Name
Title | ||
|---|---|---|
Anatomically informed convolution kernels for the projection of fMRI data on the cortical surface. |
Abstract | ||
|---|---|---|
We present here a method that aims at producing representations of functional brain data on the cortical surface from functional MRI volumes. Such representations are required for subsequent cortical-based functional analysis. We propose a projection technique based on the definition, around each node of the grey/white matter interface mesh, of convolution kernels whose shape and distribution rely on the geometry of the local anatomy. For one anatomy, a set of convolution kernels is computed that can be used to project any functional data registered with this anatomy. The method is presented together with experiments on synthetic data and real statistical t-maps. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1007/11866763_37 | MICCAI (2) |
Keywords | Field | DocType |
subsequent cortical-based functional analysis,fmri data,functional brain data,local anatomy,cortical surface,projection technique,real statistical t-maps,synthetic data,functional data,convolution kernel,functional mri volume,functional analysis | Computer vision,Pattern recognition,Convolution,Computer science,Synthetic data,Artificial intelligence,Kernel (image processing),Geodesic | Conference |
Volume | Issue | ISSN |
9 | Pt 2 | 0302-9743 |
ISBN | Citations | PageRank |
3-540-44727-X | 0 | 0.34 |
References | Authors | |
4 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Grégory Operto | 1 | 5 | 1.46 |
| Rémy Bulot | 2 | 6 | 3.27 |
| Jean-Luc Anton | 3 | 149 | 20.67 |
| Olivier Coulon | 4 | 220 | 34.76 |