Name
Abstract | ||
|---|---|---|
In task-based fMRI, the generalized linear model (GLM) is widely used to detect activated brain regions. A fundamental assumption in the GLM model for fMRI activation detection is that the brain's response, represented by the blood-oxygenation level dependent (BOLD) signals of volumetric voxels, follows the shape of stimulus paradigm. Based on this same assumption, we use the dynamic functional connectivity (DFC) curves between two ends of a white matter fiber, instead of the BOLD signal, to represent the brain's response, and apply the GLM to detect Activated Fibers (AFs). Our rational is that brain regions connected by white matter fibers tend to be more synchronized during stimulus intervals than during baseline intervals. Therefore, the DFC curves for fibers connecting active brain regions should be positively correlated with the stimulus paradigm, which is verified by our extensive experiments using multimodal task-based fMRI and diffusion tensor imaging (DTI) data. Our results demonstrate that the detected AFs connect not only most of the activated brain regions detected via traditional voxel-based GLM method, but also many other brain regions, suggesting that the voxel-based GLM method may be too conservative in detecting activated brain regions. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1007/978-3-642-22092-0_47 | IPMI |
Keywords | Field | DocType |
white matter fiber,active brain region,brain region,fmri activation detection,activated fiber,fiber-centered activation detection,glm model,task-based fmri,traditional voxel-based glm method,multimodal task-based fmri,stimulus paradigm,activated brain region,voxel-based glm method | Voxel,Computer vision,Diffusion MRI,Fiber,Pattern recognition,White matter,Computer science,Artificial intelligence,Stimulus (physiology),Dynamic functional connectivity,Activation detection | Conference |
Volume | ISSN | Citations |
22 | 1011-2499 | 5 |
PageRank | References | Authors |
0.56 | 8 | 7 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jinglei Lv | 1 | 205 | 26.70 |
| Lei Guo | 2 | 1661 | 142.63 |
| Kaiming Li | 3 | 385 | 30.92 |
| Xintao Hu | 4 | 118 | 13.53 |
| Dajiang Zhu | 5 | 320 | 36.72 |
| Junwei Han | 6 | 3501 | 194.57 |
| Tianming Liu | 7 | 1033 | 112.95 |