The Effect of Connectivity on EEG Rhythms, Power Spectral Density and Coherence Among Coupled Neural Populations: Analysis With a Neural Mass Model

Controlling Alzheimer's disease by deep brain stimulation based on a data-driven cortical network model

This work aims to explore the control effect of DBS on Alzheimer's disease (AD) from a neurocomputational perspective. Firstly, a data-driven cortical network model is constructed using the Diffusion Tensor Imaging data. Then, a typical electrophysiological feature of EEG slowing in AD is reproduced by reducing the synaptic connectivity parameters. The corresponding changes in kinetic behavior mainly include an oscillation decrease in the amplitude and frequency of the pyramidal neuron population. Subsequently, DBS current with specific parameters is introduced into three potential targets of the hippocampus, the nucleus accumbens and the olfactory tubercle, respectively. The results indicate that applying DBS to simulated mild AD patients induces an increase in relative alpha power, a decrease in relative theta power, and a significant rightward shift of the dominant frequency. This is consistent with the EEG reversal in pharmacological treatments for AD. Further, the optimal stimulation strategy of DBS is investigated through spectral and statistical analyses. Specifically, the pathological symptoms of AD could be alleviated by adjusting the critical parameters of DBS, and the control effect of DBS on various targets is that the hippocampus is superior to the olfactory tubercle and nucleus accumbens. Finally, using correlation analysis between the power increments and the nodal degrees, it is concluded that the control effect of DBS is related to the importance of the nodes in the brain network. This study provides a theoretical guidance for determining DBS targets and parameters, which may have a substantial impact on the development of DBS treatment for AD.

EEG generation mechanism of lower limb active movement intention and its virtual reality induction enhancement: a preliminary study

Introduction

Active rehabilitation requires active neurological participation when users use rehabilitation equipment. A brain-computer interface (BCI) is a direct communication channel for detecting changes in the nervous system. Individuals with dyskinesia have unclear intentions to initiate movement due to physical or psychological factors, which is not conducive to detection. Virtual reality (VR) technology can be a potential tool to enhance the movement intention from pre-movement neural signals in clinical exercise therapy. However, its effect on electroencephalogram (EEG) signals is not yet known. Therefore, the objective of this paper is to construct a model of the EEG signal generation mechanism of lower limb active movement intention and then investigate whether VR induction could improve movement intention detection based on EEG.

Methods

Firstly, a neural dynamic model of lower limb active movement intention generation was established from the perspective of signal transmission and information processing. Secondly, the movement-related EEG signal was calculated based on the model, and the effect of VR induction was simulated. Movement-related cortical potential (MRCP) and event-related desynchronization (ERD) features were extracted to analyze the enhancement of movement intention. Finally, we recorded EEG signals of 12 subjects in normal and VR environments to verify the effectiveness and feasibility of the above model and VR induction enhancement of lower limb active movement intention for individuals with dyskinesia.

Results

Simulation and experimental results show that VR induction can effectively enhance the EEG features of subjects and improve the detectability of movement intention.

Discussion

The proposed model can simulate the EEG signal of lower limb active movement intention, and VR induction can enhance the early and accurate detectability of lower limb active movement intention. It lays the foundation for further robot control based on the actual needs of users.

Decreased coherence in the model of the dorsal visual pathway associated with Alzheimer's disease

Decreased coherence in electroencephalogram (EEG) has been reported in Alzheimer's disease (AD) experimentally, which could be considered as a typical electrophysiological characteristic in AD. This work aimed to investigate the effect of AD on coherence in the dorsal visual pathway by the technique of neurocomputation. Firstly, according to the hierarchical organization of the cerebral cortex and the information flows of the dorsal visual pathway, a more physiologically plausible neural mass model including cortical areas v1, v2, and v5 was established in the dorsal visual pathway. The three interconnected cortical areas were connected by ascending and descending projections. Next, the pathological condition of loss of long synaptic projections in AD was simulated by reducing the parameters of long synaptic projections in the model. Then, the loss of long synaptic projections on coherence among different visual cortex areas was explored by means of power spectral analysis and coherence function. The results demonstrate that the coherence between these interconnected cortical areas showed an obvious decline with the gradual decrease of long synaptic projections, i.e. decrease in descending projections from area v2 to v1 and v5 to v2 and ascending projection from area v2 to v5. Hopefully, the results of this study could provide theoretical guidance for understanding the dynamical mechanism of AD.

