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Cited by 349Dementia and Cognitive Impairment ResearchAdvanced Neuroimaging Techniques and ApplicationsAlzheimer's disease research and treatments

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Effectiveness of regional DTI measures in distinguishing Alzheimer’s disease, MCI, and normal aging

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Nir, Talia M., Jahanshad, Neda, Villalon-Reina, Julio E., Toga, Arthur W., Jack, Clifford R., Weiner, Michael W., Thompson, Paul M.·2013·NeuroImage: Clinical
DOI 10.1016/j.nicl.2013.07.006PMC PMC3792746
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Abstract

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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Fig. S1 Of the 43 TBSS ROIs, these 8 ROIs were consistently more sensitive to detecting differences across diffusivity and anisotropy measures in at least two cognitive and/or diagnostic analyses. These graphs show the average FA, MD, RD and AxD in the TBSS ROIs in each diagnostic group. “TOTAL” refers to the ROI generated by combining all the atlas ROIs. Overall, we see a decrease in anisotropy and increase in diffusivity with each stage of the disease.
Fig. S1 Of the 43 TBSS ROIs, these 8 ROIs were consistently more sensitive to detecting differences across diffusivity and anisotropy measures in at least two cognitive and/or diagnostic analyses. These graphs show the average FA, MD, RD and AxD in the TBSS ROIs in each diagnostic group. “TOTAL” refers to the ROI generated by combining all the atlas ROIs. Overall, we see a decrease in anisotropy and increase in diffusivity with each stage of the disease.CC BY-NC-ND · PubMed Central
Fig. S2 Cumulative distribution function (CDF) plots of the distribution of the p-values obtained from TBSS and Full ROI linear regression analyses, subjected to multiple comparisons correction using standard FDR (Benjamini and Hochberg, 1995). If the CDF initially rises at a rate steeper than 20 times the null CDF (y = 20x), then the corresponding maps are declared to be FDR significant at q = 0.05. The larger the deviation is from this line, the greater the effect sizes. (a) NC and AD group comparisons, (b) NC and MCI group comparisons, and (c) clinical cognitive test associations in both full and TBSS ROIs reveal that FA is consistently the least sensitive measure.
Fig. S2 Cumulative distribution function (CDF) plots of the distribution of the p-values obtained from TBSS and Full ROI linear regression analyses, subjected to multiple comparisons correction using standard FDR (Benjamini and Hochberg, 1995). If the CDF initially rises at a rate steeper than 20 times the null CDF (y = 20x), then the corresponding maps are declared to be FDR significant at q = 0.05. The larger the deviation is from this line, the greater the effect sizes. (a) NC and AD group comparisons, (b) NC and MCI group comparisons, and (c) clinical cognitive test associations in both full and TBSS ROIs reveal that FA is consistently the least sensitive measure.CC BY-NC-ND · PubMed Central
Fig. S3 Average FA for each ROI across all normal control (NC) participants. ROIs are ordered from those showing the most significant differences (lowest p-value) in average FA between AD patients and NC to least significant differences. ROIs that show a significant difference (critical p < 0.023; shown in green) are not necessarily those with the highest average FA, compared to the non-significant ROIs (gray). In other words, the effect sizes we find are not simply driven by higher FA values or fiber coherence.
Fig. S3 Average FA for each ROI across all normal control (NC) participants. ROIs are ordered from those showing the most significant differences (lowest p-value) in average FA between AD patients and NC to least significant differences. ROIs that show a significant difference (critical p < 0.023; shown in green) are not necessarily those with the highest average FA, compared to the non-significant ROIs (gray). In other words, the effect sizes we find are not simply driven by higher FA values or fiber coherence.CC BY-NC-ND · PubMed Central
Fig. 1 (Top panels) Statistical maps show − log10p-values within regions where AD patients exhibit significantly higher (a) MD (FDR critical p < 0.038), (b) RD (FDR critical p < 0.041), and (c) AxD (FDR critical p < 0.027) than healthy controls (NC). The cingulum, temporal lobe (including the hippocampal part of the cingulum) and splenium of the corpus callosum show the “most significant” differences, i.e. greatest effect sizes in the maps. (d) Significant but slightly less profuse FA differences between groups were also found (FDR critical p < 0.020). (e) Beta-values (non-normalized slope of the regression) within FDR significant regions largely revealed regions with lower FA in AD, but there are small regions, notorious for crossing fibers, where AD patients have higher mean FA than controls (dark blue), contrary to what would traditionally be accepted as showing impairment. (Bottom panel) Maps show the percent difference in FA and diffusivity measures between AD patients and NC within FDR significant regions. Again, the corpus callosum, temporal lobe, cingulum (including the hippocampal part of the cingulum), and the posterior thalamic radiations show the greatest degree of difference (up to ~ 33%); we note that the significance values and effect sizes in these regions depend on the mean group differences and its standard error, which is more completely reflected in top panels. In both figures, MD and RD differences are greater and more widespread. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
