanto, 2002; Volkow et al., 2009; Volkow et al., 2007). The MBA model proposes that noise, as an alternative to stimulant medication, can compensate for low dopamine levels (Sikström & Söderlund, 2007).

In summary, the MBA model posits that cognitive performance in ADHD and inattentive children benefits from noisy environments because the dopamine system modulates the SR phenomenon. It suggests that the stochastic resonance curve is right shifted in persons with a ADHD diagnose due to lower gain or lower dopamine. levels The MBA model predicts that for a given cognitive task ADHD children and inattentive children require more external noise or stimulation compared to control children, in order to reach optimal (i.e. moderate) brain arousal level (see Figure 2). This prediction has been tested and confirmed in several different settings with various participant groups and tasks. Word recall tests in children with ADHD (Söderlund, et al., 2007), non-clinical, inattentive school children (Söderlund, et al., 2010), and low performing school children (Söderlund & Sikström, 2008). The effect has also been found in a dichotic listening task, and in a visuo-spatial working memory task in a normal student population, where half of the participants rated themselves as inattentive (Söderlund, et al., 2009). At the moment we have preliminary data

(Wilkinson, Zubko, Degutis, Milberg, & Potter, 2009). This indicates that also higher

**3. Individual differences in SR and the Moderate Brain Arousal Model (MBA)**  Most of the above-referred references of the SR-effect are made with normal populations, and the revealed effects of noise are found to be valid for the entire population. Our research group has focused on cognitive effects of SR in particular groups with attentional problems, like in ADHD, where we have found differential effects of noise on cognitive performance. Some groups of participants improve their performance, whereas the performance other groups deteriorate when exposed to noise. The question is how these differentiations can be explained. We propose the Moderate Brain Arousal model (MBA) which is developed to address and explain these differences (Sikström & Söderlund, 2007). The MBA model was developed to respond to the limitation of standard psychophysical models in explaining the noise facilitating effect in children with attention problems. The model is based on established facts concerning SR; *first*, that the SR phenomenon is highly sensitive to the intensity of the signal and; *second*, the intensity of the noise, where the cognitive or perceptual performance shows an inverted U-shaped curve when plotted against noise intensity (e.g. Moss, et al., 2004). Thus, a moderate level of noise is beneficial for performance. Too little noise does not add sufficient input to bring the signal over the activation threshold, and too much noise overpowers the signal – in both cases leading to deterioration in attention and performance. The crucial and innovative insight of the MBA model is that there are individual differences in the benefit of noise; some people need just a small amount of noise and some need a lot of noise to achieve optimal performance (see Figure 2). This is because individuals differ in internal levels of background noise and signal levels in their neural systems. That is, where noise levels are low, external noise has to be added to reach an optimal performance, and to achieve a moderate brain arousal level. Furthermore, required noise levels are linked to neurotransmitter function and in particular to dopamine. A hypo-functioning dopamine system is linked to inattention, and recent research suggests that ADHD possess low levels of extracellular dopamine (Solanto, 2002; Volkow et al., 2009; Volkow et al., 2007). The MBA model proposes that noise, as an alternative to stimulant medication, can compensate for low dopamine levels (Sikström &

In summary, the MBA model posits that cognitive performance in ADHD and inattentive children benefits from noisy environments because the dopamine system modulates the SR phenomenon. It suggests that the stochastic resonance curve is right shifted in persons with a ADHD diagnose due to lower gain or lower dopamine. levels The MBA model predicts that for a given cognitive task ADHD children and inattentive children require more external noise or stimulation compared to control children, in order to reach optimal (i.e. moderate) brain arousal level (see Figure 2). This prediction has been tested and confirmed in several different settings with various participant groups and tasks. Word recall tests in children with ADHD (Söderlund, et al., 2007), non-clinical, inattentive school children (Söderlund, et al., 2010), and low performing school children (Söderlund & Sikström, 2008). The effect has also been found in a dichotic listening task, and in a visuo-spatial working memory task in a normal student population, where half of the participants rated themselves as inattentive (Söderlund, et al., 2009). At the moment we have preliminary data

cognitive processing is susceptible for SR.

Söderlund, 2007).

showing significant effects of noise on three different cognitive tasks in parity with, or even larger, than the effects of stimulant medication (Söderlund et al., in progress).

Fig. 2. The relationship between noise levels, attention ability, and cognitive performance. Sub-attentive participants (e.g. ADHD) require more noise for maximal performance according to the MBA model.

#### **4. The difference between distractors and noise**

As shown in numerous experiments, environmental auditory noise exerts a negative influence on schoolwork and on cognitive performance in general. Comparisons have been made with various sounds that are proposed to have a negative impact on different kinds of intellectual work. Both irrelevant meaningful speech and road traffic noise have been found ta have a detrimental effect on both semantic and episodic memory recall in adults (Hygge, Boman, & Enmarker, 2003). Also school children were susceptible to these kinds of distractors when performing mathematical computations (Ljung, Sörqvist, & Hygge, 2009). Aircraft noise seems to be detrimental during most kinds of work that require attention (Hygge, Evans, & Bullinger, 2002; Matheson, et al., 2010; Stansfeld, et al., 2005), even the day after the noise exposure (Stansfeld, Hygge, Clark, & Alfred, 2010). Semantically meaningful irrelevant information is found to be distracting, but does also interact with working memory capacity; persons that possess a high working memory capacity were less distracted by irrelevant speech than peers with lower capacity (Sörqvist, 2010a, 2010b; Sörqvist, Ljungberg, & Ljung, 2010).

In the present study we further investigated different environmental soundscapes that have ecological relevance out of a school perspective, and their impact on a demanding working memory task. For this purpose we created four different background noises or soundscapes that could occur in a classroom setting: 1) speech or classroom noise; 2) white noise; 3) a mix of speech + white noise; and finally 4) a silent condition. We posed the question whether pure noise is the best way of introducing cognitive enhancement in inattentive children, or whether ecologically valid soundscapes could produce similar cognitive enhancement.

We predicted that auditory environmental stimuli would have a positive effect on inattentive persons and be detrimental to the attentive persons. In particular, based on previous data, we

Distractor or Noise? The Influence of Different Sounds

**6. Results** 

12.63, p = .002, eta2 .387)

inattentive (N=11).

15

20

25

30

**Performance**

35

40

45

on Cognitive Performance in Inattentive and Attentive Children 239

The participants were tested individually in a room during the school day. All participants used the same 15' laptop PC for the visuo-spatial test (span-board). Headphones provided the noise, and dB levels where checked for all participants ahead of every session. Before starting the experiment proper, two practice trials were conducted. The time taken to complete each test was approximately 5 minutes, depending on the performance level (the better performance the longer time). Altogether, the testing sessions lasted approximately 30 minutes including instructions and test trials. The noise conditions were presented in random order so each condition appeared equally many times in each position (first, second, third, and forth).

A 2 x 4 mixed ANOVA was conducted including all noise conditions. No main effect of noise was found, but a significant overall interaction was found between noise and group (F(18,3)= 3.44, p= .039, eta2 = .365). The difference between groups was also significant, where the attentive group outperformed the inattentive group in all conditions (F(20,1) =

Thereafter we conducted three separate 2 x 2 mixed ANOVA's, one for each noise condition. It comprised one between-subject factor, *group* (attentive vs. inattentive) and one withinsubjects factor, *encoding stimulation condition* (silence vs. white noise; silence vs. speech; silence vs. speech + white noise). The data from these tests are presented in the three graphs below. In neither of the three noise conditions we found a main effect of noise, but in two

In the first ANOVA, (silence vs. white noise, Figure 3) we found an interaction between group and noise. The inattentive group improved its performance whereas the attentive

> Inattentive Attentive

Fig. 3. Number of correctly recalled items in a visuo-spatial working memory task as a function of noise condition; silence vs. white noise in two groups: attentive (N=11) and

silence noise

out of three conditions we found a robust noise x group interaction.

predicted this effect to occur for white noise. However, we posed no direct prediction on whether speech might produce cognitive enhancement effects on inattentive children.

#### **5. Methods**

#### **5.1 Participants**

Twenty-two primary school children between 7 and 10 years old (M = 8.3 yrs) participated in the present study (14 boys and 8 girls). The twenty-two participants were screened and selected out of a group of 33 participants according their attention ability as reported by their teachers. The eleven that scored lowest on the attention scale where selected for the inattentive group while the ones that scored high on attention formed the attentive group. What was considered normal or above average in attention was decided according to their teacher's judgments. For this purpose a SNAP score with 18 questions were used (Swanson et al., 2007). Mean score for the inattentive group was 28.8 and for the attentive 1.5. 28 points is slightly below the cut off point for ADHD diagnosis (36). None of the participants were consequently diagnosed with ADHD or any other neuropsychiatric diagnoses. Participants were also considered to be within a normal range with regard to general school performance.

#### **5.2 Materials**

A visuo-spatial working memory (vsWM) test was used (spanboard; Westerberg, Hirvikoski, Forssberg, & Klingberg, 2004). This test is a sensitive measure of cognitive deficits in ADHD. The test determines working memory capacity without being affected by previous skills or knowledge. The visuo-spatial WM task consists of red dots (memory stimuli) that are presented one at a time at a computer screen in a four by four grid. Interstimulus-intervals were 4 seconds, target is shown for 2.225 sec and a 1.725 sec pause is given before the next target turns up. Participants are asked to recall location, as well as the order in which the red dots appear. The working memory load increases after every second trial, and the working memory capacity is estimated based on the number of correctly recalled dots. Dependent variable was total number of correctly recalled dots.

All noise conditions were recorded and reproduced on a CD player. The speech part of the speech and noise condition was recorded at a café at Stockholm University, where five students discussed films, books, and what they did over the weekend. The equivalent continuous sound level of the white noise was set to 78 dB(A) in the three noise conditions, in accordance with findings from earlier studies (Söderlund, et al., 2007; Söderlund, et al., 2010).

#### **5.3 Design**

We used a 2 x 4 design, where sound environment (silence vs. white noise; silence vs. speech; silence vs. speech + white noise) was the within group variable. The between group variable was teacher rated classroom attention level (attentive vs. inattentive)

#### **5.4 Procedure**

The testing was conducted at the children's school, following permission from parents and children. The regional ethic board in Stockholm approved the study. The participants were tested individually in a room during the school day.

The participants were tested individually in a room during the school day. All participants used the same 15' laptop PC for the visuo-spatial test (span-board). Headphones provided the noise, and dB levels where checked for all participants ahead of every session. Before starting the experiment proper, two practice trials were conducted. The time taken to complete each test was approximately 5 minutes, depending on the performance level (the better performance the longer time). Altogether, the testing sessions lasted approximately 30 minutes including instructions and test trials. The noise conditions were presented in random order so each condition appeared equally many times in each position (first, second, third, and forth).

#### **6. Results**

238 Current Directions in ADHD and Its Treatment

predicted this effect to occur for white noise. However, we posed no direct prediction on

Twenty-two primary school children between 7 and 10 years old (M = 8.3 yrs) participated in the present study (14 boys and 8 girls). The twenty-two participants were screened and selected out of a group of 33 participants according their attention ability as reported by their teachers. The eleven that scored lowest on the attention scale where selected for the inattentive group while the ones that scored high on attention formed the attentive group. What was considered normal or above average in attention was decided according to their teacher's judgments. For this purpose a SNAP score with 18 questions were used (Swanson et al., 2007). Mean score for the inattentive group was 28.8 and for the attentive 1.5. 28 points is slightly below the cut off point for ADHD diagnosis (36). None of the participants were consequently diagnosed with ADHD or any other neuropsychiatric diagnoses. Participants were also

whether speech might produce cognitive enhancement effects on inattentive children.

considered to be within a normal range with regard to general school performance.

recalled dots. Dependent variable was total number of correctly recalled dots.

variable was teacher rated classroom attention level (attentive vs. inattentive)

tested individually in a room during the school day.

A visuo-spatial working memory (vsWM) test was used (spanboard; Westerberg, Hirvikoski, Forssberg, & Klingberg, 2004). This test is a sensitive measure of cognitive deficits in ADHD. The test determines working memory capacity without being affected by previous skills or knowledge. The visuo-spatial WM task consists of red dots (memory stimuli) that are presented one at a time at a computer screen in a four by four grid. Interstimulus-intervals were 4 seconds, target is shown for 2.225 sec and a 1.725 sec pause is given before the next target turns up. Participants are asked to recall location, as well as the order in which the red dots appear. The working memory load increases after every second trial, and the working memory capacity is estimated based on the number of correctly

All noise conditions were recorded and reproduced on a CD player. The speech part of the speech and noise condition was recorded at a café at Stockholm University, where five students discussed films, books, and what they did over the weekend. The equivalent continuous sound level of the white noise was set to 78 dB(A) in the three noise conditions, in accordance with findings from earlier studies (Söderlund, et al., 2007; Söderlund, et al., 2010).

We used a 2 x 4 design, where sound environment (silence vs. white noise; silence vs. speech; silence vs. speech + white noise) was the within group variable. The between group

The testing was conducted at the children's school, following permission from parents and children. The regional ethic board in Stockholm approved the study. The participants were

**5. Methods 5.1 Participants** 

**5.2 Materials** 

**5.3 Design** 

**5.4 Procedure** 

A 2 x 4 mixed ANOVA was conducted including all noise conditions. No main effect of noise was found, but a significant overall interaction was found between noise and group (F(18,3)= 3.44, p= .039, eta2 = .365). The difference between groups was also significant, where the attentive group outperformed the inattentive group in all conditions (F(20,1) = 12.63, p = .002, eta2 .387)

Thereafter we conducted three separate 2 x 2 mixed ANOVA's, one for each noise condition. It comprised one between-subject factor, *group* (attentive vs. inattentive) and one withinsubjects factor, *encoding stimulation condition* (silence vs. white noise; silence vs. speech; silence vs. speech + white noise). The data from these tests are presented in the three graphs below. In neither of the three noise conditions we found a main effect of noise, but in two out of three conditions we found a robust noise x group interaction.

In the first ANOVA, (silence vs. white noise, Figure 3) we found an interaction between group and noise. The inattentive group improved its performance whereas the attentive

Fig. 3. Number of correctly recalled items in a visuo-spatial working memory task as a function of noise condition; silence vs. white noise in two groups: attentive (N=11) and inattentive (N=11).

