GPT-based normative models of brain sMRI correlate with dimensional psychopathology

Generative Pre-trained Transformer (GPT) models have been widely used for language tasks with surprising results. Furthermore, neuroimaging studies using deep generative normative modeling show promise in detecting brain abnormalities from brain structural MRI (sMRI). Meanwhile, psychiatric disorders are typically diagnosed through clinical assessment, which is particularly challenging in children and adolescents who present early symptoms or are in the early stages of the disease. Brain biomarkers research may contribute to the complex task of disentangling typical neurodevelopment from emergent psychiatric disorders. Here, we investigate whether a GPT-based normative architecture can detect psychiatric symptoms and disorders from brain sMRI of youths. The studied datasets contain measures of dimensional psychopathology: Brazilian High-Risk Cohort Study (BHRCS,n = 737) and Adolescent Brain Cognitive Development (ABCD,n = 11,031), and scores and diagnostic of psychiatric disorders: Attention Deficit Hyperactivity Disorder (ADHD-200,n = 922) and Autism Brain Imaging Data Exchange II (ABIDE-II,n = 580). We examined the associations of all brain regions with: the Child Behavior Checklist (CBCL) symptom groups, ADHD scores, and Autism Spectrum Disorder (ASD) diagnosis. Results showed the whole-brain typicality likelihood as correlated with social problems (ABCD test set) and ASD diagnosis (ABIDE-II dataset). Analysis by brain regions linked different areas to several CBCL scales, ADHD scores, and ASD diagnostic. This is the first successful study assessing all dimensional groups of CBCL symptoms, from all brain regions, based exclusively on sMRI. The normative models based on GPT are promising to investigate the gap between the phenotypes of psychiatric conditions and their neurobiological substrates.