Biology subjects

Khalil, M.

Publications and source records attributed to Khalil, M..

17 recordsLinked to original sources

bioRxiv2026

Effects of Instructional Context on Neural Features of Attention during Learning Activities in Children with and without ADHD

Attention is foundational to learning, yet the extent to which features of the instructional environment differentially shape attentional engagement is not well understood. Here we leveraged mobile EEG and video-coded behavioral observation to examine attentional engagement in 6 to 10 years old children with and without a diagnosis of ADHD across instructional conditions varying in delivery modality (video watching, online, in-person) and management of learning (teacher-led versus student-led). EEG measures, including alpha-band (8-12Hz) oscillations, spectral slope and offset, and behavioral measures of active engagement (AE%), passive engagement (PE%), fidgeting, off-task behavior, were examined as a function of instructional context. Attentional engagement, as indicated by higher AE%, lower alpha power, flatter spectral slope and lower offset, was greatest in student-led learning, followed by teacher-led in person learning, synchronous online learning, and asynchronous learning, respectively. Child by instructional context interactions revealed that patterns of attentional engagement were largely consistent regardless of diagnosis, with group effects observed for motor behaviors (PE%, fidgeting) but not for measures of visual attention. Similarly, age showed only main effects, whereby older children showed higher passive engagement, lower fidgeting and off-task behavior, flatter spectral slope, and lower offset. The results underscore: (i) the importance of considering environmental context, such as instructional modality and management of learning, when examining mechanisms of attention and individual differences therein, and (ii) the value of recording multiple behavioral and neural measures, as not all indices commonly assumed to reflect attention capture the same underlying processes.

neuroscience↗

bioRxiv2025

Inner-ear delivery of AAV2-retro robustly labels lateral olivocochlear efferents but results in off-target transfection in the contralateral cochlea and the brain

Gene delivery via adeno-associated viruses (AAVs) is a valuable tool for understanding the organization and function of auditory neuronal circuitry. In this study, we explored the use of AAVs injected into the inner ear to transfect the lateral olivocochlear (LOC) pathway, a poorly understood ipsilateral component of the auditory efferent feedback system. AAV serotypes exhibit a wide range of properties, including cell type selectivity and the capacity for retrograde transport, but none of the AAV serotypes used for inner ear delivery have been reported to successfully transfect the LOC pathway. Here, we show that unilateral inner ear delivery of AAV2-retro robustly labeled the LOC pathway. However, the ability of AAV2-retro to diffuse via the cochlear aqueduct from the perilymph into the cerebrospinal fluid resulted in off-target transfection in the brain and the labeling of LOC projections in the contralateral cochlea. The extent of contralateral transfection of LOC neurons depended on the age of the animal and the amount of virus delivered to the inner ear. These findings highlight the importance of considering the dosage and properties of AAV serotype when interpreting experimental results.

neuroscience↗

bioRxiv2025

Pharmacologic NLRP3 Inhibition Modulates Parkinson's Disease-Associated Microglial Transcriptomic Signatures and Mitigates α-Synuclein-Triggered Neurodegeneration