Distractor or Noise? The Influence of Different Sounds

compared to performance in the silent condition.

**7. Conclusions and future challenges** 

attentive participants' performance decreased.

while the performance of others is impaired.

silence as in the speech + noise condition (p= .766 vs. p = .368)

on Cognitive Performance in Inattentive and Attentive Children 241

A paired samples test, testing groups separately, revealed that the decrease for the attentive group was significant (t(10)= -2.95, p = .015), whereas the increase in performance for the

The second ANOVA (silence vs. speech, Figure 4) also showed a significant interaction, in this case, between group and speech (F(20, 1)= 6.15, p = .019, eta2 = .246). The inattentive group performed better and the attentive group performed worse in the speech condition as

A paired samples test, testing groups separately, revealed that the increase for the inattentive group was significant in the speech condition (t(10)= 3.01, p = .013) whereas the decrement for the attentive group was not (t(10)= .981, p = .350). Finally, the one-way ANOVA showed that, despite the improvement for the inattentive group, in the speech condition the difference between groups remained significant (F(20, 1)= 5.43, p = .030).

In the last ANOVA (silence vs, speech + noise, Figure 5) there was no interaction between the group and noise condition The robust different between groups remained in the speech + noise condition (F(20, 1)= 16.94, p = .001). Neither did a paired sample t-test reveal a difference between groups as an effect of noise, both groups performed at the same level in

As predicted, the results shown above confirm earlier findings showing different effects of white noise on attentive and inattentive children selected from a normal population. The sub-clinical inattentive group did indeed benefit from noise, while their attentive peers did not. Interestingly, the speech (classroom noise) condition did not lead to any detrimental effects for the inattentive group, but improved their performance as well. These results suggest that the beneficial effects of auditory environmental stimulation on inattentive people are found not only in pure noise conditions, as has been found previously, but generalize to broader sets of environmental sounds. In particular, this study demonstrates that noise enhancement can be found for speech. To what extent noise enhancement effects generalize to other auditory stimuli is still unexplored. However, these results suggest that we need to be open to the idea that wider sets of environmental stimulation may serve the benefit of cognitive enhancement in inattentive people. The cafeteria/classroom noise condition improved working memory performance for the inattentive group, whereas the

The reviewed literature has found inconsistent results regarding the effect of noise on performance in cognitive tasks. Studies have shown detrimental effects, no effects, and that noise interacts with other variables such as gender or time of the day (Baker & Holding, 1993; Baker, Holding, & Loeb, 1984; Belleville, et al., 2003; Boman, Enmarker, & Hygge, 2005; Rouleau & Belleville, 1996). It is plausible that controlling for participant characteristics such as age, attention ability and working memory capacity would provide other results. Differential effects of noise can be hidden in group means, were some participants improve

Our findings suggest a need for further studies of psychoacoustics on different soundscapes. Previous research has focused on testing different noise levels (amplitudes in dB) over larger samples of participants. However, the data presented here propose that different

inattentive group did not reach significance (t(10)= 1.02, p = .333; Figure 3).

group declined under the white noise condition (F(20, 1)= 8.17, p = .010, eta2 = .290). A oneway ANOVA showed that the difference between groups in the silent condition disappeared in the noise condition (p < .001 vs. p= .222)

Fig. 5. Number of correctly recalled items in a visuo-spatial working memory task as a function of noise condition; silence vs. speech noise in two groups: attentive (N=11) and inattentive (N=11).

group declined under the white noise condition (F(20, 1)= 8.17, p = .010, eta2 = .290). A oneway ANOVA showed that the difference between groups in the silent condition

> Inattentive Attentive

Fig. 4. Number of correctly recalled items in a visuo-spatial working memory task as a function of noise condition; silence vs. speech noise in two groups: attentive (N=11) and

silence speech

Fig. 5. Number of correctly recalled items in a visuo-spatial working memory task as a function of noise condition; silence vs. speech noise in two groups: attentive (N=11) and

silence speech

+ noise

Inattentive Attentive

disappeared in the noise condition (p < .001 vs. p= .222)

inattentive (N=11).

15

20

25

30

**Performance**

35

40

45

inattentive (N=11).

15

20

25

30

**Performance**

35

40

45

A paired samples test, testing groups separately, revealed that the decrease for the attentive group was significant (t(10)= -2.95, p = .015), whereas the increase in performance for the inattentive group did not reach significance (t(10)= 1.02, p = .333; Figure 3).

The second ANOVA (silence vs. speech, Figure 4) also showed a significant interaction, in this case, between group and speech (F(20, 1)= 6.15, p = .019, eta2 = .246). The inattentive group performed better and the attentive group performed worse in the speech condition as compared to performance in the silent condition.

A paired samples test, testing groups separately, revealed that the increase for the inattentive group was significant in the speech condition (t(10)= 3.01, p = .013) whereas the decrement for the attentive group was not (t(10)= .981, p = .350). Finally, the one-way ANOVA showed that, despite the improvement for the inattentive group, in the speech condition the difference between groups remained significant (F(20, 1)= 5.43, p = .030).

In the last ANOVA (silence vs, speech + noise, Figure 5) there was no interaction between the group and noise condition The robust different between groups remained in the speech + noise condition (F(20, 1)= 16.94, p = .001). Neither did a paired sample t-test reveal a difference between groups as an effect of noise, both groups performed at the same level in silence as in the speech + noise condition (p= .766 vs. p = .368)

#### **7. Conclusions and future challenges**

As predicted, the results shown above confirm earlier findings showing different effects of white noise on attentive and inattentive children selected from a normal population. The sub-clinical inattentive group did indeed benefit from noise, while their attentive peers did not. Interestingly, the speech (classroom noise) condition did not lead to any detrimental effects for the inattentive group, but improved their performance as well. These results suggest that the beneficial effects of auditory environmental stimulation on inattentive people are found not only in pure noise conditions, as has been found previously, but generalize to broader sets of environmental sounds. In particular, this study demonstrates that noise enhancement can be found for speech. To what extent noise enhancement effects generalize to other auditory stimuli is still unexplored. However, these results suggest that we need to be open to the idea that wider sets of environmental stimulation may serve the benefit of cognitive enhancement in inattentive people. The cafeteria/classroom noise condition improved working memory performance for the inattentive group, whereas the attentive participants' performance decreased.

The reviewed literature has found inconsistent results regarding the effect of noise on performance in cognitive tasks. Studies have shown detrimental effects, no effects, and that noise interacts with other variables such as gender or time of the day (Baker & Holding, 1993; Baker, Holding, & Loeb, 1984; Belleville, et al., 2003; Boman, Enmarker, & Hygge, 2005; Rouleau & Belleville, 1996). It is plausible that controlling for participant characteristics such as age, attention ability and working memory capacity would provide other results. Differential effects of noise can be hidden in group means, were some participants improve while the performance of others is impaired.

Our findings suggest a need for further studies of psychoacoustics on different soundscapes. Previous research has focused on testing different noise levels (amplitudes in dB) over larger samples of participants. However, the data presented here propose that different

Distractor or Noise? The Influence of Different Sounds

*Ergonomics, 1*, 21-29.

*365*(6444), 337-340.

*26*(3), 169-180.

*13*(5), 469-474.

373-387.

*Society of America, 30*, 824-827.

*of Modern Physics, 70*(1), 223-287.

10.1111/j.1469-7610.2008.02033.x

children. *Noise Health, 9*(36), 64-74.

198. doi: 10.4103/1463-1741.56212

resonance. *Neurosci Lett, 415*(3), 231-236.

on Cognitive Performance in Inattentive and Attentive Children 243

Boman, E. (2004). The effects of noise and gender on children's episodic and semantic

Boman, E., Enmarker, I., & Hygge, S. (2005). Strength of noise effects on memory as a

Broadbent, D. E. (1951). Noise, paced performance and vigilance tasks. *Medical Research* 

Broadbent, D. E. (1957). Effects of noises of high and low frequency on behaviour.

Broadbent, D. E. (1958). Effect of noise on an "intellectual" task. *Journal of the Acoustical* 

Corbett, B., & Stanczak, D. E. (1999). Neuropsychological performance of adults evidencing

Douglass, J. K., Wilkens, L., Pantazelou, E., & Moss, F. (1993). Noise enhancement of

Gammaitoni, L., Hänggi, P., Jung, P., & Marchesoni, F. (1998). Stochastic resonance. *Reviews* 

Geffner, D., Lucker, J. R., & Koch, W. (1996). Evaluation of auditory discrimination in

Gerjets, P., Graw, T., Heise, E., Westermann, R., & Rothenberger, A. (2002). Deficits of action

Gevensleben, H., Holl, B., Albrecht, B., Vogel, C., Schlamp, D., Kratz, O., . Heinrich, H.

Hygge, S., Boman, E., & Enmarker, I. (2003). The effects of road traffic noise and meaningful

Hygge, S., Evans, G. W., & Bullinger, M. (2002). A prospective study of some effects of

Klatte, M., Meis, M., Sukowski, H., & Schick, A. (2007). Effects of irrelevant speech and

Ljung, R., Sörqvist, P., & Hygge, S. (2009). Effects of road traffic noise and irrelevant speech

Manjarrez, E., Mendez, I., Martinez, L., Flores, A., & Mirasso, C. R. (2007). Effects of auditory

*Psychologie und Psychotherapie: Forschung und Praxis, 31*(2), 99-109.

Gov't]. *Scandinavian journal of psychology, 44*(1), 13-21.

Attention-Deficit Hyperactivity Disorder. *Archives in Clinical Neuropsychology, 14*(4),

information transfer in crayfish mechanoreceptors by stochastic resonance. *Nature,* 

children with ADD and without ADD. *Child Psychiatry & Human Development,* 

control and specific goal intentions in hyperkinetic disorder. II: Empirical results/Handlungskontrolldefizite und störungsspezifische Zielintentionen bei der Hyperkinetischen Störung: II: Empirische Befunde. *Zeitschrift für Klinische* 

(2009). Is neurofeedback an efficacious treatment for ADHD? A randomised controlled clinical trial. *J Child Psychol Psychiatry, 50*(7), 780-789. doi: JCPP2033

irrelevant speech on different memory systems. [Research Support, Non-U.S.

aircraft noise on cognitive performance in schoolchildren. [Clinical Trial Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, P.H.S.]. *Psychological science,* 

traffic noise on speech perception and cognitive performance in elementary school

on children's reading and mathematical performance. *Noise & health, 11*(45), 194-

noise on the psychophysical detection of visual signals: cross-modal stochastic

memory. *Scandinavian Journal of Psychology, 45*(5), 407-416.

function of noise source and age. *Noise Health, 7*(27), 11-26.

*Council, Applied Psychology Research Unit Report, 165*(51), 8.

sounds need to be investigated in relation SR. White noise might be tiring to listen to for extended periods. Future research needs to address the question of whether sounds from waterfalls, shivering leafs or sounds from bamboo grass could be beneficial as well. These noise-like sounds possibly include sufficient variability in both amplitude and frequencies to induce the required increase of variability into the nervous system.

Future studies should investigate the neurophysiological traces set by noise by EEG measures. Earlier studies have shown that ADHD patients display elevated relative theta power, theta/alpha, and theta/beta ratios during rest (Barry, Clarke, & Johnstone, 2003). We have reason to believe that noise exposure could normalize these anomalies, and increase the level of beta and gamma activity particularly. Beta and gamma activity is crucial for higher mental activities, such as focused attention. Furthermore, the expanding field of neuro-feedback is providing /investigating interesting tools to improve attention; however, small effect sizes have been shown this far (Arns, de Ridder, Strehl, Breteler, & Coenen, 2009; Gevensleben et al., 2009). The outcome effects of neuro-feedback might get boosted if combined with noise exposure and may, in particular, shorten the time needed to obtain robust and long-lasting effects. The field of noise-induced improvement is still in its infancy, and a lot of research is needed to get a good picture of the potential contributions from this new field. Nevertheless, we find the results very promising this far, and foresee a growing field of possible applications. In Swedish education and elsewhere, school failures increase. Current figures show that about 25% do not achieve a complete exam from compulsory or upper secondary school (Skolverket, 2005). Individually adapted study environments, utilizing the benefits of noise, may be one possibility to turn this downward trend.

#### **8. References**


sounds need to be investigated in relation SR. White noise might be tiring to listen to for extended periods. Future research needs to address the question of whether sounds from waterfalls, shivering leafs or sounds from bamboo grass could be beneficial as well. These noise-like sounds possibly include sufficient variability in both amplitude and frequencies

Future studies should investigate the neurophysiological traces set by noise by EEG measures. Earlier studies have shown that ADHD patients display elevated relative theta power, theta/alpha, and theta/beta ratios during rest (Barry, Clarke, & Johnstone, 2003). We have reason to believe that noise exposure could normalize these anomalies, and increase the level of beta and gamma activity particularly. Beta and gamma activity is crucial for higher mental activities, such as focused attention. Furthermore, the expanding field of neuro-feedback is providing /investigating interesting tools to improve attention; however, small effect sizes have been shown this far (Arns, de Ridder, Strehl, Breteler, & Coenen, 2009; Gevensleben et al., 2009). The outcome effects of neuro-feedback might get boosted if combined with noise exposure and may, in particular, shorten the time needed to obtain robust and long-lasting effects. The field of noise-induced improvement is still in its infancy, and a lot of research is needed to get a good picture of the potential contributions from this new field. Nevertheless, we find the results very promising this far, and foresee a growing field of possible applications. In Swedish education and elsewhere, school failures increase. Current figures show that about 25% do not achieve a complete exam from compulsory or upper secondary school (Skolverket, 2005). Individually adapted study environments,

utilizing the benefits of noise, may be one possibility to turn this downward trend.

nondisabled children. *Journal of Learning Disabilities, 29*(3), 238-246.

hyperactivity: a meta-analysis. *Clin EEG Neurosci, 40*(3), 180-189.

electroencephalography. *Clinical Neurophysiology, 114*(2), 171-183.

cochlear-implant listeners. *Hearing Research, 186*(1-2), 91-93.

performance. *J Gen Psychol, 120*(3), 339-355.

mathematics task. *Ergonomics, 27*(1), 67-80.

dementia. *Neuropsychology, 17*(1), 69-81.

