Spatio-temporal dynamics of large-scale electrophysiological networks during cognitive action control in healthy controls and Parkinson's disease patients.

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, 10 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 task-related brain network states. 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, we found three functional network states in the beta band that involved fronto-temporal, temporo-cingulate and fronto-frontal connections with typical CAC-related prefrontal and cingulate nodes (e.g., inferior frontal cortex). The presence of these networks did not differ between PD patients and HC when analyzing microstates metrics, and no robust correlations with behavior were found. In the gamma band, five networks were found, including one fronto-temporal network that was identical to the one found in the beta band. These networks also included CAC-related nodes previously identified in different neuroimaging modalities. Similarly to the beta networks, no subject-level differences were found between PD patients and HC. Interestingly, in both frequency bands, the dominant network at the subject-level was never the one that was the most durably modulated by the task. Altogether, this study identified the dynamic functional brain networks observed during CAC, but did not highlight PD-related changes in these networks that might explain behavioral changes. Although other new methods might be needed to investigate the presence of task-related networks at the subject-level, this study still highlights that task-based dynamic functional connectivity is a promising approach in understanding the cognitive dysfunctions observed in PD and beyond.

Dynamics of task-related electrophysiological networks: a benchmarking study.

Motor, sensory and cognitive functions rely on dynamic reshaping of functional brain networks. Tracking these rapid changes is crucial to understand information processing in the brain, but challenging due to the great variety of dimensionality reduction methods used at the network-level and the limited evaluation studies. Using Magnetoencephalography (MEG) combined with Source Separation (SS) methods, we present an integrated framework to track fast dynamics of electrophysiological brain networks. We evaluate nine SS methods applied to three independent MEG databases (N=95) during motor and memory tasks. We report differences between these methods at the group and subject level. We seek to help researchers in choosing objectively the appropriate SS method when tracking fast reconfiguration of functional brain networks, due to its enormous benefits in cognitive and clinical neuroscience.

In silico model reveals the key role of GABA in KCNT1-epilepsy in infancy with migrating focal seizures.

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.

Modelling acute and lasting effects of tDCS on epileptic activity.

Transcranial Direct brain stimulation (tDCS) is commonly used in order to modulate cortical networks activity during physiological processes through the application of weak electrical fields with scalp electrodes. Cathodal stimulation has been shown to decrease brain excitability in the context of epilepsy, with variable success. However, the cellular mechanisms responsible for the acute and the long-lasting effect of tDCS remain elusive. Using a novel approach of computational modeling that combines detailed but functionally integrated neurons we built a physiologically-based thalamocortical column. This model comprises 10,000 individual neurons made of pyramidal cells, and 3 types of gamma-aminobutyric acid (GABA) -ergic cells (VIP, PV, and SST) respecting the anatomy, layers, projection, connectivity and neurites orientation. Simulating realistic electric fields in term of intensity, main results showed that 1) tDCS effects are best explained by modulation of the presynaptic probability of release 2) tDCS affects the dynamic of cortical network only if a sufficient number of neurons are modulated 3)VIP GABAergic interneurons of the superficial layer of the cortex are especially affected by tDCS 4) Long lasting effect depends on glutamatergic synaptic plasticity.

KCNT1 epilepsy with migrating focal seizures shows a temporal sequence with poor outcome, high mortality and SUDEP.

Epilepsy of infancy with migrating focal seizures was first described in 1995. Fifteen years later, KCNT1 gene mutations were identified as the major disease-causing gene of this disease. Currently, the data on epilepsy of infancy with migrating focal seizures associated with KCNT1 mutations are heterogeneous and many questions remain unanswered including the prognosis and the long-term outcome especially regarding epilepsy, neurological and developmental status and the presence of microcephaly. The aim of this study was to assess data from patients with epilepsy in infancy with migrating focal seizures with KCNT1 mutations to refine the phenotype spectrum and the outcome. We used mind maps based on medical reports of children followed in the network of the French reference centre for rare epilepsies and we developed family surveys to assess the long-term outcome. Seventeen patients were included [age: median (25th-75th percentile): 4 (2-15) years, sex ratio: 1.4, length of follow-up: 4 (2-15) years]. Seventy-one per cent started at 6 (1-52) days with sporadic motor seizures (n = 12), increasing up to a stormy phase with long lasting migrating seizures at 57 (30-89) days. The others entered this stormy phase directly at 1 (1-23) day. Ten patients entered a consecutive phase at 1.3 (1-2.8) years where seizures persisted at least daily (n = 8), but presented different semiology: brief and hypertonic with a nocturnal (n = 6) and clustered (n = 6) aspects. Suppression interictal patterns were identified on the EEG in 71% of patients (n = 12) sometimes from the first EEG (n = 6). Three patients received quinidine without reported efficacy. Long-term outcome was poor with neurological sequelae and active epilepsy except for one patient who had an early and long-lasting seizure-free period. Extracerebral symptoms probably linked with KCNT1 mutation were present, including arteriovenous fistula, dilated cardiomyopathy and precocious puberty. Eight patients (47%) had died at 3 (1.5-15.4) years including three from suspected sudden unexpected death in epilepsy. Refining the electro-clinical characteristics and the temporal sequence of epilepsy in infancy with migrating focal seizures should help diagnosis of this epilepsy. A better knowledge of the outcome allows one to advise families and to define the appropriate follow-up and therapies. Extracerebral involvement should be investigated, in particular the cardiac system, as it may be involved in the high prevalence of sudden unexpected death in epilepsy in these cases.

