Biology subjects

Modolo, J.

Publications and source records attributed to Modolo, J..

At least 19 recordsLinked to original sources

bioRxiv2026

Computational Insights into the tACS Modulation in Healthy and Epileptic Brain Networks

Transcranial Electric stimulation (tES) is a safe and noninvasive technique increasingly used in treating brain disorders. Despite many studies on tES, there is still a lack of understanding of its mechanisms at the microscale network level. This is crucial for optimized parameter selection in therapy approaches such as the treatment of pharmacoresistant epilepsy. In this study, we made use of a recently published neuroinspired microscale model of the neocortex, known as NeoCoMM, and integrated a "Lambda E"-based model of tES. This updated version was used to investigate the acute effects of tES (tDCS and tACS), on the neural activity of various neuron types in both healthy and epileptic brain states. Results showed that in the case of healthy alpha and gamma rhythms, tACS induced electric field entrainment at the peak power frequency of the network oscillations as measured by the Local field Potentials (LFPs). This resonance-like entrainment was independent from the individual firing rate of cell types. For epileptic activity, tACS did not provide consistent results. Cathodal tDCS resulted in a promising decrease in hyperexcitable activity throughout simulations. These results advance our understanding of the impact of tES on network dynamics at both the extracellular and intracellular activity levels. Author summary

neuroscience↗

bioRxiv2026

Broadband gamma-band EEG changes during magnetophosphene perception induced by 20 Hz magnetic field stimulation

ObjectiveMagnetophosphenes are visual percepts induced by extremely low-frequency magnetic fields (ELF-MF; <300 Hz), yet the EEG expression of suprathreshold magnetophosphene-inducing stimulation remains poorly characterized and is not reliably captured by classical low-frequency markers. We tested whether suprathreshold 20 Hz tAMS is accompanied by broadband high-frequency EEG changes rather than focal oscillatory effects. ApproachEEG was recorded in N=13 healthy volunteers during 20 Hz sinusoidal magnetic-field exposure delivered using transcranial alternating magnetic stimulation (tAMS) in a global-head configuration. Three conditions were analyzed: no exposure (0 mT), subthreshold (5 mT), and suprathreshold (50 mT). Gamma-band activity (30-80 Hz) was quantified using complementary spectral approaches, including aperiodic-adjusted gamma power. Main resultsPerception reports sharply dissociated the three conditions, with frequent perception at 50 mT only. Suprathreshold stimulation was associated with spatially distributed increases in gamma-band activity over frontal and occipito-parietal electrodes. These effects persisted after aperiodic correction using two independent parameterization methods and did not exhibit a consistent narrowband peak, indicating broadband high-frequency changes. No focal power change over primary occipital electrodes remained significant after Bonferroni correction. SignificanceSuprathreshold magnetophosphene-inducing stimulation is not reliably captured by focal low-frequency EEG markers but is accompanied by distributed broadband high-frequency activity. Because stimulation intensity and perceptual reports were strongly coupled, these effects should be interpreted as EEG correlates of a suprathreshold stimulation-perception state rather than as isolated markers of perception.

neuroscience↗

bioRxiv2025

Accurate evaluation of neural sensitivity to weak alternating electric fields requires protocols accounting for input dependence

Background: Weak alternating electric fields ([~]1 V/m) modulate nervous system activity. Yet, the exact mechanism by which such low amplitude electric fields can modulate neural activity is still unknown despite the use of specific protocols aiming at quantifying neural sensitivity. Objective: To characterize how the measurement protocol can impact neural sensitivity to weak electric fields and bias in vivo sensitivity. Methods: We considered a variety of somatic clamp stimulation to drive the activity of biophysical morpho-realistic reconstructed neurons during extracellular alternating stimulation to quantify the sensitivity to the field depending on the nature of cells activity. Results: Cells sensitivity to alternating current stimulation depended on the type of input used to drive their activity, with a different frequency response for each protocol, with a trend for inhibitory neurons to be more sensitive to higher stimulation frequencies. Even with the same clamp protocol, sensitivity depended on the statistics of the input used. Significance: Neuronal sensitivity to alternating current stimulation is highly input-dependent, which has been largely neglected so far, and depends on the current statistic of the received inputs, and is not reliably represented by simple current clamps protocols.

neuroscience↗

bioRxiv2025

1.4 GHz amplitude-modulated radiofrequency electromagnetic waves effects on neural cultures

Non-invasive brain stimulation (NIBS) technologies have the potential to positively impact the treatment of neurological disorders. However, NIBS techniques suffer from a lack of penetration depth and focality, thereby restraining their full potential. Here, we investigate the feasibility of a novel technique based on sinusoidal radiofrequency electromagnetic fields (RF-EMF) that are amplitude-modulated in the extremely low frequency (ELF) range. To assess the neuromodulatory effects of ELF-modulated RF-EMF, we exposed primary neuronal cultures placed on Multi-Electrode Arrays (MEAs) and quantified their spiking activity based on phase entrainment. Our results failed to reveal consistent phase entrainment across cultures, while replicating a decrease of neuronal activity with purely sinusoidal 1.4 GHz stimulation. Finally, we suggest protocol improvements that might result in more reproducible effects, which is indispensable for translational applications.

