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Objective. Numerical modeling of electric fields induced by transcranial alternating current stimulation (tACS) is currently a part of the standard procedure to predict and understand neural response. Quasi-static approximation (QSA) for electric field calculations is generally applied to reduce the computational cost. Here, we aimed to analyze and quantify the validity of the approximation over a broad frequency range. Approach. We performed electromagnetic modeling studies using an anatomical head model and considered approximations assuming either a purely ohmic medium (i.e. static formulation) or a lossy dielectric medium (QS formulation). The results were compared with the solution of Maxwell’s equations in the cases of harmonic and pulsed signals. Finally, we analyzed the effect of electrode positioning on these errors. Main results. Our findings demonstrate that the QSA is valid and produces a relative error below 1% up to 1.43 MHz. The largest error is introduced in the static case, where the error is over 1% across the entire considered spectrum and as high as 20% in the brain at 10 Hz. We also highlight the special importance of considering the capacitive effect of tissues for pulsed waveforms, which prevents signal distortion induced by the purely ohmic approximation. At the neuron level, the results point a difference of sense electric field as high as 22% at focusing point, impacting pyramidal cells firing times. Significance. QSA remains valid in the frequency range currently used for tACS. However, neglecting permittivity (static formulation) introduces significant error for both harmonic and non-harmonic signals. It points out that reliable low frequency dielectric data are needed for accurate transcranial current stimulation numerical modeling.

Intracranial electrodes are used clinically for diagnostic or therapeutic purposes, notably in drug-refractory epilepsy (DRE) among others. Visualization and quantification of the energy delivered through such electrodes is key to understanding how the resulting electric fields modulate neuronal excitability, i.e. the ratio between excitation and inhibition. Quantifying the electric field induced by electrical stimulation in a patient-specific manner is challenging, because these electric fields depend on a number of factors: electrode trajectory with respect to folded brain anatomy, biophysical (electrical conductivity / permittivity) properties of brain tissue and stimulation parameters such as electrode contacts position and intensity. Here, we aimed to evaluate various biophysical models for characterizing the electric fields induced by electrical stimulation in DRE patients undergoing stereoelectroencephalography (SEEG) recordings in the context of pre-surgical evaluation. This stimulation was performed with multiple-contact intracranial electrodes used in routine clinical practice. We introduced realistic 3D models of electrode geometry and trajectory in the neocortex. For the electrodes, we compared point (0D) and line (1D) sources approximations. For brain tissue, we considered three configurations of increasing complexity: a 6-layer spherical model, a toy model with a sulcus representation, replicating results from previous approaches; and went beyond the state-of-the-art by using a realistic head model geometry. Electrode geometry influenced the electric field distribution at close distances (~3 mm) from the electrode axis. For larger distances, the volume conductor geometry and electrical conductivity dominated electric field distribution. These results are the first step towards accurate and computationally tractable patient-specific models of electric fields induced by neuromodulation and neurostimulation procedures.

Time-varying Magnetic Fields (MF) are a cornerstone of major modern healthcare technologies and are also a byproduct of our modern environment. Virtually everyone is exposed to power-frequency MF (50/60 Hz), inducing in our body electric fields and currents, potentially modulating brain function. MF-induced electric fields within the central nervous system can generate flickering visual perceptions (magnetophosphenes), which form the basis of international MF exposure guidelines and recommendations protecting workers and the general public. However, magnetophosphene perception thresholds were estimated 40 years ago in a small, unreplicated study with significant uncertainties and leaving open the question of the underlying mechanism. Here, we report magnetophosphene perception in 81 volunteers exposed to MF (eye or occipital exposure) between 0 and 50 mT at frequencies of 20, 50, 60 and 100 Hz. Frequency-dependent threshold were quantified and support an interaction between induced electric fields and retinal rod cells. Beyond fundamental and immediate implications for international guidelines and recommendations, these results have translational potential for the differential diagnosis of retinal disorders.

Along with the study of brain activity evoked by external stimuli, the past two decades witnessed an increased interest in characterizing the spontaneous brain activity occurring during resting conditions. The identification of the connectivity patterns in this so-called “resting-state” has been the subject of a great number of electrophysiology-based studies, using the Electro/Magneto-Encephalography (EEG/MEG) source connectivity method. However, no consensus has been reached yet regarding a unified (if possible) analysis pipeline, and several involved parameters and methods require cautious tuning. This is particularly challenging when different choices induce significant discrepancy in results and drawn conclusions, thereby hindering reproducibility of neuroimaging research. Hence, our objective in this study was to evaluate some of the parameters related to the EEG source connectivity analysis and shed light on their implications on the accuracy of the resulting networks. We simulated, using neural mass models, EEG data corresponding to two of the resting-state networks (RSNs), namely the default mode network (DMN) and the dorsal attentional network (DAN). We investigated the impact of five channel densities (19, 32, 64, 128, 256), three inverse solutions (weighted minimum norm estimate (wMNE), exact low resolution brain electromagnetic tomography (eLORETA), and linearly constrained minimum variance (LCMV) beamforming) and four functional connectivity measures (phase-locking value (PLV), phase-lag index (PLI), and amplitude envelope correlation (AEC) with and without source leakage correction), on the correspondence between reconstructed and reference networks. We showed that, with different analytical choices, a high variability is present in the results. More specifically, our results show that a higher number of EEG channels significantly increased the accuracy of the reconstructed networks. Additionally, our results showed a significant variability in the performance of the tested inverse solutions and connectivity measures. In our specific simulation context, eLORETA and wMNE combined with AEC computed between orthogonalized time series exhibited the highest performance in terms of similarity between reconstructed and reference connectivity matrices. Results were similar for both DMN and DAN. We believe that this work could be useful for the field of electrophysiology connectomics, by shedding light on the challenge of analytical variability and its consequences on the reproducibility of neuroimaging studies.

