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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., (Copyright © 2021. Published by Elsevier Inc.)

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 stereotactic-EEG (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 saline solutions calibrated in electrical conductivity, rat brain tissue, and 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 versus hyperexcitable brain tissue.

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., (Copyright © 2019 The Authors. Published by Elsevier Inc. All rights reserved.)

Objective: Emerging evidence shows that the modular organization of the human brain allows for better and efficient cognitive performance. Many of these cognitive functions are very fast and occur in a sub-second time scale such as the visual object recognition., Approach: Here, we investigate brain network modularity while controlling stimuli meaningfulness and measuring a participant's reaction time. We particularly raised two questions: i) does the dynamic brain network modularity change during the recognition of meaningful and meaningless visual images? And (ii) is there a correlation between network modularity and the reaction time of the participants? To tackle these issues, we collected dense-electroencephalography (EEG, 256 channels) data from 20 healthy human subjects performing a cognitive task consisting of naming meaningful (tools, animals…) and meaningless (scrambled) images. Functional brain networks in both categories were estimated at the sub-second time scale using the EEG source connectivity method. By using multislice modularity algorithms, we tracked the reconfiguration of functional networks during the recognition of both meaningful and meaningless images., Main Results: Results showed a difference in the module's characteristics of both conditions in term of integration (interactions between modules) and occurrence (probability on average of any two brain regions to fall in the same module during the task). Integration and occurrence were greater for meaningless than for meaningful images. Our findings revealed also that the occurrence within the right frontal regions and the left occipito-temporal can help to predict the ability of the brain to rapidly recognize and name visual stimuli., Significance: We speculate that these observations are applicable not only to other fast cognitive functions but also to detect fast disconnections that can occur in some brain disorders.

Cognitive deficits in Parkinson's disease are thought to be related to altered functional brain connectivity. To date, cognitive-related changes in Parkinson's disease have never been explored with dense-EEG with the aim of establishing a relationship between the degree of cognitive impairment, on the one hand, and alterations in the functional connectivity of brain networks, on the other hand. This study was aimed at identifying altered brain networks associated with cognitive phenotypes in Parkinson's disease using dense-EEG data recorded during rest with eyes closed. Three groups of Parkinson's disease patients (N = 124) with different cognitive phenotypes coming from a data-driven cluster analysis, were studied: G1) cognitively intact patients (63), G2) patients with mild cognitive deficits (46) and G3) patients with severe cognitive deficits (15). Functional brain networks were identified using a dense-EEG source connectivity method. Pairwise functional connectivity was computed for 68 brain regions in different EEG frequency bands. Network statistics were assessed at both global (network topology) and local (inter-regional connections) level. Results revealed progressive disruptions in functional connectivity between the three patient groups, typically in the alpha band. Differences between G1 and G2 ( p  < 0.001, corrected using permutation test) were mainly frontotemporal alterations. A statistically significant correlation (ρ = 0.49, p  < 0.001) was also obtained between a proposed network-based index and the patients' cognitive score. Global properties of network topology in patients were relatively intact. These findings indicate that functional connectivity decreases with the worsening of cognitive performance and loss of frontotemporal connectivity may be a promising neuromarker of cognitive impairment in Parkinson's disease.

Background: Neurological disorders are often characterized by an excessive and prolonged imbalance between neural excitatory and inhibitory processes. An ubiquitous finding among these disorders is the disrupted function of inhibitory GABAergic interneurons., Objective: The objective is to propose a novel stimulation procedure able to evaluate the efficacy of inhibition imposed by GABAergic interneurons onto pyramidal cells from evoked responses observed in local field potentials (LFPs)., Methods: Using a computational modeling approach combined with in vivo and in vitro electrophysiological recordings, we analyzed the impact of electrical extracellular, local, bipolar stimulation (ELBS) on brain tissue. We implemented the ELBS effects in a neuronal population model in which we can tune the excitation-inhibition ratio and we investigated stimulation-related parameters. Computer simulations led to sharp predictions regarding: i) the shape of evoked responses as observed in local field potentials, ii) the type of cells (pyramidal neurons and interneurons) contributing to these field responses and iii) the optimal tuning of stimulation parameters (intensity and frequency) to evoke meaningful responses. These predictions were tested in vivo (mouse). Neurobiological mechanisms were assessed in vitro (hippocampal slices)., Results: Appropriately-tuned ELBS allows for preferential activation of GABAergic interneurons. A quantitative neural network excitability index (NNEI) is proposed. It is computed from stimulation-induced responses as reflected in local field potentials. NNEI was used in four patients with focal epilepsy. Results show that it can readily reveal hyperexcitable brain regions., Conclusion: Well-tuned ELBS and NNEI can be used to locally probe brain regions and quantify the (hyper)excitability of the underlying brain tissue., (Copyright © 2016 Elsevier Inc. All rights reserved.)

