Bhushan Patil1, Mahesh Panicker1, Radhika Madhavan1, and Suresh Joel1
Clustering of resting state fMRI signals for extraction of functional brain networks has been showed to provide value in recent times. Independent component analysis (ICA) is the most commonly used technique to extract functional brain networks. More recently non-negative matrix factorization (NMF) has been successfully utilized for identification of brain functional networks in single-subject resting state fMRI data. NMF may provide complementary information for analyzing resting state fMRI data. However, the technique has not been extended to provide group inferences. This is non-trivial, since the components obtained from single subject NMF is not ordered. Using temporal concatenation, similar to group ICA, we introduce a new framework for back reconstruction of individual subject from group analysis using NMF. This framework will make comparisons between groups possible for NMF.