International audience; OBJECTIVE: This study was undertaken to investigate how gain of function (GOF) of slack channel due to a KCNT1 pathogenic variant induces abnormal neuronal cortical network activity and generates specific electroencephalographic (EEG) patterns of epilepsy in infancy with migrating focal seizures. METHODS: We used detailed microscopic computational models of neurons to explore the impact of GOF of slack channel (explicitly coded) on each subtype of neurons and on a cortical micronetwork. Then, we adapted a thalamocortical macroscopic model considering results obtained in detailed models and immature properties related to epileptic brain in infancy. Finally, we compared simulated EEGs resulting from the macroscopic model with interictal and ictal patterns of affected individuals using our previously reported EEG markers. RESULTS: The pathogenic variants of KCNT1 strongly decreased the firing rate properties of γ-aminobutyric acidergic (GABAergic) interneurons and, to a lesser extent, those of pyramidal cells. This change led to hyperexcitability with increased synchronization in a cortical micronetwork. At the macroscopic scale, introducing slack GOF effect resulted in epilepsy of infancy with migrating focal seizures (EIMFS) EEG interictal patterns. Increased excitation-to-inhibition ratio triggered seizure, but we had to add dynamic depolarizing GABA between somatostatin-positive interneurons and pyramidal cells to obtain migrating seizure. The simulated migrating seizures were close to EIMFS seizures, with similar values regarding the delay between the different ictal activities (one of the specific EEG markers of migrating focal seizures due to KCNT1 pathogenic variants). SIGNIFICANCE: This study illustrates the interest of biomathematical models to explore pathophysiological mechanisms bridging the gap between the functional effect of gene pathogenic variants and specific EEG phenotype. Such models can be complementary to in vitro cellular and animal models. This multiscale approach provides an in silico framework that can be further used to identify candidate innovative therapies.

Work in the last two decades has shown that neural mass models (NMM) can realistically reproduce and explain epileptic seizure transitions as recorded by electrophysiological methods (EEG, SEEG). In previous work, advances were achieved by increasing excitation and heuristically varying network inhibitory coupling parameters in the models. Based on these early studies, we provide a laminar NMM capable of realistically reproducing the electrical activity recorded by SEEG in the epileptogenic zone during interictal to ictal states. With the exception of the external noise input into the pyramidal cell population, the model dynamics are autonomous. By setting the system at a point close to bifurcation, seizure-like transitions are generated, including pre-ictal spikes, low voltage fast activity, and ictal rhythmic activity. A novel element in the model is a physiologically motivated algorithm for chloride dynamics: the gain of GABAergic post-synaptic potentials is modulated by the pathological accumulation of chloride in pyramidal cells due to high inhibitory input and/or dysfunctional chloride transport. In addition, in order to simulate SEEG signals for comparison with real seizure recordings, the NMM is embedded first in a layered model of the neocortex and then in a realistic physical model. We compare modeling results with data from four epilepsy patient cases. By including key pathophysiological mechanisms, the proposed framework captures succinctly the electrophysiological phenomenology observed in ictal states, paving the way for robust personalization methods based on NMMs.