Quantitative analysis and EEG markers of KCNT1 epilepsy of infancy with migrating focal seizures.

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.

Multiscale neuro-inspired models for interpretation of EEG signals in patients with epilepsy.

OBJECTIVE: The aim is to gain insight into the pathophysiological mechanisms underlying interictal epileptiform discharges observed in electroencephalographic (EEG) and stereo-EEG (SEEG, depth electrodes) recordings performed during pre-surgical evaluation of patients with drug-resistant epilepsy. METHODS: We developed novel neuro-inspired computational models of the human cerebral cortex at three different levels of description: i) microscale (detailed neuron models), ii) mesoscale (neuronal mass models) and iii) macroscale (whole brain models). Although conceptually different, micro- and mesoscale models share some similar features, such as the typology of neurons (pyramidal cells and three types of interneurons), their spatial arrangement in cortical layers, and their synaptic connectivity (excitatory and inhibitory). The whole brain model consists of a large-scale network of interconnected neuronal masses, with connectivity based on the human connectome. RESULTS: For these three levels of description, the fine-tuning of free parameters and the quantitative comparison with real data allowed us to reproduce interictal epileptiform discharges with a high degree of fidelity and to formulate hypotheses about the cell- and network-related mechanisms underlying the generation of fast ripples and SEEG-recorded epileptic spikes and spike-waves. CONCLUSIONS: The proposed models provide valuable insights into the pathophysiological mechanisms underlying the generation of epileptic events. The knowledge gained from these models effectively complements the clinical analysis of SEEG data collected during the evaluation of patients with epilepsy. SIGNIFICANCE: These models are likely to play a key role in the mechanistic interpretation of epileptiform activity.

Distinct hyperexcitability mechanisms underlie fast ripples and epileptic spikes.

OBJECTIVE: In partial epilepsies, interictal epileptic spikes (IESs) and fast ripples (FRs) represent clinically relevant biomarkers characteristic of epileptogenic networks. However, their specific significance and the pathophysiological changes leading to either FRs or IESs remain elusive. The objective of this study was to analyze the conditions in which hyperexcitable networks can generate either IESs or FRs and to reveal shared or distinct mechanisms that underlie both types of events. METHODS: This study is the first to comparatively analyze mechanisms that induce either IESs or FRs using an approach that combines computational modeling and experimental data (in vivo and in vitro). A detailed CA1 hippocampal network model is introduced. A parameter sensitivity analysis was conducted to determine which model parameters (cell related and network related) allow the most accurate simulation of FRs and IESs. RESULTS: Our model indicates that although FRs and IESs share certain common mechanisms (shifted gamma-aminobutyric acid [GABA]A reversal potential, altered synaptic transmission), there are also critical differences in terms of number of pyramidal cells involved (small vs large), spatial distribution of hyperexcitable pyramidal cells (clustered vs uniform), and firing patterns (weakly vs highly synchronized). In vitro experiments verified that subtle changes in GABAergic and glutamatergic transmission favor either FRs or IESs, as predicted by the model. INTERPRETATION: This study provides insights into the interpretation of 2 interictal markers observed in intracerebral electroencephalographic data. Depending on the degree and spatiotemporal features of hyperexcitability, not only IESs or FRs are generated but also transitions between both types of events.

Shape features of epileptic spikes are a marker of epileptogenesis in mice.

