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

Gautam Prasad; Shantanu H Joshi; Talia M Nir; Arthur W Toga; Paul M Thompson; Alzheimer's Disease Neuroimaging Initiative (ADNI)

doi:10.1016/j.neurobiolaging.2014.04.037

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

Neurobiol Aging. 2015 Jan;36 Suppl 1(0 1):S121-31. doi: 10.1016/j.neurobiolaging.2014.04.037. Epub 2014 Aug 30.

Authors

Gautam Prasad¹, Shantanu H Joshi², Talia M Nir¹, Arthur W Toga¹, Paul M Thompson³; Alzheimer's Disease Neuroimaging Initiative (ADNI)

Affiliations

¹ 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.
² Laboratory of Neuro Imaging, Institute for Neuroimaging and Informatics, Keck School of Medicine of University of Southern California, Los Angeles, CA, USA.
³ 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.

PMID: 25264345
PMCID: PMC4276322
DOI: 10.1016/j.neurobiolaging.2014.04.037

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.

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

Publication types

Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't

MeSH terms

Aged
Aged, 80 and over
Alzheimer Disease / classification
Alzheimer Disease / diagnosis*
Alzheimer Disease / pathology*
Brain / pathology*
Brain / physiopathology*
Cognitive Dysfunction / classification
Cognitive Dysfunction / diagnosis
Cognitive Dysfunction / pathology
Diagnosis, Differential
Diffusion Magnetic Resonance Imaging / methods*
Female
Humans
Image Processing, Computer-Assisted / methods*
Male
Nerve Net / pathology*
Nerve Net / physiopathology*
Neuroimaging / methods*

Abstract

Publication types

MeSH terms

Grants and funding