*Tellus, 34*, 10-16.

Abikoff, H., Courtney, M. E., Szeibel, P. J., & Koplewicz, H. S. (1996). The effects of auditory

Arns, M., de Ridder, S., Strehl, U., Breteler, M., & Coenen, A. (2009). Efficacy of

Baker, M. A., & Holding, D. H. (1993). The effects of noise and speech on cognitive task

Baker, M. A., Holding, D. H., & Loeb, M. (1984). Noise, sex and time of day effects in a

Barry, R. J., Clarke, A. R., & Johnstone, S. J. (2003). A review of electrophysiology in

Behnam, S. E., & Zeng, F. G. (2003). Noise improves suprathreshold discrimination in

Belleville, S., Rouleau, N., Van der Linden, M., & Collette, F. (2003). Effect of manipulation

Benzi, R., Parisi, G., Sutera, S., & Vulpiani, A. (1982). Stochastic resonance in climatic change.

stimulation on the arithmetic performance of children with ADHD and

neurofeedback treatment in ADHD: the effects on inattention, impulsivity and

attention-deficit/hyperactivity disorder: I. Qualitative and quantitative

and irrelevant noise on working memory capacity of patients with Alzheimer's

**8. References** 

to induce the required increase of variability into the nervous system.


Distractor or Noise? The Influence of Different Sounds

651-658. doi: 10.3758/MC.38.5.651

*Biological Cybernetics, 83*(6), L11-16.

with ADHD. *Neuroimage, 34*(3), 1182-1190.

*Child Neuropsychology, 10*(3), 155-161.

doi: 10.1007/s00221-008-1463-0

10.4103/1463-1741.70500

10.1001/jama.2009.1308

7610.2007.01749.x

on Cognitive Performance in Inattentive and Attentive Children 245

Söderlund, G. B. W., Marklund, E., & Lacerda, F. (2009). Auditory White Noise Enhances

Söderlund, G. B. W., Sikstrom, S., & Smart, A. (2007). Listen to the noise: Noise is beneficial

Söderlund, G. B. W., & Sikström, S. (2008). Positive effects of noise on cogntive performance:

Söderlund, G. B. W., Sikström, S., Loftesnes, J. M., & Sonuga-Barke, E. (2010). The effects of

Sörqvist, P. (2010b). The role of working memory capacity in auditory distraction: a review.

Sörqvist, P., Ljungberg, J. K., & Ljung, R. (2010). A sub-process view of working memory

Non-U.S. Gov't]. *Memory, 18*(3), 310-326. doi: 10.1080/09658211003601530 Usher, M., & Feingold, M. (2000). Stochastic resonance in the speed of memory retrieval.

Volkow, N. D., Wang, G. J., Kollins, S. H., Wigal, T. L., Newcorn, J. H., Telang, F., . . .

Volkow, N. D., Wang, G. J., Newcorn, J., Fowler, J. S., Telang, F., Solanto, M. V., . . . Pradhan,

Wells, C., Ward, L. M., Chua, R., & Timothy Inglis, J. (2005). Touch noise increases vibrotactile sensitivity in old and young. *Psychological Science, 16*(4), 313-320. Westerberg, H., Hirvikoski, T., Forssberg, H., & Klingberg, T. (2004). Visuo-spatial working

Wilkinson, D., Nicholls, S., Pattenden, C., Kilduff, P., & Milberg, W. (2008). Galvanic

Wilkinson, D., Zubko, O., Degutis, J., Milberg, W., & Potter, J. (2009). Improvement of a

*Neuropsychol*. doi: jnp194 [pii] 10.1348/174866409X468205

*Behavioral and Brain Functions, 6*(55). doi: doi:10.1186/1744-9081-6-55 Sörqvist, P. (2010a). High working memory capacity attenuates the deviation effect but not

*Dept of Linguistics, Stockholm University, Stockholm, Sweden*.

*International Comission on the Biological Effects of Noise*.

Cognitive Performance Under Certain Conditions: Examples from Visuo-Spatial Working Memory and Dichotic Listening Tasks. *Proceedings from FONETICS 2009,* 

for cognitive performance in ADHD. [Controlled Clinical Trial]. *Journal of child psychology and psychiatry, and allied disciplines, 48*(8), 840-847. doi: 10.1111/j.1469-

Explaining the Moderate Brain Arousal Model. *Proceedings from ICBEN,* 

background white noise on memory performance in inattentive school children.

the changing-state effect: further support for the duplex-mechanism account of auditory distraction. [Research Support, Non-U.S. Gov't]. *Memory & cognition, 38*(5),

[Research Support, Non-U.S. Gov't Review]. *Noise & health, 12*(49), 217-224. doi:

capacity: evidence from effects of speech on prose memory. [Research Support,

Swanson, J. M. (2009). Evaluating dopamine reward pathway in ADHD: clinical implications. *JAMA, 302*(10), 1084-1091. doi: 302/10/1084 [pii]

K. (2007). Brain dopamine transporter levels in treatment and drug naive adults

memory span: a sensitive measure of cognitive deficits in children with ADHD.

vestibular stimulation speeds visual memory recall. *Exp Brain Res, 189*(2), 243-248.

figure copying deficit during subsensory galvanic vestibular stimulation. *J* 


Matheson, M., Clark, C., Martin, R., van Kempen, E., Haines, M., Barrio, I. L., . . . Stansfeld,

Moss, F., Ward, L. M., & Sannita, W. G. (2004). Stochastic resonance and sensory information

Novak, P., & Novak, V. (2006). Effect of step-synchronized vibration stimulation of soles on

Pan, W., Soma, R., Kwak, S., & Yamamoto, Y. (2008). Improvement of motor functions by

Priplata, A. A., Niemi, J. B., Harry, J. D., Lipsitz, L. A., & Collins, J. J. (2003). Vibrating insoles and balance control in elderly people. *Lancet, 362*(9390), 1123-1124. Priplata, A. A., Patritti, B. L., Niemi, J. B., Hughes, R., Gravelle, D. C., Lipsitz, L. A., . . .

Rickman, D. L. (2001). The effect of classroom-based distraction on continuous performance

Rouleau, N., & Belleville, S. (1996). Irrelevant speech effect in aging: an assessment of

Russell, D. F., Wilkens, L. A., & Moss, F. (1999). Use of behavioural stochastic resonance by

Sikström, S., & Söderlund, G. B. W. (2007). Stimulus-dependent dopamine release in

Simonotto, E., Spano, F., Riani, M., Ferrari, A., Levero, F., Pilot, A., . . . Moss, F. (1999). fMRI

Solanto, M. V. (2002). Dopamine dysfunction in AD/HD: integrating clinical and basic

Stansfeld, S., Berglund, B., Clark, C., Lopez-Barrio, I., Fischer, P., Ohrstrom, E., . . . Berry, B.

Stansfeld, S., Hygge, S., Clark, C., & Alfred, T. (2010). Night time aircraft noise exposure and

Swanson, J. M., Schuck, S., Mann, M., Carlson, C., Hartman, K., Sergeant, J., . . . McCleary, R.

Skolverket. (2005). Education Results National level. . *Report, 257. (Part 1)*, 1-172.

neuroscience research. *Behavioral Brain Research, 130*(1-2), 65-71.

gait in Parkinson's disease: a pilot study. *J Neuroeng Rehabil, 3*, 9.

*255*(11), 1657-1661. doi: 10.1007/s00415-008-0950-3

*Section B: The Sciences & Engineering, 61*(10-B), 5578.

paddle fish for feeding. *Nature, 402*(6759), 291-294.

cross-national study. *Lancet, 365*(9475), 1942-1949.

1075. doi: 10.1037/0033-295X.114.4.1047

*research 1999, 26-27*, 511-516.

262. doi: 10.4103/1463-1741.70504

http://www.ADHD.net.

*Psychological Sciences and Social Sciences, 51*(6), P356-363.

patients with stroke. *Ann Neurol, 59*(1), 4-12.

1741.70503

267-281.

S. (2010). The effects of road traffic and aircraft noise exposure on children's episodic memory: the RANCH project. [Comparative Study Multicenter Study Research Support, Non-U.S. Gov't]. *Noise & health, 12*(49), 244-254. doi: 10.4103/1463-

processing: a tutorial and review of application. *Clinical Neurophysiology, 115*(2),

noisy vestibular stimulation in central neurodegenerative disorders. *J Neurol,* 

Collins, J. J. (2006). Noise-enhanced balance control in patients with diabetes and

test scores of ADHD and nonADHD children. *Dissertation Abstracts International:* 

inhibitory processes in working memory. *The Journals of Gerontology. Series B,* 

attention-deficit/hyperactivity disorder. [Review]. *Psychological review, 114*(4), 1047-

studies of visual cortical activity during noise stimulation. *Neurocomputing: An International Journal. Special double volume: Computational neuroscience: Trends in* 

F. (2005). Aircraft and road traffic noise and children's cognition and health: a

children's cognitive performance. [Multicenter Study]. *Noise & health, 12*(49), 255-

(2007). Categorical and dimensional definitions and evaluatios of ADHD: The SNAP and the SWAN rating scales. *Retrieved December 2007 from* 


**Part 4** 

**EEG Biofeedback** 


**Part 4** 

**EEG Biofeedback** 

246 Current Directions in ADHD and Its Treatment

Yamamoto, Y., Struzik, Z. R., Soma, R., Ohashi, K., & Kwak, S. (2005). Noisy vestibular

Zeng, F. G., Fu, Q. J., & Morse, R. (2000). Human hearing enhanced by noise. *Brain Research,* 

Zentall, S. S. (1986). Effects of Color Stimulation on Performance and Activity of

Zentall, S. S., & Dwyer, A. M. (1989). Color Effects on the Impulsivity and Activity of

Zentall, S. S., Falkenberg, S. D., & Smith, L. B. (1985). Effects of color stimulation and

Zentall, S. S., & Shaw, J. H. (1980). Effects of classroom noise on performance and activity of

Zentall, S. S., & Zentall, T. R. (1983). Optimal stimulation: A model of disordered activity

Hyperactive Children. *Journal of School Psychology, 27*(2), 165-173.

neurodegenerative disorders. *Ann Neurol, 58*(2), 175-181.

*of Abnormal Child Psychology, 13*(4), 501-511.

*869*(1-2), 251-255.

159-165.

*72*(6), 830-840.

471.

stimulation improves autonomic and motor responsiveness in central

Hyperactive and Nonhyperactive Children. *Journal of Educational Psychology, 78*(2),

information on the copying performance of attention-problem adolescents. *Journal* 

second-grade hyperactive and control children. *Journal of educational psychology,* 

and performance in normal and deviant children. *Psychological Bulletin, 94*(3), 446-

**13** 

Nada Pop-Jordanova

*R. Macedonia* 

**QEEG Characteristics and Biofeedback** 

*Department for Psychophysiology, University Pediatric Clinic, Skopje,* 

Attention Deficit Hyperactivity Disorder (ADHD) is one of the most frequent neuropsychiatric diagnoses during childhood, varying between 2 and 10% depending on country or cultural area. In our country (R. Macedonia), the incidence of ADHD is about 2%. The other labels of the disorder introduced in the past are hyperactivity, hyperkinesis, hyperkinetic syndrome, minimal brain dysfunction and minimal brain damage. According to DSM – IV (American Psychiatric Association, 1994) there are three main clinical forms of this disorder: inattentive, hyperactive/impulsive and combined. European diagnostic criteria for hyperkinetic disorder as defined by the ICD-10 (International Classification of Diseases, 10th revision, 1993) include children displaying developmentally inappropriate levels of attention, hyperactivity and impulsivity that begin in childhood and cause impairment to school performance, intellectual functioning, social skills, driving and occupational functioning. Generally, ICD-10 criteria are more restrictive than DSM-IV

The overlapping of all three forms with learning disabilities is very high (up to 70%), as well as with conduct problems. The inattentive (ADD) form mainly overlaps with anxiety disorders and learning disability, while hyperactive form (ADHD) mostly overlaps with conduct disorder. Some children with ADHD have movement disorders or tics and occasionally they may have seizure disorders (Barkley, 1990). Strong genetic component related to defect in chromosome 11 is showed in some studies (Anokhin et all. 2006). Concerning the involvement of specific neurotransmitter systems in the pathophysiology of ADHD some authors suggest that the catecholaminergic dysregulations are centrally

This chapter is mainly devoted to the QEEG characteristics of ADHD in children, connected to endophenotypes, event related potentials, brain rate and biofeedback treatment. Firstly, it will be explained why QEEG recording is important for the exact diagnostics and the therapy planning for ADHD children. Based on QEEG differentiation, the endophenotypes clustering enables more precise diagnostics. In addition, it will be shown that event related potentials (ERP's) are related to the possible dysfunction of executive system, as a part of the brain the most involved in this disorder. The applied calculation of brain rate is our original proposal for the evaluation of general mental activation level, considering under or over-

arousal states, so that the planning of the treatment protocols becomes more easy.

diagnosis because they need a greater degree of symptom expression.

**1. Introduction** 

involved.

**Modalities in Children with ADHD** 

Nada Pop-Jordanova

*Department for Psychophysiology, University Pediatric Clinic, Skopje, R. Macedonia* 

#### **1. Introduction**

Attention Deficit Hyperactivity Disorder (ADHD) is one of the most frequent neuropsychiatric diagnoses during childhood, varying between 2 and 10% depending on country or cultural area. In our country (R. Macedonia), the incidence of ADHD is about 2%. The other labels of the disorder introduced in the past are hyperactivity, hyperkinesis, hyperkinetic syndrome, minimal brain dysfunction and minimal brain damage. According to DSM – IV (American Psychiatric Association, 1994) there are three main clinical forms of this disorder: inattentive, hyperactive/impulsive and combined. European diagnostic criteria for hyperkinetic disorder as defined by the ICD-10 (International Classification of Diseases, 10th revision, 1993) include children displaying developmentally inappropriate levels of attention, hyperactivity and impulsivity that begin in childhood and cause impairment to school performance, intellectual functioning, social skills, driving and occupational functioning. Generally, ICD-10 criteria are more restrictive than DSM-IV diagnosis because they need a greater degree of symptom expression.

