Name
Abstract | ||
|---|---|---|
It has been shown that the presence of a blood oxygen level dependent (BOLD) signal in high-field (3T and higher) fMRI datasets can cause stimulus-correlated registration errors, especially when using a least-squares registration method. These errors can result in systematic inaccuracies in activation detection. The authors have recently proposed a new method to solve both the registration and activation detection least-squares problems simultaneously. This paper gives an outline of the new method, and demonstrates its robustness on simulated fMRI datasets containing various combinations of motion and activation. In addition to a discussion of the merits of the method and details on how it can be efficiently implemented, it is shown that, compared to the standard approach, the new method consistently reduces false-positive activations by two thirds and reduces false-negative activations by one third. |
Year | DOI | Venue |
|---|---|---|
2003 | 10.1117/12.480849 | Proceedings of SPIE |
Keywords | Field | DocType |
fMRI,least-squares,registration,BOLD,GLM,activation | Least squares,Functional magnetic resonance imaging,Pattern recognition,Blood-oxygen-level dependent,Computer science,Robustness (computer science),Artificial intelligence,Activation detection | Conference |
Volume | ISSN | Citations |
5032 | 0277-786X | 0 |
PageRank | References | Authors |
0.34 | 4 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jefi J. Orchard | 1 | 0 | 0.34 |
| CHEN GREIF | 2 | 321 | 43.63 |
| Gene H. Golub | 3 | 2558 | 856.07 |
| Bruce Bjornson | 4 | 7 | 1.61 |
| M Stella Atkins | 5 | 1076 | 109.44 |