PURPOSE: To identify reliable biomarkers for quantitatively assessing the development of epilepsy in brain. METHODS: In a kainate mouse model of temporal lobe epilepsy, we performed long-term video-electroencephalography (EEG) monitoring (several weeks) of freely moving animals, from kainic acid injection to chronic epileptic stage. Using signal processing techniques, we automatically detected single epileptic spikes (ESs), and we quantified the evolution of shape features during the epileptogenesis process. Using a computational model of hippocampal activity (neuronal population level), we investigated excitatory-related and inhibitory-related parameters involved in morphologic changes of ESs. KEY FINDINGS: The frequency of ESs increases during epileptogenesis. Regarding shape features, we found that both the initial spike component and the wave component of opposite polarity of ESs gradually increase during epileptogenesis. These very specific alterations of the shape of ESs were reproduced in a computational physiologically relevant neuronal population model. Using this model, we disclosed some key parameters (related to glutamatergic and γ-aminobutyric acid [GABA]ergic synaptic transmission) that explain the shape features of simulated ESs. Of interest, the model predicted that the decrease of GABAergic inhibition is responsible for the increase of the wave component of ESs. This prediction (at first sight counterintuitive) was verified in both in vivo and in vitro experiments. Finally, from aforementioned electrophysiologic features, we devised a novel and easily computable index (wave area/spike amplitude ratio) indicative of the progression of the disease (early vs. late stage). SIGNIFICANCE: Results suggest that dendritic inhibition in hippocampal circuits undertake dramatic changes over the latent period. These changes are responsible for observed modifications in the shape of ESs recorded in local field potential (LFP) signals. The proposed index may constitute a biomarker of epileptogenesis.

Interictal spikes, fast ripples and seizures in partial epilepsies--combining multi-level computational models with experimental data.

Epileptic seizures, epileptic spikes and high-frequency oscillations (HFOs) are recognized as three electrophysiological markers of epileptogenic neuronal systems. It can be reasonably hypothesized that distinct (hyper)excitability mechanisms underlie these electrophysiological signatures. The question is 'What are these mechanisms?'. Solving this difficult question would considerably help our understanding of epileptogenic processes and would also advance our interpretation of electrophysiological signals. In this paper, we show how computational models of brain epileptic activity can be used to address this issue. With a special emphasis on the hippocampal activity recorded in various experimental models (in vivo and in vitro) as well as in epileptic patients, we confront results and insights we can get from computational models lying at two different levels of description, namely macroscopic (neural mass) and microscopic (detailed network of neurons). At each level, we show how spikes, seizures and HFOs can (or cannot) be generated depending on the model features. The replication of observed signals, the prediction of possible mechanisms as well as their experimental validation are described and discussed; as are the advantages and limitations of the two modelling approaches.

An electroencephalography connectome predictive model of major depressive disorder severity.

Emerging evidence showed that major depressive disorder (MDD) is associated with disruptions of brain structural and functional networks, rather than impairment of isolated brain region. Thus, connectome-based models capable of predicting the depression severity at the individual level can be clinically useful. Here, we applied a machine-learning approach to predict the severity of depression using resting-state networks derived from source-reconstructed Electroencephalography (EEG) signals. Using regression models and three independent EEG datasets (N = 328), we tested whether resting state functional connectivity could predict individual depression score. On the first dataset, results showed that individuals scores could be reasonably predicted (r = 0.6, p = 4 × 10-18) using intrinsic functional connectivity in the EEG alpha band (8-13 Hz). In particular, the brain regions which contributed the most to the predictive network belong to the default mode network. We further tested the predictive potential of the established model by conducting two external validations on (N1 = 53, N2 = 154). Results showed statistically significant correlations between the predicted and the measured depression scale scores (r1 = 0.52, r2 = 0.44, p < 0.001). These findings lay the foundation for developing a generalizable and scientifically interpretable EEG network-based markers that can ultimately support clinicians in a biologically-based characterization of MDD.

Assessing HD-EEG functional connectivity states using a human brain computational model.