EEG Recordings as Biomarkers of Pain Perception: Where Do We Stand and Where to Go?

Introduction

The universality and complexity of pain, which is highly prevalent, yield its significance to both patients and researchers. Developing a non-invasive tool that can objectively measure pain is of the utmost importance for clinical and research purposes. Traditionally electroencephalography (EEG) has been mostly used in epilepsy; however, over the recent years EEG has become an important non-invasive clinical tool that has helped increase our understanding of brain network complexities and for the identification of areas of dysfunction. This review aimed to investigate the role of EEG recordings as potential biomarkers of pain perception.

Methods

A systematic search of the PubMed database led to the identification of 938 papers, of which 919 were excluded as a result of not meeting the eligibility criteria, and one article was identified through screening of the reference lists of the 19 eligible studies. Ultimately, 20 papers were included in this systematic review.

Results

Changes of the cortical activation have potential, though the described changes are not always consistent. The most consistent finding is the increase in the delta and gamma power activity. Only a limited number of studies have looked into brain networks encoding pain perception.

Conclusion

Although no robust EEG biomarkers of pain perception have been identified yet, EEG has potential and future research should be attempted. Designing strong research protocols, controlling for potential risk of biases, as well as investigating brain networks rather than isolated cortical changes will be crucial in this attempt.

Supplementary Information

The online version contains supplementary material available at 10.1007/s40122-022-00372-2.

Multi-Region Local Field Potential Signatures in Response to the Formalin-induced Inflammatory Stimulus in Male Rats

Pain can be ignited by noxious chemical (e.g., acid), mechanical (e.g., pressure), and thermal (e.g., heat) stimuli and generated by the activation of sensory neurons and their axonal terminals called nociceptors in the periphery. Nociceptive information transmitted from the periphery is projected to the central nervous system (thalamus, somatosensory cortex, insular, anterior cingulate cortex, amygdala, periaqueductal grey, prefrontal cortex, etc.) to generate a unified experience of pain. Local field potential (LFP) recording is one of the neurophysiological tools to investigate the combined neuronal activity, ranging from several hundred micrometers to a few millimeters (radius), located around the embedded electrode. The advantage of recording LFP is that it provides stable simultaneous activities in various brain regions in response to external stimuli. In this study, differential LFP activities from the contralateral anterior cingulate cortex (ACC), ventral tegmental area (VTA), and bilateral amygdala in response to peripheral noxious formalin injection were recorded in anesthetized male rats. The results indicated increased power of delta, theta, alpha, beta, and gamma bands in the ACC and amygdala but no change of gamma-band in the right amygdala. Within the VTA, intensities of the delta, theta, and beta bands were only enhanced significantly after formalin injection. It was found that the connectivity (i.t. the coherence) among these brain regions reduced significantly under the formalin-induced nociception, which suggests a significant interruption within the brain. With further study, it will sort out the key combination of structures that will serve as the signature for pain state.

Transfer Entropy as a Measure of Brain Connectivity: A Critical Analysis With the Help of Neural Mass Models

Objective: Assessing brain connectivity from electrophysiological signals is of great relevance in neuroscience, but results are still debated and depend crucially on how connectivity is defined and on mathematical instruments utilized. Aim of this work is to assess the capacity of bivariate Transfer Entropy (TE) to evaluate connectivity, using data generated from simple neural mass models of connected Regions of Interest (ROIs). Approach: Signals simulating mean field potentials were generated assuming two, three or four ROIs, connected via excitatory or by-synaptic inhibitory links. We investigated whether the presence of a statistically significant connection can be detected and if connection strength can be quantified. Main Results: Results suggest that TE can reliably estimate the strength of connectivity if neural populations work in their linear regions, and if the epoch lengths are longer than 10 s. In case of multivariate networks, some spurious connections can emerge (i.e., a statistically significant TE even in the absence of a true connection); however, quite a good correlation between TE and synaptic strength is still preserved. Moreover, TE appears more robust for distal regions (longer delays) compared with proximal regions (smaller delays): an approximate a priori knowledge on this delay can improve the procedure. Finally, non-linear phenomena affect the assessment of connectivity, since they may significantly reduce TE estimation: information transmission between two ROIs may be weak, due to non-linear phenomena, even if a strong causal connection is present. Significance: Changes in functional connectivity during different tasks or brain conditions, might not always reflect a true change in the connecting network, but rather a change in information transmission. A limitation of the work is the use of bivariate TE. In perspective, the use of multivariate TE can improve estimation and reduce some of the problems encountered in the present study.