The overlapping of all three forms with learning disabilities is very high (up to 70%), as well as with conduct problems. The inattentive (ADD) form mainly overlaps with anxiety disorders and learning disability, while hyperactive form (ADHD) mostly overlaps with conduct disorder. Some children with ADHD have movement disorders or tics and occasionally they may have seizure disorders (Barkley, 1990). Strong genetic component related to defect in chromosome 11 is showed in some studies (Anokhin et all. 2006). Concerning the involvement of specific neurotransmitter systems in the pathophysiology of ADHD some authors suggest that the catecholaminergic dysregulations are centrally involved.

This chapter is mainly devoted to the QEEG characteristics of ADHD in children, connected to endophenotypes, event related potentials, brain rate and biofeedback treatment. Firstly, it will be explained why QEEG recording is important for the exact diagnostics and the therapy planning for ADHD children. Based on QEEG differentiation, the endophenotypes clustering enables more precise diagnostics. In addition, it will be shown that event related potentials (ERP's) are related to the possible dysfunction of executive system, as a part of the brain the most involved in this disorder. The applied calculation of brain rate is our original proposal for the evaluation of general mental activation level, considering under or overarousal states, so that the planning of the treatment protocols becomes more easy.

Each site has a letter to identify the lobe and a number to identify the hemisphere location. The letters F, T, C, P and O stand for Frontal, Temporal, Central, Parietal, and Occipital, respectively. Note that in fact there exists no central lobe, the "C" letter is only used for identification purposes only. The letter "z" (zero) refers to an electrode placed on the midline. Even numbers (2, 4, 6, 8) refer to electrode positions on the right hemisphere,

One important element of the EEG signal is its rhythmicity. Rhythms differ in frequency, location, mechanism of generation and functional meaning. They evolve in time, so that a representative EEG recording usually takes three or more minutes (in our patients we use five minute recording). For compressing the information about rhythmicity over time, as a most powerful method, the Fourier analysis is used. The parameters of Fourier analysis can

When EEG is recorded from many electrodes that cover the whole cortex, it is possible to compute a 2D representation of a measured EEG characteristic. The characteristics could be

Computerized analysis of EEG signals involve a number of factors: frequency distribution, voltage (as amplitude of the signal), locus of the phenomena, wave shape morphology, inter-hemispheric symmetries, character of waveform occurrence and reactivity (changes in

The pattern of neuronal oscillations plays an important role in the evaluation and treatment of children (and adult as well) with ADHD. These patients are characterized with QEEG abnormalities in up to 80%. In this population, frontal regions are most likely to show deviations from normal development, with disturbed thalamo-cortical and septal-

The term "executive functions" refers to the coordination and control of motor and cognitive actions to attain specific goals. In neuropsychology, the term "executive functions" has long been used as a synonym for frontal lobe function. A modern view postulates several sub-components in the hypothetical executive mechanism. In a frequently cited classification, Smith and Jonides (1999) distinguished between mechanisms relating to (a) attention and inhibition, (b) task management, (c) planning, (d) monitoring and (e) coding. There is, however, no consensus on the number and the precise nature of

As we said previously, it is supposed that the main brain system impaired in ADHD is the executive system. The executive system is characterized by two parameters: general activation of the system (arousal = A) and the response associated with different operations such as working memory, action selection, action inhibition and action monitoring (focused

It is well known that EEG recorded in eyes open (EO) and eyes closed (EC) resting state is good indicator of metabolic activity in the brain cortex. Low metabolic activity in the area that generates the corresponding EEG is characterized by increase of slow activities (delta and theta waves) and decrease of beta activities. It means that in this condition, the level of activation is low (low A), as well as the amplitude of responses we named focused activity

whereas odd numbers (1, 3, 5, 7) refer to those on the left hemisphere.

be adjusted to the goal of a specific task.

an EEG parameter with changes in state).

functional subcomponents.

activation = At).

(At) is also low.

either potential or power taken at a particular frequency.

hippocampal pathways, altogether named as executive system.

Finally, biofeedback will be offer as the non-pharmaceutical treatment for ADHD, with long-term effects.

#### **2. QEEG recording**

The EEG recording is a noninvasive, painless, and safe measurement using digital technology of electrical patterns at the surface of the scalp which primarily reflect cortical electrical activity or brainwaves. The Quantitative EEG procedure uses multi-electrode EEG recording where the data are processed with various algorithms and than statistically analyzed comparing values with normative database reference values. The processed EEG could be also converted into color maps of brain functioning called brain maps.

The placement of electrodes from the first recording (introduced in 1924) was strongly precise and the system was called the10-20 International System of Electrode Placement. This system of recording is used until now. The "10" and "20" refer to the fact that the actual distances between adjacent electrodes are either 10% or 20% of the total front-back or rightleft distance of the skull (Fig.1).

Fig. 1. The 10-20 International System

Finally, biofeedback will be offer as the non-pharmaceutical treatment for ADHD, with

The EEG recording is a noninvasive, painless, and safe measurement using digital technology of electrical patterns at the surface of the scalp which primarily reflect cortical electrical activity or brainwaves. The Quantitative EEG procedure uses multi-electrode EEG recording where the data are processed with various algorithms and than statistically analyzed comparing values with normative database reference values. The processed EEG

The placement of electrodes from the first recording (introduced in 1924) was strongly precise and the system was called the10-20 International System of Electrode Placement. This system of recording is used until now. The "10" and "20" refer to the fact that the actual distances between adjacent electrodes are either 10% or 20% of the total front-back or right-

could be also converted into color maps of brain functioning called brain maps.

long-term effects.

**2. QEEG recording** 

left distance of the skull (Fig.1).

Fig. 1. The 10-20 International System

Each site has a letter to identify the lobe and a number to identify the hemisphere location. The letters F, T, C, P and O stand for Frontal, Temporal, Central, Parietal, and Occipital, respectively. Note that in fact there exists no central lobe, the "C" letter is only used for identification purposes only. The letter "z" (zero) refers to an electrode placed on the midline. Even numbers (2, 4, 6, 8) refer to electrode positions on the right hemisphere, whereas odd numbers (1, 3, 5, 7) refer to those on the left hemisphere.

One important element of the EEG signal is its rhythmicity. Rhythms differ in frequency, location, mechanism of generation and functional meaning. They evolve in time, so that a representative EEG recording usually takes three or more minutes (in our patients we use five minute recording). For compressing the information about rhythmicity over time, as a most powerful method, the Fourier analysis is used. The parameters of Fourier analysis can be adjusted to the goal of a specific task.

When EEG is recorded from many electrodes that cover the whole cortex, it is possible to compute a 2D representation of a measured EEG characteristic. The characteristics could be either potential or power taken at a particular frequency.

Computerized analysis of EEG signals involve a number of factors: frequency distribution, voltage (as amplitude of the signal), locus of the phenomena, wave shape morphology, inter-hemispheric symmetries, character of waveform occurrence and reactivity (changes in an EEG parameter with changes in state).

The pattern of neuronal oscillations plays an important role in the evaluation and treatment of children (and adult as well) with ADHD. These patients are characterized with QEEG abnormalities in up to 80%. In this population, frontal regions are most likely to show deviations from normal development, with disturbed thalamo-cortical and septalhippocampal pathways, altogether named as executive system.

The term "executive functions" refers to the coordination and control of motor and cognitive actions to attain specific goals. In neuropsychology, the term "executive functions" has long been used as a synonym for frontal lobe function. A modern view postulates several sub-components in the hypothetical executive mechanism. In a frequently cited classification, Smith and Jonides (1999) distinguished between mechanisms relating to (a) attention and inhibition, (b) task management, (c) planning, (d) monitoring and (e) coding. There is, however, no consensus on the number and the precise nature of functional subcomponents.

As we said previously, it is supposed that the main brain system impaired in ADHD is the executive system. The executive system is characterized by two parameters: general activation of the system (arousal = A) and the response associated with different operations such as working memory, action selection, action inhibition and action monitoring (focused activation = At).

It is well known that EEG recorded in eyes open (EO) and eyes closed (EC) resting state is good indicator of metabolic activity in the brain cortex. Low metabolic activity in the area that generates the corresponding EEG is characterized by increase of slow activities (delta and theta waves) and decrease of beta activities. It means that in this condition, the level of activation is low (low A), as well as the amplitude of responses we named focused activity (At) is also low.

Endophenotypes are characteristics indicating biological markers of the brain disease. Endophenotype must obey some requirements: a) to be stable and reproducible in time intervals during which behavioral patterns associated with the state of the brain remain unchanged; b) endophenotype must reflect a function of a certain brain system that in a specific way determine the human behavior; and c) it must be inherited. EEG spectra, or amplitude of the background EEG in certain frequency bands, obey these requirements and

We said in the previous text that EEG oscillations (expressed as e.g. alpha or theta rhythm) wane and wax in time. The degree of variability of the basic oscillation depends on the frequency band and the state (eyes open, eyes closed, task etc) of the subject. For example, alpha spindles in the posterior regions vary with periods of few seconds, and burst of the frontal midline theta appear with interburst periods of few deco-seconds. However, if averaged over significantly long time intervals the resulting spectra become quite stable characteristics of the brain. So, these characteristics of EEG spectra could be considered as a

Different oscillations reflect different mechanisms: alpha rhythms reflect the state of thalamocortical pathways; frontal midline theta rhythms reflect functioning of the limbic system; beta rhythms are more local reflecting state of specific cortical areas. So, defining abnormal rhythmic activities in EEG and associating these abnormalities with distinct systems fit the second requirement for endophenotypes as the biological markers of disease. Finally, there is

Generally, it can be said that endophenotype is becoming an important concept in the study of different mental disorders such as ADHD, schizophrenia etc. The term was coined in 1966 and applied in psychiatry by Gottesman and Shileds in 1972. It is gradually substituting some similar terms such as "biological markers", "vulnerability marker", "subclinical traits" or "intermediate phenotype". An endophenotype may be neurophysiological, biochemical, endocrinological, neuroanatomical, or neurophychological in nature. Endophenotype represents simpler clue to genetic mechanism than the behavioral symptoms.Thereby, endophenotypes can help to define subtypes of a particular disorder and can be used as a

For example, some researchers suggest a biological rationale for lack of inhibition as an endophenotype for ADHD. Inhibitory tasks activate the prefrontal cortex and basal ganglia, regions in which the dopamine system is associated with executive functioning. Tests of inhibitory function, such as the stop signal task, often consists of two concurrent tasks (a "Go" task and "NoGo" task) in which the subject is signaled to produce a particular response. An individual with a poor inhibitory system will have a long reaction time. This deficit, being replicated several times, is concerned to be specific to ADHD. Imaging studies have shown that this inhibitory task activates the prefrontal cortex and basal ganglia. Inhibition also correlates with family history, so that 48.1% subjects with poor inhibition have a family history

Endophenotypes may also be useful in exploring different pathways leading up to the disorder. Patients having the same diagnosis may differ strongly in the number and severity of symptoms they portray, suggesting heterogeneity in the causal pathways. Creating more

of ADHD compared with only 7.7% of normal controls. (Crosbie, Schachar, 2001).

strong experimental evidence that spectral characteristics of EEG are inherited.

**3. Endophenotypes** 

consequently can be considered as endophenotypes.

reliable and stable estimation of the brain functional state.

quantitative trait in genetic analyses of probands and families.

The early studies of EEG abnormalities in children with minimal brain dysfunction are made in the period 1930-1950. Basically, EEG studies showed the increase of delta and theta bands in central and frontal regions during EEG recording. This finding was confirmed by quantitative EEG analysis in the 1970s and later. These studies are supported by recent PET (positron emission tomography) and SPECT (single photon emission computerized tomography) scan studies, which also indicate abnormalities in cerebral metabolism in these particular brain areas.

The most studies of QEEG in ADHD population confirm elevated levels of slow wave power in frontal region indicating frontal lobe deactivation, in comparison to normal children. These children experience decreased metabolism in prefrontal regions of the brain and have been good candidates for the use of stimulant medication or neurofeedback. Other children show deficits in limbic system activity and are characterized as having oppositional behavior, emotional outbursts, and impulsiveness. These children also show significant decreased metabolism in the prefrontal lobes and the anterior cingulated gyrus. They are good candidates for the use of tricyclic antidepressants. The third subgroup comprises individuals who have increased activity in the medial superior frontal gyrus. Although these children experience ADD they are often characterized as having an attention deficit with obsessive-compulsive disorder. They have a very short attention span and are often impulsive and oppositional. These patients sometimes respond to clomipramine. By far the most common group of children with ADD consists of those with excessive slow activity in frontal and central brain regions.

Having in mind that the absolute values of EEG spectra depend on some brain unrelated features, such as thickness of the skull, a relative parameter defined as the theta-beta ratio is introduced. Multi-centric studies in USA (Monastra et al. 2001; Lubar 1991, 1997; Mann et al 1992) used the theta-beta ratio as an index of inattention. This so called *inattention index* is defined as a ratio of theta EEG power (measured within the 4-8 Hz frequency band) and beta EEG power (measured within 13-21 Hz frequency band). Usually this index is calculated by EEG recording at a single place Cz in reference to linked ears. It was found that this index is three times higher in inattentive and combine types of ADHD children at the age of 6-10 years compared with normal group. Monastra et al. (2001) found that the sensitivity of this index was 86% and his specificity is 98%.

Opposite to these findings Russian scientists from the Human Brain Institute in St Petersburg (Kropotov, 2009) showed that this index is a good measure only for a part of ADHD population. Mapping this index in normal population showed that the location of the maximum of this index changes significantly with age. For example, the maximum of theta-beta index move from central-parietal location at 7-8 years old children to frontalcentral location in adults. The conclusion was that for better results in discriminating the ADHD population from healthy subjects this index must be measured in different electrode position depending on age.

Generally, we can infer that QEEG recording is needed for more precise diagnostics of ADHD, in addition to the clinical criteria. The method is a non invasive, relatively easy for manipulation with standardized locations of the electrodes over the scalp. The recording is performed in different conditions: eyes open, eyes closed and during some cognitive tasks (Go/ NoGo paradigm, visual or auditive performance tasks, reading or math tasks). QEEG is useful if neurofeedback treatment is planned to be applied.