BackgroundParkinsons disease (PD), the second most common neurodegenerative disorder after Alzheimers disease, and the rare disorder multiple system atrophy (MSA), are both characterized by intracellular accumulation of -synuclein fibrils and early, sustained microglial reactivity in parallel to the neurodegeneration. Activation of the NLRP3 inflammasome in disease-associated reactive microglia is increasingly recognized as a key pathogenic driver and a promising therapeutic target in synucleinopathies. Dapansutrile (OLT1177(R)) is a selective, orally bioavailable NLRP3 inhibitor with a favorable safety profile in clinical trials for non-neurological indications. Here, we evaluated the therapeutic potential of dapansutrile in preclinical models of PD and MSA and explored the predictive and translational value of its effects. MethodsTwo established mouse models of synucleinopathy with nigral neurodegeneration were employed: the -synuclein preformed fibril (PFF) propagation model and the transgenic PLP--syn model expressing human wild-type -synuclein in oligodendrocytes. Pharmacokinetic analyses assessed plasma and brain exposure after oral administration. The efficacy of six-month dapansutrile treatment was examined in both preventive (post-PFF injection) and therapeutic (PLP--syn mice) paradigms, using behavioral, histopathological, and molecular readouts. Transcriptomic profiling of striatal and midbrain microglia identified differentially expressed genes (DEGs) associated with treatment and compared them with post-mortem transcriptomic signatures of disease-associated microglia in PD patients. Plasma IL-18 and neurofilament light chain (NfL) levels were evaluated as translational biomarkers. ResultsChronic oral dapansutrile treatment at clinically relevant doses improved motor performance, reduced -synuclein inclusions, attenuated gliosis, and mitigated nigral neurodegeneration in both models. Microglial transcriptomic analyses revealed that dapansutrile reversed key transcriptional signatures characteristic of PD-associated reactive microglia. Moreover, plasma IL-18 and NfL levels correlated with neuropathological and functional outcomes, supporting their potential as biomarkers of target engagement and treatment efficacy. ConclusionsThese data identify chronic NLRP3 activation as a shared and targetable mechanism in PD and MSA and highlight dapansutrile as a CNS-penetrant, clinically advanced candidate for disease modification in -synucleinopathies. The observed transcriptomic reprogramming of microglia and the parallel changes in blood biomarkers provide a strong translational bridge to clinical development.

neuroscience↗

bioRxiv2025

Comparative Landscape of Small RNAs in Tissue and Liquid Biopsies for Liver Transplant Outcomes

BackgroundIschemia-reperfusion injury (IRI) is an inevitable consequence of liver transplantation, arising during donor organ procurement and reoxygenation. Severe IRI is a leading contributor to early allograft dysfunction (EAD), a post-transplant complication associated with reduced graft survival. Current postoperative biomarkers provide limited time for intervention, highlighting a need to identify preoperative biomarkers of IRI. Meanwhile, tRNA fragments (tRFs) have emerged as novel biomarkers in various diseases but remain unexplored in the context of liver transplant. ResultsWe performed small RNA sequencing on 96 paired donor liver biopsies from 48 patients to investigate IRI-associated transcript changes. In parallel, 161 donor liver perfusates were analyzed as a non-invasive surrogate for tissue. Across samples, microRNAs (miRNAs) and tRFs were the most abundant. Perfusate expression strongly correlated with biopsies, supporting their value as a non-invasive source of small RNAs. Comparison between post-reperfusion and pre-implantation biopsies revealed that IRI reprogrammed tRF expression. Stratification by clinical outcome showed that patients who developed EAD exhibited specific small RNA signatures in both biopsy and perfusate. Receiver operating characteristic (ROC) analysis revealed a miRNA-based model that achieved an AUC of 0.772, outperforming donor risk index alone (AUC = 0.665), representing a 10.7% increase in discriminative capacity. ConclusionsThese results are the first to establish tRFs as IRI-responsive biomolecules abundant in both donor liver tissue and non-invasive perfusate. In particular, various small RNAs emerged as promising candidate biomarkers for early detection of EAD. These results lay the foundation to further investigate the prognostic utility of tRFs/miRNAs in liver transplantation.

genomics↗

bioRxiv2025

Domain-Specific EPR Spectroscopy to Monitor Facilitated Dissociation of a DNA-Transcription Factor Complex