Resting-state EEG activity predicts frontoparietal network reconfiguration and improved attentional performance

Mounting evidence indicates that resting-state EEG activity is related to various cognitive functions. To trace physiological underpinnings of this relationship, we investigated EEG and behavioral performance of 36 healthy adults recorded at rest and during visual attention tasks: visual search and gun shooting. All measures were repeated two months later to determine stability of the results. Correlation analyses revealed that within the range of 2–45 Hz, at rest, beta-2 band power correlated with the strength of frontoparietal connectivity and behavioral performance in both sessions. Participants with lower global beta-2 resting-state power (gB2rest) showed weaker frontoparietal connectivity and greater capacity for its modifications, as indicated by changes in phase correlations of the EEG signals. At the same time shorter reaction times and improved shooting accuracy were found, in both test and retest, in participants with low gB2rest compared to higher gB2rest values. We posit that weak frontoparietal connectivity permits flexible network reconfigurations required for improved performance in everyday tasks.

Efficient Classification of Motor Imagery Electroencephalography Signals Using Deep Learning Methods

Single-trial motor imagery classification is a crucial aspect of brain–computer applications. Therefore, it is necessary to extract and discriminate signal features involving motor imagery movements. Riemannian geometry-based feature extraction methods are effective when designing these types of motor-imagery-based brain–computer interface applications. In the field of information theory, Riemannian geometry is mainly used with covariance matrices. Accordingly, investigations showed that if the method is used after the execution of the filterbank approach, the covariance matrix preserves the frequency and spatial information of the signal. Deep-learning methods are superior when the data availability is abundant and while there is a large number of features. The purpose of this study is to a) show how to use a single deep-learning-based classifier in conjunction with BCI (brain–computer interface) applications with the CSP (common spatial features) and the Riemannian geometry feature extraction methods in BCI applications and to b) describe one of the wrapper feature-selection algorithms, referred to as the particle swarm optimization, in combination with a decision tree algorithm. In this work, the CSP method was used for a multiclass case by using only one classifier. Additionally, a combination of power spectrum density features with covariance matrices mapped onto the tangent space of a Riemannian manifold was used. Furthermore, the particle swarm optimization method was implied to ease the training by penalizing bad features, and the moving windows method was used for augmentation. After empirical study, the convolutional neural network was adopted to classify the pre-processed data. Our proposed method improved the classification accuracy for several subjects that comprised the well-known BCI competition IV 2a dataset.

Deriving theoretical phase locking values of a coupled cortico-thalamic neural mass model using center manifold reduction

Cognitive functions such as sensory processing and memory processes lead to phase synchronization in the electroencephalogram or local field potential between different brain regions. There are a lot of computational researches deriving phase locking values (PLVs), which are an index of phase synchronization intensity, from neural models. However, these researches derive PLVs numerically. To the best of our knowledge, there have been no reports on the derivation of a theoretical PLV. In this study, we propose an analytical method for deriving theoretical PLVs from a cortico-thalamic neural mass model described by a delay differential equation. First, the model for generating neural signals is transformed into a normal form of the Hopf bifurcation using center manifold reduction. Second, the normal form is transformed into a phase model that is suitable for analyzing synchronization phenomena. Third, the Fokker-Planck equation of the phase model is derived and the phase difference distribution is obtained. Finally, the PLVs are calculated from the stationary distribution of the phase difference. The validity of the proposed method is confirmed via numerical simulations. Furthermore, we apply the proposed method to a working memory process, and discuss the neurophysiological basis behind the phase synchronization phenomenon. The results demonstrate the importance of decreasing the intensity of independent noise during the working memory process. The proposed method will be of great use in various experimental studies and simulations relevant to phase synchronization, because it enables the effect of neurophysiological changes on PLVs to be analyzed from a mathematical perspective.