### **3. Endophenotypes**

252 Current Directions in ADHD and Its Treatment

The early studies of EEG abnormalities in children with minimal brain dysfunction are made in the period 1930-1950. Basically, EEG studies showed the increase of delta and theta bands in central and frontal regions during EEG recording. This finding was confirmed by quantitative EEG analysis in the 1970s and later. These studies are supported by recent PET (positron emission tomography) and SPECT (single photon emission computerized tomography) scan studies, which also indicate abnormalities in cerebral metabolism in these

The most studies of QEEG in ADHD population confirm elevated levels of slow wave power in frontal region indicating frontal lobe deactivation, in comparison to normal children. These children experience decreased metabolism in prefrontal regions of the brain and have been good candidates for the use of stimulant medication or neurofeedback. Other children show deficits in limbic system activity and are characterized as having oppositional behavior, emotional outbursts, and impulsiveness. These children also show significant decreased metabolism in the prefrontal lobes and the anterior cingulated gyrus. They are good candidates for the use of tricyclic antidepressants. The third subgroup comprises individuals who have increased activity in the medial superior frontal gyrus. Although these children experience ADD they are often characterized as having an attention deficit with obsessive-compulsive disorder. They have a very short attention span and are often impulsive and oppositional. These patients sometimes respond to clomipramine. By far the most common group of children with ADD consists of those with excessive slow activity in

Having in mind that the absolute values of EEG spectra depend on some brain unrelated features, such as thickness of the skull, a relative parameter defined as the theta-beta ratio is introduced. Multi-centric studies in USA (Monastra et al. 2001; Lubar 1991, 1997; Mann et al 1992) used the theta-beta ratio as an index of inattention. This so called *inattention index* is defined as a ratio of theta EEG power (measured within the 4-8 Hz frequency band) and beta EEG power (measured within 13-21 Hz frequency band). Usually this index is calculated by EEG recording at a single place Cz in reference to linked ears. It was found that this index is three times higher in inattentive and combine types of ADHD children at the age of 6-10 years compared with normal group. Monastra et al. (2001) found that the

Opposite to these findings Russian scientists from the Human Brain Institute in St Petersburg (Kropotov, 2009) showed that this index is a good measure only for a part of ADHD population. Mapping this index in normal population showed that the location of the maximum of this index changes significantly with age. For example, the maximum of theta-beta index move from central-parietal location at 7-8 years old children to frontalcentral location in adults. The conclusion was that for better results in discriminating the ADHD population from healthy subjects this index must be measured in different electrode

Generally, we can infer that QEEG recording is needed for more precise diagnostics of ADHD, in addition to the clinical criteria. The method is a non invasive, relatively easy for manipulation with standardized locations of the electrodes over the scalp. The recording is performed in different conditions: eyes open, eyes closed and during some cognitive tasks (Go/ NoGo paradigm, visual or auditive performance tasks, reading or math tasks). QEEG

particular brain areas.

frontal and central brain regions.

position depending on age.

sensitivity of this index was 86% and his specificity is 98%.

is useful if neurofeedback treatment is planned to be applied.

Endophenotypes are characteristics indicating biological markers of the brain disease. Endophenotype must obey some requirements: a) to be stable and reproducible in time intervals during which behavioral patterns associated with the state of the brain remain unchanged; b) endophenotype must reflect a function of a certain brain system that in a specific way determine the human behavior; and c) it must be inherited. EEG spectra, or amplitude of the background EEG in certain frequency bands, obey these requirements and consequently can be considered as endophenotypes.

We said in the previous text that EEG oscillations (expressed as e.g. alpha or theta rhythm) wane and wax in time. The degree of variability of the basic oscillation depends on the frequency band and the state (eyes open, eyes closed, task etc) of the subject. For example, alpha spindles in the posterior regions vary with periods of few seconds, and burst of the frontal midline theta appear with interburst periods of few deco-seconds. However, if averaged over significantly long time intervals the resulting spectra become quite stable characteristics of the brain. So, these characteristics of EEG spectra could be considered as a reliable and stable estimation of the brain functional state.

Different oscillations reflect different mechanisms: alpha rhythms reflect the state of thalamocortical pathways; frontal midline theta rhythms reflect functioning of the limbic system; beta rhythms are more local reflecting state of specific cortical areas. So, defining abnormal rhythmic activities in EEG and associating these abnormalities with distinct systems fit the second requirement for endophenotypes as the biological markers of disease. Finally, there is strong experimental evidence that spectral characteristics of EEG are inherited.

Generally, it can be said that endophenotype is becoming an important concept in the study of different mental disorders such as ADHD, schizophrenia etc. The term was coined in 1966 and applied in psychiatry by Gottesman and Shileds in 1972. It is gradually substituting some similar terms such as "biological markers", "vulnerability marker", "subclinical traits" or "intermediate phenotype". An endophenotype may be neurophysiological, biochemical, endocrinological, neuroanatomical, or neurophychological in nature. Endophenotype represents simpler clue to genetic mechanism than the behavioral symptoms.Thereby, endophenotypes can help to define subtypes of a particular disorder and can be used as a quantitative trait in genetic analyses of probands and families.

For example, some researchers suggest a biological rationale for lack of inhibition as an endophenotype for ADHD. Inhibitory tasks activate the prefrontal cortex and basal ganglia, regions in which the dopamine system is associated with executive functioning. Tests of inhibitory function, such as the stop signal task, often consists of two concurrent tasks (a "Go" task and "NoGo" task) in which the subject is signaled to produce a particular response. An individual with a poor inhibitory system will have a long reaction time. This deficit, being replicated several times, is concerned to be specific to ADHD. Imaging studies have shown that this inhibitory task activates the prefrontal cortex and basal ganglia. Inhibition also correlates with family history, so that 48.1% subjects with poor inhibition have a family history of ADHD compared with only 7.7% of normal controls. (Crosbie, Schachar, 2001).

Endophenotypes may also be useful in exploring different pathways leading up to the disorder. Patients having the same diagnosis may differ strongly in the number and severity of symptoms they portray, suggesting heterogeneity in the causal pathways. Creating more

As can be seen on Fig. 1, in frontal and central region of brain cortex the dominant frequencies are in the range of delta and theta waves, while alpha and beta waves are practically absent.

On Fig.3 we can see a pick of theta activity (P 6.54 Hz) in frontal-midline area as the most important finding. It is also combined with generally slow activity (lack of alpha and beta

Both Fig.5 and 6 show over activation of the cortex expressed by the pronounced beta brain

In addition to the QEEG, SPECT (Amen, 1997, Amen et al. 1998*)* shows the corresponding

• frontal lobe deactivation (presented clinically as ADD, which usually respond to

• temporal lobe dysfunction (very like temporal epilepsy, respond to therapy with

• homogenous cortical suppression (respond to combination antidepressives + Ritalin) • increased activity in the anterior medial aspects of the frontal lobes - gyros rectus

• hypofrontality at rest, but normal frontal activity in intellectual stress (respond to Ritalin)

Fig. 3. Subtype II Excess of frontal-midline theta

waves.

therapy with Ritalin)

anticonvulsant)

activity). The same is visible on the row EEG record shown in Fig. 4.

specifics in ADHD children which can be summarized as:

(responds to alpha adrenergic blockers like clonidine)

homogeneous subgroups of patients based on their endophenotypic functioning, may facilitate unraveling these differential causal pathways.

Recently, a QEEG spectrum classification of ADHD population has been developed defining four main subtypes: I subtype (abnormal increase of delta-theta frequency range centrally or centrally-frontally), II subtype (abnormal increase of frontal midline theta rhythm), III subtype (abnormal increase of beta activity frontally), and IV subtype (excess of alpha activities at posterior, central, or frontal leads).

In the following, I will show (on Figs. 2-8) some our examples of QEEG spectra from different subtypes of ADHD children (Pop-Jordanova 2007, 2009; Zorcec 2007, 2008).

The first and second subtypes are characterized clinically with inattention, while in the third subtype mainly hyperactivity, impulsivity and social inadaptation are prevalent. The low attention span is also the main complain of children with alpha excess.

Among the investigated Macedonian ADHD children (over 250), very slow alpha excess (subtype 4) was showed in 25% of children, and high theta/beta ratio in frontal-central cortex (subtype 1) in other 25% of children. The majority of 48% belong to the combined 1 and 2 subtypes. Very rarely (under 2%) we found subtype III were overactive cortex is typical finding (Pop-Jordanova et al. 2007; Zorcec et al. 2007, 2008).

Fig. 2. Subtype I – High delta and theta amplitudes in frontal-central cortex

homogeneous subgroups of patients based on their endophenotypic functioning, may

Recently, a QEEG spectrum classification of ADHD population has been developed defining four main subtypes: I subtype (abnormal increase of delta-theta frequency range centrally or centrally-frontally), II subtype (abnormal increase of frontal midline theta rhythm), III subtype (abnormal increase of beta activity frontally), and IV subtype (excess of alpha

In the following, I will show (on Figs. 2-8) some our examples of QEEG spectra from

The first and second subtypes are characterized clinically with inattention, while in the third subtype mainly hyperactivity, impulsivity and social inadaptation are prevalent. The low

Among the investigated Macedonian ADHD children (over 250), very slow alpha excess (subtype 4) was showed in 25% of children, and high theta/beta ratio in frontal-central cortex (subtype 1) in other 25% of children. The majority of 48% belong to the combined 1 and 2 subtypes. Very rarely (under 2%) we found subtype III were overactive cortex is

different subtypes of ADHD children (Pop-Jordanova 2007, 2009; Zorcec 2007, 2008).

attention span is also the main complain of children with alpha excess.

typical finding (Pop-Jordanova et al. 2007; Zorcec et al. 2007, 2008).

Fig. 2. Subtype I – High delta and theta amplitudes in frontal-central cortex

facilitate unraveling these differential causal pathways.

activities at posterior, central, or frontal leads).

As can be seen on Fig. 1, in frontal and central region of brain cortex the dominant frequencies are in the range of delta and theta waves, while alpha and beta waves are practically absent.

Fig. 3. Subtype II Excess of frontal-midline theta

On Fig.3 we can see a pick of theta activity (P 6.54 Hz) in frontal-midline area as the most important finding. It is also combined with generally slow activity (lack of alpha and beta activity). The same is visible on the row EEG record shown in Fig. 4.

Both Fig.5 and 6 show over activation of the cortex expressed by the pronounced beta brain waves.

In addition to the QEEG, SPECT (Amen, 1997, Amen et al. 1998*)* shows the corresponding specifics in ADHD children which can be summarized as:


(Note fast brain waves over all cortex)

Fig. 7. Subtype IV – alpha excess

Fig. 6. Row EEG recording - overactive cortex

(Note slow delta/theta waves in frontal and central area)

#### Fig. 4. Subtype II - row EEG recording

Fig. 5. Subtype III – over activated beta in frontal, central and parietal cortex


(Note fast brain waves over all cortex)

256 Current Directions in ADHD and Its Treatment

(Note slow delta/theta waves in frontal and central area)

Fig. 5. Subtype III – over activated beta in frontal, central and parietal cortex

Fig. 4. Subtype II - row EEG recording

#### Fig. 6. Row EEG recording - overactive cortex

Fig. 7. Subtype IV – alpha excess




The N2 motor inhibition component is generated in the ipsi-lateral premotor cortex, the P3b component is generated in the parietal cortical area, and the P400 monitoring component is generated in the cingulated cortex. It is supposed that dopamine is the main mediator of the

For the psychometric assessment of the executive functions in ADHD patients most frequently we use the Stroop Color Word Task – SCWT (Stroop 1935), and Wisconsin Card

The obtained results for a group of 30 children diagnosed as combined form of ADHD, aged

T-score controls Significance

controls Significance of the test P

of the test <sup>P</sup>

7-14 years are presented on Table 1 and Table 2 (Zorcec and Pop-Jordanova, 2010).

Significance of the test

Table 1. T-score and statistical significance for WCST obtained for ADHD children

*Mistakes (St) II* 50 average 55 average 0,1 *Mistakes III* 29 Very low 50 average 0,00001\* *Mistakes III/II* 28 Very low 53 average 0,00000\* *St III-St II* 50 average 53 average 0,02

Table 2. T- score and statistical significance for SCWT obtained for ADHD children

In the electrophysiological evaluation of ADHD children we used VCPT (visual cognitive performance task) with two stimulus Go/NoGo task developed specifically for the HBI database. The task consisted of 400 trials. The duration of stimuli is equal to 100 ms. Trials consisted of presentation of a pair of stimuli with inter stimulus interval of 1.1 sec. Interval between trials is equal to 3,100 ms and response interval from 100 to 1,000 ms. Subjects were

ADHD Significance of the test T-score

*N categories* 42 Low average 55 average 0,32 *N perseverations* 31 below average 51 average 0,00001\* *N mistakes* 32 below average 50 average 0,00000\* *carts total* 30 below average 52 average 0,00001\* *M categories* 31 below average 51 average 0,00001\*

The following executive components in the Go/NoGo stimulus tasks could be obtained:

200 millisecond (the conventional N2 inhibition component)

conventional P3b component)

Sorting Test – WCST (Berg 1948).

ADHD

WCST T-score

\* statistical significance

SCWT T-score

\*statistical significance

compared with healthy control

compared with healthy control

(P400).

executive system.

Fig. 8. Subtype IV- Row recording alpha excess

The evidence is not sufficient to permit conclusions about the benefits of SPECT imaging in the diagnosis and treatment of ADHD. A significant number of published studies are focused on investigating differences in regional cerebral perfusion in response to drug therapy and on serotonin and dopamine receptor and transporter activity. These studies are only preliminary. On the other side, the risks associated with SPECT imaging include exposure to low-dose radiation which is not recommendable in children.

#### **4. Event related potentials**

As we said previously, the term "executive functions" refers to the coordination and control of motor and cognitive actions to attain specific goals. The executive control is needed for optimizing behavior. The need for an executive control mechanism has been postulated for non-routine situations requiring a supervisory system (e.g. selection of appropriate action from variety of options, inhibition of inappropriate actions, and keeping in working memory the plan of the action as well as the outcome).

