Dictionary Learning-Based fMRI Data Analysis for Capturing Common and Individual Neural Activation Maps

In this paper, a novel dictionary learning (DL) method is proposed to estimate sparse neural activations from multi-subject fMRI data sets. By exploiting the label information such as the patient and the normal healthy groups, the activation maps that are commonly shared across the groups as well as...

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Bibliographic Details
Published in:IEEE journal of selected topics in signal processing 2020-10, Vol.14 (6), p.1265-1279
Main Authors: Jin, Rui, Dontaraju, Krishna K., Kim, Seung-Jun, Akhonda, Mohammad Abu Baker Siddique, Adali, Tulay
Format: Article
Language:English
Subjects:
Brain modeling
Correlation analysis
Data analysis
Datasets
Dictionaries
Dictionary learning
FMRI data analysis
Functional magnetic resonance imaging
Independent component analysis
Learning
Machine learning
Sparse matrices
sparse models
Stability analysis
Supervised learning
supervised/unsupervised learning
Unsupervised learning
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Summary:In this paper, a novel dictionary learning (DL) method is proposed to estimate sparse neural activations from multi-subject fMRI data sets. By exploiting the label information such as the patient and the normal healthy groups, the activation maps that are commonly shared across the groups as well as those that can explain the group differences are both captured. The proposed method was tested using real fMRI data sets consisting of schizophrenic subjects and healthy controls. The DL approach not only reproduced most of the maps obtained from the conventional independent component analysis (ICA), but also identified more maps that are significantly group-different, including a number of novel ones that were not revealed by ICA. The stability analysis of the DL method and the correlation analysis with separate neuropsychological test scores further strengthen the validity of our analysis.
ISSN:1932-4553
1941-0484
DOI:10.1109/JSTSP.2020.2992430