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Gajewski, P. D.

Publications and source records attributed to Gajewski, P. D..

2 recordsLinked to original sources

Cognitive exertion reshapes resting-state EEG markers across the adult lifespan

Resting-state electroencephalography (rsEEG) yields robust indices of ageing, notably individual alpha peak frequency (iAPF), alpha power, and the aperiodic exponent. Whether these markers reflect stable traits or shift with cognitive exertion remains unresolved, with direct consequences for lifespan and clinical research. We parameterised periodic and aperiodic rsEEG activity before and after cognitive tasks in a lifespan cohort (N = 390, aged 20-70), a five-year longitudinal follow-up (N = 100), and an independent older-adult dataset completing a different task (N = 71). Across datasets, cognitive exertion produced lifespan-wide iAPF slowing and increase in associated power. Notably, aperiodic shifts were age-dependent, with post-task steepening of the exponent in younger adults that progressively flattened with advancing age, resulting in a stronger effect of age on post-task exponents. Our results demonstrate that widely used spectral metrics exhibit acute state-dependency and post-task recordings offer a promising translational framework for indexing individual differences in healthy ageing and pathology. Trial registrationClinicaltrials.gov: NCT05155397

neuroscience↗

Electrophysiological resting-state signatures link polygenic scores to general intelligence

Intelligence is associated with important life outcomes. Behavioral, genetic, structural, and functional brain correlates of intelligence have been studied for decades, but questions remain as to how genetics are related to trait expression and what intermediary role brain properties play. This study investigated these mediations in a representative sample of 434 individuals, comprising young and older adults. Polygenic scores (PGS) for intelligence were calculated. Resting-state EEG recordings were analyzed using graph theory quantifying functional connectivity across different frequencies. We tested whether global and local graph metrics like efficiency and clustering mediated the association between PGS and intelligence. PGS significantly predicted variance in intelligence and were related to frequency-specific graph metrics in areas predominantly located in parieto-frontal regions, which in turn were associated with intelligence. These findings, which are based on the first study linking PGS to intelligence using EEG-derived graph metrics, advance our understanding of the neurogenetics of intelligence.

neuroscience↗