An Introduction to Variational Methods for Graphical Models
Machine Learning
The Journal of Machine Learning Research
Clustering on the Unit Hypersphere using von Mises-Fisher Distributions
The Journal of Machine Learning Research
Discovering Structure in the Space of Activation Profiles in fMRI
MICCAI '08 Proceedings of the 11th international conference on Medical Image Computing and Computer-Assisted Intervention - Part I
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
State-space models of mental processes from fMRI
IPMI'11 Proceedings of the 22nd international conference on Information processing in medical imaging
Cohort-level brain mapping: learning cognitive atoms to single out specialized regions
IPMI'13 Proceedings of the 23rd international conference on Information Processing in Medical Imaging
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We present an exploratory method for simultaneous parcellation of multisubject fMRI data into functionally coherent areas. The method is based on a solely functional representation of the fMRI data and a hierarchical probabilistic model that accounts for both inter-subject and intra-subject forms of variability in fMRI response. We employ a Variational Bayes approximation to fit the model to the data. The resulting algorithm finds a functional parcellation of the individual brains along with a set of population-level clusters, establishing correspondence between these two levels. The model eliminates the need for spatial normalization while still enabling us to fuse data from several subjects. We demonstrate the application of our method on a visual fMRI study.