Post-Traumatic Stress Constrains the Dynamic Repertoire of Neural Activity

Post-traumatic stress disorder (PTSD) is an anxiety disorder arising from exposure to a traumatic event. Although primarily defined in terms of behavioral symptoms, the global neurophysiological effects of traumatic stress are increasingly recognized as a critical facet of the human PTSD phenotype. Here we use magnetoencephalographic recordings to investigate two aspects of information processing: inter-regional communication (measured by functional connectivity) and the dynamic range of neural activity (measured in terms of local signal variability). We find that both measures differentiate soldiers diagnosed with PTSD from soldiers without PTSD, from healthy civilians, and from civilians with mild traumatic brain injury, which is commonly comorbid with PTSD. Specifically, soldiers with PTSD display inter-regional hypersynchrony at high frequencies (80-150 Hz), as well as a concomitant decrease in signal variability. The two patterns are spatially correlated and most pronounced in a left temporal subnetwork, including the hippocampus and amygdala. We hypothesize that the observed hypersynchrony may effectively constrain the expression of local dynamics, resulting in less variable activity and a reduced dynamic repertoire. Thus, the re-experiencing phenomena and affective sequelae in combat-related PTSD may result from functional networks becoming "stuck" in configurations reflecting memories, emotions, and thoughts originating from the traumatizing experience.

SIGNIFICANCE STATEMENT

The present study investigates the effects of post-traumatic stress disorder (PTSD) in combat-exposed soldiers. We find that soldiers with PTSD exhibit hypersynchrony in a circuit of temporal lobe areas associated with learning and memory function. This rigid functional architecture is associated with a decrease in signal variability in the same areas, suggesting that the observed hypersynchrony may constrain the expression of local dynamics, resulting in a reduced dynamic range. Our findings suggest that the re-experiencing of traumatic events in PTSD may result from functional networks becoming locked in configurations that reflect memories, emotions, and thoughts associated with the traumatic experience.

On self-feedback connectivity in neural mass models applied to event-related potentials

Neural mass models (NMMs) applied to neuroimaging data often do not emphasise intrinsic self-feedback within a neural population. However, based on mean-field theory, any population of coupled neurons is intrinsically endowed with effective self-coupling. In this work, we examine the effectiveness of three cortical NMMs with different self-feedbacks using a dynamic causal modelling approach. Specifically, we compare the classic Jansen and Rit (1995) model (no self-feedback), a modified model by Moran et al. (2007) (only inhibitory self-feedback), and our proposed model with inhibitory and excitatory self-feedbacks. Using bifurcation analysis, we show that single-unit Jansen-Rit model is less robust in generating oscillatory behaviour than the other two models. Next, under Bayesian inversion, we simulate single-channel event-related potentials (ERPs) within a mismatch negativity auditory oddball paradigm. We found fully self-feedback model (FSM) to provide the best fit to single-channel data. By analysing the posterior covariances of model parameters, we show that self-feedback connections are less sensitive to the generated evoked responses than the other model parameters, and hence can be treated analogously to "higher-order" parameter corrections of the original Jansen-Rit model. This is further supported in the more realistic multi-area case where FSM can replicate data better than JRM and MoM in the majority of subjects by capturing the finer features of the ERP data more accurately. Our work informs how NMMs with full self-feedback connectivity are not only more consistent with the underlying neurophysiology, but can also account for more complex features in ERP data.

A global coupling index of multivariate neural series with application to the evaluation of mild cognitive impairment

Recently, the synchronization between neural signals has been widely used as a key indicator of brain function. To understand comprehensively the effect of synchronization on the brain function, accurate computation of the synchronization strength among multivariate neural series from the whole brain is necessary. In this study, we proposed a method named global coupling index (GCI) to estimate the synchronization strength of multiple neural signals. First of all, performance of the GCI method was evaluated by analyzing simulated EEG signals from a multi-channel neural mass model, including the effects of the frequency band, the coupling coefficient, and the signal noise ratio. Then, the GCI method was applied to analyze the EEG signals from 12 mild cognitive impairment (MCI) subjects and 12 normal controls (NC). The results showed that GCI method had two major advantages over the global synchronization index (GSI) or S-estimator. Firstly, simulation data showed that the GCI method provided both a more robust result on the frequency band and a better performance on the coupling coefficients. Secondly, the actual EEG data demonstrated that GCI method was more sensitive in differentiating the MCI from control subjects, in terms of the global synchronization strength of neural series of specific alpha, beta1 and beta2 frequency bands. Hence, it is suggested that GCI is a better method over GSI and S-estimator to estimate the synchronization strength of multivariate neural series for predicting the MCI from the whole brain EEG recordings.

Performance comparison between gPDC and PCMI for measuring directionality of neural information flow

BACKGROUND

General partial directed coherence (gPDC) and permutation conditional mutual information (PCMI) have been widely used to analyze neural activities. These two algorithms are representative of linear and nonlinear methods, respectively. However, there is little known about the difference between their performances in measurements of neural information flow (NIF).

