Task-Specific Functional Brain Geometry from Model Maps
MICCAI '08 Proceedings of the 11th international conference on Medical Image Computing and Computer-Assisted Intervention - Part I
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
Probabilistic Anatomo-Functional Parcellation of the Cortex: How Many Regions?
MICCAI '08 Proceedings of the 11th International Conference on Medical Image Computing and Computer-Assisted Intervention, Part II
Exploratory fMRI Analysis without Spatial Normalization
IPMI '09 Proceedings of the 21st International Conference on Information Processing in Medical Imaging
Functional Segmentation of fMRI Data Using Adaptive Non-negative Sparse PCA (ANSPCA)
MICCAI '09 Proceedings of the 12th International Conference on Medical Image Computing and Computer-Assisted Intervention: Part II
Feature-space-based fMRI analysis using the optimal linear transformation
IEEE Transactions on Information Technology in Biomedicine
Multi-subject dictionary learning to segment an atlas of brain spontaneous activity
IPMI'11 Proceedings of the 22nd international conference on Information processing in medical imaging
A supervised clustering approach for fMRI-based inference of brain states
Pattern Recognition
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In functional connectivity analysis, networks of interest are defined based on correlation with the mean time course of a user-selected 'seed' region. In this work we propose to simultaneously estimate the optimal representative time courses that summarize the fMRI data well and the partition of the volume into a set of disjoint regions that are best explained by these representative time courses. Our approach offers two advantages. First, is removes the sensitivity of the analysis to the details of the seed selection. Second, it substantially simplifies group analysis by eliminating the need for a subject-specific threshold at which correlation values are deemed significant. This unsupervised technique generalizes connectivity analysis to situations where candidate seeds are difficult to identify reliably or are unknown. Our experimental results indicate that the functional segmentation provides a robust, anatomically meaningful and consistent model for functional connectivity in fMRI.