Among the cognitive symptoms that are associated with Parkinson’s disease (PD), alterations in cognitive action control (CAC) are commonly reported in patients. CAC enables the suppression of an automatic action, in favor of a goal-directed one. The implementation of CAC is time-resolved and arguably associated with dynamic changes in functional brain networks. However, the electrophysiological functional networks involved, their dynamic changes, and how these changes are affected by PD, still remain unknown. In this study, to address this gap of knowledge, 21 PD patients and 10 healthy controls (HC) underwent a Simon task while high-density electroencephalography (HD-EEG) was recorded. Source-level dynamic connectivity matrices were estimated using the phase-locking value in the beta (12-25 Hz) and gamma (30-45 Hz) frequency bands. Temporal independent component analyses were used as a dimension reduction tool to isolate the group-specific brain network states that were dominant during the task. Typical microstate metrics were quantified to investigate the presence of these states at the subject-level. Our results first confirmed that PD patients experienced difficulties in inhibiting automatic responses during the task. At the group-level, HC displayed a significant functional network state that involved typical CAC-related prefrontal and cingulate nodes (e.g., inferior frontal cortex). Both group- and subject-level analyses showed that this network was less present in PD to the benefit of other networks involving lateralized temporal and insular components. The presence of this prefrontal network was associated with decreased reaction time. In the gamma band, two networks (fronto-cingulate and fronto-temporal) followed one another in HC, while 3 partially overlapping networks that included fronto-temporal, fronto-occipital and cross-hemispheric temporal connections were found in PD. At the subject-level, differences between PD and HC were less marked. Altogether, this study showed that the functional brain networks observed during CAC and their temporal changes were different in PD patients as compared to HC, and that these differences partially relate to behavioral changes. This study also highlights that task-based dynamic functional connectivity is a promising approach in understanding the cognitive dysfunctions observed in PD and beyond.HighlightsCognitive action control is associated with dynamic functional networksPrefrontal and cingulate beta connectivity are prominent in healthy controlsPD patients have different dynamic networks in which prefrontal nodes are absentThe occurrence of prefrontal beta networks was associated with a decreased reaction timeFunctional networks in the gamma band were temporally organized in HC, but overlapping in PD patients

International audience; In the past years, the emergent method called "electroencephalography (EEG) source connectivity"has gained increased interest due to its ability to identify large-scale brain networks with satisfactory spatio-temporal resolution. However, many related methodological questions remain unanswered and no consensus has been reached yet over a unified EEG source connectivity pipeline. The objective evaluation of the pipeline is challenged by the absence of a ground truth when dealing with real EEG data. In this paper, we show how a recently developed, large-scale, physiologically-grounded computational model, named COALIA, can provide such "ground-truth"models by generating cortical and scalp-level realistic simulations of brain activity. We investigated the effect of three factors involved in the "EEG source connectivity"pipeline: the number of EEG sensors, the solution of the inverse problem, and the functional connectivity measure, in the context of epileptiform activity. Results showed that increasing the number of electrodes (at least channels) leads to a higher accuracy of the reconstructed cortical networks, and that the weighted minimum norm estimate (wMNE) combined with the weighted phase lag index (wPLI) has the best performance at high electrode density. Although we believe that these results are context-specific, the model-based approach presented in this paper can be extended to address other methodological aspects of the EEG source connectivity pipeline in different contexts. We aim at presenting a proof-of-concept of the potential use of COALIA in the optimization the EEG source connectivity pipeline. © 2021 IEEE.

Neural mass models are among the most popular mathematical models of brain activity, since they enable the rapid simulation of large-scale networks involving different neural types at a spatial scale compatible with electrophysiological experiments (e.g. local field potentials). However, establishing neural mass model (NMM) equations associated with specific neuronal network architectures can be tedious and is an error-prone process, restricting their use to scientists who are familiar with mathematics. In order to overcome this challenge, we have developed a user-friendly software that enables a user to construct rapidly, under the form of a graph, a neuronal network with its populations and connectivity patterns. The resulting graph is then automatically translated into the corresponding set of differential equations, which can be solved and displayed within the same software environment. The software is proposed as open access, and should assist in offering the possibility for a wider audience of scientists to develop NMM corresponding to their specific neuroscience research questions.

International audience; Understanding the dynamics of brain-scale functional networks at rest and during cognitive tasks is the subject of intense research efforts to unveil fundamental principles of brain functions. To estimate these large-scale brain networks, the emergent method called "electroencephalography (EEG) source connectivity" has generated increasing interest in the network neuroscience community, due to its ability to identify cortical brain networks with satisfactory spatio-temporal resolution, while reducing mixing and volume conduction effects. However, no consensus has been reached yet regarding a unified EEG source connectivity pipeline, and several methodological issues have to be carefully accounted to avoid pitfalls. Thus, a validation toolbox that provides flexible "ground truth" models is needed for an objective methods/parameters evaluation and, thereby an optimization of the EEG source connectivity pipeline. In this paper, we show how a recently developed large-scale model of brain-scale activity, named COALIA, can provide to some extent such ground truth by providing realistic simulations of source-level and scalp-level activity. Using a bottom-up approach, the model bridges cortical micro-circuitry and large-scale network dynamics. Here, we provide an example of the potential use of COALIA to analyze, in the context of epileptiform activity, the effect of three key factors involved in the "EEG source connectivity" pipeline: (i) EEG sensors density, (ii) algorithm used to solve the inverse problem, and (iii) functional connectivity measure. Results showed that a high electrode density (at least 64 channels) is required to accurately estimate cortical networks. Regarding the inverse solution/connectivity measure combination, the best performance at high electrode density was obtained using the weighted minimum norm estimate (wMNE) combined with the weighted phase lag index (wPLI). Although those results are specific to the considered aforementioned context (epileptiform activity), we believe that this model-based approach can be successfully applied to other experimental questions/contexts. We aim at presenting a proof-of-concept of the interest of COALIA in the network neuroscience field, and its potential use in optimizing the EEG source-space network estimation pipeline.

