Topological graph kernel on multiple thresholded functional connectivity networks for mild cognitive impairment classification.

ABSTRACT: Recently, brain connectivity networks have been used for classification of Alzheimer's disease and mild cognitive impairment (MCI) from normal controls (NC). In typical connectivity-networks-based classification approaches, local measures of connectivity networks are first extracted from each region-of-interest as network features, which are then concatenated into a vector for subsequent feature selection and classification. However, some useful structural information of network, especially global topological information, may be lost in this type of approaches. To address this issue, in this article, we propose a connectivity-networks-based classification framework to identify accurately the MCI patients from NC. The core of the proposed method involves the use of a new graph-kernel-based

SUBMITTER: Jie B 

PROVIDER: S-EPMC4116356 | biostudies-literature | 2014 Jul

REPOSITORIES: biostudies-literature