Transcription factors (TFs) regulate gene expression by interacting with specific DNA sequences in various manners, yet the mechanisms by which they dissociate from high-affinity DNA sites under physiological conditions remain not completely understood. Here, we employ continuous wave electron paramagnetic resonance (CW-EPR) spectroscopy in native solution state to resolve, with domain-level precision, the dissociation dynamics of the MYC-associated factor X (MAX) from its cognate EBOX DNA motif. Site-directed spin labeling reveals that in the absence of DNA, MAX undergoes a stepwise dissociation process--beginning with melting of the N-terminal disordered region (NTD), followed by the helix-loop-helix (HLH) domain, and culminating in leucine zipper (LZ) dimer dissociation. DNA binding reorganizes this process into a cooperative all-or-none transition, wherein destabilization of the LZ triggers concerted collapse of the entire trimeric MAX:MAX-DNA complex. Strikingly, the addition of a disordered BRCA1 fragment (residues 219-504) disrupts this cooperativity by selectively destabilizing DNA contacts in the NTD and HLH regions, without perturbing the LZ. This results in facilitated dissociation via competitive DNA binding, observed here directly at domain-level resolution. Our findings establish EPR spectroscopy as a uniquely sensitive tool for dissecting TF-DNA dynamics under physiological conditions, offering mechanistic insight into regulated unbinding processes inaccessible to ensemble methods.

biochemistry↗

bioRxiv2025

Patient-Derived Three-Dimensional Lung Tumor Models to Evaluate Response to Immunotherapy

Novel preclinical models that better mimic the in vivo tumor microenvironment are needed for advanced understanding of tumor biology and resistance/response to therapy. Herein, we report development of a novel ex vivo patient-derived three-dimensional lung tumor model (3D-LTM), that maintains features of human extracellular matrix, cell-cell interactions, and tissue architecture to evaluate a rapid response to immune checkpoint inhibitors (ICI). Within this model system, we recapitulated the heterogeneity of response to immunotherapy observed in non-small cell lung cancer (NSCLC) patients and defined signatures associated with response for predicting early response of ICI in patients. Spatial transcriptomics of the 3D-LTMs identified positive correlation of CD8+ T cell populations, CD4+ memory T cells, mast cells, NK cells, endothelial cells and non-classical monocytes with response status, whereas macrophages negatively correlated with response status. Pathway analysis of gene expression showed that chemokine signaling related pathways were activated in responder 3D-LTM tissues, whereas suppression of antigen presentation-related pathways and activation of Treg differentiation-related pathways was associated with 3D-LTMs that were not considered responders. This model system has utility for rapid testing of novel immune directed therapy outcomes and for developing biomarkers of ICI response in NSCLC.

cancer biology↗

bioRxiv2023

Cell-binding IgM in CSF is distinctive of multiple sclerosis and targets the iron transporter SCARA5

Intrathecal IgM production in multiple sclerosis (MS) is associated with a worse disease course. To investigate pathogenic relevance of autoreactive IgM in MS, CSF from two independent cohorts, including MS patients and controls, were screened for antibody binding to induced pluripotent stem cell-derived neurons and astrocytes, and a panel of CNS-related cell lines. IgM binding to a primitive neuro-ectodermal tumour cell line discriminated 10% of MS donors from controls. Transcriptomes of single IgM producing CSF B cells from patients with cell-binding IgM were sequenced and used to produce recombinant monoclonal antibodies for characterisation and antigen identification. We produced 5 cell-binding recombinant IgM antibodies, of which one, cloned from an HLA-DR+ plasma-like B cell, mediated antigen-dependent complement activation. Immunoprecipitation and mass spectrometry, and biochemical and transcriptome analysis of the target cells identified the iron transport scavenger protein SCARA5 as the antigen target of this antibody. Intrathecal injection of a SCARA5 antibody led to an increased T cell infiltration in an EAE model. CSF IgM might contribute to CNS inflammation in MS by binding to cell surface antigens like SCARA5 and activating complement, or by facilitating immune cell migration into the brain.

immunology↗

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

Cleaving DNA with DNA: cooperative tuning of structure and reactivity driven by copper ions

