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Proceedings Paper

Ranking diffusion tensor measures of brain aging and Alzheimer’s disease
Author(s): Artemis Zavaliangos-Petropulu; Talia M. Nir; Sophia I. Thomopoulos; Neda Jahanshad; Robert I. Reid; Matthew A. Bernstein; Bret Borowski; Clifford R. Jack Jr.; Michael W. Weiner; Paul M. Thompson
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Paper Abstract

Diffusion-weighted MRI (dMRI) offers a range of measures that are sensitive to brain aging and neurodegeneration. Here we analyzed data from 318 participants (mean age: 75.4±7.9 years; 143 men/175 women) from the third phase of the Alzheimer’s Disease Neuroimaging Initiative (ADNI3), who were each scanned with one of six different diffusion MRI protocols using scanners from three different manufacturers. We computed 4 standard diffusion tensor imaging (DTI) anisotropy and diffusivity indices, and one advanced anisotropy index based on the tensor distribution function (TDF), in 24 white matter regions of interest. Modeling protocol effects, we ranked the diffusion indices for their strength of correlation with 3 standard clinical measures of cognitive impairment: the ADAS-Cog, MMSE, and sum-of-boxes Clinical Dementia Rating. Across all dMRI indices and cognitive measures, the cingulum-hippocampal region and the uncinate showed some of the strongest associations with cognitive impairment; largest effect sizes were detected with axial diffusivity (AxDDTI). While fractional anisotropy (FA) derived from the DTI model was the weakest in detecting associations with cognitive measures, FA derived from the TDF detected widespread, robust associations. Protocol differences affected dMRI indices; however by modeling protocol effects, we were able to pool dMRI data from multiple acquisition protocols and detect consistent associations with cognitive impairment and age. dMRI indices computed from the upgraded scanning protocols in ADNI3 were sensitive to cognitive impairment in brain aging, offering a benchmark to compare to future multi-shell or multi-compartment diffusion indices.

Paper Details

Date Published: 21 December 2018
PDF: 17 pages
Proc. SPIE 10975, 14th International Symposium on Medical Information Processing and Analysis, 109750O (21 December 2018); doi: 10.1117/12.2506694
Show Author Affiliations
Artemis Zavaliangos-Petropulu, The Univ. of Southern California (United States)
Talia M. Nir, The Univ. of Southern California (United States)
Sophia I. Thomopoulos, The Univ. of Southern California (United States)
Neda Jahanshad, The Univ. of Southern California (United States)
Robert I. Reid, Mayo Clinic (United States)
Matthew A. Bernstein, Mayo Clinic (United States)
Bret Borowski, Mayo Clinic (United States)
Clifford R. Jack Jr., Mayo Clinic (United States)
Michael W. Weiner, Univ. of California, San Francisco (United States)
Paul M. Thompson, The Univ. of Southern California (United States)

Published in SPIE Proceedings Vol. 10975:
14th International Symposium on Medical Information Processing and Analysis
Eduardo Romero; Natasha Lepore; Jorge Brieva, Editor(s)

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