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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