Statistical analysis of structural brain connectivity

Authors:
Renske de Boer;Michiel Schaap;Fedde van der Lijn;Henri A. Vrooman;Marius de Groot;Meike W. Vernooij;M. Arfan Ikram;Evert F. S. van Velsen;Aad van der Lugt;Monique M. B. Breteler;Wiro J. Niessen
Affiliations:
Departments of Radiology & Medical Informatics, Erasmus MC, Rotterdam, The Netherlands and Department of Epidemiology, Erasmus MC, Rotterdam, The Netherlands;Departments of Radiology & Medical Informatics, Erasmus MC, Rotterdam, The Netherlands;Departments of Radiology & Medical Informatics, Erasmus MC, Rotterdam, The Netherlands;Departments of Radiology & Medical Informatics, Erasmus MC, Rotterdam, The Netherlands;Departments of Radiology & Medical Informatics, Erasmus MC, Rotterdam, The Netherlands;Department of Epidemiology, Erasmus MC, Rotterdam, The Netherlands and Department of Radiology, Erasmus MC, Rotterdam, The Netherlands;Department of Epidemiology, Erasmus MC, Rotterdam, The Netherlands and Department of Radiology, Erasmus MC, Rotterdam, The Netherlands;Departments of Radiology & Medical Informatics, Erasmus MC, Rotterdam, The Netherlands and Department of Epidemiology, Erasmus MC, Rotterdam, The Netherlands;Department of Radiology, Erasmus MC, Rotterdam, The Netherlands;Department of Epidemiology, Erasmus MC, Rotterdam, The Netherlands;Departments of Radiology & Medical Informatics, Erasmus MC, Rotterdam, The Netherlands and Imaging Science & Technology, Faculty of Applied Sciences, Delft University of Technology, Delft, ...
Venue:
MICCAI'10 Proceedings of the 13th international conference on Medical image computing and computer-assisted intervention: Part II
Year:
2010

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Abstract

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.