Brain connectivity and novel network measures for Alzheimer's disease classification

NEUROBIOLOGY OF AGING

卷:36

Article

Prasad, Gautam^1,2  Joshi, Shantanu H.²  Nir, Talia M.^1,2  Toga, Arthur W.^1,2  Thompson, Paul M.^1,2,3 

[1] Univ So Calif, Keck Sch Med, Inst Neuroimaging & Informat, Imaging Genet Ctr, Los Angeles, CA 90032 USA

[2] Univ So Calif, Keck Sch Med, Inst Neuroimaging & Informat, Lab Neuro Imaging, Los Angeles, CA 90032 USA

[3] Univ Calif Los Angeles, Sch Med, Dept Neurol, Los Angeles, CA 90024 USA

关键词: SVM;    Classification;    Sensitivity;    Specificity;    Maximum flow;    Connectivity matrix;    Alzheimer's disease;    Network measures;    Graph;    Ranking;   

DOI  :  10.1016/j.neurobiolaging.2014.04.037

来源: Elsevier

【 摘 要 】

We compare a variety of different anatomic connectivity measures, including several novel ones, that may help in distinguishing Alzheimer's disease (AD) patients from controls. We studied diffusion-weighted magnetic resonance imaging from 200 subjects scanned as part of the Alzheimer's Disease Neuroimaging Initiative. We first evaluated measures derived from connectivity matrices based on whole-brain tractography; next, we studied additional network measures based on a novel flow-based measure of brain connectivity, computed on a dense 3-dimensional lattice. Based on these 2 kinds of connectivity matrices, we computed a variety of network measures. We evaluated the measures' ability to discriminate disease with a repeated, stratified 10-fold cross-validated classifier, using support vector machines, a supervised learning algorithm. We tested the relative importance of different combinations of features based on the accuracy, sensitivity, specificity, and feature ranking of the classification of 200 people into normal healthy controls and people with early or late mild cognitive impairment or AD. (C) 2015 Elsevier Inc. All rights reserved.

【 授权许可】

Free