**5.1. General description of the simulator**

In this section, we will present simulation results that are valuable to better understand some mechanisms underlying the conditioning of muscle activity discussed previously in this chapter. The simulations were carried out in a multi-scale web-based neuromuscular simulator (dubbed ReMoto) that is freely accessible at http://remoto.leb.usp.br. A complete description of the simulator may be found elsewhere [4, 5]. Briefly, the simulator provides a detailed modeling of four spinal motor nuclei that command leg muscles responsible for ankle extension (SO; medial gastrocnemius - MG; lateral gastrocnemius - LG) and ankle flexion (TA). Each nucleus encompasses a MN pool and spinal INs mediating recurrent inhibition (by means of Renshaw cells), RI (by means of inhibitory Ia INs that receive inputs from antagonist muscles), and Ib inhibition. Individual spinal neurons are modeled following biophysical data from both cat MNs and INs, including active ionic channels responsible for the genesis of action potentials (sodium and fast potassium) and afterhyperpolarization (slow potassium). MN dendrites have an L-type calcium channel yielding a persistent inward current that is activated by the presence of neuromodulators in the spinal cord [57]. Ia and Ib afferents are present in ReMoto so as to allow studies on spinal reflexes (e.g., H-reflex) generated by electrical stimulation applied to a nerve (PTN for SO, LG and MG; CPN for TA). Model parameter values (e.g., axon conduction velocity, ionic channel time constants, maximum synaptic conductan‐ ces) and default numbers of elements (i.e. spinal neurons and afferents) are based on experi‐ mental data from cats or humans. Some of the parameter values were adjusted so that the dynamic behavior of an individual model matches those experimentally observed in cats or humans, for example, MN frequency-current (*f-I*) curves, post-synaptic potentials time course, and IN discharge patterns.

The MN pool drives muscle units, which generate both electrical (MUAPs) and mechanical activity (force twitches). For each muscle, one output is the EMG, expressed as the sum of all MUAPs, and the other output is force, being the sum of the twitches of all muscle units. Muscle twitches are modeled as the impulse responses of second-order critically-damped systems [58]. MUAPs occurring at the muscle surface are modeled by first- and second-order Hermite-Rodriguez functions [59], which are randomly attributed to each MU. MUAP amplitude and durations are chosen to match intramuscular MUAPs recorded from humans. To model the MUAP recorded by bipolar surface electrodes at the muscle's surface, each intramuscular signal is re-scaled depending on the MU positioning within the muscle cross-section [60], thus representing the spatio-temporal filtering due to the volume conductor (see section 2.2). A white Gaussian noise is added to the resultant surface EMG and this signal is band-pass filtered to mimic a real EMG signal recorded in experiments.

Volitional muscle control is represented by the generation of random trains of action potentials in the DTs, which are modeled by independent nonhomogeneous renewal point processes with Gamma-distributed ISIs. The instantaneous firing rate or the ISI of these point processes can be modulated by mathematical functions (e.g., sinusoid and ramp) in order to generate dynamic motor behaviors, such as rhythmic muscle activity.

Recently, a detailed muscle spindle model was added to the simulator's structure, so that stretch reflex responses can be studied with the simulator [61]. This model (fully described in [62]) represents the nonlinear dynamics of three intrafusal muscle fibers (bag 1, bag 2 and chain). The combination of the fibers' tensions yields the instantaneous activity of the Ia and II afferents. Each intrafusal fiber has an active element, which represents the static and dynamic fusimotor activity coming from gamma MNs. A single muscle spindle model lies in parallel with each muscle model so that muscle stretch and stretch velocity modulate intrafusal fiber tension and consequently the afferent activity. Primary (Ia) and secondary (II) afferent activities are translated into spike trains that are transmitted to the spinal cord through an ensemble of peripheral nerve axons with an associated distribution of conduction velocities (type II afferents are at the moment available only in a downloadable version at the website). In order to represent the ISI variability observed in afferent axons [63], each spike train is represented by a non-homogeneous renewal point process with Gamma-distributed ISIs, whose intensity is modulated by the correspondent muscle spindle output (i.e., Ia or II). In addition, a linear recruitment of afferents is adopted so that during low afferent activity only a small fraction of afferents are discharging and the increase in afferent activity (from muscle spindle model) results in the recruitment of additional afferent axons.

