Alzheimer’s disease classification with novel microstructural metrics from diffusion-weighted MRI

Alzheimer’s Disease Neuroimaging Initiative (ADNI)

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

Alzheimer’s disease (AD) deficits may be due in part to declining white matter (WM) integrity and disrupted connectivity. Numerous diffusionweighted MRI (dMRI) studies of AD report WM deficits based on tensor model metrics. New microstructural measures derived from additional dMRI models may carry different information about WM microstructure including the geometry of diffusion anisotropy, diffusivity, complexity, estimated number of distinguishable fiber compartments, number of crossing fibers and neurite dispersion. Here we aimed to find the most helpful dMRI metrics and brain regions from a set of 17 dMRI-derived feature maps, to predict diagnostic group (AD or healthy control). The best metrics for classification were non-tensor metrics in the hippocampus and temporal lobes, areas consistently implicated in AD.

Original languageEnglish (US)
Title of host publicationMathematics and Visualization
PublisherSpringer Heidelberg
Pages41-54
Number of pages14
Volumenone
ISBN (Print)9783319285863
DOIs
StatePublished - 2016
EventWorkshop on Computational Diffusion MRI, MICCAI 2015 - Munich, Germany
Duration: Oct 9 2015Oct 9 2015

Publication series

NameMathematics and Visualization
Volumenone
ISSN (Print)16123786
ISSN (Electronic)2197666X

Other

OtherWorkshop on Computational Diffusion MRI, MICCAI 2015
CountryGermany
CityMunich
Period10/9/1510/9/15

ASJC Scopus subject areas

  • Computer Graphics and Computer-Aided Design
  • Applied Mathematics
  • Geometry and Topology
  • Modeling and Simulation

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