International audience; Objective - We aimed to characterize epilepsy of infancy with migrating focal seizures (EIMFS), a rare, severe early onset developmental epilepsy related to KCNT1 mutation, and to define specific electroencephalography (EEG) markers using EEG quantitative analysis. The ultimate goal would be to improve early diagnosis and to better understand seizure onset and propagation of EIMFS as compared to other early onset developmental epilepsy. Methods - EEG of 7 EIMFS patients with KCNT1 mutations (115 seizures) and 17 patients with other early onset epilepsies (30 seizures) was included in this study. After detection of seizure onset and termination, spatiotemporal characteristics were quantified. Seizure propagation dynamics were analyzed using chronograms and phase coherence. Results - In patients with EIMFS, seizures started and were localized predominantly in temporal and occipital areas, and evolved with a stable frequency (4-10 Hz). Inter- and intrahemispheric migrations were present in 60% of EIMFS seizures with high intraindividual reproducibility of temporospatial dynamics. Interhemispheric migrating seizures spread in 71% from temporal or occipital channels to the homologous contralateral ones, whereas intrahemispheric seizures involved mainly frontotemporal, temporal, and occipital channels. Causality links were present between ictal activities detected under different channels during migrating seizures. Finally, time delay index (based on delays between the different ictal onsets) and phase correlation index (based on coherence of ictal activities) allowed discrimination of EIMFS and non-EIMFS seizures with a specificity of 91.2% and a sensitivity of 84.4%. Significance - We showed that the migrating pattern in EIMFS is not a random process, as suggested previously, and that it is a particular propagation pattern that follows the classical propagation pathways. It is notable that this study reveals specific EEG markers (time delay and phase correlation) accessible to visual evaluation, which will improve EIMFS diagnosis.

International audience; BACKGROUND: Electrical brain stimulation is recognized as a promising therapeutic approach for treating brain disorders such as epilepsy. However, the use of this technique is still largely empirical, since stimulation parameters and targets are chosen using a trial-and-error approach. Therefore, there is a pressing need to design optimal, rationale-based multi-site brain stimulation protocols to control epileptiform activity. APPROACH: Here, we developed biologically-inspired models of brain activity receiving stimulation at two levels of description (single- and multi-population epileptogenic networks). First, we used bifurcation analysis to determine optimal parameters able to abort epileptiform patterns. Second, we present a graph-theory based method to classify network populations in an epileptogenic network based on their contribution to seizure generation and propagation. MAIN RESULTS: The best therapeutic effects (i.e., reduction of epileptiform discharges duration and occurence rate) were obtained by the specific targeting of populations with the highest eigenvector centrality values. The timing of stimulation was also found to be critical in seizure abortion impact. SIGNIFICANCE: Overall, our results provide a proof-of-concept that using network neuroscience combined with physiology-based computational models of brain activity can provide an effective method for the rational design of brain stimulation protocols in epilepsy.

In addition to the subthalamic nucleus’ (STN) role in motor control, STN deep brain stimulation (DBS) for Parkinson’s disease (PD) has also uncovered its involvement in cognitive and limbic processing. STN neural oscillations analyzed through local field potential (LFP) recordings have been shown to contribute to emotional (mostly in the alpha band [8-12 Hz]) and cognitive processing (theta [4-7 Hz] and beta [13-30 Hz] bands). In this study, we aimed at testing the hypothesis that STN oscillatory activity is involved in explicit and implicit processing of emotions. To achieve this objective, we used a task that presented patients with fearful emotional facial expressions and asked them to identify the emotion (explicit task) or gender associated with the face (implicit task). We evaluated emotion and task effects on STN neural oscillations power and intertrial phase consistency. Our results revealed that accuracy was lower in the implicit task. Increased STN delta power and decreased alpha and beta power were observed after stimulus presentation. However, there was no influence of emotional facial expression, i.e. neutral versus fear, nor task demands. Intertrial phase consistency in the delta and theta band increased after stimulus onset, in the same time-period as delta power increased. However, similarly to oscillatory power, no changes related to emotional fear expression or task demand were found.These findings suggest that STN oscillatory activity is not specifically involved in explicit and/or implicit processing of emotions, and that power and phase synchronization changes might be more related to overall task-execution mechanisms. These conjectures remain to be confirmed.Highlights– STN LFPs were recorded during an emotional/gender recognition task in PD patients.– STN delta power increased, and alpha and beta power decreased after stimulus onset.– Power changes were not influenced by emotional fearful expression or task demands.– Delta/theta intertrial phase consistency increased after stimulus onset.– Intertrial phase consistency was not affected by emotional valence or task demands.– The observed STN activity was likely related to general task-execution mechanisms.

