Deformation-Invariant Sparse Coding for Modeling Spatial Variability of Functional Patterns in the Brain

For a given cognitive task such as language processing, the location of corresponding functional regions in the brain may vary across subjects relative to anatomy. We present a probabilistic generative model that accounts for such variability as observed in fMRI data. We relate our approach to sparse coding that estimates a basis consisting of functional regions in the brain. Individual fMRI data is represented as a weighted sum of these functional regions that undergo deformations. We demonstrate the proposed method on a language fMRI study. Our method identified activation regions that agree with known literature on language processing and established correspondences among activation regions across subjects, producing more robust group-level effects than anatomical alignment alone.

Source:
http://nac.spl.harvard.edu/publications/item/view/2280
Authors:
Institution:
Chen G./Fedorenko E.G./Kanwisher N.G./Golland P.
Massachusetts Institute of Technology, Cambridge, MA. USA.
Publication Date:
Dec-2012
Volume Number:
7263
Pages:
68-75
Citation:
In Proc. Neural Information Processing Systems (NIPS) Workshop on Machine Learning and Interpretation in Neuroimaging (MLNI), LNAI 7263:68-75, 2012.
Keywords:
Projects:ModelingFunctionalActivationPatterns
Appears in Collections:
NAC
Sponsors:
NSF IIS/CRCNS 0904625
NSF CAREER 0642971
NIH NCRR NAC P41 RR13218
NIH NEI R01 EY13455
Generated Citation:
Chen G., Fedorenko E.G., Kanwisher N.G., Golland P. Deformation-Invariant Sparse Coding for Modeling Spatial Variability of Functional Patterns in the Brain. In Proc. Neural Information Processing Systems (NIPS) Workshop on Machine Learning and Interpretation in Neuroimaging (MLNI), LNAI 7263:68-75, 2012.
Downloaded:9 times. [view map]
Paper:Download, View online

0 yorum: