Nonparametric hierarchical Bayesian model for functional brain parcellation
Name
Golland_Nonparametric hierarchical.pdf
Size
4.12 MB
Format
Adobe PDF
Checksum (MD5)
afdc4512a72c5c0e8e0c0beef3ddad8b
Author(s)
Lashkari, Danial
Sridharan, Ramesh
Vul, Edward
Hsieh, Po-Jang
Kanwisher, Nancy
Golland, Polina
Date Issued
June 2010
Journal
IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops.
Publisher
Institute of Electrical and Electronics Engineers / IEEE Computer Society
Citation
Kanwisher, N., and P. Golland, with Lashkari, D., R. Sridharan, and E. Vul, Po-Jang Hsieh. “Nonparametric Hierarchical Bayesian Model for Functional Brain Parcellation.” Computer Vision and Pattern Recognition Workshops (CVPRW), 2010 IEEE Computer Society Conference On. 2010. 15-22. Copyright © 2010, IEEE
Version
Final published version
Abstract
We develop a method for unsupervised analysis of functional brain images that learns group-level patterns of functional response. Our algorithm is based on a generative model that comprises two main layers. At the lower level, we express the functional brain response to each stimulus as a binary activation variable. At the next level, we define a prior over the sets of activation variables in all subjects. We use a Hierarchical Dirichlet Process as the prior in order to simultaneously learn the patterns of response that are shared across the group, and to estimate the number of these patterns supported by data. Inference based on this model enables automatic discovery and characterization of salient and consistent patterns in functional signals. We apply our method to data from a study that explores the response of the visual cortex to a collection of images. The discovered profiles of activation correspond to selectivity to a number of image categories such as faces, bodies, and scenes. More generally, our results appear superior to the results of alternative data-driven methods in capturing the category structure in the space of stimuli.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
McGovern Institute for Brain Research at MIT
Terms of Use
Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/CVPRW.2010.5543434