IntroductionIdentifying the neural substrates underlying the personality traits is a topic of great interest. On the other hand, it is now established that the brain is a dynamic networked system which can be studied using functional connectivity techniques. However, much of the current understanding of personality-related differences in functional connectivity has been obtained through the stationary analysis, which does not capture the complex dynamical properties of brain networks.ObjectiveIn this study, we aimed to evaluate the feasibility of using dynamic network measures to predict personality traits.MethodUsing the EEG/MEG source connectivity method combined with a sliding window approach, dynamic functional brain networks were reconstructed from two datasets: 1) Resting state EEG data acquired from 56 subjects. 2) Resting state MEG data provided from the Human Connectome Project. Then, several dynamic functional connectivity metrics were evaluated.ResultsSimilar observations were obtained by the two modalities (EEG and MEG) according to the neuroticism, which showed a negative correlation with the dynamic variability of resting state brain networks. In particular, a significant relationship between this personality trait and the dynamic variability of the temporal lobe regions was observed. Results also revealed that extraversion and openness are positively correlated with the dynamics of the brain networks.ConclusionThese findings highlight the importance of tracking the dynamics of functional brain networks to improve our understanding about the neural substrates of personality.

We assessed the effects of power-line frequency (60 Hz in North America) magnetic fields (MF) in humans using simultaneous electroencephalography (EEG) and functional magnetic resonance imaging (fMRI). Twenty-five participants were enrolled in a pseudo-double-blind experiment involving "real" or "sham" exposure to sinusoidal 60 Hz MF exposures delivered using the gradient coil of an MRI scanner following two conditions: (i) 10 s exposures at 3 mT (10 repetitions); (ii) 2 s exposures at 7.6 mT (100 repetitions). Occipital EEG spectral power was computed in the alpha range (8-12 Hz, reportedly the most sensitive to MF exposure in the literature) with/without exposure. Brain functional activation was studied using fMRI blood oxygen level-dependent (BOLD, inversely correlated with EEG alpha power) maps. No significant effects were detected on occipital EEG alpha power during or post-exposure for any exposure condition. Consistent with EEG results, no effects were observed on fMRI BOLD maps in any brain region. Our results suggest that acute exposure (2-10 s) to 60 Hz MF from 3 to 7.6 mT (30,000 to 76,000 times higher than average public exposure levels for 60 Hz MF) does not induce detectable changes in EEG or BOLD signals. Combined with previous findings in which effects were observed on the BOLD signal after 1 h exposure to 3 mT, 60 Hz MF, this suggests that MF exposure in the low mT range (

Current guidelines and recommendations limiting human exposure to extremely low frequency (ELF) magnetic field (MF) are based on the most reliable acute human neurophysiological exposure responses. However, the currently available perception thresholds are established on small sample size and non-replicated experiments. The current human study aimed to test MF levels leading to mahnetophosphenes perceptions at 20, 50, 60 and 100 Hz. Perception reported button-press were recorded in these 4 frequency groups in 55 randomly assigned magnetic flux density conditions. Results showed a frequency-dependent threshold for initial perception: 2.51 T/s at 20 Hz, 6.28 T/s at 50 Hz, 7.54 T/s at 60 Hz, and 12.57 T/s at 100 Hz; and for the 50% detection rate: 4.73 T/s at 20 Hz, 8.83 T/s at 50 Hz, 10.55 T/s at 60 Hz, and 33.46 T/s at 100 Hz. Since it is a linear correlate of the induced in-situ E-fields, the dB/dt metric was chosen to analyse the the data. It confirmed lower magnetophoshene perception at lower frequencies for a similar in-situ induced E-field.

We aimed at identifying the potential relationship between the dynamical properties of the human functional network at rest and one of the most prominent traits of personality, namely resilience. To tackle this issue, we used resting-state EEG data recorded from 45 healthy subjects. Resilience was quantified using the 10-item Connor-Davidson Resilience Scale (CD-RISC). By using a sliding windows approach, brain networks in each EEG frequency band (delta, theta, alpha, and beta) were constructed using the EEG source-space connectivity method. Brain networks dynamics were evaluated using the network flexibility, linked with the tendency of a given node to change its modular affiliation over time. The results revealed a negative correlation between the psychological resilience and the brain network flexibility for a limited number of brain regions within the delta, alpha, and beta bands. This study provides evidence that network flexibility, a metric of dynamic functional networks, is strongly correlated with psychological resilience as assessed from personality testing. Beyond this proof-of-principle that reliable EEG-based quantities representative of personality traits can be identified, this motivates further investigation regarding the full spectrum of personality aspects and their relationship with functional networks., Author Summary In this study, we investigated the possible correlation between one of the most important personality traits, resilience, with a metric of dynamic functional networks called flexibility. From EEG resting-state recordings in N = 45 volunteers, we unveiled such a correlation and identified the brain regions involved in psychological resilience, from frequency-specific networks.

