Biology subjects

Paban, V.

Publications and source records attributed to Paban, V..

7 recordsLinked to original sources

bioRxiv2026

The Lifespan Architecture of Human EEG

Human brain maturation and aging are highly nonlinear, yet their organization at the level of large-scale electrophysiological activity remains poorly understood. We analyzed resting-state electroencephalography (EEG) recordings from 1,763 healthy individuals aged 5-85 years to map lifespan trajectories across spectral, complexity, and morphological features. Age-sensitive features clustered into distinct nonlinear trajectories, most commonly showing rapid change during childhood and adolescence followed by stabilization in adulthood, while a smaller subset exhibited turning points in midlife. These trajectories also differed in their spatial expression, ranging from highly conserved scalp-wide patterns to heterogeneous profiles in which the same feature followed distinct trajectories across scalp regions. Transition ages revealed recurring regional sequences, suggesting that diverse EEG features share common reorganization chronologies. Together, these results show that lifespan EEG variation is structured through complementary temporal trajectories, spatial architectures, and regional transition sequences. This normative framework provides a basis for investigating brain development and aging and for identifying atypical patterns associated with neurological and psychiatric disorders.

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↗

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↗

bioRxiv2020

The dynamic modular fingerprints of the human brain at rest

The human brain is a dynamic modular network that can be decomposed into a set of modules and its activity changes permanently over time. At rest, several brain networks, known as Resting-State Networks (RSNs), emerge and cross-communicate even at sub-second temporal scale. Here, we seek to decipher the fast reshaping in spontaneous brain modularity and its relationship to RSNs. We use Electro/Magneto-Encephalography (EEG/MEG) to track dynamics of modular brain networks, in three independent datasets (N= 568) of healthy subjects at rest. We show the presence of striking spatiotemporal network pattern consistent over participants. We also show that some RSNs, such as default mode network and temporal network, are not necessary unified units but rather can be divided into multiple sub-networks over time. Using the resting state questionnaire, our results revealed also that brain network dynamics are strongly correlated to mental imagery at rest. These findings add new perspectives to brain dynamic analysis and highlight the importance of tracking fast reconfiguration of electrophysiological networks at rest.

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↗

bioRxiv2018

Psychological resilience correlates with EEG source-space brain network flexibility

ObjectiveWe aimed at identifying the potential relationship between the dynamical properties of the human functional network at rest and one of the most prominent traits of personality, namely resilience.\n\nApproachTo tackle this issue, we used resting-state EEG data recorded from 45 healthy subjects. Resilience was quantified using the 10-items Connor-Davidson Resilience Scale (CD-RISC). Using a sliding windows approach, brain networks in each EEG frequency band (delta, theta, alpha and beta) were constructed using the EEG source-space connectivity method. Brain networks dynamics were evaluated using the network flexibility, linked with the tendency of a given node to change its modular affiliation over time.\n\nMain ResultsThe results revealed a negative correlation between the psychological resilience and the brain network flexibility for a limited number of brain regions within the delta, alpha and beta bands.\n\nSignificanceThis study provides evidence that network flexibility, a metric of dynamic functional networks, is strongly correlated with psychological resilience as assessed from personality testing. Beyond this proof-of-principle that reliable EEG-based quantities representative of personality traits can be identified, this motivates further investigation regarding the full spectrum of personality aspects and their relationship with functional networks.

neuroscience↗