NDLI: Characterizing and differentiating task-based and resting state fMRI signals via two-stage sparse representations

Content Provider	Springer Nature Link
Author	Zhang, Shu Li, Xiang Lv, Jinglei Jiang, Xi Guo, Lei Liu, Tianming
Copyright Year	2015
Abstract	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.
Starting Page	21
Ending Page	32
Page Count	12
File Format	PDF
ISSN	19317557
Journal	Brain Imaging and Behavior
Volume Number	10
Issue Number	1
e-ISSN	19317565
Language	English
Publisher	Springer US
Publisher Date	2015-03-03
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Task-based fMRI Resting-state fMRI Sparse coding Online dictionary learning Neurosciences Neuroradiology Neuropsychology Psychiatry
Content Type	Text
Resource Type	Article
Subject	Neurology Radiology, Nuclear Medicine and Imaging Cognitive Neuroscience Behavioral Neuroscience Neurology (clinical) Cellular and Molecular Neuroscience Psychiatry and Mental Health

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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