The executive system comprises a complex brain system such as several cortical and sub cortical structures interconnected with each other. The cortical structures include the prefrontal areas interconnected with the corresponding thalamic nuclei. The striatum is the most important in the subcortical circuits and it is considered as a cognitive map of cortical representations of actions. Together with basal ganglia the prefrontal cortex performs executive functions associated with engagement, disengagement, monitoring operations and working memory.

These operations of executive system are reflected in event related potentials (ERPs) evoked in different paradigms like Go/NoGo, oddball and working memory tasks. In our practice with ADHD children, we use the Human Brain Institute (HBI) normative database for comparing the obtained ERPs from the patients and norms.

The following executive components in the Go/NoGo stimulus tasks could be obtained:


The N2 motor inhibition component is generated in the ipsi-lateral premotor cortex, the P3b component is generated in the parietal cortical area, and the P400 monitoring component is generated in the cingulated cortex. It is supposed that dopamine is the main mediator of the executive system.

For the psychometric assessment of the executive functions in ADHD patients most frequently we use the Stroop Color Word Task – SCWT (Stroop 1935), and Wisconsin Card Sorting Test – WCST (Berg 1948).

The obtained results for a group of 30 children diagnosed as combined form of ADHD, aged 7-14 years are presented on Table 1 and Table 2 (Zorcec and Pop-Jordanova, 2010).


\* statistical significance

258 Current Directions in ADHD and Its Treatment

The evidence is not sufficient to permit conclusions about the benefits of SPECT imaging in the diagnosis and treatment of ADHD. A significant number of published studies are focused on investigating differences in regional cerebral perfusion in response to drug therapy and on serotonin and dopamine receptor and transporter activity. These studies are only preliminary. On the other side, the risks associated with SPECT imaging include

As we said previously, the term "executive functions" refers to the coordination and control of motor and cognitive actions to attain specific goals. The executive control is needed for optimizing behavior. The need for an executive control mechanism has been postulated for non-routine situations requiring a supervisory system (e.g. selection of appropriate action from variety of options, inhibition of inappropriate actions, and keeping in working

The executive system comprises a complex brain system such as several cortical and sub cortical structures interconnected with each other. The cortical structures include the prefrontal areas interconnected with the corresponding thalamic nuclei. The striatum is the most important in the subcortical circuits and it is considered as a cognitive map of cortical representations of actions. Together with basal ganglia the prefrontal cortex performs executive functions associated with engagement, disengagement, monitoring operations and

These operations of executive system are reflected in event related potentials (ERPs) evoked in different paradigms like Go/NoGo, oddball and working memory tasks. In our practice with ADHD children, we use the Human Brain Institute (HBI) normative database for

exposure to low-dose radiation which is not recommendable in children.

Fig. 8. Subtype IV- Row recording alpha excess

memory the plan of the action as well as the outcome).

comparing the obtained ERPs from the patients and norms.

**4. Event related potentials** 

working memory.

Table 1. T-score and statistical significance for WCST obtained for ADHD children compared with healthy control


\*statistical significance

Table 2. T- score and statistical significance for SCWT obtained for ADHD children compared with healthy control

In the electrophysiological evaluation of ADHD children we used VCPT (visual cognitive performance task) with two stimulus Go/NoGo task developed specifically for the HBI database. The task consisted of 400 trials. The duration of stimuli is equal to 100 ms. Trials consisted of presentation of a pair of stimuli with inter stimulus interval of 1.1 sec. Interval between trials is equal to 3,100 ms and response interval from 100 to 1,000 ms. Subjects were

The brain rate (EEG spectrum weighted frequency) can be considered as an integral state attribute correlated to brain electric, mental and metabolic activity. In particular, it can serve as a preliminary diagnostic indicator of general mental activation (i.e. consciousness level), in addition to heart rate, blood pressure or temperature as standard indicators of general

In our research it was shown that brain rate can be used to discriminate between the groups of under-arousal (UA) and over-arousal (OA) disorders, to assess the quality of sleep, as well as to indicate the IQ changes caused by some environmental toxins (Pop-Jordanova 2009; Pop-Jordanov and Pop-Jordanova 2009, 2010). Brain rate is also suitable to reveal the patterns of sensitivity/rigidity of EEG spectrum, including frequency bands related to permeability of corresponding neuronal circuits. Based on all this findings, the individually

The main characteristic of the integral (polychromatic) EEG spectrum is its mean frequency,

*f fV <sup>f</sup> df V V <sup>f</sup> df <sup>V</sup>*

= =

*<sup>V</sup> f f <sup>P</sup> <sup>f</sup> V V V*

where, *i* denotes the frequency band (for delta *i=1*, theta *i=2*, etc.), and *Vi* - the corresponding mean amplitude of the electric potential. (Pop-Jordanova N., Pop-Jordanov J.,

In the following I will present some results obtained for brain rate (Demerdzieva, 2011) calculated for a group of 50 patients diagnosed as ADHD (age 119. 98; SD = 25.32 months, two females and 48 males) compared with a group of 50 healthy controls (mean age 117.84;

Frontal Central Posterior Left Midline Right

EC vs. VCPT 0.000000 0.007530 0.000000 0.014954 0.000000

Table 5. Summary of significant interactions between groups, conditions and regions for

EC vs. EO 0.033569 0.015872 0.000000 0.000083 0.000000 0.000014

EC vs. ACPT 0.000005 0.000196 0.000000 0.000004 0.000000 0.000089

0.000000 0.000000 0.000000 0.029686 0.000082

SD = 24.89 months, and the same gender ratio as the ADHD group) (Table 5).

== =

*i b ii i i ii i*

;

() , ()

bodily activation.

2005)

Group effect

Condition effect

Normal's vs. ADHD

EO vs. VCPT 0.000001

VCPT vs.ACPT 0.000005

brain rate results (evaluated with post hoc Bonferroni test)

adapted neurofeedback protocols can be elaborated.

weighted over the whole spectrum (brain rate - *fb*), defined as

*or*

1 *b*

instructed to press a button with index finger of their right hand as fast as possible every time when animal or angry face was followed by an animal or angry face (Go condition), respectively, and to withhold the suppressing on the other three trials (NoGo condition).

The VCPT during QEEG for ADHD children showedvery high ommission and commission errors, shorter reaction time (RT) and higher variation of the reaction time (var RT), compared with the results obtained for control healthy children.(Table 3)


\*statistical significance

Table 3. Statistical significance for VCPT forADHD children compared with tests norms

For the P3Go component (activation processes) we did not obtained significant differences for the latency as well as for the amplitude, while for P3NoGo component (inhibition processes) we obtained not significant differences for the latency, but significant differences concerned to the amplidude. (Tabl. 4)


\*statistical significance

Table 4. Statistical significance for P3Go and P3NoGo for ADHD children compared with tests norms

Generally, our research (Zorcec and Pop-Jordanova 2010) dedicated to the psychometric and electrophysiological evaluation of children diagnosed as ADHD showed significant presence of the perseverative mistakes and difficulties in the mental flexibility. The results obtained for VCPT showed significantly higher ommission and commission errors, lower reaction time (RT) as well as higher variation of time reaction (var RT) compared to the tests norms. The P3Go component values in tle latency and amplitude did not differ from the norms, but the P3NoGo component showed significant difference in the amplitude.

#### **5. Brain rate evaluation**

Reviewing the EEG studies of patients with ADHD it can be concluded that most of them have generalized or intermittent spectrum shift. It is the reason that we introduced the brain rate calculation (Pop-Jordanova and Pop-Jordanov, 2005) in addition to theta/beta ratio for both aims, in the assessment procedure and as a neurofeedback parameter.

instructed to press a button with index finger of their right hand as fast as possible every time when animal or angry face was followed by an animal or angry face (Go condition), respectively, and to withhold the suppressing on the other three trials (NoGo condition).

The VCPT during QEEG for ADHD children showedvery high ommission and commission errors, shorter reaction time (RT) and higher variation of the reaction time (var RT),

VCPT ADHD Norm *t*-test P *omission errors (Go)* 32, 25 4 15, 65 0, 00001\* *commission errors (NoGo)* 4, 75 1 7, 58 0, 0000\* *RT (ms) Go* 456, 89 486 - 9, 17 0, 00001\* *var RT* 18, 97 11, 7 8, 78 0, 00000\*

Table 3. Statistical significance for VCPT forADHD children compared with tests norms

*P3Go (ms)* 327, 15 327, 89 - 0, 12 0, 9 *P3Go (mV)* 9, 73 8, 55 0, 77 0, 44 *P3NoGo (ms)* 402, 05 415, 78 - 0, 69 0, 49 *P3NoGo (mV)* 4, 67 6, 23 - 2, 89 0, 006\*

Table 4. Statistical significance for P3Go and P3NoGo for ADHD children compared

norms, but the P3NoGo component showed significant difference in the amplitude.

both aims, in the assessment procedure and as a neurofeedback parameter.

Generally, our research (Zorcec and Pop-Jordanova 2010) dedicated to the psychometric and electrophysiological evaluation of children diagnosed as ADHD showed significant presence of the perseverative mistakes and difficulties in the mental flexibility. The results obtained for VCPT showed significantly higher ommission and commission errors, lower reaction time (RT) as well as higher variation of time reaction (var RT) compared to the tests norms. The P3Go component values in tle latency and amplitude did not differ from the

Reviewing the EEG studies of patients with ADHD it can be concluded that most of them have generalized or intermittent spectrum shift. It is the reason that we introduced the brain rate calculation (Pop-Jordanova and Pop-Jordanov, 2005) in addition to theta/beta ratio for

For the P3Go component (activation processes) we did not obtained significant differences for the latency as well as for the amplitude, while for P3NoGo component (inhibition processes) we obtained not significant differences for the latency, but significant differences

ADHD norm *t*-test p

compared with the results obtained for control healthy children.(Table 3)

\*statistical significance

\*statistical significance

**5. Brain rate evaluation** 

with tests norms

concerned to the amplidude. (Tabl. 4)

The brain rate (EEG spectrum weighted frequency) can be considered as an integral state attribute correlated to brain electric, mental and metabolic activity. In particular, it can serve as a preliminary diagnostic indicator of general mental activation (i.e. consciousness level), in addition to heart rate, blood pressure or temperature as standard indicators of general bodily activation.

In our research it was shown that brain rate can be used to discriminate between the groups of under-arousal (UA) and over-arousal (OA) disorders, to assess the quality of sleep, as well as to indicate the IQ changes caused by some environmental toxins (Pop-Jordanova 2009; Pop-Jordanov and Pop-Jordanova 2009, 2010). Brain rate is also suitable to reveal the patterns of sensitivity/rigidity of EEG spectrum, including frequency bands related to permeability of corresponding neuronal circuits. Based on all this findings, the individually adapted neurofeedback protocols can be elaborated.

The main characteristic of the integral (polychromatic) EEG spectrum is its mean frequency, weighted over the whole spectrum (brain rate - *fb*), defined as

$$f\_b = \frac{1}{V} \int fV(f)df \quad V = \oint V(f)df$$

$$or$$

$$f\_b = \sum\_i f\_i P\_i = \sum\_i f\_i \frac{V\_i}{V}; \quad V = \sum\_i V\_i$$

where, *i* denotes the frequency band (for delta *i=1*, theta *i=2*, etc.), and *Vi* - the corresponding mean amplitude of the electric potential. (Pop-Jordanova N., Pop-Jordanov J., 2005)

In the following I will present some results obtained for brain rate (Demerdzieva, 2011) calculated for a group of 50 patients diagnosed as ADHD (age 119. 98; SD = 25.32 months, two females and 48 males) compared with a group of 50 healthy controls (mean age 117.84; SD = 24.89 months, and the same gender ratio as the ADHD group) (Table 5).


Table 5. Summary of significant interactions between groups, conditions and regions for brain rate results (evaluated with post hoc Bonferroni test)

The brain rate cocept is shown to be useful in the case of ADHD adults as well (Markovska-Simoska and Pop-Jordanova, 2011). Maximum values of *fb* for sagittal topography are obtained in central region, while the minimum in frontal region, corresponding to increased arousal (which is in agreement with the neurophysiological considerations). Maximum values of *fb* for lateral topography are obtained in the left and right sides, while the minimum in the midline region, which indicates higher excitability of lateral regions. As expected, a positive correlation between *fb* values and the QEEG spectra subtypes was obtained. Lower values were found for first and second subtype, and higher for the third and fourth subtype. This can be explained by the comparable arousal level for first and second subtype, and higher arousal in the third and fourth subtype. On the other hand, there was no correlation between behavioral symptoms (obtained with Barkley's scale) and *fb* values (i.e. spectrum gravity), as well as between QEEG ADHD subtypes and behavioral

symptoms, illustrating the heterogeneous and multifactorial character of ADHD.

arousal can simply be achieved (Pop-Jordanov and Pop-Jordanova, 2009).

practice concerning brain rate it was shown that:

bodily activation.

Jordanova, 2007, 2008, 2009).

electrodermal response and neurofeedback.

(Pop-Jordanova, 2009).

**6. Biofeedback treatment** 

Generally we can summarize that through different clinical experiments and pediatric

• Brain rate may serve as an indicator of general mental arousal level, similar to heart rate (Kaniusas et al, 2007), blood pressure and temperature as standard indicators of general

• By comparing eyes closed and eyes open brain rate values the diagnoses of inner

• As a measure of arousal level, brain rate can be applied to discriminate between subgroups (clusters) of "mixed" disorders (e.g. ADHD, OCD, headache) (Pop-

• Brain rate can be more useful for selecting patients which need neurofeedback training

Most prevalent approaches in the treatment of ADHD involve the use of stimulants, occasionally supplemented by tricyclic antidepressants, alpha-blockers and, in rare cases, antipsychotic drugs or selective serotonin reuptake inhibitors. In addition, the extensive behavior management, cognitive-behavioral therapy, individual psychotherapy and family system approaches have been applied. In the last two decades, biofeedback modalities have

Biofeedback modalities can be divided into peripheral (based on electromyography, electrodermal response, heart rate, temperature, blood volume pulse) or central i.e. neurofeedback (based on electroencephalography). In what follows, we will concentrate on

Electrodermal response (EDR) is a complex reaction with a number of control centers in CNS. Three systems related to arousal, emotion and locomotion are responsible for the control of electrodermal activity (Bouscin 1992). The reticular formation controls EDR in connection to states of arousal, the limbic structures (hypothalamus, cingulated gyres and hippocampus) are involved in EDR activity related to emotional responses and thermoregulation, while the motor cortex and parts of the basal ganglia are involved in locomotion. In particular, skin potential and skin conductance used as parameters in EDR

been offered in the treatment of different conditions and diseases (Schwartz 1987).

