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Abstract #4161

A BP ANNs Study on the Dynamics of Resting-state fMRI Functional Connectivity for the Depression

Chuangjian Cai 1 , Xue Xiao 1 , Yan Zhu 2 , and Kui Ying 3

1 Department of Biomedical Engineering, Tsinghua University, Beijing, Beijing, China, 2 Yuquan Hospital, Tsinghua University, Beijing, China, 3 Department of Engineering Physics, Tsinghua University, Beijing, China

Machine learning techniques for fMRI help to identify the features of some brain disease, such as depression. We utilized dynamic functional connectivity analysis of resting state fMRI with BP ANNs to investigate the dynamic differences and differentiate between the depression and the control group, with cross validation and permutation test to test its feasibility. A general rate of 95.45% was achieved, better than the traditional method that combines support Vector Machine and static analysis only. The new method certificated the internal regularity of dynamic functional connectivity, found brain regions with highly discriminative power and supplied an effective model for dynamic functional connectivity investigation.

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