Journal
NEUROBIOLOGY OF AGING
Volume 36, Issue -, Pages S121-S131Publisher
ELSEVIER SCIENCE INC
DOI: 10.1016/j.neurobiolaging.2014.04.037
Keywords
SVM; Classification; Sensitivity; Specificity; Maximum flow; Connectivity matrix; Alzheimer's disease; Network measures; Graph; Ranking
Categories
Funding
- National Institute of Biomedical Imaging and Bioengineering (NIBIB) [R01 EB008281, R01 EB008432, R21 EB01651]
- National Institute on Aging (NIA) [P50 AG016570-019002, R01 AG040060, U01 AG024904-01]
- NIBIB
- National Institute of Mental Health
- US National Library of Medicine [R01 LM05639]
- National Center for Research Resources [AG016570, AG040060, EB01651, MH097268, LM05639, RR019771]
- Alzheimer's Disease Neuroimaging Initiative (ADNI) (National Institutes of Health [NIH]) [U01 AG024904]
- NIA
- Abbott
- Alzheimer's Association
- Alzheimer's Drug Discovery Foundation
- Amorfix Life Sciences Ltd
- AstraZeneca
- Bayer HealthCare
- BioClinica Inc
- Biogen Idec Inc
- Bristol-Myers Squibb Company
- Eisai Inc
- Elan Pharmaceuticals Inc
- Eli Lilly and Company
- F. Hoffmann-La Roche Ltd
- GE Healthcare
- Innogenetics, N.V.
- IXICO Ltd
- Janssen Alzheimer Immunotherapy Research & Development, LLC
- Johnson & Johnson Pharmaceutical Research & Development LLC
- Medpace Inc
- Merck Co Inc
- Meso Scale Diagnostics LLC
- Novartis Pharmaceuticals Corporation
- Pfizer Inc
- Servier
- Synarc Inc
- Takeda Pharmaceutical Company
- Canadian Institutes of Health Research
- NIH grants from the National Institute of General Medical Sciences [P30 AG010129, K01 AG030514]
- NATIONAL CENTER FOR RESEARCH RESOURCES [R21RR019771] Funding Source: NIH RePORTER
- NATIONAL INSTITUTE OF BIOMEDICAL IMAGING AND BIOENGINEERING [R01EB008281, U54EB020403, P41EB015922, R01EB008432] Funding Source: NIH RePORTER
- NATIONAL INSTITUTE OF MENTAL HEALTH [R01MH097268] Funding Source: NIH RePORTER
- NATIONAL INSTITUTE ON AGING [P50AG005142, K01AG030514, RF1AG041915, R01AG040060, P50AG016570, P30AG010129, U01AG024904] Funding Source: NIH RePORTER
- NATIONAL LIBRARY OF MEDICINE [R01LM005639] Funding Source: NIH RePORTER
Ask authors/readers for more resources
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.
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