Abstract
In order to make a deep analysis of depression, the construction and recognition of functional brain network model based on depression is mainly studied. Firstly, the relevant information of the subjects is introduced in turn. The data of fMRI (functional magnetic resonance imaging) are pretreated, static and dynamic functional connections are analyzed, statistical analysis and testing are carried out, and then the results are analyzed and discussed. The results show that static functional connectivity provides a new research perspective and means for further understanding the connection patterns of brain functional networks in unipolar depression and bipolar depression. Dynamic functional connectivity analysis (DFA) extends the previous studies on unipolar depression and bipolar depression, and provides a potential biological marker for clinical identification of unipolar depression and bipolar depression.
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Author Lin Wen declares that he has no conflict of interest. Author Shan Liu declares that he has no conflict of interest. Author Yurong Cao declares that he has no conflict of interest. Author Guiling Li declares that he has no conflict of interest.
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Wen, L., Liu, S., Cao, Y. et al. Construction and Recognition of Functional Brain Network Model Based on Depression. J Med Syst 43, 236 (2019). https://doi.org/10.1007/s10916-019-1198-4
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DOI: https://doi.org/10.1007/s10916-019-1198-4