CALM Team, | bioRxiv Science

bioRxiv2026

Functional connectivity correlates of the hierarchical p-factor model in youth at neurodevelopmental risk

BackgroundEmotional and cognitive difficulties often co-occur in neurodevelopmental conditions. While transdiagnostic, dimensional approaches offer a more precise framework for understanding mental health than diagnostic categories, their neural correlates in youth with learning difficulties remain poorly understood. This study investigates associations between transdiagnostic mental health dimensions and resting-state functional connectivity in struggling learners. MethodsCross-sectional behavioural data from the Centre for Attention, Learning and Memory (CALM) for struggling learners (N = 378) was used to replicate a hierarchical model of mental health from the Conners Parent Rating Short Form, the Revised Childrens Anxiety and Depression Scale and the Strengths and Difficulties Questionnaire. Functional connectomes were derived from resting-state fMRI data (N = 67), and partial least squares regression related mental health dimensions to connectivity within and between large-scale brain networks. ResultsThe replicated model comprised a general p-factor, two broad domains (internalising and externalising), and three specific dimensions (specific internalising, neurodevelopmental and social maladjustment). Symptom severity was associated with two connectivity patterns: greater default mode network coupling to frontoparietal and attention networks, and reduced connectivity between visual and somatomotor systems. These effects were strongest for the neurodevelopmental and social maladjustment dimensions, respectively. ConclusionsThese findings align with population-level evidence linking mental health dimensions to brain network organization, extending it to struggling learners and offering new insight into the neural basis of mental health vulnerability in neurodevelopmentally at-risk youth.

neuroscience↗

bioRxiv2023

Study design features that improve effect sizes in cross-sectional and longitudinal brain-wide association studies

Brain-wide association studies (BWAS) are a fundamental tool in discovering brain-behavior associations. Several recent studies showed that thousands of study participants are required for good replicability of BWAS because the standardized effect sizes (ESs) are much smaller than the reported standardized ESs in smaller studies. Here, we perform analyses and meta-analyses of a robust effect size index using 63 longitudinal and cross-sectional magnetic resonance imaging studies from the Lifespan Brain Chart Consortium (77,695 total scans) to demonstrate that optimizing study design is critical for increasing standardized ESs and replicability in BWAS. A meta-analysis of brain volume associations with age indicates that BWAS with larger variability in covariate have larger reported standardized ES. In addition, the longitudinal studies we examined reported systematically larger standardized ES than cross-sectional studies. Analyzing age effects on global and regional brain measures from the United Kingdom Biobank and the Alzheimers Disease Neuroimaging Initiative, we show that modifying longitudinal study design through sampling schemes improves the standardized ESs and replicability. Sampling schemes that improve standardized ESs and replicability include increasing between-subject age variability in the sample and adding a single additional longitudinal measurement per subject. To ensure that our results are generalizable, we further evaluate these longitudinal sampling schemes on cognitive, psychopathology, and demographic associations with structural and functional brain outcome measures in the Adolescent Brain and Cognitive Development dataset. We demonstrate that commonly used longitudinal models can, counterintuitively, reduce standardized ESs and replicability. The benefit of conducting longitudinal studies depends on the strengths of the between-versus within-subject associations of the brain and non-brain measures. Explicitly modeling between-versus within-subject effects avoids averaging the effects and allows optimizing the standardized ESs for each separately. Together, these results provide guidance for study designs that improve the replicability of BWAS.

neuroscience↗