Biology subjects

HASSAN, M.

Publications and source records attributed to HASSAN, M..

11 recordsLinked to original sources

bioRxiv2026

How Much Does Electrode Density Matter? A Multilevel Evaluation from EEG Signals to Cortical Networks

Background: High-density electroencephalography (EEG) improves spatial sampling but entails greater acquisition cost, preparation time, participant burden, and computational demand. However, the extent to which electrode reduction affects successive levels of EEG analysis from global signal properties to cortical network inference remains insufficiently understood. Methods: We systematically subsampled two independent resting-state EEG datasets comprising 120 children recorded with 128 channels and 98 adults recorded with 256 channels. Density-dependent effects were assessed across 128-, 256-, 64-, 32-, and 19-channel configurations using a multilevel framework encompassing signal representation and quality, global and regional spectral power, EEG microstates, scalp-level effective connectivity, traveling waves, and source-level cortical hub topology. Results: Sensitivity to electrode reduction increased with the spatial specificity of the outcome. Global relative spectral power and explained variance were comparatively stable across configurations. In contrast, signal-to-noise ratio, entropy, reconstruction error, regional spectral distributions, traveling-wave estimates, effective connectivity, and microstate dynamics became progressively more density-sensitive. Although canonical microstate classes remained broadly identifiable at lower densities, their global explained variance, temporal parameters, and transition structure were altered. Source-level hub identity and centrality ranking showed the greatest vulnerability, with markedly reduced cross-density agreement at 32 and 19 channels. Across both datasets, several spatially dependent measures showed a transition around 64 channels, whereas estimates obtained with 128 and 256 channels were generally more similar. Conclusions: Electrode density affects EEG outcomes according to the spatial scale of the intended inference. Sparse montages may preserve broad global summaries but are less reliable for regional, spatiotemporal, and source-network analyses. Under the resting-state conditions examined here, approximately 64 channels represented a pragmatic transition for several spatially dependent measures. This value should be interpreted as context-specific guidance rather than a universal threshold.

neuroscience↗

bioRxiv2026

Robust EEG brain-behavior associations emerge only at large sample sizes

Electroencephalography (EEG) offers unique access to the oscillatory dynamics that shape human cognition and behavior. Yet reported associations between EEG features and behavioral measures often fail to replicate, raising fundamental concerns about the robustness of this literature. A key reason is the predominance of small sample sizes (median [~]30 participants), but the consequences for reproducibility have not been systematically evaluated. Here, we provide the first large-scale empirical test of this key issue using the largest available harmonised resting-state EEG dataset, comprising 2,292 participants across healthy individuals and clinical populations. We extracted a comprehensive set of spectral, temporal, complexity, and dynamical features, and assessed their correlations with behavioral, cognitive, and mental health measures. Using extensive resampling across a wide range of sample sizes, we show that small samples produce unstable associations with inflated and inconsistent effect sizes, whereas larger cohorts converge on smaller but reproducible effects. These findings establish sample size as a critical determinant of reliability in EEG research and call for a re-evaluation of how oscillatory dynamics are linked to human cognition. By moving beyond low-powered correlations toward large-scale, reproducible approaches, the field will be better positioned to uncover the true role of neural oscillations in shaping behavior.

neuroscience↗

bioRxiv2025

Transdiagnostic electrophysiological subtypes reveal brain-behavior dimensions in youth psychiatry

Conventional psychiatric diagnoses often fail to reflect the underlying neurobiological and behavioral complexity of mental health conditions. Here, we propose a transdiagnostic, data-driven framework for stratifying youth based on large-scale multisite electroencephalography (EEG) data from 1,707 individuals aged 5-18 years, including healthy controls and individuals diagnosed with attention-deficit/hyperactivity disorder (ADHD), autism spectrum disorder (ASD), anxiety disorders (ANX), and learning disabilities (LD), along with their common comorbidities. By applying normative modeling to quantify individual deviations from typical brain functional maturation, and integrating multidimensional EEG features across spectral, temporal, complexity, and dynamical domains via similarity network fusion clustering, we identified three robust neurophysiological biotypes. These biotypes showed distinct electrophysiological and behavioral profiles, and captured meaningful brain-behavior relationships. Our findings suggest that biologically informed subtypes capture meaningful neuropsychiatric heterogeneity in youth, challenging conventional diagnostic boundaries in psychiatric nosology.

neuroscience↗

bioRxiv2025

Social, cognitive and sensory dimensions of cortical network overconnectivity in young children with autism spectrum disorder

The heterogeneity in both the neurobiological mechanisms and the phenotypic presentation of autism spectrum disorder (ASD) poses a major challenge to clinical and translational research. Early inefficiencies in functional connectivity (FC) have been associated with ASD, yet it remains unclear whether and how abnormal brain network properties may account for individual differences across ASD-related symptomatology and behaviors. We applied source-level reconstruction to resting-state high-definition EEG data in a cohort of 113 young children (40 with ASD) to identify early global and local alterations of cortical network connectivity. We subsequently used regularized canonical correlation analysis (rCCA) to characterize specific FC patterns linked to variation in cognitive, social and sensory dimensions within the autism spectrum. We found increased low-frequency FC in frontotemporal cross-hemispheric networks and lateral-occipital regions of young ASD children. RCCA revealed three distinct FC patterns in recurrent ASD-related networks, each contributing to predict individual differences in cognitive, social and sensory features. These linked FC-behavior dimensions may shed light on atypical brain network topology conferring risk for specific phenotypic manifestations of ASD, which may implicate unique underlying neurobiological mechanisms.

neuroscience↗

bioRxiv2024

Characterizing the heterogeneity of neurodegenerative diseases through EEG normative modeling

Neurodegenerative diseases such as Parkinsons (PD) and Alzheimers (AD) exhibit considerable heterogeneity of functional brain features within patient populations, complicating diagnosis, treatment, prognosis, and drug discovery. Here, we use electroencephalography (EEG) and normative modeling to investigate neurophysiological oscillatory mechanisms underpinning this heterogeneity. To this aim, we use resting-state EEG activity collected by 14 clinical units, in healthy older persons (n=499) and patients with PD (n=237) and AD (n=197), aged over 40 years old. Spectral and source connectivity analyses of EEG activity provided EEG features for normative modeling and deviation measures in the PD and AD patients. Normative models confirmed significant deviations of the EEG features in PD and AD patients over population norms, characterized by high heterogeneity and frequency-dependence. The percentage of patients with at least one deviating EEG feature was [~]30% for spectral measures and [~]80% for functional source connectivity. Notably, the spatial overlap of the deviant EEG features did not exceed 60% for spectral analysis and 25% for functional source connectivity analysis. Furthermore, the patient-specific deviations were correlated with relevant clinical measures, such as the UPDRS for PD (=0.24, p=0.025) and the MMSE for AD (=-0.26, p=0.01), indicating that greater deviations from normative EEG features are associated with worsening score values. These results suggest that the deviation percentage from EEG norms may enrich clinical assessment in PD and AD patients at individual levels in the framework of Precision Neurology.

neuroscience↗

bioRxiv2024

Dynamic Resting-State EEG Alpha Connectivity: Quantifying Brain Network State Evolution in Individuals with Psychosis

This study investigates brain dynamic connectivity patterns in psychosis and their relationship with psychopathological profile and cognitive functioning using a novel dynamic connectivity pipeline on resting-state EEG. Data from seventy-eight individuals with first-episode psychosis (FEP) and sixty control subjects (CTR) were analyzed. Source estimation was performed using eLORETA, and connectivity matrices in the alpha band were computed with the weighted phase-lag index. A modified k-means algorithm was employed to cluster connectivity matrices into distinct brain network states (BNS), from which metrics were extracted. The segmentation revealed five distinct BNSs. FEP exhibited significantly lower connectivity power in BNS 2 and 5 and a greater duration dispersion in BNS 1 than CTR. Negative correlations were identified between BNS metrics and negative symptoms in FEP. In CTR, correlations were found between BNS metrics and cognitive domains. This analysis method highlights the variability of neural dynamics in psychosis and their relationship with negative symptoms.

neuroscience↗

bioRxiv2024

Beyond homogeneity: Charting the landscape of heterogeneity in psychiatric electroencephalography

Electroencephalography (EEG) has been thoroughly studied for decades in psychiatry research. Yet its integration into clinical practice as a diagnostic/prognostic tool remains unachieved. We hypothesize that a key reason is the underlying patients heterogeneity, overlooked in psychiatric EEG research relying on a case-control approach. We combine HD-EEG with normative modeling to quantify this heterogeneity using two well-established and extensively investigated EEG characteristics -spectral power and functional connectivity-across a cohort of 1674 patients with attention-deficit/hyperactivity disorder, autism spectrum disorder, learning disorder, or anxiety, and 560 matched controls. Normative models showed that deviations from population norms among patients were highly heterogeneous and frequency-dependent. Deviation spatial overlap across patients did not exceed 40% and 24% for spectral and connectivity, respectively. Considering individual deviations in patients has significantly enhanced comparative analysis, and the identification of patient-specific markers has demonstrated a correlation with clinical assessments, representing a crucial step towards attaining precision psychiatry through EEG.

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

Predicting real-life creativity using resting state electroencephalography

Neuroscience research has shown that specific functional brain patterns can be related to creativity during multiple tasks but also at rest. Nevertheless, the electrophysiological correlates of a highly creative brain remain largely unexplored. This study aims to uncover resting-state networks related to real-life creativity using high-density electroencephalography (HD-EEG) and to test whether the strength of functional connectivity within these networks could predict individual creativity. We acquired resting-state HD-EEG data from 90 participants who completed a creativity questionnaire. We then employed connectome-based predictive modeling; a machine-learning technique that predicts behavioral measures from brain connectivity features. Using a support vector regression, our results revealed functional connectivity patterns related to high and low creativity in the gamma frequency band. In leave-one-out cross-validation, the combined model of high and low creativity networks predicted creativity scores with very good accuracy (r= 0.34, p= 0.0009). Furthermore, the models predictive power was established by an external validation on an independent dataset (N= 41), where we found a statistically significant relationship between the observed and predicted creativity scores (r= 0.37, p= 0.01). These findings reveal large-scale networks that could predict individual real-life creativity at rest, providing a crucial foundation for developing EEG network-based markers of creativity.

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↗

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↗