#### *5.1.1. Simulated H and T reflexes*

H and T reflexes can be studied in ReMoto by activating (electrically or mechanically) the monosynaptic pathway encompassing Ia afferents, MN pool, and muscle (including the spindle). Oligosynaptic pathways [23] that may contribute to the H and T reflexes are not yet available in the simulator. Due to its multi-scale structure one may evaluate neu‐ rophysiological mechanisms and test hypotheses that are unfeasible with human experi‐ ments. Recent results [64] of conditioning effects on H and T reflexes are presented below, with emphasis on RI, which is an important inhibitory pathway associated with the control of movements [65, 66].

**Figure 15.** Schematic diagram of the neuromuscular system used to simulate H-reflex, T-reflex and V-wave of the SO muscle. An electrical pulse with appropriate amplitude delivered to the PTN elicits a direct M-wave and a test reflex (Hreflex), which can be observed in the simulated EMG. Similarly, the V-wave can be generated after a supramaximal stimulus delivered to the PTN during a sustained voluntary contraction evoked by the activity of DTs. Test T reflexes can be observed in the EMG after the application of an idealized SO muscle stretch (ΔL) that evokes a phasic response of muscle spindles and a burst of firing in Ia afferents. To simulate a conditioned H- or T-reflex due to RI, the antago‐ nist CPN was stimulated with a CT interval equal to -3ms. For the T-reflex, an additional 7ms interval was added to

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Test T-reflexes can be simulated by applying an idealized stretch (10ms triangular-shaped stretch) to muscle fibers (see the schematic in Figure 15 and the time course in the lower panel of Figure 16b) in order to evoke a response in the muscle spindle model, which reflexively activates the spinal MNs by means of Ia afferents. The upper panel in Figure 16b shows the Treflex generated with amplitude similar to the H-reflex described in the paragraph above (~25%MMAX). It is worth noting that in these simulations a similar number of spinal MNs were recruited by the afferent volley evoked by the electrical (H-reflex) and mechanical stimulus (T-reflex), suggesting that despite the asynchronous discharge in Ia fibers during the T-reflex (see Ia afferent discharges in Figure 16b; [20]), the excitatory post-synaptic effect is similar between the electrically- and mechanically-evoked reflexes. A remarkable difference between these reflexes is the latency in which each wave is observed in the simulated EMG. The T-reflex

account for the difference in reflex latencies.

The friendly interface of ReMoto allows the easy set up of H- and T-reflex simulations using the structure depicted in Figure 15. The SO motor nucleus encompasses 900 type-specified MNs (800 S-type, 50 FR-type, and 50 FF-type), which receives synaptic contacts from Ia afferents (400 with 90% connectivity) of the PTN. In order to generate test H-reflexes, electrical stimuli (1ms rectangular pulses) are delivered at the nerve in a point equivalent to the popliteal fossa (0.66m from the spinal cord and 0.14m to the muscle end-plate). Figure 16a shows the M-wave and H-reflex generated by a 13mA stimulus without conditioning, as well as the discharge times of Ia afferents and MUs that were recruited directly by the electrical stimulus (early recruited MUs) and reflexively by Ia-to-MN excitation, respectively.

Experimental and Simulated EMG Responses in the Study of the Human Spinal Cord http://dx.doi.org/10.5772/54870 77

Volitional muscle control is represented by the generation of random trains of action potentials in the DTs, which are modeled by independent nonhomogeneous renewal point processes with Gamma-distributed ISIs. The instantaneous firing rate or the ISI of these point processes can be modulated by mathematical functions (e.g., sinusoid and ramp) in order to generate

