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Exploratory fMRI Analysis without Spatial Normalization
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Institution: |
Computer Science and Artificial Intelligence Laboratory, MIT, USA. |
Publisher: |
Inf Process Med Imaging IPMI 2009 |
Publication Date: |
Jul-2009 |
Citation: |
Inf Process Med Imaging. 2009;21:398-410. |
PubMed ID: |
19694280 |
Keywords: |
Projects:fMRIClustering |
Appears in Collections: |
NAC, NA-MIC |
Sponsors: |
NIH NIBIB NAMIC U54 EB005149 NIH NCRR NAC P41 RR13218 MIT McGovern Institute Neurotechnology Program NSF CAREER Grant 0642971 |
Generated Citation: |
Lashkari D, Golland P. Exploratory fMRI Analysis without Spatial Normalization. Inf Process Med Imaging. 2009;21:398-410. PMID: 19694280. |
| Downloaded: | 127 times. [view map] |
| Paper: | Download, View online |
| Export citation: |
We present an exploratory method for simultaneous parcellation of multisubject fMRI data into functionally coherent areas. The method is based on a solely functional representation of the fMRI data and a hierarchical probabilistic model that accounts for both inter-subject and intra-subject forms of variability in fMRI response. We employ a Variational Bayes approximation to fit the model to the data. The resulting algorithm finds a functional parcellation of the individual brains along with a set of population-level clusters, establishing correspondence between these two levels. The model eliminates the need for spatial normalization while still enabling us to fuse data from several subjects. We demonstrate the application of our method on a visual fMRI study.
Additional Material
1 File (197.91kB)
Lashkari-IPMI2009-fig3.jpg (197.91kB)