Biology subjects

Tabbal, J.

Publications and source records attributed to Tabbal, J..

8 recordsLinked to original sources

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↗

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

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

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↗

bioRxiv2022

Analysis of task-related MEG functional brain networks using dynamic mode decomposition

ObjectiveFunctional connectivity networks explain the different brain states during diverse motor, cognitive, and sensory functions. Extracting spatial network configurations and their temporal evolution is crucial for understanding the brain function during diverse behavioral tasks. ApproachIn this study, we introduce the use of dynamic mode decomposition (DMD) to extract the dynamics of brain networks. We compared DMD with principal component analysis (PCA) using real magnetoencephalography (MEG) data during motor and memory tasks. Main ResultsThe framework generates dominant spatial brain networks and their time dynamics during simple tasks, such as button press and left-hand movement, as well as more complex tasks, such as picture naming and memory tasks. Our findings show that the DMD-based approach provides a better temporal resolution than the PCA-based approach. SignificanceWe believe that DMD has a very high potential for deciphering the spatiotemporal dynamics of electrophysiological brain network states during tasks.

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↗

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

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↗