Recently, a detailed muscle spindle model was added to the simulator's structure, so that stretch reflex responses can be studied with the simulator [61]. This model (fully described in [62]) represents the nonlinear dynamics of three intrafusal muscle fibers (bag 1, bag 2 and chain). The combination of the fibers' tensions yields the instantaneous activity of the Ia and II afferents. Each intrafusal fiber has an active element, which represents the static and dynamic fusimotor activity coming from gamma MNs. A single muscle spindle model lies in parallel with each muscle model so that muscle stretch and stretch velocity modulate intrafusal fiber tension and consequently the afferent activity. Primary (Ia) and secondary (II) afferent activities are translated into spike trains that are transmitted to the spinal cord through an ensemble of peripheral nerve axons with an associated distribution of conduction velocities (type II afferents are at the moment available only in a downloadable version at the website). In order to represent the ISI variability observed in afferent axons [63], each spike train is represented by a non-homogeneous renewal point process with Gamma-distributed ISIs, whose intensity is modulated by the correspondent muscle spindle output (i.e., Ia or II). In addition, a linear recruitment of afferents is adopted so that during low afferent activity only a small fraction of afferents are discharging and the increase in afferent activity (from muscle

H and T reflexes can be studied in ReMoto by activating (electrically or mechanically) the monosynaptic pathway encompassing Ia afferents, MN pool, and muscle (including the spindle). Oligosynaptic pathways [23] that may contribute to the H and T reflexes are not yet available in the simulator. Due to its multi-scale structure one may evaluate neu‐ rophysiological mechanisms and test hypotheses that are unfeasible with human experi‐ ments. Recent results [64] of conditioning effects on H and T reflexes are presented below, with emphasis on RI, which is an important inhibitory pathway associated with

The friendly interface of ReMoto allows the easy set up of H- and T-reflex simulations using the structure depicted in Figure 15. The SO motor nucleus encompasses 900 type-specified MNs (800 S-type, 50 FR-type, and 50 FF-type), which receives synaptic contacts from Ia afferents (400 with 90% connectivity) of the PTN. In order to generate test H-reflexes, electrical stimuli (1ms rectangular pulses) are delivered at the nerve in a point equivalent to the popliteal fossa (0.66m from the spinal cord and 0.14m to the muscle end-plate). Figure 16a shows the M-wave and H-reflex generated by a 13mA stimulus without conditioning, as well as the discharge times of Ia afferents and MUs that were recruited directly by the electrical stimulus

(early recruited MUs) and reflexively by Ia-to-MN excitation, respectively.

dynamic motor behaviors, such as rhythmic muscle activity.

76 Electrodiagnosis in New Frontiers of Clinical Research

spindle model) results in the recruitment of additional afferent axons.

*5.1.1. Simulated H and T reflexes*

the control of movements [65, 66].

**Figure 15.** Schematic diagram of the neuromuscular system used to simulate H-reflex, T-reflex and V-wave of the SO muscle. An electrical pulse with appropriate amplitude delivered to the PTN elicits a direct M-wave and a test reflex (Hreflex), which can be observed in the simulated EMG. Similarly, the V-wave can be generated after a supramaximal stimulus delivered to the PTN during a sustained voluntary contraction evoked by the activity of DTs. Test T reflexes can be observed in the EMG after the application of an idealized SO muscle stretch (ΔL) that evokes a phasic response of muscle spindles and a burst of firing in Ia afferents. To simulate a conditioned H- or T-reflex due to RI, the antago‐ nist CPN was stimulated with a CT interval equal to -3ms. For the T-reflex, an additional 7ms interval was added to account for the difference in reflex latencies.

Test T-reflexes can be simulated by applying an idealized stretch (10ms triangular-shaped stretch) to muscle fibers (see the schematic in Figure 15 and the time course in the lower panel of Figure 16b) in order to evoke a response in the muscle spindle model, which reflexively activates the spinal MNs by means of Ia afferents. The upper panel in Figure 16b shows the Treflex generated with amplitude similar to the H-reflex described in the paragraph above (~25%MMAX). It is worth noting that in these simulations a similar number of spinal MNs were recruited by the afferent volley evoked by the electrical (H-reflex) and mechanical stimulus (T-reflex), suggesting that despite the asynchronous discharge in Ia fibers during the T-reflex (see Ia afferent discharges in Figure 16b; [20]), the excitatory post-synaptic effect is similar between the electrically- and mechanically-evoked reflexes. A remarkable difference between these reflexes is the latency in which each wave is observed in the simulated EMG. The T-reflex is shifted by approximately 7ms with respect to latency of the H-reflex, which represents the conduction time between the point of mechanical (muscle tendon) and electrical (popliteal fossa) stimulations [44, 45] (see also the vertical line in Figure 13b for experimental data).

