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Effectiveness of regional DTI measures in distinguishing Alzheimer’s disease, MCI, and normal aging
Open accessAbstract
The Alzheimer's Disease Neuroimaging Initiative (ADNI) recently added diffusion tensor imaging (DTI), among several other new imaging modalities, in an effort to identify sensitive biomarkers of Alzheimer's disease (AD). While anatomical MRI is the main structural neuroimaging method used in most AD studies and clinical trials, DTI is sensitive to microscopic white matter (WM) changes not detectable with standard MRI, offering additional markers of neurodegeneration. Prior DTI studies of AD report lower fractional anisotropy (FA), and increased mean, axial, and radial diffusivity (MD, AxD, RD) throughout WM. Here we assessed which DTI measures may best identify differences among AD, mild cognitive impairment (MCI), and cognitively healthy elderly control (NC) groups, in region of interest (ROI) and voxel-based analyses of 155 ADNI participants (mean age: 73.5 ± 7.4; 90 M/65 F; 44 NC, 88 MCI, 23 AD). Both VBA and ROI analyses revealed widespread group differences in FA and all diffusivity measures. DTI maps were strongly correlated with widely-used clinical ratings (MMSE, CDR-sob, and ADAS-cog). When effect sizes were ranked, FA analyses were least sensitive for picking up group differences. Diffusivity measures could detect more subtle MCI differences, where FA could not. ROIs showing strongest group differentiation (lowest p-values) included tracts that pass through the temporal lobe, and posterior brain regions. The left hippocampal component of the cingulum showed consistently high effect sizes for distinguishing groups, across all diffusivity and anisotropy measures, and in correlations with cognitive scores.
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“Among the three cognitive assessments, CDR-sob showed the strongest correlations with dMRI indices, in line with prior ADNI brain imaging studies (Hua et al., 2009; Nir et al., 2013).”
From Diffusion MRI Indices and Their Relation to Cognitive Impairment in Brain Aging: The Updated Multi-protocol Approach in ADNI3 (2019) · cites this paper as Nir et al., 2013
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“Similarly, in ADNI2, AxDDTI was the most sensitive to differences between CN and MCI diagnosis (Nir et al., 2013).”
From Diffusion MRI Indices and Their Relation to Cognitive Impairment in Brain Aging: The Updated Multi-protocol Approach in ADNI3 (2019) · cites this paper as Nir et al., 2013
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“As in our prior analysis of ADNI2 (Nir et al., 2013), FADTI was the least sensitive DTI measure.”
From Diffusion MRI Indices and Their Relation to Cognitive Impairment in Brain Aging: The Updated Multi-protocol Approach in ADNI3 (2019) · cites this paper as Nir et al., 2013
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“dMRI has since been used in numerous studies to understand the effects of AD on white matter (WM) microstructure and brain connectivity (Daianu et al., 2013a,b; Nir et al., 2013; Prasad et al., 2013).”
From Diffusion MRI Indices and Their Relation to Cognitive Impairment in Brain Aging: The Updated Multi-protocol Approach in ADNI3 (2019) · cites this paper as Nir et al., 2013
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“Lower FADTI and higher MDDTI are most frequently reported in studies of AD (Kavcic et al., 2008; Clerx et al., 2012; Nir et al., 2013; Maggipinto et al., 2017; Mayo et al., 2017)”
From Diffusion MRI Indices and Their Relation to Cognitive Impairment in Brain Aging: The Updated Multi-protocol Approach in ADNI3 (2019) · cites this paper as Nir et al., 2013
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“These are all regions that show early degenerative changes in MCI and AD (Mielke et al., 2009; Nir et al., 2013; Maggipinto et al., 2017; Powell et al., 2018).”
From Diffusion MRI Indices and Their Relation to Cognitive Impairment in Brain Aging: The Updated Multi-protocol Approach in ADNI3 (2019) · cites this paper as Nir et al., 2013
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Metadata
- Authors
- Nir, T. M., Jahanshad, N., Villalon-Reina, J. E., Toga, A. W., Jack, C. R., Weiner, M. W., & Thompson, P. M.
- Affiliations
- University of California, Los Angeles (US)
- Mayo Clinic (US)
- University of California, San Francisco (US)
- Corresponding author
- Thompson, Paul M.
- ORCID
- Venue
- NeuroImage: Clinical
- Publisher
- Elsevier BV
- Published
- 2013
- Volume
- 3
- Pages
- 180-195
- ISSN
- 2213-1582 (ISSN-L)
- PMC
- PMC3792746
- OpenAlex
- W2028748733
- Cited by
- 348
- References
- 131
- Citations by year
- 2026: 14 · 2025: 37 · 2024: 28 · 2023: 33 · 2022: 29 · 2021: 31 · 2020: 23 · 2019: 32 · 2018: 22 · 2017: 21 · 2016: 27 · 2015: 30 · 2014: 21
- FWCI
- 18.49
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- NeuroImage Clinical (publishedVersion, cc-by-nc-nd)
- Europe PMC (PubMed Central) (publishedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- eScholarship (California Digital Library) (submittedVersion, cc-by-nc-nd)
- Funders
- ADNI (U01 AG024904)
- National Institute on Aging
- National Institute of Biomedical Imaging and Bioengineering
- 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.
- Genentech, Inc.
- 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 (P30 AG010129, K01 AG030514)
- NIA
- NIBIB
- National Library of Medicine
- National Center for Research Resources (AG016570, EB01651, LM05639, RR019771)
- Topics
- Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced Neuroimaging Techniques and Applications
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Alzheimer's disease research and treatments
- Keywords
- Alzheimer's disease, mild cognitive impairment, normal aging, diffusion tensor imaging, fractional anisotropy, white matter diffusivity, white matter microstructure, temporal lobe, neurodegeneration biomarkers, cognitive impairment detection, voxel-based analysis, region of interest analysis
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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