Abnormal reemergence of depolarizing GABAA current during postnatal brain maturation may play a major role in paediatric epilepsies, Dravet syndrome (DS) being among the most severe. To study the impact of depolarizing GABA onto distinct patterns of EEG activity, we extended a neural mass model as follows: one sub-population of pyramidal cells was added as well as two sub-populations of interacting interneurons, perisomatic-projecting interneurons (basket-like) with fast synaptic kinetics GABAA (fast, I1) and dendritic-projecting interneurons with slow synaptic kinetics GABAA (slow, I2). Basket-like cells were interconnected to reproduce mutual inhibition mechanisms (I1➔I1). The firing rate of interneurons was adapted to mimic the genetic alteration of voltage gated sodium channels found in DS patients, SCN1A(+/-). We implemented the "dynamic depolarizing GABAA" mediated post-synaptic potential in the model, as some studies reported that the chloride reversal potential can switch from negative to more positive value depending on interneuron activity. The "shunting inhibition" promoted by GABAA receptor activation was also implemented. We found that increasing the proportion of depolarizing GABAA mediated IPSP (I1➔I1 and I1➔P) only (i.e., other parameters left unchanged) was sufficient to sequentially switch the EEG activity from background to (1) interictal isolated polymorphic epileptic spikes, (2) fast onset activity, (3) seizure like activity and (4) seizure termination. The interictal and ictal EEG patterns observed in 4 DS patients were reproduced by the model via tuning the amount of depolarizing GABAA postsynaptic potential. Finally, we implemented the modes of action of benzodiazepines and stiripentol, two drugs recommended in DS. Both drugs blocked seizure-like activity, partially and dose-dependently when applied separately, completely and with a synergic effect when combined, as has been observed in DS patients. This computational modeling study constitutes an innovative approach to better define the role of depolarizing GABA in infantile onset epilepsy and opens the way for new therapeutic hypotheses, especially in Dravet syndrome., (Copyright © 2016. Published by Elsevier Inc.)

Developing a clear understanding of the relationship between cerebral blood flow (CBF) response and neuronal activity is of significant importance because CBF increase is essential to the health of neurons, for instance through oxygen supply. This relationship can be investigated by analyzing multimodal (fMRI, PET, laser Doppler…) recordings. However, the important number of intermediate (non-observable) variables involved in the underlying neurovascular coupling makes the discovery of mechanisms all the more difficult from the sole multimodal data. We present a new computational model developed at the population scale (voxel) with physiologically relevant but simple equations to facilitate the interpretation of regional multimodal recordings. This model links neuronal activity to regional CBF dynamics through neuro-glio-vascular coupling. This coupling involves a population of glial cells called astrocytes via their role in neurotransmitter (glutamate and GABA) recycling and their impact on neighboring vessels. In epilepsy, neuronal networks generate epileptiform discharges, leading to variations in astrocytic and CBF dynamics. In this study, we took advantage of these large variations in neuronal activity magnitude to test the capacity of our model to reproduce experimental data. We compared simulations from our model with isolated epileptiform events, which were obtained in vivo by simultaneous local field potential and laser Doppler recordings in rats after local bicuculline injection. We showed a predominant neuronal contribution for low level discharges and a significant astrocytic contribution for higher level discharges. Besides, neuronal contribution to CBF was linear while astrocytic contribution was nonlinear. Results thus indicate that the relationship between neuronal activity and CBF magnitudes can be nonlinear for isolated events and that this nonlinearity is due to astrocytic activity, highlighting the importance of astrocytes in the interpretation of regional recordings.

For efficient information processing during cognitive activity, functional brain networks have to rapidly and dynamically reorganize on a sub-second time scale. Tracking the spatiotemporal dynamics of large scale networks over this short time duration is a very challenging issue. Here, we tackle this problem by using dense electroencephalography (EEG) recorded during a picture naming task. We found that (i) the picture naming task can be divided into six brain network states (BNSs) characterized by significantly high synchronization of gamma (30-45 Hz) oscillations, (ii) fast transitions occur between these BNSs that last from 30 msec to 160 msec, (iii) based on the state of the art of the picture naming task, we consider that the spatial location of their nodes and edges, as well as the timing of transitions, indicate that each network can be associated with one or several specific function (from visual processing to articulation) and (iv) the comparison with previously-used approach aimed at localizing the sources showed that the network-based approach reveals networks that are more specific to the performed task. We speculate that the persistence of several brain regions in successive BNSs participates to fast and efficient information processing in the brain., (Copyright © 2015 Elsevier Ltd. All rights reserved.)

