Joint multi-site domain adaptation and multi-modality feature selection for the diagnosis of psychiatric disorders

Abstract Identifying biomarkers for computer-aided diagnosis (CAD) is crucial for early intervention of psychiatric disorders. Multi-site data have been utilized to increase the sample size and improve statistical power, while multi-modality classification offers significant advantages over traditional single-modality based approaches for diagnosing psychiatric disorders. However, inter-site heterogeneity and intra-modality heterogeneity present challenges to multi-site and multi-modality based classification. In this paper, brain functional and structural networks (BFNs/BSNs) from multiple sites were constructed to establish a joint multi-site multi-modality framework for psychiatric diagnosis. To do this we developed a hypergraph based multi-source domain adaptation (HMSDA) which allows us to transform source domain subjects into a target domain. A local ordinal structure based multi-task feature selection (LOSMFS) approach was developed by integrating the transformed functional and structural connections (FCs/SCs). The effectiveness of our method was validated by evaluating diagnosis of both schizophrenia (SZ) and autism spectrum disorder (ASD). The proposed method obtained accuracies of 92.2%±2.22% and 84.8%±2.68% for the diagnosis of SZ and ASD, respectively. We also compared with 6 domain adaptation (DA), 10 multi-modality feature selection, and 8 multi-site and multi-modality methods. Results showed the proposed HMSDA + LOSMFS effectively integrates multi-site and multi-modality data to enhance psychiatric diagnosis and identify disorder-specific diagnostic brain connections.