Characterizing and differentiating task-based and resting state fMRI signals via two-stage sparse representations.
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, in the first stage, the whole-brain tfMRI or rsfMRI signals of each subject were composed into a big data matrix, which was then factorized into a subject-specific dictionary matrix and a weight coefficient matrix for sparse representation. In the second stage, all of the dictionary matrices from both tfMRI/rsfMRI data across multiple subjects were composed into another big data-matrix, which was further sparsely represented by a cross-subjects common dictionary and a weight matrix. This framework has been applied on the recently publicly released Human Connectome Project (HCP) fMRI data and experimental results revealed that there are distinctive and descriptive atoms in the cross-subjects common dictionary that can effectively characterize and differentiate tfMRI and rsfMRI signals, achieving 100% classification accuracy. Moreover, our methods and results can be meaningfully interpreted, e.g., the well-known default mode network (DMN) activities can be recovered from the very noisy and heterogeneous aggregated big-data of tfMRI and rsfMRI signals across all subjects in HCP Q1 release.
Cortical Architecture Imaging and Discovery Lab, Department of Computer Science and Bioimaging Research Center, The University of Georgia, Athens, GA, USA.
Shu
S
Zhang
Cortical Architecture Imaging and Discovery Lab, Department of Computer Science and Bioimaging Research Center, The University of Georgia, Athens, GA, USA.
Xiang
X
Li
Cortical Architecture Imaging and Discovery Lab, Department of Computer Science and Bioimaging Research Center, The University of Georgia, Athens, GA, USA.|School of Automation, Northwestern Polytechnic University, Xi'an, China.
Jinglei
J
Lv
Cortical Architecture Imaging and Discovery Lab, Department of Computer Science and Bioimaging Research Center, The University of Georgia, Athens, GA, USA.
Xi
X
Jiang
School of Automation, Northwestern Polytechnic University, Xi'an, China.
Lei
L
Guo
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.
Tianming
T
Liu
D001921:Brain / Q000502:physiology*; D063132:Connectome / Q000379:methods; D004644:Emotions / Q000502:physiology; D005715:Gambling / Q000503:physiopathology; D006801:Humans; D007802:Language; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008570:Memory, Short-Term / Q000502:physiology; D009043:Motor Activity / Q000502:physiology; D009483:Neuropsychological Tests; D012146:Rest; D012919:Social Behavior
Online dictionary learning; Resting-state fMRI; Sparse coding; Task-based fMRI