2011
DOI: 10.1007/978-3-642-22092-0_46
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Multi-subject Dictionary Learning to Segment an Atlas of Brain Spontaneous Activity

Abstract: Thirion. Multisubject dictionary learning to segment an atlas of brain spontaneous activity. Information Processing in Medical Imaging, Jul 2011, Kaufbeuren, Germany. Springer, 6801, pp.562-573, 2011, Lecture Notes in Computer Science. <10.1007 Multi-subject dictionary learning to segment an atlas of brain spontaneous activity Abstract. 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 f… Show more

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Cited by 148 publications
(165 citation statements)
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“…The algorithm iterates over small batches of voxels (hundreds) to incrementally improve the dictionary. When the number of voxels is large, such an approach can be orders of magnitude faster than the alternate optimization strategies used by [18,3], because these require solving brain-wide sparse regression for each update of the dictionary. Szabo et al [14] extend this approach to structured dictionaries by replacing the 1 norm on α v with a structure-inducing norm, such as the 21 norm used in the group lasso.…”
Section: Efficient Learning Of Rfx-structured Dictionariesmentioning
confidence: 99%
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“…The algorithm iterates over small batches of voxels (hundreds) to incrementally improve the dictionary. When the number of voxels is large, such an approach can be orders of magnitude faster than the alternate optimization strategies used by [18,3], because these require solving brain-wide sparse regression for each update of the dictionary. Szabo et al [14] extend this approach to structured dictionaries by replacing the 1 norm on α v with a structure-inducing norm, such as the 21 norm used in the group lasso.…”
Section: Efficient Learning Of Rfx-structured Dictionariesmentioning
confidence: 99%
“…Laird et al [6] have recently shown that the modes that it extracts from task-activation data capture meaningful structure in the space of cognitive processes. Beyond ICA, Varoquaux et al [18] use dictionary learning to segment a functional parcellation from resting-state. Very interesting preliminary work by Chen et al [3] integrates spatial normalization with dictionary learning to estimate jointly an inter-subject warping and functional regions.…”
Section: Introductionmentioning
confidence: 99%
“…While multi-subject analyses do stabilize model estimation (see Fig. 1), at very high model order the spatial maps may reflect modes of inter-subject variability, such as spatial gradients of some of the networks [19].…”
Section: Introductionmentioning
confidence: 98%
“…Combined with PCA in a sparse PCA procedure, it regularizes model estimation by constraining the fitted signal [19]. As ℓ 1 penalizations perform poorly with correlated features, it is useful to impose sparsity on groups of neighboring voxels, for instance using sparse structured penalties.…”
Section: Introductionmentioning
confidence: 99%
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