Fig. 1 (Top panels) Statistical maps show − log10p-values within regions where AD patients exhibit significantly higher (a) MD (FDR critical p < 0.038), (b) RD (FDR critical p < 0.041), and (c) AxD (FDR critical p < 0.027) than healthy controls (NC). The cingulum, temporal lobe (including the hippocampal part of the cingulum) and splenium of the corpus callosum show the “most significant” differences, i.e. greatest effect sizes in the maps. (d) Significant but slightly less profuse FA differences between groups were also found (FDR critical p < 0.020). (e) Beta-values (non-normalized slope of the regression) within FDR significant regions largely revealed regions with lower FA in AD, but there are small regions, notorious for crossing fibers, where AD patients have higher mean FA than controls (dark blue), contrary to what would traditionally be accepted as showing impairment. (Bottom panel) Maps show the percent difference in FA and diffusivity measures between AD patients and NC within FDR significant regions. Again, the corpus callosum, temporal lobe, cingulum (including the hippocampal part of the cingulum), and the posterior thalamic radiations show the greatest degree of difference (up to ~ 33%); we note that the significance values and effect sizes in these regions depend on the mean group differences and its standard error, which is more completely reflected in top panels. In both figures, MD and RD differences are greater and more widespread. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)CC BY-NC-ND · PubMed Central
Fig. 2 Effect sizes for FA, MD, RD and AxD measures in AD, MCI and NC group comparisons and clinical cognitive test associations reveal that FA is consistently the least sensitive measure. This type of plot is used to compare effect sizes in statistical maps based on different diffusivity measures. We used it to help decide which DTI measures best distinguish the diagnostic groups. Here we show cumulative distribution function (CDF) plots of the distribution of the p-values obtained from (a,d) voxel-wise and (b–c) ROI linear regressions, which are subjected to multiple comparisons correction using standard FDR (Benjamini and Hochberg, 1995). (a) VBA comparisons of NC to AD patients revealed that RD was the most sensitive measure (denoted by the higher critical p-values controlling the FDR/the highest non-zero x-coordinate where the CDF crosses the y = 20x line) followed closely by MD. (b) ROI comparisons of NC vs AD confirm that FA was the least sensitive measure. (c) ROI comparisons of NC vs MCI reveal that AxD measures had the largest effect size, followed by MD (VBA comparisons were not significant). (d) Voxel-wise and (e) ROI linear regressions on associations between cognitive scores (MMSE, CDR-sob, and ADAS-cog) in the entire cohort and anisotropy and diffusivity measures confirm that FA associations, while FDR significant, show the smallest effect size, while MD and RD have the largest effect sizes.
Fig. 2 Effect sizes for FA, MD, RD and AxD measures in AD, MCI and NC group comparisons and clinical cognitive test associations reveal that FA is consistently the least sensitive measure. This type of plot is used to compare effect sizes in statistical maps based on different diffusivity measures. We used it to help decide which DTI measures best distinguish the diagnostic groups. Here we show cumulative distribution function (CDF) plots of the distribution of the p-values obtained from (a,d) voxel-wise and (b–c) ROI linear regressions, which are subjected to multiple comparisons correction using standard FDR (Benjamini and Hochberg, 1995). (a) VBA comparisons of NC to AD patients revealed that RD was the most sensitive measure (denoted by the higher critical p-values controlling the FDR/the highest non-zero x-coordinate where the CDF crosses the y = 20x line) followed closely by MD. (b) ROI comparisons of NC vs AD confirm that FA was the least sensitive measure. (c) ROI comparisons of NC vs MCI reveal that AxD measures had the largest effect size, followed by MD (VBA comparisons were not significant). (d) Voxel-wise and (e) ROI linear regressions on associations between cognitive scores (MMSE, CDR-sob, and ADAS-cog) in the entire cohort and anisotropy and diffusivity measures confirm that FA associations, while FDR significant, show the smallest effect size, while MD and RD have the largest effect sizes.CC BY-NC-ND · PubMed Central