tivity between Ia afferents and IaINs was set at 100%, while a 20% connectivity was adopted in the IaINs-to-MNs pathway. Similarly to experimental studies, a CT interval equal to -3ms was adopted for the H-reflex simulations (i.e. the conditioning stimulus was delivered 3ms before the test stimulus). To account for the difference in reflex latencies, 7ms was added to

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Top panels in Figure 17 (a and b) show the EMGs of the SO muscle for a control condition (red curves) and when a conditioning stimulus was applied to the CPN (black curves). RI reduced the H-reflex amplitude by ~40% of its control value (Figure 17a), whereas the amount of inhibition observed in T-reflex was ~53% of its control value (Figure 17b). This difference was not statistically significant (*t*-Student test; *p* > 0.05; *n* = 5), supporting the hypothesis that the

**Figure 17.** Conditioning effects of the reciprocal inhibition (RI) on H and T reflexes. Data based on [64], but figures are unpublished. **a)** Simulated SO EMGs showing M waves and the H reflexes evoked with (black curves) and without (red curves) a conditioning stimulus delivered to the CPN (five repetitions for each condition). **b)** The same as **a** but for T reflexes. **c)** Raster plots of MU discharges at the muscle end-plate for a single simulation of H-reflex in a control condi‐ tion (left-side graph) and with a conditioning stimulus delivered to the CPN (right-side graph). **d)** The same as **c** but for a single simulation of T-reflex. **e)** Membrane potential time course of a single MN during a H-reflex simulation. The left-side graph shows an action potential generated in a control condition, whereas the right-side graph shows the post-synaptic potentials observed when a conditioning stimulus is delivered to the CPN. **f)** The same as **e** but for a single MN during a T-reflex simulation. The zero in all displayed abscissas indicates the moment when the stimulus

the CT interval in T-reflex simulations [44, 45].

(either electrical or mechanical) was delivered.

post-synaptic effect is similar in both H and T reflexes [45].

**Figure 16.** Simulated H and T reflexes (data based on [64], but figures are unpublished). **a)** From top to bottom: SO EMG showing the M-wave and the test H-reflex; raster plots of MU discharges at the muscle end-plate; raster plots of Ia afferent discharges at the popliteal fossa; and electrical stimulus delivered to the PTN at the popliteal fossa. **b)** From top to bottom: SO EMG showing the test T-reflex; raster plots of MU discharges at the muscle end-plate; raster plots of Ia afferent discharges at the popliteal fossa; and idealized mechanical stimulus (normalized to the optimal muscle length, *L0* ) delivered directly to the muscle spindle model. In both simulations, Ia afferents had a random low-frequen‐ cy background discharge, which is compatible with the experimental data recorded from humans [63, 67]. Differences in latencies between the H- and T-reflex are due to the different places of stimulus application.

#### *5.1.2. Conditioning effects from the activation of the reciprocal inhibition pathway*

In this set of simulations, we have evaluated the conditioning effects of the RI pathway on the amplitude of H and T reflexes. Test reflexes were evoked as described in the preceding section; nevertheless, a conditioning stimulus was applied to the CPN, which innervates the antagonist muscle (see schematic in Figure 15), in order to elicit an afferent volley to the inhibitory Ia INs (IaINs) that make inhibitory synapses on the SO MN pool. The stimulus amplitude (1ms duration) delivered to the CPN was 1.1MT (i.e. 10% above the MT). In addition, the connec‐ tivity between Ia afferents and IaINs was set at 100%, while a 20% connectivity was adopted in the IaINs-to-MNs pathway. Similarly to experimental studies, a CT interval equal to -3ms was adopted for the H-reflex simulations (i.e. the conditioning stimulus was delivered 3ms before the test stimulus). To account for the difference in reflex latencies, 7ms was added to the CT interval in T-reflex simulations [44, 45].

is shifted by approximately 7ms with respect to latency of the H-reflex, which represents the conduction time between the point of mechanical (muscle tendon) and electrical (popliteal fossa) stimulations [44, 45] (see also the vertical line in Figure 13b for experimental data).

