期刊
NEUROIMAGE
卷 220, 期 -, 页码 -出版社
ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.neuroimage.2020.117111
关键词
Dynamic functional connectivity (FC); Single-scale time-dependent (SSTD) window-sizes; Sliding-window analysis; Regression and classification analysis; Empirical mode decomposition (EMD)
资金
- National Institutes of Health [1R01EB014284, P20GM109025]
- Peter and Angela Dal Pezzo funds
- Lynn and William Weidner
- Stacie and Chuck Matthewson
- Belator, UFC
- August Rapone Family Foundation
- Top Rank
- Haymon Boxing
- NIH Institutes and Centers [1U54MH091657]
- McDonnell Center for Systems Neuroscience at Washington University
During the past ten years, dynamic functional connectivity (FC) has been extensively studied using the sliding-window method. A fixed window-size is usually selected heuristically, since no consensus exists yet on choice of the optimal window-size. Furthermore, without a known ground-truth, the validity of the computed dynamic FC remains unclear and questionable. In this study, we computed single-scale time-dependent (SSTD) window-sizes for the sliding-window method. SSTD window-sizes were based on the frequency content at every time point of a time series and were computed without any prior information. Therefore, they were time-dependent and data-driven. Using simulated sinusoidal time series with frequency shifts, we demonstrated that SSTD window-sizes captured the time-dependent period (inverse of frequency) information at every time point. We further validated the dynamic FC values computed with SSTD window-sizes with both a classification analysis using fMRI data with a low sampling rate and a regression analysis using fMRI data with a high sampling rate. Specifically, we achieved both a higher classification accuracy in predicting cognitive impairment status in fighters and a larger explained behavioral variance in healthy young adults when using dynamic FC matrices computed with SSTD window-sizes as features, as compared to using dynamic FC matrices computed with the conventional fixed window-sizes. Overall, our study computed and validated SSTD window-sizes in the sliding-window method for dynamic FC analysis. Our results demonstrate that dynamic FC matrices computed with SSTD window-sizes can capture more temporal dynamic information related to behavior and cognitive function.
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