Meta-guided dual path convolutional neural network for depression diagnosis with functional MR images

Deep learning methods with functional magnetic resonance imaging (fMRI) data are successful in diagnosis of depression. However, developing robust models remains challenging due to small datasets and individual heterogeneity. Domain knowledge has the potential to enhance deep-learning-based diagnosis. Previous imaging studies reported abnormalities in brain regions, and meta-analysis can identify spatially convergent abnormal regions. In the present study, we proposed a meta-guided deep-learning framework integrating meta-analysis findings as domain knowledge. We designed the preprocessing of the meta-map for framework integration and developed meta convolutional block and meta dual-path block using the meta-map’s disease-associated regions as spatial guidance to learn ReHo features. Our framework achieved 76.9 % accuracy using a cohort of 385 subjects (192 healthy controls and 193 depressed patients). The effectiveness of the meta-map was comprehensively validated through extensive comparative experiments and systematic ablation studies. Experiments validated the meta-map’s effectiveness in addressing limited sample size and heterogeneity issues. This study introduces the findings of traditional literature beyond the given medical dataset, providing a more promising approach to addressing the problem of small-sized medical datasets for psychiatric disorders.