Objective.Electro/Magnetoencephalography (EEG/MEG) source-space network analysis is increasingly recognized as a powerful tool for tracking fast electrophysiological brain dynamics. However, an objective and quantitative evaluation of pipeline steps is challenging due to the lack of realistic 'controlled' data. Here, our aim is two-folded: (a) provide a quantitative assessment of the advantages and limitations of the analyzed techniques and (b) introduce (and share) a complete framework that can be used to optimize the entire pipeline of EEG/MEG source connectivity.Approach.We used a human brain computational model containing both physiologically based cellular GABAergic and Glutamatergic circuits coupled through Diffusion Tensor Imaging, to generate high-density EEG recordings. We designed a scenario of successive gamma-band oscillations in distinct cortical areas to emulate a virtual picture-naming task. We identified fast time-varying network states and quantified the performance of the key steps involved in the pipeline: (a) inverse models to reconstruct cortical-level sources, (b) functional connectivity measures to compute statistical interdependency between regional signals, and (c) dimensionality reduction methods to derive dominant brain network states (BNS).Main results.Using a systematic evaluation of the different decomposition techniques, results show significant variability among tested algorithms in terms of spatial and temporal accuracy. We outlined the spatial precision, the temporal sensitivity, and the global accuracy of the extracted BNS relative to each method. Our findings suggest a good performance of weighted minimum norm estimate/ Phase Locking Value combination to elucidate the appropriate functional networks and ICA techniques to derive relevant dynamic BNS.Significance.We suggest using such brain models to go further in the evaluation of the different steps and parameters involved in the EEG/MEG source-space network analysis. This can reduce the empirical selection of inverse model, connectivity measure, and dimensionality reduction method as some of the methods can have a considerable impact on the results and interpretation.

Transcranial current stimulation in epilepsy: A systematic review of the fundamental and clinical aspects.

Purpose: Transcranial electrical current stimulation (tES or tCS, as it is sometimes referred to) has been proposed as non-invasive therapy for pharmacoresistant epilepsy. This technique, which includes direct current (tDCS) and alternating current (tACS) stimulation involves the application of weak currents across the cortex to change cortical excitability. Although clinical trials have demonstrated the therapeutic efficacy of tES, its specific effects on epileptic brain activity are poorly understood. We sought to summarize the clinical and fundamental effects underlying the application of tES in epilepsy. Methods: A systematic review was performed in accordance with the PRISMA guidelines. A database search was performed in PUBMED, MEDLINE, Web of Science and Cochrane CENTRAL for articles corresponding to the keywords "epilepsy AND (transcranial current stimulation OR transcranial electrical stimulation)". Results: A total of 56 studies were included in this review. Through these records, we show that tDCS and tACS epileptic patients are safe and clinically relevant techniques for epilepsy. Recent articles reported changes of functional connectivity in epileptic patients after tDCS. We argue that tDCS may act by affecting brain networks, rather than simply modifying local activity in the targeted area. To explain the mechanisms of tES, various cellular effects have been identified. Among them, reduced cell loss, mossy fiber sprouting, and hippocampal BDNF protein levels. Brain modeling and human studies highlight the influence of individual brain anatomy and physiology on the electric field distribution. Computational models may optimize the stimulation parameters and bring new therapeutic perspectives. Conclusion: Both tDCS and tACS are promising techniques for epilepsy patients. Although the clinical effects of tDCS have been repeatedly assessed, only one clinical trial has involved a consistent number of epileptic patients and little knowledge is present about the clinical outcome of tACS. To fill this gap, multicenter studies on tES in epileptic patients are needed involving novel methods such as personalized stimulation protocols based on computational modeling. Furthermore, there is a need for more in vivo studies replicating the tES parameters applied in patients. Finally, there is a lack of clinical studies investigating changes in intracranial epileptiform discharges during tES application, which could clarify the nature of tES-related local and network dynamics in epilepsy.

A personalizable autonomous neural mass model of epileptic seizures.

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.

Signal processing and computational modeling for interpretation of SEEG-recorded interictal epileptiform discharges in epileptogenic and non-epileptogenic zones.

Objective.In partial epilepsies, interictal epileptiform discharges (IEDs) are paroxysmal events observed in epileptogenic zone (EZ) and non-epileptogenic zone (NEZ). IEDs' generation and recurrence are subject to different hypotheses: they appear through glutamatergic and gamma-aminobutyric acidergic (GABAergic) processes; they may trigger seizures or prevent seizure propagation. This paper focuses on a specific class of IEDs, spike-waves (SWs), characterized by a short-duration spike followed by a longer duration wave, both of the same polarity. Signal analysis and neurophysiological mathematical models are used to interpret puzzling IED generation.Approach.Interictal activity was recorded by intracranial stereo-electroencephalography (SEEG) electrodes in five different patients. SEEG experts identified the epileptic and non-epileptic zones in which IEDs were detected. After quantifying spatial and temporal features of the detected IEDs, the most significant features for classifying epileptic and non-epileptic zones were determined. A neurophysiologically-plausible mathematical model was then introduced to simulate the IEDs and understand the underlying differences observed in epileptic and non-epileptic zone IEDs.Main results.Two classes of SWs were identified according to subtle differences in morphology and timing of the spike and wave component. Results showed that type-1 SWs were generated in epileptogenic regions also involved at seizure onset, while type-2 SWs were produced in the propagation or non-involved areas. The modeling study indicated that synaptic kinetics, cortical organization, and network interactions determined the morphology of the simulated SEEG signals. Modeling results suggested that the IED morphologies were linked to the degree of preserved inhibition.Significance.This work contributes to the understanding of different mechanisms generating IEDs in epileptic networks. The combination of signal analysis and computational models provides an efficient framework for exploring IEDs in partial epilepsies and classifying EZ and NEZ.

