Name
Abstract | ||
|---|---|---|
Inferring the functional specificity of brain regions from functional Magnetic Resonance Images (fMRI) data is a challenging statistical problem. While the General Linear Model (GLM) remains the standard approach for brain mapping, supervised learning techniques (\emph{a.k.a.} decoding) have proven to be useful to capture multivariate statistical effects distributed across voxels and brain regions. Up to now, much effort has been made to improve decoding by incorporating prior knowledge in the form of a particular regularization term. In this paper we demonstrate that further improvement can be made by accounting for non-linearities using a ranking approach rather than the commonly used least-square regression. Through simulation, we compare the recovery properties of our approach to linear models commonly used in fMRI based decoding. We demonstrate the superiority of ranking with a real fMRI dataset. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/PRNI.2012.23 | PRNI |
Keywords | DocType | Volume |
functional specificity,standard approach,brain region,functional magnetic resonance images,brain mapping,multivariate statistical effect,general linear model,ranking approach,improved brain pattern recovery,real fmri dataset,ranking approaches,challenging statistical problem,glm,support vector machines,correlation,logistics,predictive models,vectors,decoding,supervised learning,computational modeling,learning artificial intelligence,ranking,statistical analysis | Journal | abs/1207.3520 |
Citations | PageRank | References |
2 | 0.43 | 8 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Fabian Pedregosa | 1 | 4164 | 179.32 |
| Elodie Cauvet | 2 | 9 | 1.27 |
| Gael Varoquaux | 3 | 5309 | 285.24 |
| Christophe Pallier | 4 | 186 | 18.82 |
| Bertrand Thirion | 5 | 5047 | 270.40 |
| Alexandre Gramfort | 6 | 4791 | 234.87 |