Abstract
As the size of brain imaging data such as fMRI grows explosively, it provides us with unprecedented and abundant information about the brain. How to reduce the size of fMRI data but not lose much information becomes a more and more pressing issue. Recent literature studies tried to deal with it by dictionary learning and sparse representation methods, however, their computation complexities are still high, which hampers the wider application of sparse representation method to large scale fMRI datasets. To effectively address this problem, this work proposes to represent resting state fMRI (rs-fMRI) signals of a whole brain via a statistical sampling based sparse representation. First we sampled the whole brain’s signals via different sampling methods, then the sampled signals were aggregate into an input data matrix to learn a dictionary, finally this dictionary was used to sparsely represent the whole brain’s signals and identify the resting state networks. Comparative experiments demonstrate that the proposed signal sampling framework can speed-up by ten times in reconstructing concurrent brain networks without losing much information. The experiments on the 1000 Functional Connectomes Project further demonstrate its effectiveness and superiority.
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Acknowledgments
We thank all investigators contributing data to the 1000 Functional Connectomes project, without whom this analysis could not have been performed. T Liu was supported by NIH R01 DA-033393, NIH R01 AG-042599, NSF CAREER Award IIS-1149260, NSF CBET-1302089 and NSF BCS-1439051. B Ge was supported by NSFC 61403243, 2015JM6312, the Fundamental Research Funds for the Central Universities from China (No. GK201402008) and Interdisciplinary Incubation Project of Learning Science of Shaanxi Normal University. The authors would like to thank the anonymous reviewers for their constructive comments.
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Ge, B., Makkie, M., Wang, J. et al. Signal sampling for efficient sparse representation of resting state FMRI data. Brain Imaging and Behavior 10, 1206–1222 (2016). https://doi.org/10.1007/s11682-015-9487-0
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DOI: https://doi.org/10.1007/s11682-015-9487-0