Identifying population differences in whole-brain structural networks: a machine learning approach.

Models of whole-brain connectivity are valuable for understanding neurological function, development and disease. This paper presents a machine learning based approach to classify subjects according to their approximated structural connectivity patterns and to identify features which represent the key differences between groups. Brain networks are extracted from diffusion magnetic resonance images obtained by a clinically viable acquisition protocol. Connections are tracked between 83 regions of interest automatically extracted by label propagation from multiple brain atlases followed by classifier fusion. Tracts between these regions are propagated by probabilistic tracking, and mean anisotropy measurements along these connections provide the feature vectors for combined principal component analysis and maximum uncertainty linear discriminant analysis. The approach is tested on two populations with different age distributions: 20-30 and 60-90 years. We show that subjects can be classified successfully (with 87.46% accuracy) and that the features extracted from the discriminant analysis agree with current consensus on the neurological impact of ageing.