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A relatively underexplored question in fMRI is whether there are intrinsic differences in terms of signal composition patterns that can effectively characterize and differentiate task-based or resting state fMRI (tfMRI or rsfMRI) signals. In this paper, we propose a novel two-stage sparse representation framework to examine the fundamental difference between tfMRI and rsfMRI signals. Specifically,...
Human brain function has been widely believed as a network behavior. However, most previous activation detection methods in the task-based fMRI field were voxel-based, instead of network-based. For instance, the general linear model (GLM) has been widely used to fit the external stimulus curve with the fMRI BOLD signal of each voxel. In this paper, we present a novel network-based activation detection...
Segregation and integration are two general principles of the brain's functional architecture; therefore brain network analysis is of significant importance in understanding brain function. Critical to brain network analysis and construction is the identification of reliable, reproducible and accurate network nodes, or Regions of Interest (ROIs). In this paper, based on functional ROIs derived from...
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