Associations between anterior hypothalamic subunits and ADHD and autistic traits revealed by deep learning MRI segmentation

The hypothalamus is a central regulator of neuroendocrine function and social behavior, yet its internal organization has remained difficult to examine in vivo in human neurodevelopmental research. Based on neuroendocrine models, we hypothesized that anterior hypothalamic subunits enriched in magnocellular neurosecretory nuclei—approximating the paraventricular (PVN) and supraoptic (SON) regions—form a structurally coherent axis linked to dimensional traits associated with autism spectrum disorder (ASD) and attention-deficit/hyperactivity disorder (ADHD). To test this hypothesis, we applied a convolutional neural network–based automated segmentation framework to high-resolution T1-weighted MRI data from a non-clinical adult sample (n = 23), enabling volumetric quantification of ten hypothalamic subunits. After adjustment for biological sex, age, and total intracranial volume, PVN- and SON-associated volumes showed a significant positive structural covariance (r = 0.51, p = 0.022), consistent with a coherent anterior magnocellular-rich network. Reduced SON-associated volume was significantly associated with higher autistic traits (Autism-Spectrum Quotient: r = − 0.45, p = 0.046) and greater ADHD symptom severity (CAARS: r = − 0.59, p = 0.006). Bootstrapped path analyses (5,000 iterations) further supported this network organization, revealing a robust association between PVN- and SON-associated volumes (β = 0.51, p = 0.013) and a significant indirect association between PVN-associated volume and ADHD symptoms via SON-associated volume (β = − 0.29, p = 0.006), with a marginal indirect association observed for autistic traits. Together, these preliminary findings provide in vivo evidence linking anterior hypothalamic structure to dimensional neurodevelopmental traits and highlight a potential neuroendocrine pathway relevant to psychiatric vulnerability.