Name
Abstract | ||
|---|---|---|
Task-based functional magnetic resonance imaging (tfMRI) has been widely used to study functional brain networks under task performance. Modeling tfMRI data is challenging due to at least two problems: the lack of the ground truth of underlying neural activity and the highly complex intrinsic structure of tfMRI data. To better understand brain networks based on fMRI data, data-driven approaches ha... |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/TMI.2017.2715285 | IEEE Transactions on Medical Imaging |
Keywords | DocType | Volume |
Convolution,Data models,Brain modeling,Hidden Markov models,Decoding,Machine learning,Time series analysis | Journal | 37 |
Issue | ISSN | Citations |
7 | 0278-0062 | 6 |
PageRank | References | Authors |
0.45 | 4 | 8 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Heng Huang | 1 | 3080 | 203.21 |
| Xintao Hu | 2 | 118 | 13.53 |
| Yu Zhao | 3 | 64 | 10.07 |
| Milad Makkie | 4 | 32 | 4.03 |
| Qinglin Dong | 5 | 27 | 3.93 |
| Shijie Zhao | 6 | 66 | 10.85 |
| Lei Guo | 7 | 181 | 11.67 |
| Tianming Liu | 8 | 1033 | 112.95 |