Abstract:
: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.
journal_name
Neuroimagejournal_title
NeuroImageauthors
Robinson EC,Hammers A,Ericsson A,Edwards AD,Rueckert Ddoi
10.1016/j.neuroimage.2010.01.019subject
Has Abstractpub_date
2010-04-15 00:00:00pages
910-9issue
3eissn
1053-8119issn
1095-9572pii
S1053-8119(10)00024-8journal_volume
50pub_type
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