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

• Published in: IEEE journal of selected topics in signal processing Vol. 14; no. 6; pp. 1265 - 1279
• Main Authors: Jin, Rui, Dontaraju, Krishna K., Kim, Seung-Jun, Akhonda, Mohammad Abu Baker Siddique, Adali, Tulay
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.10.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• 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
• ISSN: 1932-4553; 1941-0484
• DOI: 10.1109/JSTSP.2020.2992430

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.