Name
Abstract | ||
|---|---|---|
The tracking of dynamic functional connectivity (dFC) states in resting-state fMRI scans aims to reveal how the brain sequentially processes stimuli and thoughts. Despite the recent advances in statistical methods, estimating the high dimensional dFC states from a small number of available time points remains a challenge. This paper shows that the challenge is reduced by a statistical method used for the estimation of large covariance matrices from small number of samples. We present a computationally efficient formulation of our approach that scales dFC analysis up to full resolution resting-state fMRI scans. Experiments on synthetic data demonstrate that our approach produces dFC estimates that are closer to the ground-truth than state-of-the-art estimation approaches. When comparing methods on the rs-fMRI scans of 162 subjects, we found that our approach is better at extracting functional networks and capturing differences in rs-fMRI acquisition and diagnosis. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1007/978-3-030-32391-2_4 | CNI@MICCAI |
DocType | Volume | Citations |
Conference | 11848 | 0 |
PageRank | References | Authors |
0.34 | 0 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Nicolas Honnorat | 1 | 31 | 6.92 |
| Ehsan Adeli Mosabbeb | 2 | 261 | 39.27 |
| Qingyu Zhao | 3 | 2 | 4.09 |
| Adolf Pfefferbaum | 4 | 174 | 20.61 |
| Edith V Sullivan | 5 | 150 | 19.25 |
| Kilian Pohl | 6 | 577 | 46.78 |