Clustering on the Unit Hypersphere using von Mises-Fisher Distributions
The Journal of Machine Learning Research
Detection of spatial activation patterns as unsupervised segmentation of fMRI data
MICCAI'07 Proceedings of the 10th international conference on Medical image computing and computer-assisted intervention - Volume Part I
Exploratory fMRI Analysis without Spatial Normalization
IPMI '09 Proceedings of the 21st International Conference on Information Processing in Medical Imaging
A General and Unifying Framework for Feature Construction, in Image-Based Pattern Classification
IPMI '09 Proceedings of the 21st International Conference on Information Processing in Medical Imaging
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We present a method for discovering patterns of activation observed through fMRI 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.