Biology subjects

Silk, T.

Publications and source records attributed to Silk, T..

8 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

Sleep-like Slow Waves During Wakefulness Mediate Attention and Vigilance Difficulties in Adult Attention-Deficit/Hyperactivity Disorder

BackgroundAttention-Deficit/Hyperactivity Disorder (ADHD) is characterised by behavioural variability and heightened inattention associated with increased mind wandering (MW) and mind blanking (MB). Individuals with ADHD frequently experience sleep disorders and excessive daytime sleepiness, suggesting interactions between attention and arousal systems. Research examining brain activity using electroencephalography (EEG) has demonstrated that sleep-like slow waves (SW) during wakefulness are linked to inattention in neurotypical individuals, particularly following sleep deprivation, yet their role in ADHD remains unclear. This study investigated whether individuals with ADHD present with altered waking SW distribution compared to neurotypical controls and whether SW explain attentional difficulties in ADHD. MethodsAdults with (n = 32) and without ADHD (n = 31) completed a sustained attention task while EEG recorded brain activity. Mental state probes (on-task, MW, MB) were embedded within the task. Sleep-like SW reflect a slowing of cortical activity and was detected from EEG activity. Omission/commission errors, reaction time (RT), RT variability, mental state reports and subjective sleepiness were analysed. Mediation analysis examined whether SW density explained ADHD-related performance differences. ResultsIndividuals with ADHD exhibited more commission errors, MW and MB, and higher SW density (SW/min), particularly over parieto-temporal electrodes. Increased SW density correlated with higher omission errors, slower RTs, greater RT variability, and elevated sleepiness ratings. On-task reports were negatively correlated with SW density. Mediation analysis revealed that SW density significantly accounted for ADHD-related attentional difficulties. ConclusionsWake SW may explain attentional difficulties in ADHD, providing a potential mechanistic link between sleep disturbances and attentional fluctuations.

neuroscience↗

bioRxiv2021

Multivariate Pattern Analysis (MVPA) Reveals Distinct Neural Profiles of Frontoparietal Networks in Boys with Attention-Deficit/Hyperactivity Disorder and Boys with Persistent Depressive Disorder

Working memory deficits are common in attention-deficit/hyperactivity disorder (ADHD) and depression, two common neurodevelopmental disorders with overlapping cognitive profiles but distinct clinical presentation. Multivariate techniques have previously been utilized to understand working memory processes in functional brain networks in healthy adults, but have not yet been applied to investigate how working memory processes within the same networks differ within typical and atypical developing populations. We used multivariate pattern analysis (MVPA) to identify whether brain networks discriminated between spatial vs. verbal working memory processes in ADHD and Persistent Depressive Disorder (PDD). 36 male clinical participants and 19 typically developing (TD) boys participated in a fMRI scan while completing a verbal and a spatial working memory task. Within a priori functional brain networks (frontoparietal, default mode, salience) the TD group demonstrated differential response patterns to verbal and spatial working memory. The PDD group showed weaker differentiation than TD, with lower classification accuracies observed in primarily the left frontoparietal network. The neural profiles of the ADHD and PDD differed specifically in the SN where the ADHD groups neural profile suggests significantly less specificity in neural representations of spatial and verbal working memory. We highlight within-group classification as an innovative tool for understanding the neural mechanisms of how cognitive processes may deviate in clinical disorders, an important intermediary step towards improving translational psychiatry.

neuroscience↗

bioRxiv2020

A longitudinal analysis of puberty-related cortical development

The brain undergoes extensive structural changes during adolescence, concurrent to puberty-related physical and hormonal changes. While animal research suggests these biological processes are related to one another, our knowledge of brain development in humans is largely based on age-related processes. Thus, the current study characterized puberty-related changes in human brain structure, by combining data from two longitudinal neuroimaging cohorts. Beyond normative changes in cortical thickness, we examined whether individual differences in the rate of pubertal maturation (or "pubertal tempo") was associated with variations in cortical trajectories. Participants (N = 192; scans = 366) completed up to three waves of MRI assessments between 8.5 and 14.5 years of age, as well as questionnaire assessments of pubertal stage at each wave. Generalized additive mixture models were used to characterize trajectories of cortical development. Results revealed widespread linear puberty-related changes across much of the cortex. Many of these changes, particularly within the frontal and parietal cortices, were independent of age-related development. Males exhibiting faster pubertal tempo demonstrated greater thinning in the precuneus and frontal cortices than same-aged and -sex peers. Findings suggest that the unique influence of puberty on cortical development may be more extensive than previously identified, and also emphasize important individual differences in the coupling of these developmental processes.