International audience; Objective - Among electrophysiological signals, local field potentials (LFPs) are extensively used to study brain activity, either in vivo or in vitro. LFPs are recorded with extracellular electrodes implanted in brain tissue. They reflect intermingled excitatory and inhibitory processes in neuronal assemblies. In cortical structures, LFPs mainly originate from the summation of post-synaptic potentials (PSPs), either excitatory (ePSPs) or inhibitory (iPSPs) generated at the level of pyramidal cells. The challenging issue, addressed in this paper, is to estimate, from a single extracellularly-recorded signal, both ePSP and iPSP components of the LFP. Approach - The proposed method is based on a model-based reverse engineering approach in which the measured LFP is fed into a physiologically-grounded neural mass model (mesoscopic level) to estimate the synaptic activity of a sub-population of pyramidal cells interacting with local GABAergic interneurons. Main results - The method was first validated using simulated LFPs for which excitatory and inhibitory components are known a priori and can thus serve as a ground truth. It was then evaluated on in vivo data (PTZ-induced seizures, rat; PTZ-induced excitability increase, mouse; epileptiform discharges, mouse) and on in clinico data (human seizures recorded with depth-EEG electrodes). Significance - Under these various conditions, results showed that the proposed reverse engineering method provides a reliable estimation of the average excitatory and inhibitory post-synaptic potentials originating of the measured LFPs. They also indicated that the method allows for monitoring of the excitation/inhibition ratio. The method has potential for multiple applications in neuroscience, typically when a dynamical tracking of local excitability changes is required.

Increasing evidence links disorders of consciousness (DOC) with disruptions in functional connectivity between distant brain areas. However, to which extent the balance of brain network segregation and integration is modified in DOC patients remains unclear. Using high-density electroencephalography (EEG), the objective of our study was to characterize the local and global topological changes of DOC patients' functional brain networks.Resting state high-density-EEG data were collected and analyzed from 82 participants: 61 DOC patients recovering from coma with various levels of consciousness (EMCS (n = 6), MCS+ (n = 29), MCS- (n = 17) and UWS (n = 9)), and 21 healthy subjects (i.e., controls). Functional brain networks in five different EEG frequency bands and the broadband signal were estimated using an EEG connectivity approach at the source level. Graph theory-based analyses were used to evaluate their relationship with decreasing levels of consciousness as well as group differences between healthy volunteers and DOC patient groups.Results showed that networks in DOC patients are characterized by impaired global information processing (network integration) and increased local information processing (network segregation) as compared to controls. The large-scale functional brain networks had integration decreasing with lower level of consciousness. Keywords: Disorders of consciousness, High-density electroencephalography, Functional brain networks, Unresponsive wakefulness syndrome, Minimally conscious state

International audience; Understanding the origin of the main physiological processes involved in consciousness is a major challenge of contemporary neuroscience, with crucial implications for the study of Disorders of Consciousness (DOC). The difficulties in achieving this task include the considerable quantity of experimental data in this field, along with the non-intuitive, nonlinear nature of neuronal dynamics. One possibility of integrating the main results from the experimental literature into a cohesive framework, while accounting for nonlinear brain dynamics, is the use of physiologically-inspired computational models. In this study, we present a physiologically-grounded computational model, attempting to account for the main micro-circuits identified in the human cortex, while including the specificities of each neuronal type. More specifically, the model accounts for thalamo-cortical (vertical) regulation of cortico-cortical (horizontal) connectivity, which is a central mechanism for brain information integration and processing. The distinct neuronal assemblies communicate through feedforward and feedback excitatory and inhibitory synaptic connections implemented in a template brain accounting for long-range connectome. The EEG generated by this physiologically-based simulated brain is validated through comparison with brain rhythms recorded in humans in two states of consciousness (wakefulness, sleep). Using the model, it is possible to reproduce the local disynaptic disinhibition of basket cells (fast GABAergic inhibition) and glutamatergic pyramidal neurons through long-range activation of vasoactive intestinal-peptide (VIP) interneurons that induced inhibition of somatostatin positive (SST) interneurons. The model (COALIA) predicts that the strength and dynamics of the thalamic output on the cortex control the local and long-range cortical processing of information. Furthermore, the model reproduces and explains clinical results regarding the complexity of transcranial magnetic stimulation TMS-evoked EEG responses in DOC patients and healthy volunteers, through a modulation of thalamo-cortical connectivity that governs the level of cortico-cortical communication. This new model provides a quantitative framework to accelerate the study of the physiological mechanisms involved in the emergence, maintenance and disruption (sleep, anesthesia, DOC) of consciousness.

International audience; OBJECTIVE:We aimed at providing an accurate estimation of human brain tissue electrical conductivity in clinico, using local, low-intensity pulsed stimulation.METHODS:Using the quasi-static approximation of Maxwell equations, we derived an analytical model of the electric field generated by intracerebral stereoelectroencephalography (SEEG) electrodes. We coupled this electric field model with a model of the electrode-electrolyte interface to provide an explicit, analytical expression of brain tissue conductivity based on the recorded brain tissue response to pulse stimulation.RESULTS:We validated our biophysical model using i) saline solutions calibrated in electrical conductivity, ii) rat brain tissue, and iii) electrophysiological data recorded in clinico from two epileptic patients during sEEG.CONCLUSION:This new model-based method offers a fast and reliable estimation of brain tissue electrical conductivity, by accounting for contributions from the electrode-electrolyte interface.SIGNIFICANCE:This method outperforms standard bioimpedance measurements since it provides absolute (as opposed to relative) changes in brain tissue conductivity. Application for diagnosis is envisioned since conductivity values strongly differ when estimated in the healthy vs. hyperexcitable brain tissue.

