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Abstract #4008

Optimum window-size in sliding-window dynamic functional connectivity analysis

Xiaowei Zhuang1, Zhengshi Yang1, Virendra Mishra1, Karthik Sreenivasan1, Bernick Charles1, and Dietmar Cordes1,2
1Lou Ruvo Center for Brain Health, Cleveland Clinic, Las Vegas, NV, United States, 2University of Colorado, Boulder, Boulder, CO, United States

In this abstract, we proposed an optimum window-size in a sliding-window approach for dynamic functional connectivity analysis. The proposed window-size was derived from the instantaneous period and energy of each intrinsic mode functions (IMF) obtained from empirical mode decomposition. IMFs track local periodic changes of non-stationary time series and therefore can capture subtle temporal variations. Using dynamic functional connectivity matrix computed with the proposed window-size as features, a higher accuracy was obtained in classifying cognitively impaired fighters from cognitively normal ones; and a larger behavioral variance was found in HCP data.

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