We present a framework for statistical analysis in large cohorts of structural brain connectivity, derived from diffusion weighted MRI. A brain network is defined between subcortical gray matter structures and a cortical parcellation obtained with FreeSurfer. Connectivity is established through minimum cost paths with an anisotropic local cost function and is quantified per connection. The connectivity network potentially encodes important information about brain structure, and can be analyzed using multivariate regression methods. The proposed framework can be used to study the relation between connectivity and e.g. brain function or neurodegenerative disease. As a proof of principle, we perform principal component regression in order to predict age and gender, based on the connectivity networks of 979 middle-aged and elderly subjects, in a 10-fold cross-validation. The results are compared to predictions based on fractional anisotropy and mean diffusivity averaged over the white matter and over the corpus callosum. Additionally, the predictions are performed based on the best predicting connection in the network. Principal component regression outperformed all other prediction models, demonstrating the age and gender information encoded in the connectivity network. 1 Introduction Both functional and anatomical connectivity of the brain are areas of increasing research interest. Functional connectivity is mainly established through functional magnetic resonance imaging (fMRI) while diffusion MRI has been applied for assessing anatomical or structural connectivity. Both types have been analyzed by modeling connectivity as a complex network and applying graph theory approaches to study the network topology. A review of graph theoretical analysis of complex brain networks is given in 1. T. Jiang et al. (Eds.): MICCAI 2010, Part II, LNCS 6362, pp. 101–108, 2010. c Springer-Verlag Berlin Heidelberg 2010
Renske de Boer Monique M.B.Breteler Wiro J.Niessen Michiel Schaap Fedde van der Lijn Henri A.Vrooman Marius de Groot MeikeW.Vernooij M.Arfan Ikram Evert F.S.van Velsen Aad van der Lugt
Biomedical Imaging Group Rotterdam, Departments of Radiology &Medical Informatics, Erasmus MC, Rotte Department of Epidemiology, Erasmus MC, Rotterdam, The Netherlands Biomedical Imaging Group Rotterdam, Departments of Radiology & Medical Informatics, Erasmus MC, Rott Biomedical Imaging Group Rotterdam, Departments of Radiology &Medical Informatics, Erasmus MC, Rotte Department of Epidemiology, Erasmus MC, Rotterdam, The Netherlands Department of Radiology, Erasmus Department of Radiology, Erasmus MC, Rotterdam, The Netherlands
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