Deep Learning in Neuroimaging: Approaches, Applications and Challenges in Mental Disorders

COEX 

Deep learning (DL) has found success in various domains like image recognition and natural language processing, yet its application in neuroimaging presents unique challenges due to the complexity of data—higher dimensionality, limited sample sizes, and heterogeneous modalities, often lacking a solid ground truth. This course addresses four crucial aspects of DL in neuroimaging. First, we delve into classification/regression, showcasing DL's superiority over standard machine learning for novel clinical and neurobiological insights. Second, we spotlight DL leveraging dynamic functional information, incorporating time series and advanced connectivity approaches. Third, we explore how DL can capitalize on complementary information from different neuroimaging modalities, enhancing prediction accuracy through cross-modality-based representations. Lastly, we examine model visualization and interpretation techniques, crucial for biomarker discovery and subtype identification. Each section summarizes research examples, outlines future directions, and discusses challenges, aiming to propel neuroimaging towards refined and personalized diagnoses and treatments through the powerful lens of DL.