Large-scale sparse functional networks from resting state fMRI
… Aiming to overcome these challenges, we propose a brain decomposition method for
computing subject specific, sparse function networks (SFNs) without losing comparability across …
computing subject specific, sparse function networks (SFNs) without losing comparability across …
Large-scale DCMs for resting-state fMRI
… 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) …
resting-state functional MRI (fMRI). … reduction to discover the most likely sparse graph (or model) …
Hierarchical extraction of functional connectivity components in human brain using resting-state fMRI
D Sahoo, TD Satterthwaite… - IEEE Transactions on …, 2020 - ieeexplore.ieee.org
… 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 …
hierarchically-… Xu et al., “Large-scale functional network overlap is a general property of brain …
Interactions between large-scale functional brain networks are captured by sparse coupled HMMs
… 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 …
of large-scale functional network … Then, building upon a novel sparse coupled hidden …
Identifying sparse connectivity patterns in the brain using resting-state fMRI
… 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 …
… Resting-state fMRI (rs-fMRI) is a powerful tool for understanding the large-scale functional …
Characterizing and differentiating task-based and resting state fMRI signals via two-stage sparse representations
… 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). …
fMRI data. More results could be found in supplemental materials (Supplemental Figs. 1–6). …
[HTML][HTML] Identification of functional networks in resting state fMRI data using adaptive sparse representation and affinity propagation clustering
… 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 …
parcelation and may not be enough to discover subtle differences between subjects. The AAL …
Sparse representation of whole-brain fMRI signals for identification of functional networks
… 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. …
multiple functional networks via sparse representation of whole-brain task-based fMRI signals. …
[HTML][HTML] Large-scale probabilistic functional modes from resting state fMRI
… 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 …
… for identifying modes of coherent activity from resting state fMRI (rfMRI) data. Our model …
Signal sampling for efficient sparse representation of resting state FMRI data
… 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 …
resting state fMRI (rs-fMRI) signals of a whole brain via a statistical sampling based sparse …