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Start Over Author "The Computational, Cognitive and Clinical Neuroimaging Lab" Topic [info.info-im]computer science [cs]/medical imaging
2 results on '"The Computational, Cognitive and Clinical Neuroimaging Lab"'

1. A framework for inter-subject prediction of functional connectivity from structural networks

Author

Daniel Rueckert, Robert Leech, Fani Deligianni, Gaël Varoquaux, Bertrand Thirion, Christian Ledig, David J. Sharp, Department of Computing [London], Biomedical Image Analysis Group [London] (BioMedIA), Imperial College London-Imperial College London, Imaging and Biophysics Unit, University College of London [London] (UCL), Modelling brain structure, function and variability based on high-field MRI data (PARIETAL), Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Service NEUROSPIN (NEUROSPIN), Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay, Service NEUROSPIN (NEUROSPIN), Computational, Cognitive and Clinical Neuroimaging Laboratory (C3NL), Imperial College London, The Computational, Cognitive and Clinical Neuroimaging Lab, Université Paris-Saclay-Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), and Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)

Subjects
Computer science, structural brain connectivity, ACM: J.: Computer Applications/J.3: LIFE AND MEDICAL SCIENCES, Machine learning, computer.software_genre, functional brain connectivity, statistical associations, 030218 nuclear medicine & medical imaging, 03 medical and health sciences, symbols.namesake, 0302 clinical medicine, Salience (neuroscience), medicine, [INFO.INFO-IM]Computer Science [cs]/Medical Imaging, Graphical model, Electrical and Electronic Engineering, Gaussian process, Default mode network, Radiological and Ultrasound Technology, medicine.diagnostic_test, Artificial neural network, business.industry, Functional connectivity, [SCCO.NEUR]Cognitive science/Neuroscience, Computer Science Applications, symbols, ACM: I.: Computing Methodologies/I.5: PATTERN RECOGNITION/I.5.1: Models/I.5.1.4: Statistical, [SDV.IB]Life Sciences [q-bio]/Bioengineering, Artificial intelligence, business, Functional magnetic resonance imaging, computer, predictive modeling, 030217 neurology & neurosurgery, Software
Abstract

Functional connections between brain regions are supported by structural connectivity. Both functional and structural connectivity are estimated from in vivo magnetic resonance imaging and offer complementary information on brain organization and function. However, imaging only provides noisy measures, and we lack a good neuroscientific understanding of the links between structure and function. Therefore, inter-subject joint modeling of structural and functional connectivity, the key to multimodal biomarkers, is an open challenge. We present a probabilistic framework to learn across subjects a mapping from structural to functional brain connectivity. Expanding on our previous work [1], our approach is based on a predictive framework with multiple sparse linear regression. We rely on the randomized LASSO to identify relevant anatomo-functional links with some confidence interval. In addition, we describe resting-state functional magnetic resonance imaging in the setting of Gaussian graphical models, on the one hand imposing conditional independences from structural connectivity and on the other hand parameterizing the problem in terms of multivariate autoregressive models. We introduce an intrinsic measure of prediction error for functional connectivity that is independent of the parameterization chosen and provides the means for robust model selection. We demonstrate our methodology with regions within the default mode and the salience network as well as, atlas-based cortical parcellation.

Published
2013
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2. A probabilistic framework to infer brain functional connectivity from anatomical connections

Author

Bertrand Thirion, Daniel Rueckert, Emma C. Robinson, Gaël Varoquaux, David J. Sharp, Fani Deligianni, A. David Edwards, Department of Computing [London], Biomedical Image Analysis Group [London] (BioMedIA), Imperial College London-Imperial College London, Laboratoire de Neuroimagerie Assistée par Ordinateur (LNAO), Commissariat à l'énergie atomique et aux énergies alternatives (CEA), Modelling brain structure, function and variability based on high-field MRI data (PARIETAL), Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Service NEUROSPIN (NEUROSPIN), Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay, Neuroimagerie cognitive - Psychologie cognitive expérimentale (UNICOG-U992), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris Saclay (COmUE)-Institut National de la Santé et de la Recherche Médicale (INSERM), The Computational, Cognitive and Clinical Neuroimaging Lab, Imperial College London, Institute of Clinical Sciences, Gábor Székely, Horst Hahn, Service NEUROSPIN (NEUROSPIN), Université Paris-Saclay-Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Paris-Saclay, and Varoquaux, Gaël

Subjects
Computer science, Brain activity and meditation, [INFO.INFO-TS] Computer Science [cs]/Signal and Image Processing, brain, ACM: G.: Mathematics of Computing/G.3: PROBABILITY AND STATISTICS/G.3.5: Multivariate statistics, ACM: J.: Computer Applications/J.3: LIFE AND MEDICAL SCIENCES, [INFO.INFO-IM] Computer Science [cs]/Medical Imaging, Linear prediction, Machine learning, computer.software_genre, Brain mapping, 030218 nuclear medicine & medical imaging, 03 medical and health sciences, 0302 clinical medicine, [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, [INFO.INFO-IM]Computer Science [cs]/Medical Imaging, [SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processing, learning, Basis (linear algebra), business.industry, Model selection, functional connectivity, Function (mathematics), Covariance, anatomical connectivity, Conditional independence, Artificial intelligence, business, computer, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, 030217 neurology & neurosurgery, ACM: I.: Computing Methodologies/I.2: ARTIFICIAL INTELLIGENCE/I.2.6: Learning
Abstract

International audience; We present a novel probabilistic framework to learn across several subjects a mapping from brain anatomical connectivity to functional connectivity, i.e. the covariance structure of brain activity. This prediction problem must be formulated as a structured-output learning task, as the predicted parameters are strongly correlated. We introduce a model selection framework based on cross-validation with a parametrization-independent loss function suitable to the manifold of covariance matrices. Our model is based on constraining the conditional independence structure of functional activity by the anatomical connectivity. Subsequently, we learn a linear predictor of a stationary multivariate autoregressive model. This natural parameterization of functional connectivity also enforces the positive-definiteness of the predicted covariance and thus matches the structure of the output space. Our results show that functional connectivity can be explained by anatomical connectivity on a rigorous statistical basis, and that a proper model of functional connectivity is essential to assess this link.

Published
2011
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