Name
Abstract | ||
|---|---|---|
Task-related motion is a major source of noise in functional magnetic-resonance imaging (fMRI) time series. The motion effect usually persists even after perfect spatial realignment is achieved. Here, we propose a new method to remove a certain type of task-related motion effect that persists after realignment. The procedure consists of the following: the decomposition of the realigned time-series data into spatially-independent components using independent-component analysis (ICA); the automatic classification and rejection of the ICs of the task-related residual motion effects; and finally, a reconstruction without them. To classify the ICs, we utilized the associated task-related changes in signal intensity and variance. The effectiveness of the method was verified using an fMRI experiment that explicitly included head motion as a main effect. The results indicate that our ICA-based method removed the task-related motion effects more effectively than the conventional voxel-wise regression-based method. |
Year | DOI | Venue |
|---|---|---|
2005 | 10.1016/j.neuroimage.2004.12.027 | NeuroImage |
Keywords | Field | DocType |
Voxel,Motion,Regression,Functional MRI,Independent-component analysis (ICA) | Voxel,Residual,Signal intensity,Computer vision,Pattern recognition,Regression,Head movements,Artificial intelligence,Independent component analysis,Mathematics,Principal component analysis,Main effect | Journal |
Volume | Issue | ISSN |
25 | 3 | 1053-8119 |
Citations | PageRank | References |
16 | 1.74 | 6 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Takanori Kochiyama | 1 | 98 | 14.14 |
| Tomoyo Morita | 2 | 16 | 1.74 |
| T Okada | 3 | 108 | 14.38 |
| Yoshiharu Yonekura | 4 | 23 | 3.47 |
| Michikazu Matsumura | 5 | 16 | 1.74 |
| Norihiro Sadato | 6 | 55 | 8.22 |