Characterizing and differentiating task-based and resting state fMRI signals via two-stage sparse representations
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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, 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.
KeywordsTask-based fMRI Resting-state fMRI Sparse coding Online dictionary learning
T Liu was supported by NSF CAREER Award (IIS-1149260), NIH R01 DA-033393, NIH R01 AG-042599, NSF CBET-1302089 and NSF BCS-1439051. L Guo was supported by the NSFC #61273362.
Conflict of Interest
Shu Zhang, Xiang Li, Jinglei Lv, Xi Jiang, Lei Guo, and Tianming Liu declare that they have no conflicts of interest.
Data used in this study were previously collected and archived in a data bank.
- Abolghasemi, V., Ferdowsi, S., Sanei, S. (2013). Fast and incoherent dictionary learning algorithms with application to fMRI. Signal, Image and Video Processing.Google Scholar
- Barch, D.M., Burgess, G.C., Harms, M.P., Petersen, S.E., Schlaggar, B.L., Corbetta, M., Glasser, M.F., Curtiss, S., Dixit, S., Feldt, C., Nolan, D., Bryant, E., Hartley, T., Footer, O., Bjork, J.M., Poldrack, R., Smith, S., Johansen-Berg, H., Snyder, A.Z., Van Essen, D.C., WU-Minn HCP Consortium. (2013). Function in the human connectome: task-fMRI and individual differences in behavior. Neuroimage.Google Scholar
- Calhoun, V.D., et al. (2011). fMRI Activation in a visual-perception task: network of areas detected using the general linear model and independent components analysis. NeuroImage, 14(5), 1080–1088, 2001.Google Scholar
- Chih C.C., & Chih J.L. LIBSVM: a library for support vector machines. ACM Transactions on Intelligent Systems and Technology, 2:27:1--27:27.Google Scholar
- Daubechies, I., Roussos, E., Takerkart, S., Benharrosh, M., Golden, C., D’Ardenne, K., Richter, W., Cohen, J. D., & Haxby, J. (2009). Independent component analysis for brain fMRI does not select for independence. Proceedings of the National Academy of Sciences of the United States of America, 106(26), 10415–10422.CrossRefPubMedPubMedCentralGoogle Scholar
- Foland, L., & Glover, G.H. (2004). Scanner quality assurance for longitudinal or multicenter fMRI studies, In International Society for Magnetic Resonance Imaging. 12th Annual Meeting of the International Society for Magnetic Resonance Imaging (ISMRM).Google Scholar
- Friston, KJ., Holmes, AP., Worsley, KJ. (1994). Statistical parametric maps in functional imaging: a general linear approach. Human Brain Mapping, V2-I4: 189–210.Google Scholar
- Lee, J., Jeong, Y., Ye, J.C. (2013). Group sparse dictionary learning and inference for resting-state fMRI analysis of Alzheimer’s disease. ISBI.Google Scholar
- Li, X., Zhu, D., Jiang, X., Jin, C., Zhang, X., Guo, L., Zhang, J., Hu, X., Li, J., Liu, T. (2013). Dynamic functional connectomics signatures for characterization and differentiation of PTSD Patients, in press, Human Brain Mapping.Google Scholar
- Lv, J., Jiang, X., Li, X., Zhu, D., Chen, H., Zhang, T., Zhang, S., Hu, X., Han, J., Huang, H., Zhang, J., Guo, L., Liu, T. (2014a). Sparse representation of whole-brain FMRI signals for identification of functional networks, in press, Medical Image Analysis.Google Scholar
- Lv, J., Jiang, X., Li, X., Zhu, D., Zhang, S., Zhao, S., Chen, H., Zhang, T., Hu, X., Han, J, Ye, J, Guo, L, Liu, T. (2014b). Holistic atlases of functional networks and interactions reveal reciprocal organizational architecture of cortical function, accepted, IEEE Transactions on Biomedical Engineering.Google Scholar
- Mairal, J., Bach, Francis., Ponce, J., Sapiro, G. (2009). Online dictionary learning for sparse coding. In Proceedings of the International Conference on Machine Learning (ICML).Google Scholar
- Smith, S.M., Beckmann, C.F., Andersson, J., Auerbach, E.J., Bijsterbosch, J., Douaud, G., Duff, E., Feinberg, D.A., Griffanti, L., Harms, M.P., Kelly, M., Laumann, T., Miller, K.L., Moeller, S., Petersen, S., Power, J., Salimi-Khorshidi, G., Snyder, A.Z., Vu, A.T., Woolrich, M.W., Xu, J., Yacoub, E., Uğurbil, K., Van Essen, D.C., Glasser, M.F., WU-Minn HCP Consortium. (2013). Resting-state fMRI in the Human Connectome Project. Neuroimage.Google Scholar