78 Electrodiagnosis in New Frontiers of Clinical Research

**Figure 16.** Simulated H and T reflexes (data based on [64], but figures are unpublished). **a)** From top to bottom: SO EMG showing the M-wave and the test H-reflex; raster plots of MU discharges at the muscle end-plate; raster plots of Ia afferent discharges at the popliteal fossa; and electrical stimulus delivered to the PTN at the popliteal fossa. **b)** From top to bottom: SO EMG showing the test T-reflex; raster plots of MU discharges at the muscle end-plate; raster plots of Ia afferent discharges at the popliteal fossa; and idealized mechanical stimulus (normalized to the optimal muscle

In this set of simulations, we have evaluated the conditioning effects of the RI pathway on the amplitude of H and T reflexes. Test reflexes were evoked as described in the preceding section; nevertheless, a conditioning stimulus was applied to the CPN, which innervates the antagonist muscle (see schematic in Figure 15), in order to elicit an afferent volley to the inhibitory Ia INs (IaINs) that make inhibitory synapses on the SO MN pool. The stimulus amplitude (1ms duration) delivered to the CPN was 1.1MT (i.e. 10% above the MT). In addition, the connec‐

in latencies between the H- and T-reflex are due to the different places of stimulus application.

*5.1.2. Conditioning effects from the activation of the reciprocal inhibition pathway*

) delivered directly to the muscle spindle model. In both simulations, Ia afferents had a random low-frequen‐ cy background discharge, which is compatible with the experimental data recorded from humans [63, 67]. Differences

length, *L0*

Top panels in Figure 17 (a and b) show the EMGs of the SO muscle for a control condition (red curves) and when a conditioning stimulus was applied to the CPN (black curves). RI reduced the H-reflex amplitude by ~40% of its control value (Figure 17a), whereas the amount of inhibition observed in T-reflex was ~53% of its control value (Figure 17b). This difference was not statistically significant (*t*-Student test; *p* > 0.05; *n* = 5), supporting the hypothesis that the post-synaptic effect is similar in both H and T reflexes [45].

**Figure 17.** Conditioning effects of the reciprocal inhibition (RI) on H and T reflexes. Data based on [64], but figures are unpublished. **a)** Simulated SO EMGs showing M waves and the H reflexes evoked with (black curves) and without (red curves) a conditioning stimulus delivered to the CPN (five repetitions for each condition). **b)** The same as **a** but for T reflexes. **c)** Raster plots of MU discharges at the muscle end-plate for a single simulation of H-reflex in a control condi‐ tion (left-side graph) and with a conditioning stimulus delivered to the CPN (right-side graph). **d)** The same as **c** but for a single simulation of T-reflex. **e)** Membrane potential time course of a single MN during a H-reflex simulation. The left-side graph shows an action potential generated in a control condition, whereas the right-side graph shows the post-synaptic potentials observed when a conditioning stimulus is delivered to the CPN. **f)** The same as **e** but for a single MN during a T-reflex simulation. The zero in all displayed abscissas indicates the moment when the stimulus (either electrical or mechanical) was delivered.

