Discovering Structure in the Space of Activation Profiles in fMRI

We present a method for discovering patterns of activation observed through fMIRI in experiments with multiple stimuli/tasks. We introduce an explicit parameterization for the profiles of activation and represent fMRI time courses as such profiles using linear regression estimates. Working in the space of activation profiles, we design a mixture model that finds the major activation patterns along with their localization maps and derive an algorithm for fitting the model to the fMRI data. The method enables functional group analysis independent of spatial correspondence among subjects. We validate this model in the context of category selectivity in the visual cortex, demonstrating good agreement with prior findings based on hypothesis-driven methods.

[1]  L. Chalupa,et al.  The visual neurosciences , 2004 .

[2]  C. F. Beckmann,et al.  Tensorial extensions of independent component analysis for multisubject FMRI analysis , 2005, NeuroImage.

[3]  R Baumgartner,et al.  Fuzzy clustering of gradient‐echo functional MRI in the human visual cortex. Part I: Reproducibility , 1997, Journal of magnetic resonance imaging : JMRI.

[4]  L. K. Hansen,et al.  On Clustering fMRI Time Series , 1999, NeuroImage.

[5]  Polina Golland,et al.  Detection of Spatial Activation Patterns as Unsupervised Segmentation of fMRI Data , 2007, MICCAI.

[6]  S. Ruan,et al.  A multistep Unsupervised Fuzzy Clustering Analysis of fMRI time series , 2000, Human brain mapping.

[7]  L. K. Hansen,et al.  Feature‐space clustering for fMRI meta‐analysis , 2001, Human brain mapping.

[8]  Karl J. Friston,et al.  Statistical parametric maps in functional imaging: A general linear approach , 1994 .

[9]  K. Mardia.  Statistics of Directional Data , 1972 .

[10]  R Baumgartner,et al.  Fuzzy clustering of gradient‐echo functional MRI in the human visual cortex. Part II: Quantification , 1997, Journal of magnetic resonance imaging : JMRI.

[11]  S Makeig,et al.  Analysis of fMRI data by blind separation into independent spatial components , 1998, Human brain mapping.

[12]  Kanti V. Mardia,et al.  Statistics of Directional Data , 1972 .

[13]  D. Rubin,et al.  Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .

[14]  Terry M. Peters,et al.  Medical Image Computing and Computer-Assisted Intervention - MICCAI 2003 , 2003, Lecture Notes in Computer Science.

[15]  Olivier D. Faugeras,et al.  Feature Detection in fMRI Data: The Information Bottleneck Approach , 2003, MICCAI.

[16]  Inderjit S. Dhillon,et al.  Clustering on the Unit Hypersphere using von Mises-Fisher Distributions , 2005, J. Mach. Learn. Res..

[17]  Nicholas Ayache,et al.  Medical Image Computing and Computer-Assisted Intervention - MICCAI 2007, 10th International Conference, Brisbane, Australia, October 29 - November 2, 2007, Proceedings, Part I , 2007, MICCAI.