Biology subjects

Lagopoulos, J.

Publications and source records attributed to Lagopoulos, J..

4 recordsLinked to original sources

bioRxiv2025

Resting-State EEG Aperiodic Exponent Moderates the Association Between Age and Memory Performance in Older Adults

Memory functions are susceptible to age-related cognitive decline, making it essential to explore the underlying neurophysiological mechanisms that contribute to memory function during healthy ageing. Resting-state EEG (rsEEG) parameters, particularly the aperiodic exponent, a marker of cortical excitation-inhibition balance, and individual alpha peak frequency, a correlate of neural processing efficiency, have demonstrated associations with ageing and cognitive functions. This study investigated associations between these rsEEG markers and performance across multiple memory systems in healthy older adults (n = 99) aged 50-84 years, specifically the direct associations of these markers on memory across episodic, working, and visual short-term memory systems, assessed via computerised tasks, as well as their moderating effects on age-memory relationships. While no direct associations were seen between rsEEG markers and memory performance across tasks beyond the contribution of age, gender and education, results revealed significant moderating effects of the aperiodic exponent on age-related performance in episodic and visual short-term memory. Notably, for individuals with a higher exponent, age was not significantly associated with episodic or visual short-term memory performance, whereas those with average and lower exponent values showed poorer performance with older age. These findings suggest that average and lower aperiodic exponents may reflect a marker of decrement in age-related memory performance and higher exponents may index an underlying protective mechanism against age-related memory decline. This investigation extends the current understanding of cognitive ageing mechanisms by identifying the aperiodic exponent as a potential biomarker explaining individual differences in cognitive ageing trajectories in older adult populations, particularly in episodic and visual short-term memory systems, and establishes a framework for studying neuroprotective mechanisms and developing interventions to preserve cognitive function in older adults.

neuroscience↗

bioRxiv2021

EEG-based clusters differentiate psychological distress, sleep quality and cognitive function in adolescents

1IntroductionTo better understand the relationships between brain activity, cognitive function and mental health risk in adolescence there is value in identifying data-driven subgroups based on measurements of brain activity and function, and then comparing cognition and mental health symptoms between such subgroups. MethodsHere we implement a multi-stage analysis pipeline to identify data-driven clusters of 12-year-olds (M = 12.64, SD = 0.32) based on frequency characteristics calculated from resting state, eyes-closed electroencephalography (EEG) recordings. EEG data was collected from 59 individuals as part of their baseline assessment in the Longitudinal Adolescent Brain Study (LABS) being undertaken in Queensland, Australia. Applying multiple unsupervised clustering algorithms to these EEG features, we identified well-separated subgroups of individuals. To study patterns of difference in cognitive function and mental health symptoms between core clusters, we applied Bayesian regression models to probabilistically identify differences in these measures between clusters. ResultsWe identified 5 core clusters which were associated with distinct subtypes of resting state EEG frequency content. EEG features that were influential in differentiating clusters included Individual Alpha Frequency, relative power in 4 Hz bands up to 16 Hz, and 95% Spectral Edge Frequency. Bayesian models demonstrated substantial differences in psychological distress, sleep quality and cognitive function between these clusters. By examining associations between neurophysiology and health measures across clusters, we have identified preliminary risk and protective profiles linked to EEG characteristics. ConclusionIn this work we have developed a flexible and scaleable pipeline to identify subgroups of individuals in early adolescence on the basis of resting state EEG activity. These findings provide new clues about neurophysiological subgroups of adolescents in the general population, and associated patterns of health and cognition that are not observed at the whole group level. This approach offers potential utility in clinical risk prediction for mental and cognitive health outcomes throughout adolescent development.

neuroscience↗

bioRxiv2020

Elucidating the neuroanatomical correlates of social connectedness, sleep quality and psychological distress in early adolescence

BackgroundAdolescence is an important period for developing ones sense of self. Social connectedness has been linked to a sense of self which in turn has links to resilience in mental disorders. Adolescence is also a period of increased risk of chronic sleep deprivation during a time of ongoing white matter (WM) maturation. The complex relationship between these variables and their relationship with the onset on mental disorders during adolescence remains largely unexplored. MethodsN = 64 participants aged 12 years (M = 12.6) completed the Pittsburgh Sleep Quality Index (PSQI), Social connectedness scale (SCS) and a diffusion weighted Magnetic Resonance Imaging (MRI) scan to investigate the relationship of these variables to predict psychological distress via the Kessler psychological distress scale (K10) in early adolescents. Multiple regression analysis was used with K10 entered as the dependent variable and SCS, PSQI, and values of white matter integrity as the predictor variables. ResultsResults showed that while all four variables collectively accounted for a significant proportion of the variance in K10 (41.1%), SCS and PSQI were the only predictors that accounted for a significant proportion of variance uniquely. ConclusionsThese findings suggest interventions aimed at increasing levels of social connectedness and sleep quality during adolescence may reduce psychological distress. Future longitudinal reporting of this combination of variables is suggested.

neuroscience↗

bioRxiv2019

Brain Aging in Major Depressive Disorder: Results from the ENIGMA Major Depressive Disorder working group

BackgroundMajor depressive disorder (MDD) is associated with an increased risk of brain atrophy, aging-related diseases, and mortality. We examined potential advanced brain aging in MDD patients, and whether this process is associated with clinical characteristics in a large multi-center international dataset. MethodsWe performed a mega-analysis by pooling brain measures derived from T1-weighted MRI scans from 29 samples worldwide. Normative brain aging was estimated by predicting chronological age (10-75 years) from 7 subcortical volumes, 34 cortical thickness and 34 surface area, lateral ventricles and total intracranial volume measures separately in 1,147 male and 1,386 female controls from the ENIGMA MDD working group. The learned model parameters were applied to 1,089 male controls and 1,167 depressed males, and 1,326 female controls and 2,044 depressed females to obtain independent unbiased brain-based age predictions. The difference between predicted "brain age" and chronological age was calculated to indicate brain predicted age difference (brain-PAD). FindingsOn average, MDD patients showed a higher brain-PAD of +0.90 (SE 0.21) years (Cohens d=0.12, 95% CI 0.06-0.17) compared to controls. Relative to controls, first-episode and currently depressed patients showed higher brain-PAD (+1.2 [0.3] years), and the largest effect was observed in those with late-onset depression (+1.7 [0.7] years). In addition, higher brain-PAD was associated with higher self-reported depressive symptomatology (b=0.05, p=0.004). InterpretationThis highly powered collaborative effort showed subtle patterns of abnormal structural brain aging in MDD. Substantial within-group variance and overlap between groups were observed. Longitudinal studies of MDD and somatic health outcomes are needed to further assess the predictive value of these brain-PAD estimates. FundingThis work was supported, in part, by NIH grants U54 EB020403 and R01 MH116147.

neuroscience↗