TY - GEN
T1 - Ranking diffusion tensor measures of brain aging and Alzheimer's disease
AU - Zavaliangos-Petropulu, Artemis
AU - Nir, Talia M.
AU - Thomopoulos, Sophia I.
AU - Jahanshad, Neda
AU - Reid, Robert I.
AU - Bernstein, Matthew A.
AU - Borowski, Bret
AU - Jack, Clifford R.
AU - Weiner, Michael W.
AU - Thompson, Paul M.
N1 - Funding Information:
Data collection and sharing for ADNI was funded by National Institutes of Health Grant U01 AG024904 and the DOD (Department of Defense award number W81XWH-12-2-0012). Additional support was provided by NIA grant RF1 AG04191 and P41 EB015922.
Publisher Copyright:
© SPIE. Downloading of the abstract is permitted for personal use only.
PY - 2018
Y1 - 2018
N2 - 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 (Ax DDT I). 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.
AB - 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 (Ax DDT I). 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.
KW - ADNI3
KW - Aging
KW - Alzheimer's Disease
KW - Diffusion
KW - Diffusion Tensor Imaging
UR - http://www.scopus.com/inward/record.url?scp=85060534869&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85060534869&partnerID=8YFLogxK
U2 - 10.1117/12.2506694
DO - 10.1117/12.2506694
M3 - Conference contribution
AN - SCOPUS:85060534869
T3 - Proceedings of SPIE - The International Society for Optical Engineering
BT - 14th International Symposium on Medical Information Processing and Analysis
A2 - Lepore, Natasha
A2 - Romero, Eduardo
A2 - Brieva, Jorge
PB - SPIE
T2 - 14th International Symposium on Medical Information Processing and Analysis, SIPAIM 2018
Y2 - 24 October 2018 through 26 October 2018
ER -