title: Signal sampling for efficient sparse representation of resting state FMRI data.
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.
authors.affiliation: School of Physics & Information Technology, Shaanxi Normal University, Xi'an, China.|Cortical Architecture Imaging and Discovery Lab, Department of Computer Science and Bioimaging Research Center, The University of Georgia, Athens, GA, USA.
authors.forename: Bao
authors.initials: B
authors.lastname: Ge
authors.affiliation: Cortical Architecture Imaging and Discovery Lab, Department of Computer Science and Bioimaging Research Center, The University of Georgia, Athens, GA, USA.
authors.forename: Milad
authors.initials: M
authors.lastname: Makkie
authors.affiliation: Institute of Bioinformatics, The University of Georgia, Athens, GA, USA.
authors.forename: Jin
authors.initials: J
authors.lastname: Wang
authors.affiliation: School of Automation, Northwestern Polytechnical University, Xi'an, China.|Cortical Architecture Imaging and Discovery Lab, Department of Computer Science and Bioimaging Research Center, The University of Georgia, Athens, GA, USA.
authors.forename: Shijie
authors.initials: S
authors.lastname: Zhao
authors.affiliation: Cortical Architecture Imaging and Discovery Lab, Department of Computer Science and Bioimaging Research Center, The University of Georgia, Athens, GA, USA.
authors.forename: Xi
authors.initials: X
authors.lastname: Jiang
authors.affiliation: Cortical Architecture Imaging and Discovery Lab, Department of Computer Science and Bioimaging Research Center, The University of Georgia, Athens, GA, USA.
authors.forename: Xiang
authors.initials: X
authors.lastname: Li
authors.affiliation: School of Automation, Northwestern Polytechnical University, Xi'an, China.|Cortical Architecture Imaging and Discovery Lab, Department of Computer Science and Bioimaging Research Center, The University of Georgia, Athens, GA, USA.
authors.forename: Jinglei
authors.initials: J
authors.lastname: Lv
authors.affiliation: Cortical Architecture Imaging and Discovery Lab, Department of Computer Science and Bioimaging Research Center, The University of Georgia, Athens, GA, USA.
authors.forename: Shu
authors.initials: S
authors.lastname: Zhang
authors.affiliation: Cortical Architecture Imaging and Discovery Lab, Department of Computer Science and Bioimaging Research Center, The University of Georgia, Athens, GA, USA.
authors.forename: Wei
authors.initials: W
authors.lastname: Zhang
authors.affiliation: School of Automation, Northwestern Polytechnical University, Xi'an, China.
authors.forename: Junwei
authors.initials: J
authors.lastname: Han
authors.affiliation: School of Automation, Northwestern Polytechnical University, Xi'an, China.
authors.forename: Lei
authors.initials: L
authors.lastname: Guo
authors.affiliation: Cortical Architecture Imaging and Discovery Lab, Department of Computer Science and Bioimaging Research Center, The University of Georgia, Athens, GA, USA. tliu@cs.uga.edu.
authors.forename: Tianming
authors.initials: T
authors.lastname: Liu
mesh_terms: D000465:Algorithms; D001921:Brain / Q000000981:diagnostic imaging* / Q000502:physiology*; D001931:Brain Mapping / Q000379:methods*; D003627:Data Interpretation, Statistical; D006801:Humans; D008279:Magnetic Resonance Imaging / Q000379:methods; D009434:Neural Pathways / Q000000981:diagnostic imaging / Q000502:physiology; D012146:Rest; D013997:Time Factors
keywords: DICCCOL; DTI; Resting state fMRI; Resting state networks; Sampling