Exploratory fMRI Analysis without Spatial Normalization
IPMI '09 Proceedings of the 21st International Conference on Information Processing in Medical Imaging
Online Learning for Matrix Factorization and Sparse Coding
The Journal of Machine Learning Research
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
Behavioral interpretations of intrinsic connectivity networks
Journal of Cognitive Neuroscience
Online group-structured dictionary learning
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
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Functional Magnetic Resonance Imaging (fMRI) studies map the human brain by testing the response of groups of individuals to carefully-crafted and contrasted tasks in order to delineate specialized brain regions and networks. The number of functional networks extracted is limited by the number of subject-level contrasts and does not grow with the cohort. Here, we introduce a new group-level brain mapping strategy to differentiate many regions reflecting the variety of brain network configurations observed in the population. Based on the principle of functional segregation, our approach singles out functionally-specialized brain regions by learning group-level functional profiles on which the response of brain regions can be represented sparsely. We use a dictionary-learning formulation that can be solved efficiently with on-line algorithms, scaling to arbitrary large datasets. Importantly, we model inter-subject correspondence as structure imposed in the estimated functional profiles, integrating a structure-inducing regularization with no additional computational cost. On a large multi-subject study, our approach extracts a large number of brain networks with meaningful functional profiles.