Sparsity and Low-Rank Based Methods for Capturing Common and Discriminative Features in Multi-Subject fMRI Data
Loading...
Links to Files
Permanent Link
Author/Creator
Author/Creator ORCID
Date
2019-01-01
Type of Work
Department
Computer Science and Electrical Engineering
Program
Engineering, Electrical
Citation of Original Publication
Rights
Access limited to the UMBC community. Item may possibly be obtained via Interlibrary Loan thorugh a local library, pending author/copyright holder's permission.
This item may be protected under Title 17 of the U.S. Copyright Law. It is made available by UMBC for non-commercial research and education. For permission to publish or reproduce, please see http://aok.lib.umbc.edu/specoll/repro.php or contact Special Collections at speccoll(at)umbc.edu
This item may be protected under Title 17 of the U.S. Copyright Law. It is made available by UMBC for non-commercial research and education. For permission to publish or reproduce, please see http://aok.lib.umbc.edu/specoll/repro.php or contact Special Collections at speccoll(at)umbc.edu
Abstract
Data-driven analysis for functional magnetic resonance imaging (fMRI) data has played an important role for uncovering salient brain functional networks that are shared across multiple subjects. On the other hand, recent fMRI studies indicate that there is significant and consistent heterogeneity present across different subject groups and individuals. While independent component analysis (ICA) has been a major tool to perform data-driven analysis of fMRI data, dictionary learning (DL) approaches are increasingly receiving attention due to their modeling capability and flexibility. In this work, a series of DL algorithms are formulated and their efficacy in estimating good spatial maps is explored. First, multi-subject fMRI data analysis methods based on sparse DL are proposed. The component spatial maps are identified by exploiting the sparsity of the maps while the clusters of the subjects are pursued simultaneously by postulating that the fMRI volumes admit subspace clustering structures. Furthermore, a framework is developed to capitalize on the available class labels and capture not only the commonly shared components across the population, but also the unique components that contribute to discrimination. A systematic comparison with conventional ICA is performed based on real fMRI data consisting of healthy controls and patients with schizophrenia.