Long term evolution of fast ripples during epileptogenesis.

Objective.Fast ripples (FRs) have received considerable attention in the last decade since they represent an electrophysiological biomarker of the epileptogenic zone (EZ). However, the real dynamics underlying the occurrence, amplitude, and time-frequency content of FRs generation during epileptogenesis are still not well understood. This work aims at characterizing and explaining the evolution of these features.Approach.Intracortical electroencephalographic signals recorded in a kainate mouse model of temporal lobe epilepsy were processed in order to compute specific FR features. Then realistic physiologically based computational modeling was employed to explore the different elements that can explain the mechanisms of epileptogenesis and simulate the recorded FR in the early and late latent period.Main results.Results indicated that continuous changes of FR features are mainly portrayed by the epileptic (pathological) tissue size and synaptic properties. Furthermore, the microelectrodes characteristics were found to dramatically affect the observability and spectral/temporal content of FRs. Consequently, FRs evolution seems to mirror the continuous pathophysiological mechanism changes that occur during epileptogenesis as long as the microelectrode properties are taken into account.Significance.Our study suggests that FRs can account for the pathophysiological changes which might explain the EZ generation and evolution and can contribute in the treatment plan of pharmaco-resistant epilepsies.

Computational modeling of high-frequency oscillations at the onset of neocortical partial seizures: from 'altered structure' to 'dysfunction'.

In this paper, a neural mass model is proposed to analyze some mechanisms underlying the generation of fast oscillations (80 Hz and beyond) at the onset of seizures. This model includes one sub-population of pyramidal cells and one sub-population of interneurons targeting the perisomatic region of pyramidal cells where fast GABAergic currents are mediated. We identified some conditions for which the model can reproduce the features of high-frequency, chirp-like (from approximately 100 to approximately 70 Hz) signatures observed in real depth-EEG signals recorded in epileptic patients at seizure onset ("fast onset activity"). These conditions included appropriate alterations in (i) the strengths of GABAergic and glutamatergic connections, and (ii) the amplitude of average EPSPs/IPSPs. Results revealed that a subtle balance between excitatory and inhibitory feedbacks is required in the model for reproducing a 'realistic' fast activity, i.e., showing a reduction of frequency with a simultaneous increase in amplitude, as actually observed in epileptogenic cerebral cortex. Results also demonstrated that the number of scenarios (variation, in time, of model parameters) leading to chirp-like signatures was rather limited. First, to produce high-frequency output signals, the model should operate in a "resonance" region, at the frontier between a stable and an unstable region. Second both EPSP and IPSP amplitudes should decrease with time in order to obey the frequency/amplitude constraint. These scenarios obtained through a mathematical analysis of the model show how some alteration in the structure of neural networks can lead to dysfunction. They also provide insights into potentially important mechanisms for high-frequency epileptic activity generation.

Decoding the circuitry of consciousness: From local microcircuits to brain-scale networks.

Identifying the physiological processes underlying the emergence and maintenance of consciousness is one of the most fundamental problems of neuroscience, with implications ranging from fundamental neuroscience to the treatment of patients with disorders of consciousness (DOCs). One major challenge is to understand how cortical circuits at drastically different spatial scales, from local networks to brain-scale networks, operate in concert to enable consciousness, and how those processes are impaired in DOC patients. In this review, we attempt to relate available neurophysiological and clinical data with existing theoretical models of consciousness, while linking the micro- and macrocircuit levels. First, we address the relationships between awareness and wakefulness on the one hand, and cortico-cortical and thalamo-cortical connectivity on the other hand. Second, we discuss the role of three main types of GABAergic interneurons in specific circuits responsible for the dynamical reorganization of functional networks. Third, we explore advances in the functional role of nested oscillations for neural synchronization and communication, emphasizing the importance of the balance between local (high-frequency) and distant (low-frequency) activity for efficient information processing. The clinical implications of these theoretical considerations are presented. We propose that such cellular-scale mechanisms could extend current theories of consciousness.

COALIA: A Computational Model of Human EEG for Consciousness Research.

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.

Reconstruction of post-synaptic potentials by reverse modeling of local field potentials.

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.