3D Convolutional Neural Network Model for Detection of Major Depressive Disorder from Grey Matter Images

Major depressive disorder is a mental health condition characterized by ongoing feelings of sadness, trouble focusing or making decisions, and a frequent sense of fatigue or hopelessness that lasts for a prolonged period. If left undiagnosed, it can have serious consequences, including suicide. This study proposes a 3D convolutional neural network model to detect major depressive disorder using 3D grey matter images from magnetic resonance imaging. The proposed 3D convolutional architecture comprises multiple hierarchical convolutional and pooling layers, designed to automatically learn spatial patterns from magnetic resonance imaging data. The model was optimized via Bayesian hyperparameter tuning, achieving an accuracy of 72.26%, an area under the receiver operating characteristic curve of 0.80, and an area under the precision–recall curve of 0.81 on a large multisite dataset comprising 1276 patients and 1104 healthy controls. Gradient-weighted class activation mapping is utilized to find brain regions associated with major depressive disorder. From this study, six regions were identified, namely, the frontal lobe, parietal lobe, temporal lobe, thalamus, insular cortex and corpus callosum which may be affected by major depressive disorder.