Approximately the same number of spinal MNs was de-recruited by the RI in both reflexes (see Figure 17c and d), with a more pronounced effect on high-threshold neurons. This finding (which is readily accessible in the simulator, but not in human experiments) can be explained by the higher input conductance of these cells, which yield smaller compound excitatory postsynaptic potentials (EPSP). Hence, these cells will be operating near their firing thresholds, which means that they will be more easily de-recruited by the arrival of small IPSPs (see rightside graphs in Figure 17e and f). Another result that is unique to the simulations is the recording of intracellular membrane potentials. In the lower panels of Figure 17 (e and f), the membrane potential of a single MN is shown. In a control condition (i.e. without conditioning), this MN is recruited by both electrically- and mechanically-evoked synaptic volleys (left-side graphs). Similarly, the arrival of an IPSP is effective in de-recruiting this MN in both reflexes (right-side graphs), suggesting that RI has a similar effect on these responses. In addition, the compound EPSP observed in the MN soma has a similar time course for both reflexes, reinforcing the hypothesis that post-synaptic effects are similar between H- and T-reflexes [45].

### **5.2. Simulated V-wave**

As described in section 4.4, the V-wave is believed to reflect the level of the efferent drive maintained by a voluntary command. To test this hypothesis, we have used the neuromuscular simulator described above to generate V waves in the SO muscle [68]. The structure depicted in Figure 15 (with exception of the conditioning stimulus) was also used in this simulation, with the MN pool encompassing 900 type-identified MNs and 100 independent axons representing the DTs. The spike train associated to each DT axon was modeled as Poisson point processes with a given mean intensity and the connectivity between DTs and the MN pool was fixed at 30%. At time 1s, a supramaximal electrical stimulus was delivered to the PTN evoking an MMAX and subsequently a V-wave. Changes in V-wave amplitude (normalized with respect to the MMAX) were evaluated by changes in the mean ISI of DTs, mimicking the neuronal plasticity that is supposed to occur after training [52, 55, 56].

**Figure 18.** V-waves (arrows) preceded by M-waves in ReMoto simulation of SO EMG (upper panels). Raster plots of MN spikes (lower panels). **a)** Lower-intensity descending drive. **b)** Higher intensity descending drive. Data based on

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81

In this chapter we have discussed several aspects regarding the use of surface EMG in a variety of human neurophysiology protocols. Different conditioning effects on the interference pattern and phasic responses, which can be used to infer spinal cord mechanisms, were presented and discussed. Finally, in order to refine the understanding of some underlying mechanisms involved in motor control, as well as to facilitate the interpretation of EMG data, we have introduced a comprehensive web-based simulator of the neuromuscular system with openaccess and a friendly interface. The simulation results can be used to test hypothesis raised from the analysis of experimental data and to propose new questions to be addressed in different experimental protocols. The techniques and models presented here might be useful for researchers/clinicians who intend to conduct experiments on both healthy subjects and

The authors are grateful to CNPq (Brazilian Science Foundation), FAPESP (São Paulo Research Foundation) and Canadian Bureau for International Education (PDRF Program) for their

[68].

**6. Conclusion**

patients with neuromuscular disorders.

**Acknowledgements**

financial support.

Figure 18 shows the simulated SO EMG (upper panels) for two different intensities of the descending drive (mean ISI equal to 3.8ms in Figure 18a and 3ms in Figure 18b), which were chosen to match the ratio V/MMAX observed in the literature [52]. The increase in the ratio reflects the increase in the number of active MNs (from 233 to 426; see lower panels in Figure 18), which roughly corresponded to the number of MNs discharging before the electrical stimulation. This information cannot be accessed in human experiments, emphasizing, therefore, the relevance of mathematical modeling and computer simulations.

The reader can notice that the background EMG activity before the stimulus delivery is slightly different between the two simulated conditions. However, the interference pattern is more susceptible to nonlinear summation and cancellation of MUAPs. Therefore, the V-wave may be a more reproducible and reliable measure of the efferent drive, which can increase or decrease due to different factors, e.g, neuronal plasticity following training and hyperexcitability of spinal MNs following neurological diseases, such as stroke and amyotrophic lateral sclerosis [54, 56].

Experimental and Simulated EMG Responses in the Study of the Human Spinal Cord http://dx.doi.org/10.5772/54870 81

**Figure 18.** V-waves (arrows) preceded by M-waves in ReMoto simulation of SO EMG (upper panels). Raster plots of MN spikes (lower panels). **a)** Lower-intensity descending drive. **b)** Higher intensity descending drive. Data based on [68].