NEW METHOD

Comparison of these two approaches was effectively performed based on the neural mass model (NMM) and real local field potentials.

RESULTS

The results showed that the sensitivity of PCMI was more robust than that of gPDC. The coupling strengths calculated by PCMI were closer to theoretical values in the bidirectional mode of NMM. Furthermore, there was a small Coefficient of Variance (C.V.) for the PCMI results. The gPDC was more sensitive to alterations in the directionality index or the coupling strength of NMM; the gPDC method was more likely to detect a difference between two distinct types of coupling strengths compared to that of PCMI, and gPDC performed well in the identification of the coupling strength in the unidirectional mode.

COMPARISON TO EXISTING METHOD(S)

A comparison between gPDC and PCMI was performed and the advantages of the approaches are discussed.

CONCLUSIONS

The performance of the PCMI is better than that of gPDC in measuring the characteristics of connectivity between neural populations. However, gPDC is recommended to distinguish the differences in connectivity between two states in the same pathway or to detect the coupling strength of the unidirectional mode, such as the hippocampal CA3-CA1 pathway.

Seizure prediction in hippocampal and neocortical epilepsy using a model-based approach

OBJECTIVES

The aim of this study is to develop a model based seizure prediction method.

METHODS

A neural mass model was used to simulate the macro-scale dynamics of intracranial EEG data. The model was composed of pyramidal cells, excitatory and inhibitory interneurons described through state equations. Twelve model's parameters were estimated by fitting the model to the power spectral density of intracranial EEG signals and then integrated based on information obtained by investigating changes in the parameters prior to seizures. Twenty-one patients with medically intractable hippocampal and neocortical focal epilepsy were studied.

RESULTS

Tuned to obtain maximum sensitivity, an average sensitivity of 87.07% and 92.6% with an average false prediction rate of 0.2 and 0.15/h were achieved using maximum seizure occurrence periods of 30 and 50 min and a minimum seizure prediction horizon of 10s, respectively. Under maximum specificity conditions, the system sensitivity decreased to 82.9% and 90.05% and the false prediction rates were reduced to 0.16 and 0.12/h using maximum seizure occurrence periods of 30 and 50 min, respectively.

CONCLUSIONS

The spatio-temporal changes in the parameters demonstrated patient-specific preictal signatures that could be used for seizure prediction.

SIGNIFICANCE

The present findings suggest that the model-based approach may aid prediction of seizures.

Cross-conditional entropy and coherence analysis of pharmaco-EEG changes induced by alprazolam

RATIONALE

Quantitative analysis of electroencephalographic signals (EEG) and their interpretation constitute a helpful tool in the assessment of the bioavailability of psychoactive drugs in the brain. Furthermore, psychotropic drug groups have typical signatures which relate biochemical mechanisms with specific EEG changes.

OBJECTIVES

To analyze the pharmacological effect of a dose of alprazolam on the connectivity of the brain during wakefulness by means of linear and nonlinear approaches.

METHODS

EEG signals were recorded after alprazolam administration in a placebo-controlled crossover clinical trial. Nonlinear couplings assessed by means of corrected cross-conditional entropy were compared to linear couplings measured with the classical magnitude squared coherence.

RESULTS

Linear variables evidenced a statistically significant drug-induced decrease, whereas nonlinear variables showed significant increases. All changes were highly correlated to drug plasma concentrations. The spatial distribution of the observed connectivity changes clearly differed from a previous study: changes before and after the maximum drug effect were mainly observed over the anterior half of the scalp. Additionally, a new variable with very low computational cost was defined to evaluate nonlinear coupling. This is particularly interesting when all pairs of EEG channels are assessed as in this study.

CONCLUSIONS

Results showed that alprazolam induced changes in terms of uncoupling between regions of the scalp, with opposite trends depending on the variables: decrease in linear ones and increase in nonlinear features. Maps provided consistent information about the way brain changed in terms of connectivity being definitely necessary to evaluate separately linear and nonlinear interactions.

