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Gargiulo, P.

Publications and source records attributed to Gargiulo, P..

2 recordsLinked to original sources

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↗

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↗