neuroscience↗

bioRxiv2024

Functional connectivity is dominated by aperiodic, rather than oscillatory, coupling

Functional connectivity has attracted significant interest in the identification of specific circuits underlying brain (dys-)function. Classical analyses to estimate functional connectivity (i.e., filtering electrophysiological signals in canonical frequency bands and using connectivity metrics) assume that these reflect oscillatory networks. However, this approach conflates non-oscillatory, aperiodic neural activity with oscillations; raising the possibility that these functional networks may reflect aperiodic rather than oscillatory activity. Here, we provide the first study quantifying, in two different human electroencephalography (EEG) databases, the contribution of aperiodic activity on reconstructed oscillatory functional networks in resting state. We found that more than 99% of delta, theta, and gamma functional networks, more than 90% of beta functional networks and between 23 and 55% of alpha functional networks were actually driven by aperiodic activity. While there is no universal consensus on how to identify and quantify neural oscillations, our results demonstrate that oscillatory functional networks are drastically sparser than commonly assumed. These findings suggest that most functional connectivity studies focusing on resting state actually reflect aperiodic networks instead of oscillations-based networks. We highly recommend that oscillatory network analyses first check the presence of aperiodicity-unbiased neural oscillations before estimating their statistical coupling to strengthen the robustness, interpretability, and reproducibility of functional connectivity studies.

neuroscience↗

bioRxiv2023

Frequency-dependent phase entrainment of cortical cell types during tACS: Converging modeling evidence

BackgroundTranscranial alternating current stimulation (tACS) enables non-invasive modulation of brain activity, holding promise for clinical and research applications. Yet, it remains unclear how the stimulation frequency affects various neuron types. ObjectiveTo quantify the frequency-dependent behavior of key neocortical cell types. MethodsWe used both detailed (anatomical multicompartments) and simplified (three compartments) single-cell modeling approaches based on the Hodgkin-Huxley formalism to study neocortical excitatory and inhibitory cells under various-amplitude tACS frequencies within the 5-50 Hz range at rest and during basal 10 Hz activity. ResultsL5 pyramidal cells exhibited the highest polarizability at DC, ranging from 0.21 to 0.25mm and decaying exponentially with frequency. Inhibitory neurons displayed membrane resonance in the 5-15 Hz range with lower polarizability, although bipolar cells had higher polarizability. Layer 5 PC demonstrated the highest entrainment close to 10 Hz, which decayed with frequency. In contrast, inhibitory neurons entrainment increased with frequency, reaching level akin to PC. Results from simplified models could replicate the phase preferences, while amplitudes tend to follow opposite trends in PC. ConclusiontACS-induced membrane polarization is frequency-dependent, revealing observable resonance behavior. This finding motivates further experimental studies of cell-specific frequency-dependent membrane responses to weak electric stimuli. Whilst optimal phase entrainment of sustained activity is achieved in PC when tACS frequency matches the activity, inhibitory neurons tend to be entrained at higher frequencies. Consequently, this presents the potential for precise, cell-specific targeting. O_LISingle-cell models of frequency response to tACS for a wide variety of neurons C_LIO_LISimplified and morphologically accurate models were compared C_LIO_LIPyramidal cells (PC) show resonant-like response close to intrinsic firing frequency C_LIO_LIEntrainement of GABAergic neurons increases with the frequency of tACS C_LIO_LIMembrane polarization resonance in GABAergic neurons within the 5-15 Hz range C_LI

neuroscience↗

bioRxiv2023

Changes in electrophysiological aperiodic activity during cognitive control in Parkinson's disease

Cognitive symptoms in Parkinsons disease (PD) are common and can significantly affect patients quality of life. Therefore, there is an urgent clinical need to identify a signature derived from behavioral and/or neuroimaging indicators that could predict which patients are at increased risk for early and rapid cognitive decline. Recently, converging evidence identified electroencephalogram (EEG) aperiodic activity as meaningful physiological information associated with age, development, cognitive and perceptual states or pathologies. In this study, we aimed to investigate aperiodic activity in PD during cognitive control and characterize its possible association with behavior. Here, we recorded high-density EEG (HD-EEG) in 30 healthy controls and 30 PD patients during a Simon task. We analyzed task-related behavioral data in the context of the activation-suppression model and extracted aperiodic parameters (offset, exponent) at both scalp and source levels. Our results showed behavioral alterations of cognitive control as well as higher offsets in patients in the parieto-occipital areas, suggesting increased excitability in PD. A small congruence effect on aperiodic parameters in pre- and post-central brain areas was also found, possibly associated with task execution. Significant differences in aperiodic parameters between the resting state, pre- and post-stimulus phases all across the scalp and cortex confirmed that the observed changes in aperiodic activity are linked to task execution. No correlation was found between aperiodic activity and behavior or clinical features. Our findings provide evidence that EEG aperiodic activity in PD is characterized by greater offsets, and that aperiodic parameters differ depending on arousal state. However, our results do not support the hypothesis that the behavior-related differences observed in PD are related to aperiodic changes. Overall, this study highlights the importance of considering aperiodic activity contributions in brain disorders and further investigating the relationship between aperiodic activity and behavior.

