Classification of major depressive disorder based on functional and structural MRI

Depression is one of the most common mental health disorders and has been a major focus of research, particularly through the lens of automated diagnostic methods. While many studies have explored magnetic resonance imaging techniques separately, the integration of multiple neuroimaging modalities has received less attention. To address this gap, we introduce a multimodal automatic classification method that leverages both resting-state functional magnetic resonance imaging and structural magnetic resonance imaging. Our approach employs a multi-stream 3D Convolutional Neural Network model to facilitate joint training on diverse features extracted from rs-fMRI and sMRI data. By classifying a combined group of 830 MDD patients and 771 normal controls from the REST-meta-MDD dataset, our model achieves an impressive accuracy of 69.38% using a feature combination of CSF, REHO, and fALFF. This result signifies a notable enhancement in classification performance, contributing valuable insights into the capabilities of multimodal imaging in MDD diagnosis.