Abstract/SummaryA copper-dependent self-cleaving DNA (DNAzyme or dexoxyribozyme) previously isolated by in vitro selection has been analyzed by a combination of Molecular Dynamics simulations and advanced EPR/ESR spectroscopy, providing insights on the structural and mechanistic features of the cleavage reaction at unprecedented resolution. The minimized 46-nucleotide deoxyribozyme forms duplex and triplex substructures that flank a highly conserved catalytic core. The self-cleaving construct forms a bimolecular complex that has a distinct substrate and enzyme domains. Cleavage of the substrate is directed at one of two adjacent nucleotides and proceeds via an oxidative cleavage mechanism that is unique to the position cleaved. The use of isotopologues of nucleotides allowed us to provide atomic resolution for the copper-substrate complex. The spectroscopic analysis overcomes the major drawbacks related to the metal-soup scenario, also known as super-stoichiometric ratios of cofactors versus substrate, conventionally required for the cleavage reaction within those nucleic acids-based enzymes. Our results pave the way for analysis on mixtures where metals/lanthanides are used as cofactors, having demonstrated that our approach may reach resolution of single nucleotide and beyond. Furthermore, the insertion of cleavage reaction within more complex architectures is now a realistic option towards the applicability of spectroscopic studies, both in vitro and in vivo matrices.

biophysics↗

bioRxiv2023

Cell population growth as manifestation of stochastic heterogeneity of cell phenotype

Recent studies at individual cell resolution have revealed phenotypic heterogeneity in nominally clonal tumor cell populations. The heterogeneity affects cell growth behaviors, which can result in departure from the idealized exponential growth. Here we measured the stochastic time courses of growth of an ensemble of populations of HL60 leukemia cells in cultures, starting with distinct initial cell numbers to capture the departure from the exponential growth model in the initial growth phase. Despite being derived from the same cell clone, we observed significant variations in the early growth patterns of individual cultures with statistically significant differences in growth kinetics and the presence of subpopulations with different growth rates that endured for many generations. Based on the hypothesis of existence of multiple inter-converting subpopulations, we developed a branching process model that captures the experimental observations.

cancer biology↗

bioRxiv2022

A Clinically Useful and Biologically Informative Genomic Classifier for Papillary Thyroid Cancer

Clinical management of papillary thyroid cancer depends on estimations of prognosis. Standard care, which relies on prognostication based on clinicopathologic features, is inaccurate. We applied a machine learning algorithm (HighLifeR) to 502 cases annotated by The Cancer Genome Atlas Project to derive an accurate molecular prognostic classifier. Unsupervised analysis of the 82 genes that were most closely associated with recurrence after surgery enabled identification of three unique molecular subtypes. One subtype had a high recurrence rate, an immunosuppressed microenvironment, and enrichment of the EZH2-HOTAIR pathway. Two other unique molecular subtypes with a lower rate of recurrence were identified, including one subtype with a paucity of BRAFV600E mutations and a high rate of RAS mutations. The genomic risk classifier, in addition to tumor size and lymph node status, enabled effective prognostication that outperformed the American Thyroid Association clinical risk stratification. The genomic classifier we derived can potentially be applied preoperatively to direct clinical decision-making. Distinct biological features of molecular subtypes also have implications regarding sensitivity to radioactive iodine, EZH2 inhibitors, and immune checkpoint inhibitors.

cancer biology↗

bioRxiv2022

Synapses, predictions, and prediction errors: a neocortical computational study of MDD using the temporal memory algorithm of HTM.