neuroscience↗

bioRxiv2023

Effect of analytical variability in estimating EEG-based functional connectivity

The significant degree of variability and flexibility in neuroimaging analysis approaches has recently raised concerns. When running any neuroimaging study, the researcher is faced with a large number of methodological choices, often made arbitrarily. This can produce substantial variability in the results, ultimately hindering research replicability, and thus, robust conclusions. Here, we addressed the analytical variability in the EEG source connectivity pipeline and its effects on outcomes consistency. Like most neuroimaging analyses, the EEG source connectivity analysis involves the processing of high-dimensional data and is characterized by a complex workflow that leads to high analytical variability. In this study, we focused on source functional connectivity variability induced by three key factors along the analysis pipeline: 1) number of EEG electrodes, 2) inverse solution algorithms, and 3) functional connectivity metrics. Outcomes variability was assessed in terms of group-level consistency, inter-, and intra-subjects similarity, using resting-state EEG data (n = 88). As expected, our results showed that different choices related to the number of electrodes, source reconstruction algorithm, and functional connectivity measure substantially affect group-level consistency, between-, and within-subjects similarity. We believe that the significant impact of such methodological variability represents a critical issue for neuroimaging studies that should be prioritized. HighlightsO_LIThe significant impact of methodological variability is a recognized critical priority issue for neuroimaging studies. C_LIO_LIAnalytical variability related to the number of electrodes, source reconstruction algorithm, and functional connectivity measure is a prominent issue in the EEG source connectivity analysis. C_LIO_LIGroup-level consistency, between-, and within-subjects similarity are substantially affected by analytical variability in the EEG source connectivity analysis. C_LI

neuroscience↗

bioRxiv2023

Biophysical modeling of the electric field magnitude and distribution induced by electrical stimulation with intracerebral electrodes

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.

neuroscience↗

bioRxiv2022

Effect of channel density, inverse solutions and connectivity measures on EEG resting-state networks: a simulation study

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.

neuroscience↗

bioRxiv2021

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.

neuroscience↗

bioRxiv2021

Impaired dynamics of prefrontal beta-band networks during cognitive action control in Parkinson's disease

Among the cognitive symptoms that are associated with Parkinsons 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. HighlightsO_LICognitive action control is associated with dynamic functional networks C_LIO_LIPrefrontal and cingulate beta connectivity are prominent in healthy controls C_LIO_LIPD patients have different dynamic networks in which prefrontal nodes are absent C_LIO_LIThe occurrence of prefrontal beta networks was associated with a decreased reaction time C_LIO_LIFunctional networks in the gamma band were temporally organized in HC, but overlapping in PD patients C_LI

neuroscience↗

bioRxiv2020

Low-frequency subthalamic neural oscillations are involved in explicit and implicit facial emotional processing - a local field potential study

In addition to the subthalamic nucleus (STN) role in motor control, STN deep brain stimulation (DBS) for Parkinsons 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.

neuroscience↗

bioRxiv2020

Mean-field modeling of brain-scale dynamics for the evaluation of EEG source-space networks

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 for 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.

neuroscience↗

bioRxiv2019

Brain network dynamics correlates with personality traits

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.

bioinformatics↗

bioRxiv2019

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 VIP interneurons that induced inhibition of 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.

neuroscience↗

bioRxiv2018

Decreased integration of EEG source-space networks in disorders of consciousness

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.\n\nResting 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 group differences between healthy volunteers and patient groups.\n\nResults 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.

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bioRxiv2018

Subthalamic nucleus local field potentials recordings reveal subtle effects of promised reward during conflict resolution in Parkinson’s disease

Cognitive action control depends on cortical-subcortical circuits, involving notably the subthalamic nucleus (STN), as evidenced by local field potentials recordings (LFPs) studies. The STN consistently shows an increase in theta oscillations power during conflict resolution. Some studies have shown that cognitive action control in Parkinsons disease (PD) could be influenced by the occurrence of monetary reward. In this study, we investigated whether incentive motivation could modulate STN activity, and notably STN theta activity, during response conflict resolution. To achieve this objective, we recorded STN LFPs during a motivated Simon task in PD patients who had undergone deep brain stimulation surgery. Behavioral results revealed that promised rewards increased the difficulty in resolving conflict situations, thus replicating previous findings. Signal analyses locked on the imperative stimulus onset revealed the typical pattern of increased theta power in a conflict situation. However, this conflict-related modulation of theta power was not influenced by the size of the reward cued. We nonetheless identified a significant effect of the reward size on local functional organization (indexed by inter-trial phase clustering) of theta oscillations, with higher organization associated with high rewards while resolving conflict. When focusing on the period following the onset of the reward cue, we unveiled a stronger beta power decrease in higher reward conditions. However, these LFPs results were not correlated to behavioral results. Our study suggests that the STN is involved in how reward information can influence computations during conflict resolution. However, considering recent studies as well as the present results, we suspect that these effects are subtle.

neuroscience↗