## **6. Conclusion**

Approximately the same number of spinal MNs was de-recruited by the RI in both reflexes (see Figure 17c and d), with a more pronounced effect on high-threshold neurons. This finding (which is readily accessible in the simulator, but not in human experiments) can be explained by the higher input conductance of these cells, which yield smaller compound excitatory postsynaptic potentials (EPSP). Hence, these cells will be operating near their firing thresholds, which means that they will be more easily de-recruited by the arrival of small IPSPs (see rightside graphs in Figure 17e and f). Another result that is unique to the simulations is the recording of intracellular membrane potentials. In the lower panels of Figure 17 (e and f), the membrane potential of a single MN is shown. In a control condition (i.e. without conditioning), this MN is recruited by both electrically- and mechanically-evoked synaptic volleys (left-side graphs). Similarly, the arrival of an IPSP is effective in de-recruiting this MN in both reflexes (right-side graphs), suggesting that RI has a similar effect on these responses. In addition, the compound EPSP observed in the MN soma has a similar time course for both reflexes, reinforcing the

hypothesis that post-synaptic effects are similar between H- and T-reflexes [45].

plasticity that is supposed to occur after training [52, 55, 56].

As described in section 4.4, the V-wave is believed to reflect the level of the efferent drive maintained by a voluntary command. To test this hypothesis, we have used the neuromuscular simulator described above to generate V waves in the SO muscle [68]. The structure depicted in Figure 15 (with exception of the conditioning stimulus) was also used in this simulation, with the MN pool encompassing 900 type-identified MNs and 100 independent axons representing the DTs. The spike train associated to each DT axon was modeled as Poisson point processes with a given mean intensity and the connectivity between DTs and the MN pool was fixed at 30%. At time 1s, a supramaximal electrical stimulus was delivered to the PTN evoking an MMAX and subsequently a V-wave. Changes in V-wave amplitude (normalized with respect to the MMAX) were evaluated by changes in the mean ISI of DTs, mimicking the neuronal

Figure 18 shows the simulated SO EMG (upper panels) for two different intensities of the descending drive (mean ISI equal to 3.8ms in Figure 18a and 3ms in Figure 18b), which were chosen to match the ratio V/MMAX observed in the literature [52]. The increase in the ratio reflects the increase in the number of active MNs (from 233 to 426; see lower panels in Figure 18), which roughly corresponded to the number of MNs discharging before the electrical stimulation. This information cannot be accessed in human experiments, emphasizing,

The reader can notice that the background EMG activity before the stimulus delivery is slightly different between the two simulated conditions. However, the interference pattern is more susceptible to nonlinear summation and cancellation of MUAPs. Therefore, the V-wave may be a more reproducible and reliable measure of the efferent drive, which can increase or decrease due to different factors, e.g, neuronal plasticity following training and hyperexcitability of spinal MNs following neurological diseases, such as stroke and amyotrophic

therefore, the relevance of mathematical modeling and computer simulations.

**5.2. Simulated V-wave**

80 Electrodiagnosis in New Frontiers of Clinical Research

lateral sclerosis [54, 56].

In this chapter we have discussed several aspects regarding the use of surface EMG in a variety of human neurophysiology protocols. Different conditioning effects on the interference pattern and phasic responses, which can be used to infer spinal cord mechanisms, were presented and discussed. Finally, in order to refine the understanding of some underlying mechanisms involved in motor control, as well as to facilitate the interpretation of EMG data, we have introduced a comprehensive web-based simulator of the neuromuscular system with openaccess and a friendly interface. The simulation results can be used to test hypothesis raised from the analysis of experimental data and to propose new questions to be addressed in different experimental protocols. The techniques and models presented here might be useful for researchers/clinicians who intend to conduct experiments on both healthy subjects and patients with neuromuscular disorders.

#### **Acknowledgements**

The authors are grateful to CNPq (Brazilian Science Foundation), FAPESP (São Paulo Research Foundation) and Canadian Bureau for International Education (PDRF Program) for their financial support.
