Multi-View Ensemble Classification of Brain Connectivity Images for Neurodegeneration Type Discrimination.

ABSTRACT: Brain connectivity analyses using voxels as features are not robust enough for single-patient classification because of the inter-subject anatomical and functional variability. To construct more robust features, voxels can be aggregated into clusters that are maximally coherent across subjects. Moreover, combining multi-modal neuroimaging and multi-view data integration techniques allows generating multiple independent connectivity features for the same patient. Structural and functional connectivity features were extracted from multi-modal MRI images with a clustering technique, and used for the multi-view classification of different phenotypes of neurodegeneration by an ensemble learning method (random forest). Two different multi-view models (intermediate and late data integration) were

SUBMITTER: Fratello M 

PROVIDER: S-EPMC5443864 | biostudies-literature | 2017 Apr

REPOSITORIES: biostudies-literature