Biology subjects

Hermens, D. F.

Publications and source records attributed to Hermens, D. F..

6 recordsLinked to original sources

bioRxiv2026

Adolescent brain-age differences between profiles based on suicidal ideation, distress and wellbeing

BackgroundPredicted brain-age derived from structural neuroimaging is being increasingly explored as a marker of brain maturation and biological age, with mental ill-health linked to advanced biological ageing in adults. Longitudinal evidence in adolescents remains limited, and the relationship between suicidality, psychological distress, wellbeing and brain-age gap (BrainAGE; predicted brain-age minus chronological age) is poorly understood. MethodsData were drawn from 135 adolescents (54.8% female; 12-16.9 years; n=688 observations) from the Longitudinal Adolescent Brain Study. Latent profile analysis (LPA) used person-level means and standard deviations of distress (K10), wellbeing (COMPAS-W) and suicidal ideation (SIDAS). BrainAGE was estimated using the CentileBrain Global-BrainAGE pipeline from FreeSurfer-derived morphometric features. Associations between cluster membership and BrainAGE were examined using linear regression and linear mixed-effects models, adjusted for chronological age and sex. ResultsThree profiles emerged: low distress (N=110; 50% female), moderate distress with greater suicidal ideation (N=14; 64% female) and moderate distress with lower suicidal ideation (N=11; 91% female). The moderate distress with lower suicidal ideation cluster showed significantly higher BrainAGE relative to the low distress cluster (B=1.87, SE=0.76, p=.015). Whereas, the moderate distress with greater suicidal ideation cluster did not differ to the low distress cluster. Chronological age was positively associated with BrainAGE (B=0.67, SE=0.07, p<.001). ConclusionsDistinct adolescent mental health profiles may be associated with BrainAGE, depending on the levels and stability of distress and suicidality. However, the longitudinal associations were less robust across small extensions of the developmental range studied, warranting some cautious interpretation and need for replication.

neuroscience↗

bioRxiv2026

Global Structural Brain-Age in Adolescence: Application of an Established Method to Short-Interval, Longitudinal Data

Brain-age estimates from grey matter structure provide a promising tool to examine the neurobiology of mental health. However, whether existing models generalise to longitudinal adolescent data remains unclear. The CentileBrain Global-BrainAGE Lifespan Model, was applied to N=138 participants (74 females, 64 males) from the Longitudinal Adolescent Brain Study. Participants completed 2-14 MRI scans between the ages of 12-17 years (752 datapoints). Model fit, prediction accuracy and longitudinal consistency were examined. Results showed moderate-to-good longitudinal consistency and reliability, consistent with high-performing cross-sectional age-to-brain-age correlations in youth cohorts. However, the model systematically overestimated brain-age changes relative to chronological changes, and prediction accuracy was unstable, with the mean absolute error increasing with age. Despite sex-specific brain-age calculations, on average, females showed older brain-ages compared with males. Further, younger adolescents were underpredicted, while older adolescents were overpredicted, indicating that standard model adjustments and assumptions may not be applicable.

neuroscience↗

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↗

bioRxiv2024

Geometric eigenmode brain fingerprinting and its longitudinal associations with adolescent mental health and wellbeing

Background Brain fingerprinting research posits that individual uniqueness can be identified by structural and functional features that may also be linked to mental health outcomes. Global structural features of the brain can be succinctly and directly captured from magnetic resonance imaging (MRI) via the eigenmodes of the cortical surface - known as geometric eigenmodes. This research investigates how the uniqueness of geometric eigenmodes changes across adolescence and their longitudinal relation to mental health and wellbeing. MethodsThe current study utilised n=613 MRI, self-report and demographic datasets from N=116 community-recruited adolescents enrolled in the Longitudinal Adolescent Brain Study (LABS), between the ages of 12-17 years. MPRAGE scans at each participants visit were used to derive 225 left-hemisphere geometric eigenmodes. Eigenmodes were clustered into 14 eigengroups and developmental trajectories of their uniqueness and longitudinal associations with mental wellbeing and psychological distress were examined. ResultsAll eigengroups become significantly more unique longitudinally, and higher mode (shorter wavelength) eigengroups were more unique than lower mode groups in adolescence. Less uniqueness in eigengroup 6 was significantly associated with higher psychological distress and lower mental wellbeing at concurrent and future timepoints. ConclusionGeometric eigengroup brain fingerprinting offers a novel way to examine neurodevelopment. This study provides evidence that eigengroups have distinct trajectories from adolescence to adulthood, consistent with other imaging studies demonstrating increasing uniqueness in this period. Importantly, they are associated with mental health state and thus may represent neurobiological markers for mental illness onset, building on previous LABS research demonstrating that the functional uniqueness of the cognitive control network predicts psychological distress four months later.

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↗