Shared and Subject-Specific Dictionary Learning Algorithm for Multi-Subject fMRI Data Analysis (ShSSDL).

IEEE Trans Biomed Eng. 2018 Feb 16;:

Authors: Iqbal A, Seghouane K, Adali T

Abstract
OBJECTIVE: Analysis of functional magnetic resonance imaging (fMRI) data from multiple subjects is at the heart of many medical imaging studies, and approaches based on dictionary learning (DL) are recently noted as promising solutions to the problem. However, the DL-based methods for fMRI analysis proposed to date do not naturally extend to multisubject analysis. In this paper, we propose a dictionary learning algorithm for multi-subject fMRI data analysis.
METHODS: The proposed algorithm (named ShSSDL) is derived based on a temporal concatenation, which is particularly attractive for the analysis of multi-subject task-related fMRI data sets. It differs from existing dictionary learning algorithms in both its sparse coding and dictionary update stages and has the advantage of learning a dictionary shared by all subjects as well as a set of subject-specific dictionaries.
RESULTS: Performance of the proposed dictionary learning algorithm is illustrated using simulated and real fMRI datasets. The results show that it can successfully extract shared as well as subject specific latent components.
CONCLUSION: In addition to offering a new dictionary learning approach, when applied on multi-subject fMRI data analysis, the proposed algorithm generates a group level as well as a set of subject specific spatial maps.
SIGNIFICANCE: The proposed algorithm has the advantage of learning simultaneously multiple dictionaries providing us with a shared as well discriminative source of information about the analyzed fMRI data sets.

PMID: 29993508 [PubMed - as supplied by publisher]