neuroscience↗

bioRxiv2019

Spectroscopy and Machine Learning Based Rapid Point-of-Care Assessment of Core Needle Cancer Biopsies

Solid tumor needle biopsies are essential to confirm malignancy and assess for actionable characteristics or genetic alterations to guide treatment selection. Ensuring that sufficient and suitable material is acquired for tumor profiling, while minimizing patient risk, remains a critical unmet need. Here, we evaluated the performance characteristics of transmission optical spectroscopy for rapid identification of malignant tissue in core needle biopsies (CNB). Human kidney biopsy specimens (545 CNB from 102 patients, 5583 spectra for analysis) were analyzed directly on core biopsy needles with a custom-built optical spectroscopy instrument. Machine learning classifiers were trained to differentiate malignant from normal tissue spectra. Classifiers were compared using receiver operating characteristics analysis and sensitivity and specificity were calculated relative to a histopathologic gold standard. The best performing algorithm was the random forest (sensitivity 96% and 93%, specificity 90% and 93% at the level of individual spectra and full CNB, respectively). Ex-vivo spectroscopy paired with machine learning paves the way towards rapid and accurate characterization of CNB at the time of tissue acquisition and improving tumor biopsy quality.

bioinformatics↗

bioRxiv2019

Longitudinal white matter development in children is associated with puberty, attentional difficulties, and mental health

The pubertal period involves dynamic white matter development. This period also corresponds with rapid gains in higher cognitive functions including attention, as well as increased risk of developing mental health difficulties. This longitudinal study comprised children aged 9-13 years (n=130). Diffusion magnetic resonance imaging (dMRI) data were acquired (b=2800 s/mm2, 60 directions) at two time-points. We derived measures of fibre density and morphology using the fixel-based analysis framework and performed a tract-based mixed-effects modelling analysis to understand patterns of white matter development with respect to age, pubertal stage, attentional difficulties, and internalising and externalising problems. We observed significant increases in apparent fibre density across a large number of white matter pathways, including major association and commissural pathways. We observed a linear relationship between fibre density and morphology with pubertal stage, in the right superior longitudinal fasciculus and in the right inferior longitudinal fasciculus. In terms of symptom severity, fibre density was positively associated with attentional dysfunction in the right uncinate fasciculus. Overall, white matter development across ages 9-13 years involved the expansion of major white matter fibre pathways, with key right-lateralised association pathways linked with pubertal development and attentional difficulties.

neuroscience↗

bioRxiv2017

Age, sex, and puberty related development of the corpus callosum: a multi-technique diffusion MRI study

PurposeThe corpus callosum is integral to the central nervous system, and continually develops with age by virtue of increasing axon diameter and ongoing myelination. Magnetic resonance imaging (MRI) techniques offer a means to disentangle these two aspects of white matter development. We investigate the profile of microstructural metrics across the corpus callosum, and assess the impact of age, sex and pubertal development on these processes.\n\nMethodsThis study made use of two independent paediatric populations. Multi-shell diffusion MRI data were analysed to produce a suite of diffusion tensor imaging (DTI), neurite orientation density and dispersion imaging (NODDI), and apparent fibre density (AFD) metrics. A multivariate profile analysis was performed for each diffusion metric across 10 subdivisions of the corpus callosum.\n\nResultsAll diffusion metrics significantly varied across the length of the corpus callosum. AFD exhibited a strong relationship with age across the corpus callosum (partial{eta} 2 = .65), particularly in the posterior body of the corpus callosum (partial{eta} 2 = .72). In addition, females had significantly higher AFD compared with males, most markedly in the anterior splenium (partial{eta} 2 = .14) and posterior genu (partial{eta} 2 = .13). Age-matched pubertal group differences were localised to the splenium.\n\nConclusionWe present evidence of a strong relationship between apparent fibre density and age, sex, and puberty during development. These results are consistent with ex vivo studies of fibre morphology, providing insights into the dynamics of axonal development in childhood and adolescence using diffusion MRI.\n\nTarget journalsBrain Structure & Function; HBM; NeuroImage; Developmental Cognitive Neuroscience

neuroscience↗