Journal
NEUROIMAGE
Volume 50, Issue 3, Pages 1085-1098Publisher
ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.neuroimage.2009.12.106
Keywords
fMRI; Clustering; High level vision; Category selectivity
Funding
- McGovern Institute Neurotechnology Program
- NIH [NIBIB NAMIC U54-EB005149, NCRR NAC P41-RR1 3218]
- NEI [13455]
- NSF [CAREER 0642971]
- NDSEG
- [IIS/CRCNS 0904625]
- Div Of Information & Intelligent Systems
- Direct For Computer & Info Scie & Enginr [904625] Funding Source: National Science Foundation
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We present a method for discovering patterns of selectivity in fMRI data for experiments with multiple stimuli/tasks. We introduce a representation of the data as profiles of selectivity using linear regression estimates, and employ mixture model density estimation to identify functional systems with distinct types of selectivity. The method characterizes these systems by their selectivity patterns and spatial maps. both estimated Simultaneously via the EM algorithm. We demonstrate a corresponding method for group analysis that avoids the need for spatial correspondence among Subjects. Consistency of the selectivity profiles across subjects provides a way to assess the validity of the discovered systems. We validate this model in the context of category selectivity in visual cortex, demonstrating good agreement with the findings based on prior hypothesis-driven methods. (C) 2010 Elsevier Inc. All rights reserved.
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