Although magnetophosphene perception is the most reliable reported effect on acute human neurophysiological responses to extremely low frequency (ELF) magnetic field (MF) exposure, current knowledge is based on small sample size, non-replicated experiments. In this study, we established MF levels triggering magnetophosphenes at 20, 50, 60 and 100 Hz in humans. Magnetophosphene perception and EEG were collected in 55 magnetic flux density conditions randomly delivered in each frequency group (2 experiments, total n=145). Results indicate that threshold values 1) need to be reported as a function of dB/dt instead of flux density, and 2) are frequency-dependent (higher sensitivity to lower frequencies). No clear trend was found in EEG data.

While electrical stimulation of brain tissue has been thoroughly investigated over the last decades, ongoing questions remain regarding the neurophysiological effects of low-level electric fields (on the order of 1 V/m) on brain activity. Electric fields at such levels are, for example, induced by transcranial direct/alternating current stimulation (tDCS/tACS). Action potentials can be indeed elicited when applied (supra-threshold) electric fields are in the 10–100 V/m range, while lower (subthreshold) electric fields result in more limited and subtler membrane polarization effects. In this review, we address the question of the mechanisms underlying the immediate effects (also referred to as acute, concurrent or short-term) and the lasting effects (also referred to as long-term or aftereffects) of low-level electric fields on brain tissue. We review recent evidence at the in vitro and in vivo (animal and human) level, and also present mechanistic insights gained from in silico models, which are still few but have received increased attention over the recent past years. We highlight the convergent evidence towards potential mechanisms, and also discuss discrepancies between in vitro studies and human tDCS/tACS studies that require further investigation to bridge the gap between the single-cell and large-scale network level. Possible novel avenues of research are discussed.

Among electrophysiological signals, Local Field Potentials (LFPs) are extensively used to study brain activity, either in vivo or in vitro. LFPs are recorded with extracellular electrodes implanted in brain tissue. They reflect intermingled excitatory and inhibitory processes in neuronal assemblies. In cortical structures, LFPs mainly originate from the summation of post-synaptic potentials (PSPs), either excitatory (ePSPs) and inhibitory (iPSPs) generated at the level of pyramidal cells. The challenging issue, addressed in this paper, is to estimate, from a single extracellularly-recorded signal, both ePSP and iPSP components of the LFP. The proposed method is based on a model-based reverse engineering approach in which the measured LFP is fed into a physiologically-grounded neural mass model (mesoscopic level) in order to estimate the synaptic activity of a sub-population of pyramidal cells interacting with local GABAergic interneurons. The method was first validated using simulated LFPs for which excitatory and inhibitory components are known a priori and can thus serve as a ground truth. It was then evaluated on in vivo data (PTZ-induced seizures, rat; PTZ-induced excitability increase, mouse; epileptiform discharges, mouse) and on in clinico data (human seizures recorded with depth-EEG electrodes). Under these various conditions, results showed that the proposed reverse engineering method provides a reliable estimation of the average excitatory and inhibitory post-synaptic potentials at the origin of the measured LFPs. They also indicated that the method allows for monitoring of the excitation/inhibition ratio. The method has potential for multiple applications in neuroscience, typically when a time tracking of local excitability changes is required.

Understanding the basic mechanisms underlying the generation of interictal and ictal activity in epilepsy is of prime importance for improving therapeutic strategies or developing new ones. However, the main difficulty lies in the integration of the enormous volume of experimental and clinical data at different spatial (cell to networks) and temporal (millisecond to years) scales into a unified framework. Over the past decades, computational (i.e., “in silico”) models of epilepsy have considerably developed and have progressively gained acceptance. They are now considered as efficient tools for integrating data coming from experimental models of epilepsy, as well as from clinical epileptology. Based on a mathematical formulation of how brain activity is generated/processed within small- and/or large-scale neural networks, modeling approaches to epilepsy seek the same objectives: provide mechanistic insights into the generation of observed epileptiform activity and generate novel experimentally testable hypotheses about underlying pathophysiological mechanisms. In this chapter, computational models developed in the field of epilepsy are reviewed, with special emphasis on models involving the neocortex and/or thalamus. Before describing the models proposed so far, we briefly present the major types of modeling approaches, with special attention to their basic assumptions, possibilities, and limitations. Readers may refer to the book entitled Computational Neuroscience in Epilepsy for a quite comprehensive state-of-the-art overview of this domain.