Fig. 3 Statistical maps show the − log10p-values within regions where l-MCI subjects exhibit significantly higher AxD (FDR critical p < 2.46 × 10− 5) than healthy controls (NC). The left hippocampal part of the cingulum was also significant in MD and RD maps. This region is small, but it passes the conventional FDR correction for multiple comparisons, and is in a region implicated in early changes in AD (medial temporal regions). With a greater sample size, this effect may be detectable in the other hemisphere as well.
Fig. 3 Statistical maps show the − log10p-values within regions where l-MCI subjects exhibit significantly higher AxD (FDR critical p < 2.46 × 10− 5) than healthy controls (NC). The left hippocampal part of the cingulum was also significant in MD and RD maps. This region is small, but it passes the conventional FDR correction for multiple comparisons, and is in a region implicated in early changes in AD (medial temporal regions). With a greater sample size, this effect may be detectable in the other hemisphere as well.CC BY-NC-ND · PubMed Central
Fig. 4 Statistical maps show that clinical scores on the (a) MMSE, (b) CDR-sob, and (c) ADAS-cog are related to detectable differences in fractional anisotropy, and mean, radial and axial diffusivity in the entire cohort. These maps show the − log10p-values within regions that significantly correlate with cognitive scores. RD and MD show the most widespread and robust associations. The hippocampal part of the cingulum and surrounding temporal lobes show the “most significant” associations (i.e., lowest p-values and highest voxel-wise effect sizes) between cognitive performance and white matter integrity.
Fig. 4 Statistical maps show that clinical scores on the (a) MMSE, (b) CDR-sob, and (c) ADAS-cog are related to detectable differences in fractional anisotropy, and mean, radial and axial diffusivity in the entire cohort. These maps show the − log10p-values within regions that significantly correlate with cognitive scores. RD and MD show the most widespread and robust associations. The hippocampal part of the cingulum and surrounding temporal lobes show the “most significant” associations (i.e., lowest p-values and highest voxel-wise effect sizes) between cognitive performance and white matter integrity.CC BY-NC-ND · PubMed Central
Fig. 5 Statistical maps show where CDR-sob scores are significantly positively associated with diffusivity measures in the MCI group, considered on its own (n = 88). Higher diffusivity, indicative of greater WM deficits, is associated with greater CDR-sob scores indicative of greater impairment. We find the “most significant” association (i.e., strongest voxel-wise effect) in the splenium of the corpus callosum and the posterior cingulum.
Fig. 5 Statistical maps show where CDR-sob scores are significantly positively associated with diffusivity measures in the MCI group, considered on its own (n = 88). Higher diffusivity, indicative of greater WM deficits, is associated with greater CDR-sob scores indicative of greater impairment. We find the “most significant” association (i.e., strongest voxel-wise effect) in the splenium of the corpus callosum and the posterior cingulum.CC BY-NC-ND · PubMed Central
Fig. 6 Of the 43 full ROIs, these 9 ROIs were consistently more sensitive to detecting differences across diffusivity and anisotropy measures in at least two cognitive and/or diagnostic analyses. These graphs show the average FA, MD, RD and AxD in the ROIs in each diagnostic group. “TOTAL” refers to the ROI generated by combining all the atlas ROIs. Overall, we see a decrease in anisotropy and increase in diffusivity with each stage of the disease.
Fig. 6 Of the 43 full ROIs, these 9 ROIs were consistently more sensitive to detecting differences across diffusivity and anisotropy measures in at least two cognitive and/or diagnostic analyses. These graphs show the average FA, MD, RD and AxD in the ROIs in each diagnostic group. “TOTAL” refers to the ROI generated by combining all the atlas ROIs. Overall, we see a decrease in anisotropy and increase in diffusivity with each stage of the disease.CC BY-NC-ND · PubMed Central

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Citations

Cites

Research works printed but not linked: 2

  1. Daianu, 2012, Analyzing the structural k-core of brain connectivity networks in normal aging and Alzheimer's disease, MICCAI NIBAD, 52
  2. Gutman, 2012, Creating unbiased minimal deformation templates for brain volume registration, Proceedings of the Organization for Human Brain Mapping Annual Conference

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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)
DOI
10.1016/j.nicl.2013.07.006
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
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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