Maximum values of *fb* for sagittal topography were obtained in central and posterior regions, which is statistically significant, F (3, 390) = 24.849, p =.00000 (Fig. 9, left panel). Anyway these results are lower than those in healthy controls. According to the different conditions, obtained results were also statistically significant, F (9, 949.31) = 72.294, p = 0.0000. Maximum values for *fb* were obtained in the posterior region during EC and minimum values in the frontal region again during EC condition for both ADHD and healthy groups..

Fig. 9. Results for fb values for sagittal topography according groups-left panel and according conditions-right panel (EC-eyes closed, EO-eyes opened, VCPT– visual continuous performance test, ACPT- auditory continuous performance test)

Maximum values of *fb* for lateral topography are obtained in the left and right sides (Fig.10, left panel), which indicates higher excitability of lateral regions. The results are again significantly higher in normal group which indicates under arousal (UA) in children with ADHD (corresponding to subtype I prevalence). According different conditions in lateral topography the lower *fb* values were obtained in midline for all four conditions (Fig.10, right panel).

Fig. 10. Results for fb values for lateral topography according groups - left panel and according conditions - right panel (EC-eyes closed, EO-eyes opened, VCPT-visual continuous performance test, ACPT- auditory continuous performance test)

The brain rate cocept is shown to be useful in the case of ADHD adults as well (Markovska-Simoska and Pop-Jordanova, 2011). Maximum values of *fb* for sagittal topography are obtained in central region, while the minimum in frontal region, corresponding to increased arousal (which is in agreement with the neurophysiological considerations). Maximum values of *fb* for lateral topography are obtained in the left and right sides, while the minimum in the midline region, which indicates higher excitability of lateral regions. As expected, a positive correlation between *fb* values and the QEEG spectra subtypes was obtained. Lower values were found for first and second subtype, and higher for the third and fourth subtype. This can be explained by the comparable arousal level for first and second subtype, and higher arousal in the third and fourth subtype. On the other hand, there was no correlation between behavioral symptoms (obtained with Barkley's scale) and *fb* values (i.e. spectrum gravity), as well as between QEEG ADHD subtypes and behavioral symptoms, illustrating the heterogeneous and multifactorial character of ADHD.

Generally we can summarize that through different clinical experiments and pediatric practice concerning brain rate it was shown that:


#### **6. Biofeedback treatment**

262 Current Directions in ADHD and Its Treatment

Maximum values of *fb* for sagittal topography were obtained in central and posterior regions, which is statistically significant, F (3, 390) = 24.849, p =.00000 (Fig. 9, left panel). Anyway these results are lower than those in healthy controls. According to the different conditions, obtained results were also statistically significant, F (9, 949.31) = 72.294, p = 0.0000. Maximum values for *fb* were obtained in the posterior region during EC and minimum values in the frontal region

Fig. 9. Results for fb values for sagittal topography according groups-left panel and according conditions-right panel (EC-eyes closed, EO-eyes opened, VCPT– visual continuous performance test, ACPT- auditory continuous performance test)

the lower *fb* values were obtained in midline for all four conditions (Fig.10, right panel).

Fig. 10. Results for fb values for lateral topography according groups - left panel and according conditions - right panel (EC-eyes closed, EO-eyes opened, VCPT-visual continuous performance test, ACPT- auditory continuous performance test)

Maximum values of *fb* for lateral topography are obtained in the left and right sides (Fig.10, left panel), which indicates higher excitability of lateral regions. The results are again significantly higher in normal group which indicates under arousal (UA) in children with ADHD (corresponding to subtype I prevalence). According different conditions in lateral topography

again during EC condition for both ADHD and healthy groups..

Most prevalent approaches in the treatment of ADHD involve the use of stimulants, occasionally supplemented by tricyclic antidepressants, alpha-blockers and, in rare cases, antipsychotic drugs or selective serotonin reuptake inhibitors. In addition, the extensive behavior management, cognitive-behavioral therapy, individual psychotherapy and family system approaches have been applied. In the last two decades, biofeedback modalities have been offered in the treatment of different conditions and diseases (Schwartz 1987).

Biofeedback modalities can be divided into peripheral (based on electromyography, electrodermal response, heart rate, temperature, blood volume pulse) or central i.e. neurofeedback (based on electroencephalography). In what follows, we will concentrate on electrodermal response and neurofeedback.

Electrodermal response (EDR) is a complex reaction with a number of control centers in CNS. Three systems related to arousal, emotion and locomotion are responsible for the control of electrodermal activity (Bouscin 1992). The reticular formation controls EDR in connection to states of arousal, the limbic structures (hypothalamus, cingulated gyres and hippocampus) are involved in EDR activity related to emotional responses and thermoregulation, while the motor cortex and parts of the basal ganglia are involved in locomotion. In particular, skin potential and skin conductance used as parameters in EDR

with a normative database for state modulation. The obtained topographic maps show covariance between all sites at relevant frequencies compared with a normative database,

Many studies of QEEG for the ADHD group confirmed an increased theta activity predominantly in frontal regions, and a decreased beta activity in comparison to normal children (Mann et al 1992). In this context increased theta/beta ratio is reported as a typical finding in ADHD children (Lubar 1991; Monastra et al. 2001; Muller, 2006). So, the typical ADHD finding can be underarousal. As we said previously, the recent cluster analysis identified several subgroups of ADHD based on different EEG topographies. As a consequence, manly four distinct subgroups of ADHD with regard to electrophysiology are

Neurofeedback treatment is particularly indicated in ADHD patients who show excessive EEG slowing in the superior frontal cortex or the midline central cortex (i.e. the first two subtypes) The most relevant neurological EEG correlate in these ADHD cases is usually assumed in the place where the highest ratio of theta/beta activity or lowest *fb* is seen, so that placement of the electrode between Cz and Fz is the best for training. We followed two consecutive treatment protocols. (1) Training to increase the SMR EEG rhythm (11 - 13 Hz) and at the same time, starting to inhibit (decrease) slow activity in the theta range (4 - 8 Hz); this approach is primarily used for the hyperactive component of ADHD. (2) Training to focus attention aiming at increasing higher beta activity (16 - 20 Hz), while training for decreasing the slow activity continued. The training is performed with 40 sessions, 60 min duration per session, one per week. To obtain stress diminishing, before neurofeedback we

Practically all neurofeedback interventions can be roughly reduced to the need of mastering flexibility in increasing or decreasing the general mental activation, i.e. mental arousal (which is somehow coupled with metabolic activity). Thereby, in practice, whenever a certain band is trained, the other bands are affected too (it may even appear that e.g. "…the changes that occurred as a result of stimulating in the alpha frequency were not in alpha but were in beta…"(Lubar 1997). Therefore, the introduced brain rate *f*b could be employed as a complementary biofeedback parameter, characterizing the whole EEG spectrum (as distinct from e.g. theta-beta ratio). The rationale is that, according to the mentioned empirical results, the EEG frequency shifts are related to mental activation / deactivation, as the main

Using brain rate as a neurofeedback parameter for a group of ADHD children (N=50 mean age 11.11 years) Pop-Jordanova et al. (2005 and 2008) obtained the shifting of the spectrum from under-arousal to normal mental arousal, and it corresponded to improved attention and cognition as well as better school performance. Thereby obtained change of brain rate (i.e. arousal level) appeared to be more realistic in respect to the changes of psychological state of children than the drastic reduction of theta/beta ratio, which appeared to be even halved.

If we introduce brain rate as a general indicator of mental arousal in ADHD example, we can see that the first two subtypes are correlated with lower brain rate, (underarousal-UA) the third subtype with higher brain rate (overarousal-OA), while the four subtype is related to excess of alpha activity and "normal" arousal state. In the first three subtypes of ADHD

the protocol for UA and OA is clear, while for the "normal" arousal it is not.

illustrating functional cortical interactions.

use peripheral biofeedback for all children.

defined (Kropotov, 2009).

objective of the treatment.

biofeedback are related to both sympathetic and parasympathetic arousal (Andreassi 2000; Mangina 1996).

Treatment by EDR biofeedback is generally based on training patients in strategies for lowering arousal and maintaining a healthful sympathetic/parasympathetic tone. Consequently, EDR biofeedback modality is a first choice for introvert persons, where high inner arousal is a typical finding and biofeedback training is supposed to lower sympathetic activity. Changes in electrodemal activity can be reliably detected within one second of stimulus presentation, often following a single event. It is important to know that electrodermal conductance precede any other signals related to neuroimaging such a positron emission tomography (PET), blood oxygen level-dependent functional magnetic resonance (BOLD), single photon emission computerized tomography (SPECT) etc. In other words, the changes of electroderamal activity can be registered before the changes obtained by the other neuroimaging techniques.

Neurofeedback (NF) i.e. EEG biofeedback refers to a specific operant-conditioning paradigm where an individual learns how to influence the electrical activity (frequency, amplitude or synchronization) of his brain. It involves teaching skills through the rewarding experience of inducing EEG changes reflected in a perceivable signal (light or sound). Neurofeedback has been shown to be particularly useful in reference to pathologies characterized by dysfunctional regulation of cortical arousal, such as epilepsy and attention deficit hyperactivity disorder (Lubar 1991, 1997; Birbaumer 1999; Mann et al. 1992; Monastra 2001; Pop-Jordanova et al 2005). Our team also used EEG biofeedback in anorectic girls (Pop-Jordanova 2000, 2003), posttraumatic stress disorder - PTSD (Pop-Jordanova 2004), headaches (Pop-Jordanova 2008), as well as for optimal school (Pop-Jordanova, Cakalaroska, 2008) and music performance (Markovska-Simoska et al. 2008).

As we explained previously, the application of spectral analysis to EEG shows that in some brain dysfunctions the EEG amplitude in certain frequency bands significantly differs from the EEG amplitude of healthy subjects. For example, a relatively large group of children with ADHD reveals an excess of the theta/beta ratio i.e. decrease of *fb* in central – frontal leads. This EEG abnormality is associated with hypo activation of frontal lobes. Neurofeedback provides a best tool for correcting such deviations from normality.

Neurofeedback is based on three scientific facts. First, EEG parameters reflect brain dysfunction in a particular disease (in the case of specific subgroup of ADHD children this means the corresponding changes of theta beta ratio and of *fb*). Second, subject can voluntarily change the state of his/her brain so that changes can be associated with increasing or decreasing the relevant parameter. Third, the brain can memorize this new state and keep it for longer time not only in lab conditions but also in other environments, such as school, home etc.

For applying neurofeedback therapy, QEEG evaluation of each patient is needed. In our studies the QEEG is obtained by standard MITSAR EEG recordings (21 electrodes), with the administration of standardized tests: eyes-open (EO), eyes-closed (EC), visual continuous performance (VCPT), auditory continuous performance (ACPT), reading test and math test. EEG data are analyzed for frequency content using the fast Fourier transformation. Statistical analysis compares subject's data with a normative database corrected for time-ofday variations. Data are also evaluated for percentage change across states and compared

biofeedback are related to both sympathetic and parasympathetic arousal (Andreassi 2000;

Treatment by EDR biofeedback is generally based on training patients in strategies for lowering arousal and maintaining a healthful sympathetic/parasympathetic tone. Consequently, EDR biofeedback modality is a first choice for introvert persons, where high inner arousal is a typical finding and biofeedback training is supposed to lower sympathetic activity. Changes in electrodemal activity can be reliably detected within one second of stimulus presentation, often following a single event. It is important to know that electrodermal conductance precede any other signals related to neuroimaging such a positron emission tomography (PET), blood oxygen level-dependent functional magnetic resonance (BOLD), single photon emission computerized tomography (SPECT) etc. In other words, the changes of electroderamal activity can be registered before the changes obtained

Neurofeedback (NF) i.e. EEG biofeedback refers to a specific operant-conditioning paradigm where an individual learns how to influence the electrical activity (frequency, amplitude or synchronization) of his brain. It involves teaching skills through the rewarding experience of inducing EEG changes reflected in a perceivable signal (light or sound). Neurofeedback has been shown to be particularly useful in reference to pathologies characterized by dysfunctional regulation of cortical arousal, such as epilepsy and attention deficit hyperactivity disorder (Lubar 1991, 1997; Birbaumer 1999; Mann et al. 1992; Monastra 2001; Pop-Jordanova et al 2005). Our team also used EEG biofeedback in anorectic girls (Pop-Jordanova 2000, 2003), posttraumatic stress disorder - PTSD (Pop-Jordanova 2004), headaches (Pop-Jordanova 2008), as well as for optimal school (Pop-Jordanova, Cakalaroska,

As we explained previously, the application of spectral analysis to EEG shows that in some brain dysfunctions the EEG amplitude in certain frequency bands significantly differs from the EEG amplitude of healthy subjects. For example, a relatively large group of children with ADHD reveals an excess of the theta/beta ratio i.e. decrease of *fb* in central – frontal leads. This EEG abnormality is associated with hypo activation of frontal lobes.

Neurofeedback is based on three scientific facts. First, EEG parameters reflect brain dysfunction in a particular disease (in the case of specific subgroup of ADHD children this means the corresponding changes of theta beta ratio and of *fb*). Second, subject can voluntarily change the state of his/her brain so that changes can be associated with increasing or decreasing the relevant parameter. Third, the brain can memorize this new state and keep it for longer time not only in lab conditions but also in other environments,

For applying neurofeedback therapy, QEEG evaluation of each patient is needed. In our studies the QEEG is obtained by standard MITSAR EEG recordings (21 electrodes), with the administration of standardized tests: eyes-open (EO), eyes-closed (EC), visual continuous performance (VCPT), auditory continuous performance (ACPT), reading test and math test. EEG data are analyzed for frequency content using the fast Fourier transformation. Statistical analysis compares subject's data with a normative database corrected for time-ofday variations. Data are also evaluated for percentage change across states and compared

Neurofeedback provides a best tool for correcting such deviations from normality.

Mangina 1996).

by the other neuroimaging techniques.

such as school, home etc.

