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Traditional task-based fMRI activation detection methods, such as the general linear model (GLM), assume that the fMRI signals of activated brain regions follow the external stimulus paradigm. Typically, these activated regions are detected independently in a voxel-wise fashion, and the interaction among voxels is nevertheless neglected. Despite the wide use and remarkable success of GLM, the temporal...
Task-based functional magnetic resonance imaging (tfMRI) is widely used to localize brain regions or networks in response to various cognitive tasks. However, given two groups of tfMRI data acquired under distinct task paradigms, it is not clear whether there exist intrinsic inter-group differences in signal composition patterns, and if so, whether these differences could be used for data discrimination...
With the growing number of audio excerpts through various media and distribution channels, advanced audio analysis approaches have received significant interest in the multimedia field. However, current audio analysis approaches are still far from satisfactory due to the semantic gaps between the low-level acoustic features and high-level semantics perceived by human brain. In order to alleviate the...
For decades, it has been largely unknown to what extent multiple functional networks spatially overlap/interact with each other and jointly realize the total cortical function. Here, by developing novel sparse representation of whole-brain fMRI signals and by using the recently publicly released large-scale Human Connectome Project high-quality fMRI data, we show that a number of reproducible and...
As brain imaging data such as fMRI is growing explosively, how to reduce its size but not to lose much information becomes a pressing problem. To address this problem, this work aims to represent resting state fMRI (rs-fMRI) signals of a whole brain via a statistical sampling based sparse representation. Specifically, we improve the online dictionary learning and sparse coding algorithm by adding...
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 and 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. In the first...
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