Multivariate Statistical Analysis of Whole Brain Structural Networks Obtained Using Probabilistic Tractography

Authors:
Emma C. Robinson;Michel Valstar;Alexander Hammers;Anders Ericsson;A. David Edwards;Daniel Rueckert
Affiliations:
Department of Computing, Imperial College, , London, UK SW7 2BZ;Department of Computing, Imperial College, , London, UK SW7 2BZ;MRC Clinical Sciences Centre and Division of Neuroscience, Faculty of Medicine, Imperial College, , London, UK W12 ONN;Department of Computing, Imperial College, , London, UK SW7 2BZ;Department of Paediatrics, Imperial College, , London, UK W12 ONN;Department of Computing, Imperial College, , London, UK SW7 2BZ
Venue:
MICCAI '08 Proceedings of the 11th international conference on Medical Image Computing and Computer-Assisted Intervention - Part I
Year:
2008

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Abstract

This paper presents a new framework for the analysis of anatomical connectivity derived from diffusion tensor MRI. The framework has been applied to estimate whole brain structural networks using diffusion data from 174 adult subjects. In the proposed approach, each brain is first segmented into 83 anatomical regions via label propagation of multiple atlases and subsequent decision fusion. For each pair of anatomical regions the probability of connection and its strength is then estimated using a modified version of probabilistic tractography. The resulting brain networks have been classified according to age and gender using non-linear support vector machines with GentleBoost feature extraction. Classification performance was tested using a leave-one-out approach and the mean accuracy obtained was 85.4%.