Name
Abstract | ||
|---|---|---|
Voxelwise modeling (VM) is a powerful framework to predict single voxel responses evoked by a rich set of stimulus features present in complex natural stimuli. However, because VM disregards correlations across neighboring voxels, its sensitivity in detecting functional selectivity can be diminished in the presence of high levels of measurement noise. Here, we introduce spatially-informed voxelwise modeling (SPIN-VM) to take advantage of response correlations in spatial neighborhoods of voxels. To optimally utilize shared information, SPIN-VM performs regularization across spatial neighborhoods in addition to model features, while still generating single-voxel response predictions. We demonstrated the performance of SPIN-VM on a rich dataset from a natural vision experiment. Compared to VM, SPIN-VM yields higher prediction accuracies and better capture locally congruent information representations across cortex. These results suggest that SPIN-VM offers improved performance in predicting single-voxel responses and recovering coherent information representations. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1016/j.neuroimage.2018.11.044 | NeuroImage |
Keywords | Field | DocType |
fMRI,Voxelwise modeling,Response correlations,Coherent representation,Spatial regularization,Computational neuroscience | Voxel,Level of measurement,Pattern recognition,Psychology,Cognitive psychology,Regularization (mathematics),Artificial intelligence,Stimulus (physiology),Congruence (geometry),Coherent information | Journal |
Volume | ISSN | Citations |
186 | 1053-8119 | 0 |
PageRank | References | Authors |
0.34 | 18 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Emin Çelik | 1 | 0 | 0.34 |
| Salman Ul Hassan Dar | 2 | 12 | 1.30 |
| Özgür Yilmaz | 3 | 15 | 3.42 |
| Ümit Keleş | 4 | 0 | 0.34 |
| Tolga Çukur | 5 | 36 | 8.84 |