… Aiming to overcome these challenges, we propose a brain decomposition method for
computing subject specific, sparse function networks (SFNs) without losing comparability across …

… functional connectivity methods based on symmetric correlations that are ubiquitous in
resting-state functional MRI (fMRI). … reduction to discover the most likely sparse graph (or model) …

… matrices generated from fMRI. The goal of the paper is to extract sparse interpretable
hierarchically-… Xu et al., “Large-scale functional network overlap is a general property of brain …

… fMRI deconvolution technique to express resting-state temporal fluctuations as a combination
of large-scale functional network … Then, building upon a novel sparse coupled hidden …

… Our results from simulated as well as real resting state fMRI data show that SCPs are accurate
… Resting-state fMRI (rs-fMRI) is a powerful tool for understanding the large-scale functional …

… our framework could identify task-evoked functional component in the large scale combined
fMRI data. More results could be found in supplemental materials (Supplemental Figs. 1–6). …

… In the present work, we only use the AAL template to define nodes, which is a large-scale
parcelation and may not be enough to discover subtle differences between subjects. The AAL …

… networks in a voxel-wise way and on the whole brain scale. This paper presents a novel, …
multiple functional networks via sparse representation of whole-brain task-based fMRI signals. …

… brain activity from functional magnetic resonance imaging (fMRI) when the subject is ‘at rest’.
… for identifying modes of coherent activity from resting state fMRI (rfMRI) data. Our model …

… the wider application of sparse representation method to large scale fMRI datasets. To …
resting state fMRI (rs-fMRI) signals of a whole brain via a statistical sampling based sparse …