CSMSDL: A common sequential dictionary learning algorithm for multi-subject FMRI data sets analysis

• Published in: 2017 IEEE International Conference on Image Processing (ICIP) pp. 4113 - 4117
• Main Authors: Seghouane, Abd-Krim, Iqbal, Asif
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.09.2017
• Subjects: Computational modeling; Data analysis; Dictionaries; dictionary learning; Encoding; Functional magnetic resonance imaging; Functional magnetic resonance imaging (fMRI); Machine learning; multi-subjects; Sparse matrices; sparsity
• ISSN: 2381-8549
• DOI: 10.1109/ICIP.2017.8297056

Sequential dictionary learning algorithms has gained widespread acceptance in functional magnetic resonance imaging (fMRI) data analysis. However, many problems in fMRI data analysis involve the analysis of multiple-subject fMRI data sets and the existing algorithms do not extend naturally to this case. In this paper we propose an algorithm dedicated to multiple-subject fMRI data analysis. The algorithm is named SMSDL for sequential multi-subject dictionary learning and differs from existing dictionary learning algorithms in its dictionary update stage. This algorithm is derived by using a variation of the power algorithm in the dictionary update stage to extract the common information among the multiple-subject fMRI data sets. The results of the proposed dictionary learning algorithm is a set of time courses which are common to the whole group of subjects and an individual spatial response pattern for each of the subjects in the group. The performance of the proposed algorithm are illustrated through a simulation and an application on real fMRI datasets.