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- W2890401963 abstract "Functional magnetic resonance imaging (fMRI) provides a noninvasive means for investigating dynamic brain activity. To date, most work in this area has focused on measuring static functional connectivity (FC) for computational efficiency due to high data dimensionality and redundancy. Because cerebral activity is dynamic and condition-dependent, however, the correlation of spontaneous fluctuations among brain regions dynamically changes even in the resting state. Thus, quantifying the dynamic functional connectivity (dFC) can potentially provide valuable insights for both individual discrimination and group comparison. In this chapter, we will first briefly introduce the background of dFC analysis and its application to Alzheimer’s disease. Then we will describe the tensor-based statistic model for dFC analysis, which includes three parts: (1) A data-driven approach to measure FC across brain regions; (2) a data-driven approach to characterize dFC across time; and (3) a statistic model to represent the intrinsic intersubject variation patterns of dFC." @default.
- W2890401963 created "2018-09-27" @default.
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- W2890401963 date "2019-01-01" @default.
- W2890401963 modified "2023-10-18" @default.
- W2890401963 title "Characterizing dynamic functional connectivity using data-driven approaches and its application in the diagnosis of alzheimer’s disease" @default.
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- W2890401963 doi "https://doi.org/10.1016/b978-0-12-813838-0.00010-8" @default.
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