BackgroundSynapses and spines are central in major depressive disorder (MDD) pathophysiology, recently highlighted by ketamines and psilocybins rapid antidepressant effects. The Bayesian brain and interoception perspectives formalize MDD as being "stuck" in affective states constantly predicting negative energy balance. We examined how synaptic atrophy relates to the predictive function of the neocortex and thus to symptoms, using temporal memory (TM), an unsupervised machine-learning algorithm. TM represents a single neocortical layer, learns in real-time using local Hebbian-learning rules, and extracts and predicts temporal sequences. MethodsWe trained a TM model on random sequences of upper-case alphabetical letters, representing sequences of affective states. To model depression, we progressively destroyed synapses in the TM model and examined how that affected the predictive capacity of the network. ResultsDestroying 50% of the synapses slightly reduced the number of predictions, followed by a marked drop with further destruction. However, reducing the synapses by 25% dropped the confidence in the predictions distinctly. So even though the network was making accurate predictions, the network was no longer confident about these predictions. ConclusionsThese findings explain how interoceptive cortices could be stuck in limited affective states with high prediction error. Growth of new synapses, e.g., with ketamine and psilocybin, would allow representing more futuristic predictions with higher confidence. To our knowledge, this is the first study to use the TM model to connect changes happening at synaptic levels to the Bayesian formulation of psychiatric symptomatology, making it possible to understand treatment mechanisms and possibly, develop new treatments. Graphical abstract O_FIG_DISPLAY_L [Figure 1] M_FIG_DISPLAY C_FIG_DISPLAY

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

Assessing HD-EEG functional connectivity states using ahuman brain computational model

ObjectiveElectro/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: 1) provide a quantitative assessment of the advantages and limitations of the analyzed techniques and 2) introduce (and share) a complete framework that can be used to optimize the entire pipeline of EEG/MEG source connectivity. ApproachWe 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: 1) inverse models to reconstruct cortical-level sources, 2) functional connectivity measures to compute statistical interdependency between regional signals, and 3) dimensionality reduction methods to derive dominant brain network states (BNS). Main ResultsUsing 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 wMNE/PLV combination to elucidate the appropriate functional networks and ICA techniques to derive relevant dynamic brain network states. SignificanceWe 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.

neuroscience↗

bioRxiv2021

A tomato prolyl-4-hydroxylase causes relocation of abscission zone and alters abscission kinetics

The detachment of organs is controlled by highly regulated molecular mechanisms. The position of the tomato abscission zone (AZ) is defined by the ratio of the proximal to distal part of the pedicel. In this study, the ratio was altered due to a shift in the position of the AZ which was attributed to shorter and longer pedicels of SlP4H3 RNAi and OEX lines due to changes on cell division and expansion in AZ and distal part. This might be associated with LM2- and JIM8-AGPs which increased in OEX and decreased in RNAi lines throughout the pedicel. The JIM13 AGPs were downregulated in the flower AZ of OEX lines, pointing to a role on abscission regulation. In addition, Co-IP in flower AZ with SlP4H3-GFP fusion proteins showed interaction with LM2-, JIM13- and JIM8-epitopes suggesting proline hydroxylation by SlP4H3. The lower content of methyl-esterified HGs and higher of demethyl-esterified HGs in the AZs of RNAi lines might be responsible for increased rigidity of the AZ cell walls, accounting for the higher force required for AZ tissue detachment to occur. Moreover, ethylene-induced flower abscission was accelerated in the RNAi lines and delayed in OEX lines, while exactly the opposite response was observed in the red ripe fruit AZs. This was partly attributed to alterations in the expression of cell wall hydrolases. Overall, these results indicate that P4Hs might regulate molecular and structural features of cell walls in the AZ as well as abscission progression by regulating the structure and function of AGPs. Sentence SummaryAlterations on the expression of tomato prolyl 4 hydroxylase 3 shifts the position of the pedicel abscission zone and alters ethylene induced abscission progression according to the developmental context of the pedicel.

plant biology↗

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↗

bioRxiv2020

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 random selection of methods 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 show that the independent component analysis (ICA)-based methods and especially those exploring high order statistics are the most efficient, in terms of spatiotemporal accuracy and subject-level analysis. We seek to help researchers in choosing objectively the appropriate methodology when tracking fast reconfiguration of functional brain networks, due to its enormous benefits in cognitive and clinical neuroscience.

neuroscience↗