2008) and music performance (Markovska-Simoska et al. 2008).

with a normative database for state modulation. The obtained topographic maps show covariance between all sites at relevant frequencies compared with a normative database, illustrating functional cortical interactions.

Many studies of QEEG for the ADHD group confirmed an increased theta activity predominantly in frontal regions, and a decreased beta activity in comparison to normal children (Mann et al 1992). In this context increased theta/beta ratio is reported as a typical finding in ADHD children (Lubar 1991; Monastra et al. 2001; Muller, 2006). So, the typical ADHD finding can be underarousal. As we said previously, the recent cluster analysis identified several subgroups of ADHD based on different EEG topographies. As a consequence, manly four distinct subgroups of ADHD with regard to electrophysiology are defined (Kropotov, 2009).

Neurofeedback treatment is particularly indicated in ADHD patients who show excessive EEG slowing in the superior frontal cortex or the midline central cortex (i.e. the first two subtypes) The most relevant neurological EEG correlate in these ADHD cases is usually assumed in the place where the highest ratio of theta/beta activity or lowest *fb* is seen, so that placement of the electrode between Cz and Fz is the best for training. We followed two consecutive treatment protocols. (1) Training to increase the SMR EEG rhythm (11 - 13 Hz) and at the same time, starting to inhibit (decrease) slow activity in the theta range (4 - 8 Hz); this approach is primarily used for the hyperactive component of ADHD. (2) Training to focus attention aiming at increasing higher beta activity (16 - 20 Hz), while training for decreasing the slow activity continued. The training is performed with 40 sessions, 60 min duration per session, one per week. To obtain stress diminishing, before neurofeedback we use peripheral biofeedback for all children.

Practically all neurofeedback interventions can be roughly reduced to the need of mastering flexibility in increasing or decreasing the general mental activation, i.e. mental arousal (which is somehow coupled with metabolic activity). Thereby, in practice, whenever a certain band is trained, the other bands are affected too (it may even appear that e.g. "…the changes that occurred as a result of stimulating in the alpha frequency were not in alpha but were in beta…"(Lubar 1997). Therefore, the introduced brain rate *f*b could be employed as a complementary biofeedback parameter, characterizing the whole EEG spectrum (as distinct from e.g. theta-beta ratio). The rationale is that, according to the mentioned empirical results, the EEG frequency shifts are related to mental activation / deactivation, as the main objective of the treatment.

Using brain rate as a neurofeedback parameter for a group of ADHD children (N=50 mean age 11.11 years) Pop-Jordanova et al. (2005 and 2008) obtained the shifting of the spectrum from under-arousal to normal mental arousal, and it corresponded to improved attention and cognition as well as better school performance. Thereby obtained change of brain rate (i.e. arousal level) appeared to be more realistic in respect to the changes of psychological state of children than the drastic reduction of theta/beta ratio, which appeared to be even halved.

If we introduce brain rate as a general indicator of mental arousal in ADHD example, we can see that the first two subtypes are correlated with lower brain rate, (underarousal-UA) the third subtype with higher brain rate (overarousal-OA), while the four subtype is related to excess of alpha activity and "normal" arousal state. In the first three subtypes of ADHD the protocol for UA and OA is clear, while for the "normal" arousal it is not.

Amen, DG. (1997) Brain SPECT imaging: Implication for EEG biofeedback. Keynote address

Amen DG, Hanks C, Prunella J. (2008) Preliminary evidence differentiating ADHD using brain SPECT imaging in older patients, *J Psychoactive Drugs*. 40(2):139-46. Andreassi, JL. (2000) Psychophysiology. Human behavior and physiological response. LEA,

Anokhin, AP, Heath AC, Myers E. (2006) Genetic and environmental influences on frontal

Barkley, R. (1990) Attention Deficit Hyperactivity Disorder: A Handbook for Diagnosis and

Berg, A. (1948) A simple objective technique for measuring flexibility in thinking, *J. Gen.* 

Birbaumer, N, Roberts LE, Lutzenbrger W, Rockstroh B, Elbert T. (1999) Area-specific self-

Crosbie J, Schachar R. Deficient inhibition as a marker for familial ADHD. *Am J Psychiatry*.

Culbert, TC, Kajander RL, Reaney JB. (1996) Biofeedback with children and adolescent: clinical observations and patient perspectives. *J Dev Pediatr* 17(5):342–350 Demerzieva, A. (2011) EEG spectra power characteristics of Attention Deficit Hyperactivity

Gottesman I I. and Shields J., (1972) Schizophrenia and Genetics: A Twin Study Vantage

Kaniusas, E, Varoneckas G, Alonderis A, Podlipsky A. (2007) Heart rate variability and EEG

Lubar JF. (1991) Discourse on development on EEG diagnostics and biofeedback treatment for attention deficit/hyperactivity disorders. *Biofeedback Self-Regul* 16: 201–225 Lubar JF. (1997) Neurocortical dynamics: Implications for understanding the role of 388

Mann, C., Lubar J., Zimmerman A., Miller C., and Muenchen R. (1992) Quantitative analysis

Mangina CA, Beuzeron-Mangina JH. (1996) Direct electrical stimulation of specific human brain structures and bilateral electrodermal activity. *Int J Psychophysiol* 22:1–8 Markovska-Simoska S, Pop-Jordanova N, Georgiev D. (2008) Simultaneous EEG and EMG

Markovska-Simoska S., Pop-Jordanova N. (2011) Quantitative EEG Spectrum-weighted

Monastra VJ, Lubar JF, Linden M. (2001) The development of quantitative

disorder: reliability and validity studies. *Neuropsychology* 15(1):136–144

Kropotov, J. (2009) Quantitative EEG, ERP's and neurotherapy. Elsevier, Amsterdam

Point. Personality and Psychopathology Series, New York and London, Academic

during sleep using spectrum-weighted frequencies – a case study, COST B27.

neurofeedback and related techniques for the enhancement of attention. *Appl* 

of EEG in boys with attention-deficit/hyperactivity disorder: A controlled study

Frequency (Brain Rate) Distribution in Adults with ADHD, *CNS spectr* 16 (5): 579-587

electroencephalographic scanning process for attention deficit-hyperactivity

regulation of slow cortical potential on the sagittal midline and its effects on

EEG asymmetry: A twin study. *Biol. Psychol*. 71 (3): 289-295

behavior. *Electroencephalogr Clin Neurophysiol* 84:352–361

Boucsein, W. (1992) Electrodermal activity. Plenum, New York.

Disorder in childhood, *Epilepsija:* 124-136

*Psychophysiol Biofeedback* 22(2):111–126

with clinical implications. *Paediatric Neurology*, 8: 30-36

biofeedback for peak performance in musician. *Prilozi* 1:239–253

presented at 28th annual meeting of the Association for Applied Psychophysiology

**8. References** 

London.

*Psychol.* 39: 15-22

2001; 158:1884-1890.

EU/ESF, Brussels.

Press.

and Biofeedback, San Diego, CA.

Treatment, Guilford Press, New York

The detailed analysis of QEEG after the neurofeedback training with brain rate as a parameter could detect which bands have been most changed. For instance, in some cases shifting the brain rate to higher values could result in increasing high alpha or beta frequencies; in other, the same change can appear due to diminishing the power of theta or delta bands. As a result, the QEEG comparison before and after the brain rate training can be informative for assessing the individual spectrum band sensitivity.

### **7. Conclusions**

The frontal regions are most likely to show deviations from normal development in the case of ADHD, with disturbed thalamo-cortical and septal-hippocampal pathways, altogether named as executive system.

Application of spectral analysis to EEG shows that the EEG amplitude in certain frequency bands significantly differs from the EEG amplitude of healthy subjects. For example, a relatively large group of children with ADHD reveals an excess of the theta/beta ratio i.e. decrease of brain rate in central – frontal leads. This EEG abnormality is mostly associated with under activation of frontal lobes.

For the exact diagnosis of ADHD, it is recommendable QEEG to be combined with DSM-IV (or ICD-10) behavioral based approach.

There are four main endophenotypes in ADHD population: I subtype (abnormal increase of delta-theta frequency range centrally or centrally-frontally), II subtype (abnormal increase of frontal midline theta rhythm), III subtype (abnormal increase of beta activity frontally), and IV subtype (excess of alpha activities at posterior, central, or frontal leads). Thereby, we consider the second subtype as a subgroup of the first subtype.

The dysfunction of the executive system can be evaluated by event related potentials (ERP's) as well. Children with ADHD showed significant presence of the perseverative mistakes and difficulties in the mental flexibility. The results obtained for visual cognitive performance test (VCPT) showed significantly higher ommission and commission errors, lower reaction time (RT) as well as higher variation of time reaction (var RT) compared to the tests norms.

Distribution of brain rate values for sagittal and lateral topographies reflects the arousal levels in the corresponding conditions. There is a positive correlation between brain rate values and the QEEG spectra subtypes.

Neurofeedback provides a tool for correcting deviations from normality, especially for the subtypes I, II and III. For obtaining better therapeutic results, before neurofeedback, the use of peripheral biofeedback (such as electrodermal activity) aiming to obtain stabilization of sympathetic/parasympathetic system is recommended for all patients.

Brain rate can be used as a multiband biofeedback parameter in mediating the under arousal or over arousal states, complementary to few-band parameters and the skin conduction

Follow-up research is needed in order to determine more precisely the specificity and sensitivity of QEEG and brain rate approach related to neurophysical substrates of ADHD.

#### **8. References**

266 Current Directions in ADHD and Its Treatment

The detailed analysis of QEEG after the neurofeedback training with brain rate as a parameter could detect which bands have been most changed. For instance, in some cases shifting the brain rate to higher values could result in increasing high alpha or beta frequencies; in other, the same change can appear due to diminishing the power of theta or delta bands. As a result, the QEEG comparison before and after the brain rate training can

The frontal regions are most likely to show deviations from normal development in the case of ADHD, with disturbed thalamo-cortical and septal-hippocampal pathways, altogether

Application of spectral analysis to EEG shows that the EEG amplitude in certain frequency bands significantly differs from the EEG amplitude of healthy subjects. For example, a relatively large group of children with ADHD reveals an excess of the theta/beta ratio i.e. decrease of brain rate in central – frontal leads. This EEG abnormality is mostly associated

For the exact diagnosis of ADHD, it is recommendable QEEG to be combined with DSM-IV

There are four main endophenotypes in ADHD population: I subtype (abnormal increase of delta-theta frequency range centrally or centrally-frontally), II subtype (abnormal increase of frontal midline theta rhythm), III subtype (abnormal increase of beta activity frontally), and IV subtype (excess of alpha activities at posterior, central, or frontal leads). Thereby, we

The dysfunction of the executive system can be evaluated by event related potentials (ERP's) as well. Children with ADHD showed significant presence of the perseverative mistakes and difficulties in the mental flexibility. The results obtained for visual cognitive performance test (VCPT) showed significantly higher ommission and commission errors, lower reaction time (RT) as well as higher variation of time reaction (var RT) compared to

Distribution of brain rate values for sagittal and lateral topographies reflects the arousal levels in the corresponding conditions. There is a positive correlation between brain rate

Neurofeedback provides a tool for correcting deviations from normality, especially for the subtypes I, II and III. For obtaining better therapeutic results, before neurofeedback, the use of peripheral biofeedback (such as electrodermal activity) aiming to obtain stabilization of

Brain rate can be used as a multiband biofeedback parameter in mediating the under arousal or over arousal states, complementary to few-band parameters and the skin conduction

Follow-up research is needed in order to determine more precisely the specificity and sensitivity of QEEG and brain rate approach related to neurophysical substrates of

be informative for assessing the individual spectrum band sensitivity.

consider the second subtype as a subgroup of the first subtype.

sympathetic/parasympathetic system is recommended for all patients.

**7. Conclusions** 

the tests norms.

ADHD.

named as executive system.

with under activation of frontal lobes.

(or ICD-10) behavioral based approach.

values and the QEEG spectra subtypes.


**14** 

*Iran* 

Mohammad Ali Nazari *University of Tabriz, Tabriz,* 

**EEG Findings in ADHD and the Application** 

**of EEG Biofeedback in Treatment of ADHD** 

As defined in the 4th edition of Diagnostic and Statistical Manual of Mental Disorders (American Psychiatric Association, 1994), Attention deficit hyperactivity disorder (ADHD) is characterized by a persistent pattern of inattention, hyperactivity, and impulsiveness, though it can present with or without hyperactivity. ADHD is the most common childhood mental health disorder, with an estimated prevalence of 7% to 10% in boys and 3% in girls aged 4-11 years (Sgrok et al., 2000). This disorder substantially affects the individual's normal cognitive and behavioral functioning. For example, children with ADHD can have a great deal of difficulty focusing on lessons presented by their teachers and remembering how to do their homework. They may often be easily distracted whereby they pay attention

The numerous studies support a model that defines ADHD as an inherited disorder whose core symptoms are founded in neuroanatomic, neurochemical, and neurophysiologic abnormalities of the brain (Monastra, 2005). Deficits associated with ADHD support a hypothesis that anatomical and biochemical abnormalities of the prefrontal cortex constitute the physical basis of this disorder (Barkley, 1997). In this line, neurodiagnostic procedures (e.g., positron emission tomography [PET], single photon emission tomography [SPECT] and magnetic resonance imaging [MRI]) studies have provided evidence of the neurological basis of ADHD (Boutros, et al., 2009). Nevertheless, new theories on the pathogenesis of psychopathological phenomena conceptualize as a consequence of the failure to integrate the activity of different brains' areas (Boutros et al., 2009). It needs techniques tapping the dynamics of complex interaction over time among cerebral regions involved in the

Electrophysiological techniques enable monitoring brain processing in real time, providing the best methods to describe the time course of brain electrical activities. Growth of this field came from the newer and quantifiable techniques such as quantitative electroencephalography (QEEG). QEEG methods provide a set of non-invasive tools that are capable of quantitatively assessing resting and evoked activity of the brain with sensitivity and temporal resolution

QEEG studies have explored brainwave profile in children with ADHD, compared to normal children. These brainwaves could be trained via operant conditioning (called EEG

superior to those of any other imaging methods (Hughes & John, 1999).

**1. Introduction** 

to other things than what they should.

integration of cognitive processing.

