Abastract 

• Trait anxiety, a major vulnerability factor for internalizing psychopathology, remains poorly understood through integrated biological frameworks. Prior neuroimaging studies largely rely on static measures, neglecting the role of temporal dynamics and the systemic impact of physiological burden (allostatic load). We addressed this by developing a multimodal deep learning framework that integrates rs-fMRI time series and allostatic load biomarkers to predict trait anxiety. Modeling temporal dynamics significantly enhanced prediction accuracy (Pearson r=0.257). Crucially, including systemic physiological markers provided a vital, complementary signal to neuroimaging data (p=0.0008). Attention-based analysis identified the limbic and visual networks as core predictive hubs. We uncovered a dynamic signature: lower dynamic functional connectivity (dFC) transition frequency in the limbic network (ρ=−0.256) and higher dFC transition frequency in the visual network (ρ=0.226) in slow-4 band predicted increased anxiety. Among physiological markers, metabolic and immune indices were the most important predictors. Finally, correlating regional attention scores with the Allen Human Brain Atlas revealed significant enrichment for pathways governing synaptic signaling, developmental programs, and cellular stress response, linking the network signature to fundamental molecular mechanisms. This study provides a multilayered biological framework, highlighting the convergence of dynamic limbic-visual network function and systemic dysregulation in the neurobiological basis of trait anxiety..

Abastract 

• Youth depression is a major public health concern with long-term consequences and strong ties to suicidal behavior. Early detection is crucial for effective intervention, yet reliable predictive methods remain limited. To address this, we combined genetic predisposition with white matter microstructure data. Using Tract-Weighted Imaging (TWI) from diffusion MRI in 9–10-year-olds from the Adolescent Brain Cognitive Development (ABCD) cohort, we trained a 3D convolutional neural network (3DCNN) pretrained with Polygenic Risk Scores (PGS) for depression. This model outperformed from-scratch and traditional machine learning approaches, achieving an AUC of 0.62 in identifying Major Depressive Disorder (MDD) at baseline. Explainable AI revealed reduced fractional anisotropy in commissural tracts as key features. The model demonstrated robust zero-shot prediction of MDD and suicidal behavior two years later (MDD 2y: AUC = 0.61; MDD + Suicide Attempt: AUC = 0.66). Transfer learning validated its performance in an ethnically diverse cohort (AUC = 0.67). These findings highlight the potential of combining genetic and white matter data for early youth depression detection.  

Abstract 

• Episodic memory is a cornerstone of cognitive development and is essential for adaptive functioning across the lifespan. However, its development can be profoundly impacted by early-life adversity, making it critical to elucidate how such experiences influence brain connectivity and memory processes. In this study, we examined the effects of early adversity on hippocampal networks and episodic memory in Korean participants meeting inclusion criteria (N=152, age: 10-18, W=81), combining multi-modal MRI, genetic, environmental, and psychological data. Behaviorally, participants unexposed to early adversity demonstrated age-related improvements in episodic memory, a pattern absent in the adversity-exposed group. Neuroimaging findings revealed that adversity-exposed participants had reduced global structural connectivity efficiency, disrupted hippocampal-cingulate pathways in the brain networks. These results highlight the long-lasting impacts of early adversity on brain networks and memory development, emphasizing the significance of environmental influences on cognitive trajectories. This work underscores the value of integrating diverse disciplines to better understand and address the complexities of neurodevelopmental disruptions.