The pathophysiological interpretation of EEG signals recorded with depth electrodes [i.e., local field potentials (LFPs)] during interictal (between seizures) or ictal (during seizures) periods is fundamental in the presurgical evaluation of patients with drug-resistant epilepsy. Our objective was to explain specific shape features of interictal spikes in the hippocampus (observed in LFPs) in terms of cell- and network-related parameters of neuronal circuits that generate these events. We developed a neural network model based on "minimal" but biologically relevant neuron models interconnected through GABAergic and glutamatergic synapses that reproduce the main physiological features of the CA1 subfield. Simulated LFPs were obtained by solving the forward problem (dipole theory) from networks including a large number ( approximately 3000) of cells. Insertion of appropriate parameters allowed the model to simulate events that closely resemble actual epileptic spikes. Moreover, the shape of the early fast component ("spike'') and the late slow component ("negative wave'') was linked to the relative contribution of glutamatergic and GABAergic synaptic currents in pyramidal cells. In addition, the model provides insights about the sensitivity of electrode localization with respect to recorded tissue volume and about the relationship between the LFP and the intracellular activity of principal cells and interneurons represented in the network.

Voltage-gated calcium currents are important for the survival and growth of embryonic cockroach brain neurons in primary culture. In the present experiments, we have studied, using the patch-clamp technique, the evolution with time in culture of the voltage-dependency and of the pharmacological properties of the calcium conductance of these neurons during the formation of a network. We have observed a progressive increase of the high-voltage-activated calcium conductance and a 10mV shift of the voltage-dependency of activation towards more negative potentials. The proportion of the R-like calcium current component increased during network formation. At the same time, the highly omega-AgaTxIVA-sensitive P-like component of the current is progressively replaced by a component which is less sensitive to the toxin. The origin and functional implications of these modifications are discussed.

The contribution of voltage-gated calcium channels (VGCC) to the development of cultured embryonic cockroach brain neurons was assessed using pharmacological agents. VGCC currents were recorded using the patch-clamp technique and were found to be blocked dose-dependently by micromolar concentrations of mibefradil. The activation and inactivation properties of the calcium channels enable a sizeable calcium current to flow at rest (about -30 and -20 mV in high-potassium culture media). As expected, the cytoplasmic-free calcium concentration was found to rise when the extracellular potassium concentration was raised from 3 to 15 and 30 mM. The effects of VGCC blockers and calcium chelators were different in fresh and in mature cultures in which the neurons were connected to each other to form a defined network. In fresh cultures, the two non-selective VGCC blockers (verapamil and mibefradil) induced a dose-dependent cell death that was proportional to their blocking effect on I(Ba). This effect could not be prevented by addition of fetal calf serum to the culture medium. A similar effect was obtained using intra- or extracellular calcium chelating agents (10 microM BAPTA-AM or 10 mM EGTA). Quite unexpectedly, blockade of the P/Q-like (omega-Aga WA-sensitive) component of the calcium current by 500 nM of omega-AgaTx IVA had no lethal effect, suggesting that the corresponding channels are not involved in the survival mechanism. As expected from their lack of effect on I(Ba), isradipine, nifedipine, and omega-CgTx GVIA did not induce cell death. When the neurons started growing neurites, their sensitivity to calcium channel blockade by mibefradil decreased, indicating a correlation between neurite outgrowth and resistance to calcium depletion. In mature cultures, the neurons became resistant to mibefradil, verapamil, and BAPTA-AM. However, these agents, as well as omega-AgaTx IVA, had a significant inhibitory effect on the increase in diameter of the connectives that linked adjacent clusters of neurons. This effect has been shown to result, in the case of mibefradil, from an inhibition of neurite outgrowth characterized by a significant reduction of the number of primary neurites and secondary branchings but not to a significant modification of the diameter of individual neurites. These results support the view that, as in vertebrates, calcium influx through VGCC plays an important role in survival and neurite outgrowth of cultured embryonic insect neurons. The differential contribution of the P/Q-like and R-like (omega-Aga WA-sensitive) calcium channels in these processes is discussed.