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

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...

Full description

Saved in:
Bibliographic Details
Published in:IEEE transactions on biomedical engineering Vol. 65; no. 11; pp. 2519 - 2528
Main Authors: Iqbal, Asif, Seghouane, Abd-Krim, Adali, Tulay
Format: Journal Article
Language:English
Published: United States IEEE 01.11.2018
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithms
Computer simulation
Data analysis
Data processing
Datasets
Dictionaries
Dictionary learning
Encoding
Functional magnetic resonance imaging
functional magnetic resonance imaging (fMRI)
Heart
Learning
Machine learning
Magnetic resonance imaging
Medical imaging
multi-subject analysis
sparse decomposition
Sparse matrices
Task analysis
temporal concatenation
ISSN:0018-9294, 1558-2531, 1558-2531
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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 DL algorithm for multisubject fMRI data analysis. Methods: The proposed algorithm [named shared and subject-specific dictionary learning (ShSSDL)] is derived based on a temporal concatenation, which is particularly attractive for the analysis of multisubject task-related fMRI datasets. It differs from existing DL 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: The performance of the proposed DL 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 DL approach, when applied on multisubject 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 datasets.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
content type line 23
ISSN:0018-9294
1558-2531
1558-2531
DOI:10.1109/TBME.2018.2806958