NDLI: Characterizing and differentiating task-based and resting state FMRI signals via two-stage dictionary learning

Content Provider	IEEE Xplore Digital Library
Author	Shu Zhang Xiang Li Jinglei Lv Xi Jiang Bao Ge Lei Guo Tianming Liu
Copyright Year	2015
Description	Author affiliation: Dept. of Comput. Sci., Univ. of Georgia, Athens, GA, USA (Shu Zhang; Xiang Li; Jinglei Lv; Xi Jiang; Bao Ge; Tianming Liu) \|\| Sch. of Autom., Northwestern Polytech. Univ., Xi'an, China (Lei Guo)
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 and 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. In the first stage, subject-wise whole-brain tfMRI and rsfMRI signals are factorized into dictionary matrix and the corresponding loading coefficients via dictionary learning method. In the second stage, dictionaries learned at the first stage across multiple subjects are aggregated into the matrix which serve as the input for another round of dictionary learning, obtaining groupwise common dictionaries and their loading coefficients. This framework had been applied on the recently publicly released Human Connectome Project (HCP) data, and experimental results revealed that there exist distinctive and descriptive atoms in the groupwise common dictionary that can effectively differentiate tfMRI and rsfMRI signals, achieving 100% classification accuracy. Moreover, certain common dictionaries learned by our framework have a clear neuroscientific interpretation. For example, the well-known default mode network (DMN) activities can be recovered from the heterogeneous and noisy large-scale groupwise whole-brain signals.
Starting Page	675
Ending Page	678
File Size	690963
Page Count	4
File Format	PDF
ISBN	9781479923748
DOI	10.1109/ISBI.2015.7163963
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-04-16
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Dictionaries Loading Testing Time series analysis Training Noise Sparse matrices big data tfMRI rsfMRI sparse representation classification
Content Type	Text
Resource Type	Article

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