Biology subjects

Boyes, A.

Publications and source records attributed to Boyes, A..

4 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↗

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↗

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↗