Algorithmic graph theory
Independent component analysis: algorithms and applications
Neural Networks
Atomic Decomposition by Basis Pursuit
SIAM Review
Dictionary learning algorithms for sparse representation
Neural Computation
Expectation-maximization for sparse and non-negative PCA
Proceedings of the 25th international conference on Machine learning
A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems
SIAM Journal on Imaging Sciences
A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems
SIAM Journal on Imaging Sciences
IEEE Transactions on Information Theory
Online Learning for Matrix Factorization and Sparse Coding
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
ICA-based sparse features recovery from FMRI datasets
ISBI'10 Proceedings of the 2010 IEEE international conference on Biomedical imaging: from nano to Macro
MICCAI'10 Proceedings of the 13th international conference on Medical image computing and computer-assisted intervention: Part I
De-noising by soft-thresholding
IEEE Transactions on Information Theory
Characterization of task-free/task-performance brain states
MICCAI'12 Proceedings of the 15th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part II
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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Fluctuations in brain on-going activity can be used to reveal its intrinsic functional organization. To mine this information, we give a new hierarchical probabilistic model for brain activity patterns that does not require an experimental design to be specified. We estimate this model in the dictionary learning framework, learning simultaneously latent spatial maps and the corresponding brain activity time-series. Unlike previous dictionary learning frameworks, we introduce an explicit difference between subject-level spatial maps and their corresponding population-level maps, forming an atlas. We give a novel algorithm using convex optimization techniques to solve efficiently this problem with non-smooth penalties well-suited to image denoising. We show on simulated data that it can recover population-level maps as well as subject specificities. On resting-state fMRI data, we extract the first atlas of spontaneous brain activity and show how it defines a subject-specific functional parcellation of the brain in localized regions.