Multi-subject fMRI connectivity analysis using sparse dictionary learning and multiset canonical correlation analysis

In this paper, we propose an effective technique to analyze task-based functional connectivity across multiple subjects for functional magnetic resonance imaging (fMRI) data. Instead of applying the assumption of group-independence or multiset correlation maximization, an alternative approach is adopted based on a combined framework of sparse dictionary learning (SDL) and multi-set canonical correlation analysis (MCCA) to obtain connectivity maps. The proposed technique encapsulates commonality and uniqueness solely based on sparsity of cross dataset corresponding components. It is validated using real fMRI data and its superior performance is illustrated using a simulation study, which shows its better capability in obtaining connectivity maps that are more specific.

[1]  Abd-Krim Seghouane,et al.  A single SVD sparse dictionary learning algorithm for FMRI data analysis , 2014, 2014 IEEE Workshop on Statistical Signal Processing (SSP).

[2]  Abd-Krim Seghouane,et al.  A small sample model selection criterion based on Kullback's symmetric divergence , 2004, IEEE Transactions on Signal Processing.

[3]  Paul Horst,et al.  Relations amongm sets of measures , 1961 .

[4]  Shun-ichi Amari,et al.  The AIC Criterion and Symmetrizing the Kullback–Leibler Divergence , 2007, IEEE Transactions on Neural Networks.

[5]  Jeonghyeon Lee,et al.  Group sparse dictionary learning and inference for resting-state fMRI analysis of Alzheimer'S disease , 2013, 2013 IEEE 10th International Symposium on Biomedical Imaging.

[6]  H. Hotelling.  Relations Between Two Sets of Variates , 1936 .

[7]  Sungho Tak,et al.  A Data-Driven Sparse GLM for fMRI Analysis Using Sparse Dictionary Learning With MDL Criterion , 2011, IEEE Transactions on Medical Imaging.

[8]  Vince D. Calhoun,et al.  A review of group ICA for fMRI data and ICA for joint inference of imaging, genetic, and ERP data , 2009, NeuroImage.

[9]  I Daubechies,et al.  Independent component analysis for brain fMRI does not select for independence , 2009 .

[10]  Abd-Krim Seghouane,et al.  Constrained maximum likelihood based efficient dictionary learning for fMRI analysis , 2014, 2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI).

[11]  Muhammad Hanif,et al.  A sequential dictionary learning algorithm with enforced sparsity , 2015, 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[12]  S. Petersen,et al.  The maturing architecture of the brain's default network , 2008, Proceedings of the National Academy of Sciences.

[13]  Vince D. Calhoun,et al.  Joint Blind Source Separation by Multiset Canonical Correlation Analysis , 2009, IEEE Transactions on Signal Processing.

[14]  Abd-Krim Seghouane,et al.  Improving functional connectivity detection in FMRI by combining sparse dictionary learning and canonical correlation analysis , 2013, 2013 IEEE 10th International Symposium on Biomedical Imaging.