International audience; Humans are surrounded by sources of daily exposure to power-frequency (60 Hz in North America) magnetic fields (MFs). Such time-varying MFs induce electric fields and currents in living structures which possibly lead to biological effects. The present pilot study examined possible extremely low frequency (ELF) MF effects on human neuromotor control in general, and physiological postural tremor and electroencephalography (EEG) in particular. Since the EEG cortical mu-rhythm (8-12 Hz) from the primary motor cortex and physiological tremor are related, it was hypothesized that a 60 Hz MF exposure focused on this cortical region could acutely modulate human physiological tremor. Ten healthy volunteers (age: 23.8 ± 4 SD) were fitted with a MRI-compatible EEG cap while exposed to 11 MF conditions (60 Hz, 0 to 50 mTrms, 5 mTrms increments). Simultaneously, physiological tremor (recorded from the contralateral index finger) and EEG (from associated motor and somatosensory brain regions) were measured. Results showed no significant main effect of MF exposure conditions on any of the analyzed physiological tremor characteristics. In terms of EEG, no significant effects of the MF were observed for C1, C3, C5 and CP1 electrodes. However, a significant main effect was found for CP3 and CP5 electrodes, both suggesting a decreased mu-rhythm spectral power with increasing MF flux density. This is however not confirmed by Bonferroni corrected pairwise comparisons. Considering both EEG and tremor findings, no effect of the MF exposure on human motor control was observed. However, MF exposure had a subtle effect on the mu-rhythm amplitude in the brain region involved in tactile perception. Current findings are to be considered with caution due to the small size of this pilot work, but they provide preliminary insights to international agencies establishing guidelines regarding electromagnetic field exposure with new experimental data acquired in humans exposed to high mT-range MFs. © 2017 by the authors. Licensee MDPI, Basel, Switzerland.

Deep brain stimulation (DBS) is usually performed to treat advanced Parkinson’s disease (PD) patients with electrodes permanently implanted in basal ganglia while the stimulator delivers electrical impulses continuously and independently of any feedback (open-loop stimulation). Conversely, in closed-loop stimulation, electrical stimulation is delivered as a function of neuronal activities recorded and analyzed online. There is an emerging development of closed-loop DBS in the treatment of PD and a growing discussion about proposing cortical stimulation rather than DBS for this purpose. Why does it make sense to “close the loop” to treat parkinsonian symptoms? Could closed-loop stimulation applied to the cortex become a valuable therapeutic strategy for PD? Can mathematical modeling contribute to the development of this technique? We review the various evidences in favor of the use of closed-loop cortical stimulation for the treatment of advanced PD, as an emerging technique which might offer substantial clinical benefits for PD patients.

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Recent technological advances including brain imaging (higher resolution in space and time), miniaturization of integrated circuits (nanotechnologies), and acceleration of computation speed (Moore’s Law), combined with interpenetration between neuroscience, mathematics, and physics have led to the development of more biologically plausible computational models and novel therapeutic strategies. Today, mathematical models of irreversible medical conditions such as Parkinson’s disease (PD) are developed and parameterised based on clinical data. How do these evolutions have a bearing on deep brain stimulation (DBS) of patients with PD? We review how the idea of DBS, a standard therapeutic strategy used to attenuate neurological symptoms (motor, psychiatric), has emerged from past experimental and clinical observations, and present how, over the last decade, computational models based on different approaches (phase oscillator models, spiking neuron network models, population-based models) have started to shed light onto DBS mechanisms. Finally, we explore a new mathematical modelling approach based on neural field equations to optimize mechanisms of brain stimulation and achieve finer control of targeted neuronal populations. We conclude that neuroscience and mathematics are crucial partners in exploring brain stimulation and this partnership should also include other domains such as signal processing, control theory and ethics.

Parkinson's disease (PD) is a neurodegenerative disease as well as a dynamical disease. Its symptoms can be alleviated by high-frequency deep brain stimulation (DBS), which has become standard therapeutic strategy for about two decades. Today, the subthalamic nucleus (STN) is the preferred target for DBS in PD. The physiological mechanisms underlying the effects of this procedure are still far from clear, and several hypotheses have been proposed to explain the effect of DBS. However, no consensus has yet been reached. In this review paper, existing interpretations of DBS effects during STN stimulation in PD are examined. Based on results of a population-based computational model, we argue that the alleviation of PD symptoms originates in the functional decoupling of neurons in the stimulated area. This mechanism, which we call stimulation-induced functional decoupling (SIFD), is then tested against various observations, paradoxes, and modeling results. Finally, we suggest that current hypotheses indeed reflect various facets of SIFD and the resonant properties of STN neurons. This review is the first to propose an explanation of the effects of DBS by integrating and building bridges across several levels of description, including synapses, neuron population, and population network.

Deep brain stimulation (DBS) is a standard neurosurgical procedure used to treat motor symptoms in about 5% of patients with Parkinson's disease (PD). Despite the indisputable success of this procedure, the biological mechanisms underlying the clinical benefits of DBS have not yet been fully elucidated. The paper starts with a brief review on the use of DBS to treat PD symptoms. The second section introduces a computational model based on the population density approach and the Izhikevich neuron model. We explain why this model is appropriate for investigating macroscopic network effects and exploring the physiological mechanisms which respond to this treatment strategy (i.e., DBS). Finally, we present new insights into the ways this computational model may help to elucidate the dynamic network effects produced in a cerebral structure when DBS is applied.