title: Brain connectivity and novel network measures for Alzheimer's disease classification.
abstract: 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.
authors.affiliation: Imaging Genetics Center, Institute for Neuroimaging and Informatics, Keck School of Medicine of University of Southern California, Los Angeles, CA, USA; Laboratory of Neuro Imaging, Institute for Neuroimaging and Informatics, Keck School of Medicine of University of Southern California, Los Angeles, CA, USA.
authors.forename: Gautam
authors.initials: G
authors.lastname: Prasad
authors.affiliation: Laboratory of Neuro Imaging, Institute for Neuroimaging and Informatics, Keck School of Medicine of University of Southern California, Los Angeles, CA, USA.
authors.forename: Shantanu H
authors.initials: SH
authors.lastname: Joshi
authors.affiliation: Imaging Genetics Center, Institute for Neuroimaging and Informatics, Keck School of Medicine of University of Southern California, Los Angeles, CA, USA; Laboratory of Neuro Imaging, Institute for Neuroimaging and Informatics, Keck School of Medicine of University of Southern California, Los Angeles, CA, USA.
authors.forename: Talia M
authors.initials: TM
authors.lastname: Nir
authors.affiliation: Imaging Genetics Center, Institute for Neuroimaging and Informatics, Keck School of Medicine of University of Southern California, Los Angeles, CA, USA; Laboratory of Neuro Imaging, Institute for Neuroimaging and Informatics, Keck School of Medicine of University of Southern California, Los Angeles, CA, USA.
authors.forename: Arthur W
authors.initials: AW
authors.lastname: Toga
authors.affiliation: Imaging Genetics Center, Institute for Neuroimaging and Informatics, Keck School of Medicine of University of Southern California, Los Angeles, CA, USA; Laboratory of Neuro Imaging, Institute for Neuroimaging and Informatics, Keck School of Medicine of University of Southern California, Los Angeles, CA, USA; Department of Neurology, University of California, Los Angeles, School of Medicine, Los Angeles, CA, USA. Electronic address: pthomp@usc.edu.
authors.forename: Paul M
authors.initials: PM
authors.lastname: Thompson
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D000544:Alzheimer Disease / Q000145:classification / Q000175:diagnosis* / Q000473:pathology*; D001921:Brain / Q000473:pathology* / Q000503:physiopathology*; D060825:Cognitive Dysfunction / Q000145:classification / Q000175:diagnosis / Q000473:pathology; D003937:Diagnosis, Differential; D038524:Diffusion Magnetic Resonance Imaging / Q000379:methods*; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008297:Male; D009415:Nerve Net / Q000473:pathology* / Q000503:physiopathology*; D059906:Neuroimaging / Q000379:methods*
keywords: Alzheimer's disease; Classification; Connectivity matrix; Graph; Maximum flow; Network measures; Ranking; SVM; Sensitivity; Specificity