Title :
Spatial patterns and functional profiles for discovering structure in fMRI data
Author :
Golland, Polina ; Lashkari, Danial ; Venkataraman, Archana
Author_Institution :
Comput. Sci. & Artificial Intell. Lab., Massachusetts Inst. of Technol., Cambridge, MA
Abstract :
We explore unsupervised, hypothesis-free methods for fMRI analysis in two different types of experiments. First, we employ clustering to identify large-scale functionally homogeneous systems. We formulate a generative mixture model, derive the EM algorithm and apply it to delineate functional systems. We also investigate spectral clustering in application to this problem and demonstrate that both methods give rise to similar partitions of the brain based on resting state fMRI data. Second, we demonstrate how to extend this approach to include information about the experimental protocol. Specifically, we formulate a mixture model in the space of possible profiles of brain response to stimuli. In both applications, our methods confirm previously known results in brain mapping and point to new research directions for exploratory analysis of fMRI data.
Keywords :
biomedical MRI; brain; medical computing; brain mapping; fMRI data; functional profiles; generative mixture model; large-scale functionally homogeneous systems; spatial patterns; spectral clustering; Artificial intelligence; Brain modeling; Computer science; Image analysis; Independent component analysis; Laboratories; Paper technology; Pattern analysis; Principal component analysis; Protocols;
Conference_Titel :
Signals, Systems and Computers, 2008 42nd Asilomar Conference on
Conference_Location :
Pacific Grove, CA
Print_ISBN :
978-1-4244-2940-0
Electronic_ISBN :
1058-6393
DOI :
10.1109/ACSSC.2008.5074650