Cross-Domain Identification of Multisite Major Depressive Disorder Using End-to-End Brain Dynamic Attention Network.

Establishing objective and quantitative imaging markers at individual level can assist in accurate diagnosis of Major Depressive Disorder (MDD). However, the clinical heterogeneity of MDD and the shift to multisite data decreased identification accuracy. To address these issues, the Brain Dynamic Attention Network (BDANet) is innovatively proposed, and analyzed bimodal scans from 2055 participants of the Rest-meta-MDD consortium. The end-to-end BDANet contains two crucial components. The Dynamic BrainGraph Generator dynamically focuses and represents topological relationships between Regions of Interest, overcoming limitations of static methods. The Ensemble Classifier is constructed to obfuscate domain sources to achieve inter-domain alignment. Finally, BDANet dynamically generates sample-specific brain graphs by downstream recognition tasks. The proposed BDANet achieved an accuracy of 81.6%. The regions with high attribution for classification were mainly located in the insula, cingulate cortex and auditory cortex. The level of brain connectivity in p24 region was negatively correlated ( [Formula: see text]) with the severity of MDD. Additionally, sex differences in connectivity strength were observed in specific brain regions and functional subnetworks ( [Formula: see text] or [Formula: see text]). These findings based on a large multisite dataset support the conclusion that BDANet can better solve the problem of the clinical heterogeneity of MDD and the shift of multisite data. It also illustrates the potential utility of BDANet for personalized accurate identification, treatment and intervention of MDD.

Computer-aided diagnosis of schizophrenia based on node2vec and Transformer.

OBJECTIVE: Compared with the healthy control (HC) group, the brain structure and function of schizophrenia (SZ) patients are significantly abnormal, so brain imaging methods can be used to achieve the aided diagnosis of SZ. However, a brain network based on brain imaging data is non-Euclidean, and its intrinsic features cannot be learned effectively by general deep learning models. Furthermore, in the majority of existing studies, brain network features were manually specified as the input of machine learning models. METHODS: In this study, brain functional network constructed from the subject's fMRI data is analyzed, and its small-world value is calculated and t-tested; the node2vec algorithm in graph embedding is introduced to transform the constructed brain network into low-dimensional dense vectors, and the brain network's non-Euclidean spatial structure characteristics are retained to the greatest extent, so that its intrinsic features can be extracted by deep learning models; GridMask is used to randomly mask part of the information in the vectors to enhance the data; and then features can be extracted using the Transformer model to identify SZ. RESULTS: It is again shown that the small-world value of the brain network in SZ is significantly lower than that in HC by t-test (p=0.014¡0.05). 97.78% classification accuracy is achieved by the proposed methods (node2vec + GridMask + Transformer) in 30 SZ patients and 30 healthy people. CONCLUSION: The experiment shows that the node2vec used in this paper can effectively solve the problem of brain network features being difficult to learn by general deep learning models. The high-precision computer-aided diagnosis of SZ can be obtained by combining node2vec with Transformer and GridMask. SIGNIFICANCE: The proposed methods in the paper are expected to be used for aided diagnosis of SZ.