Estimation of genuine and random synchronization in multivariate neural series

Synchronization is an important mechanism that helps in understanding information processing in a normal or abnormal brain. In this paper, we propose a new method to estimate the genuine and random synchronization indexes in multivariate neural series, denoted as GSI (genuine synchronization index) and RSI (random synchronization index), by means of a correlation matrix analysis and surrogate technique. The performance of the method is evaluated by using a multi-channel neural mass model (MNMM), including the effects of different coupling coefficients, signal to noise ratios (SNRs) and time-window widths on the estimation of the GSI and RSI. Results show that the GSI and the RSI are superior in description of the synchronization in multivariate neural series compared to the S-estimator. Furthermore, the proposed method is applied to analyze a 21-channel scalp electroencephalographic recording of a 35 year-old male who suffers from mesial temporal lobe epilepsy. The GSI and the RSI at different frequency bands during the epileptic seizure are estimated. The present results could be helpful for us to understand the synchronization mechanism of epileptic seizures.

The generation of rhythms within a cortical region: analysis of a neural mass model

Rhythms in brain electrical activity are assumed to play a significant role in many cognitive and perceptual processes. It is thus of great value to analyze these rhythms and their mutual relationships in large scale models of cortical regions. In the present work, we modified the neural mass model by Wendling et al. (Eur. J. Neurosci. 15 (2002) 1499-1508) by including a new inhibitory self-loop among GABAA,fast interneurons. A theoretical analysis was performed to demonstrate that, thanks to this loop, GABAA,fast interneurons can produce a gamma rhythm in the power spectral density (PSD) even without the participation of the other neural populations. Then, the model of a whole cortical region, built upon four interconnected neural populations (pyramidal cells, excitatory, GABAA,slow and GABAA,fast interneurons) was investigated by changing the internal connectivity parameters. Results show that different rhythm combinations (beta and gamma, alpha and gamma, or a wide spectrum) can be obtained within the same region by simply altering connectivity values, without the need to change synaptic kinetics. Finally, two or three cortical regions were connected by using different topologies of long range connections. Results show that long-range connections directed from pyramidal neurons to GABAA,fast interneurons are the most efficient to transmit rhythms from one region to another. In this way, PSD with three or four peaks can be obtained using simple connectivity patterns. The model can be of value to gain a deeper insight into the mechanisms involved in the generation of gamma rhythms and provide a better understanding of cortical EEG spectra.

A neural mass model to simulate different rhythms in a cortical region

An original neural mass model of a cortical region has been used to investigate the origin of EEG rhythms. The model consists of four interconnected neural populations: pyramidal cells, excitatory interneurons and inhibitory interneurons with slow and fast synaptic kinetics, GABA(A, slow) and GABA(A,fast) respectively. A new aspect, not present in previous versions, consists in the inclusion of a self-loop among GABA(A,fast) interneurons. The connectivity parameters among neural populations have been changed in order to reproduce different EEG rhythms. Moreover, two cortical regions have been connected by using different typologies of long range connections. Results show that the model of a single cortical region is able to simulate the occurrence of multiple power spectral density (PSD) peaks; in particular the new inhibitory loop seems to have a critical role in the activation in gamma (gamma) band, in agreement with experimental studies. Moreover the effect of different kinds of connections between two regions has been investigated, suggesting that long range connections toward GABA(A,fast) interneurons have a major impact than connections toward pyramidal cells. The model can be of value to gain a deeper insight into mechanisms involved in the generation of gamma rhythms and to provide better understanding of cortical EEG spectra.

Changes in EEG power spectral density and cortical connectivity in healthy and tetraplegic patients during a motor imagery task

Knowledge of brain connectivity is an important aspect of modern neuroscience, to understand how the brain realizes its functions. In this work, neural mass models including four groups of excitatory and inhibitory neurons are used to estimate the connectivity among three cortical regions of interests (ROIs) during a foot-movement task. Real data were obtained via high-resolution scalp EEGs on two populations: healthy volunteers and tetraplegic patients. A 3-shell Boundary Element Model of the head was used to estimate the cortical current density and to derive cortical EEGs in the three ROIs. The model assumes that each ROI can generate an intrinsic rhythm in the beta range, and receives rhythms in the alpha and gamma ranges from other two regions. Connectivity strengths among the ROIs were estimated by means of an original genetic algorithm that tries to minimize several cost functions of the difference between real and model power spectral densities. Results show that the stronger connections are those from the cingulate cortex to the primary and supplementary motor areas, thus emphasizing the pivotal role played by the CMA_L during the task. Tetraplegic patients exhibit higher connectivity strength on average, with significant statistical differences in some connections. The results are commented and virtues and limitations of the proposed method discussed.