Decoding Cortical Folding with Deep Learning: Toward Neurodevelopmental Biomarkers of Psychiatric Disorders

Abstract Background Cortical folding is primarily determined during prenatal and early postnatal brain development and remains relatively stable throughout life. Accumulating evidence suggests that psychiatric disorders, including schizophrenia (SCZ), Bipolar Disorder (BD), and Autism Spectrum Disorder (ASD), result from intricate interactions between early neurodevelopmental disruptions and subsequent environmental influences. Therefore, cortical folding patterns are promising candidates as stable imaging biomarkers reflecting the neurodevelopmental component of psychiatric conditions. This study aims to demonstrate that deep learning can be used to learn meaningful representations of cortical folding patterns that enable the individual-level prediction of major psychiatric disorders, namely SCZ, BD, and ASD. Method We introduce a dedicated deep learning architecture leveraging self-supervised pre-training on large datasets of the general population to extract subject-level representations of cortical folding from structural magnetic resonance imaging (MRI) data. Results We demonstrate that the learned representations allow significant single-subject prediction of clinical status for SCZ, BD, and ASD. Conclusion The proposed folding-based representation presents a novel approach for identifying imaging biomarkers related to the neurodevelopmental origins of psychiatric disorders. It opens the possibility of inferring early-life brain alterations from adult MRI data, offering potential tools for stratification and precision psychiatry.