Automated neuroimaging pipeline for structural feature selection using deep learning segmentation applied to adolescent mental disorders

Abstract

Mental disorders are a severe public health concern still without clear biological underpinnings. Magnetic resonance imaging has emerged as a tool for interrogating biological differences in disorders, leading to structural changes as potential biomarkers for diagnostics and mechanistic understanding. To date, there are few reliable and consistently reported findings due to a need for studies with large sample sizes. Imaging analysis has previously been manually intensive, limiting the scope of such studies. Here we present an automated neuroimaging pipeline for the identification of structural volume differences between disordered and control populations. As a proof of concept, it is applied to various mental disorders screened for in the Adolescent Brain Cognitive Development study.

Margot Wagner

Postdoctoral Researcher

Interested in the use of data science and AI in mental health and using neuroscience to inspire next generation AI tools.