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

Deligianni, F. , Varoquaux, G., Thirion, B., Sharp, D. J., Ledig, C., Leech, R. and Rueckert, D. (2013) A framework for inter-subject prediction of functional connectivity from structural networks. IEEE Transactions on Medical Imaging, 32(12), pp. 2200-2214. (doi:10.1109/TMI.2013.2276916)

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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.

Item Type:Articles
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Deligianni, Dr Fani
Authors: Deligianni, F., Varoquaux, G., Thirion, B., Sharp, D. J., Ledig, C., Leech, R., and Rueckert, D.
College/School:College of Science and Engineering > School of Computing Science
Journal Name:IEEE Transactions on Medical Imaging
Publisher:IEEE
ISSN:0278-0062
ISSN (Online):1558-254X
Published Online:06 August 2013

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