White matter microstructural metrics are sensitively associated with clinical staging in Alzheimer's disease

Yang, Yisu; Schilling, Kurt G.; Shashikumar, Niranjana; Jasodanand, Varuna; Moore, Elizabeth E.; Pechman, Kimberly R.; Bilgel, Murat; Beason‐Held, Lori L.; An, Yang; Shafer, Andrea T.; Risacher, Shannon L.; Landman, Bennett A.; Jefferson, Angela L.; Saykin, Andrew J.; Resnick, Susan M.; Hohman, Timothy J.; Archer, Derek B.

doi:10.1002/dad2.12425

Cited by 7 publications

(5 citation statements)

References 36 publications

(101 reference statements)

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“…61-63 In previous work, we have shown that the FW measure, particularly in limbic tracts, is sensitive to abnormal aging. 28,49 This study extends this work by showing that APOE -ε4 carriers, who are known to be at greater risk for AD, show these same changes in the FW metric in limbic tracts. This suggests that processes that increase interstitial spaces, such as atrophy, inflammation, or edema, are present to a greater extent in APOE -ε4 carriers in tracts known to be affected in AD.…”

Section: Discussionsupporting

confidence: 74%

“…28,53 While some studies have shown that projection fibers are also associated with abnormal cognitive aging, these associations are less robust. 10,28,49 Given that females are disproportionately affected by AD, the observed sex differences in FA FWcorr in our study suggest that white matter integrity may contribute to this disparity. However, we also found that in tracts with sex differences in FW, females tended to have lower values than males longitudinally.…”

Section: Discussionmentioning

confidence: 64%

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Sex, racial, andAPOE-ε4 allele differences in longitudinal white matter microstructure in multiple cohorts of aging and Alzheimer’s disease

Peterson,

Sathe,

Zaras

et al. 2024

Preprint

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Structured AbstractINTRODUCTIONThe effects of sex, race, and Apolipoprotein E (APOE) – Alzheimer’s disease (AD) risk factors – on white matter integrity are not well characterized.METHODSDiffusion MRI data from nine well-established longitudinal cohorts of aging were free-water (FW)-corrected and harmonized. This dataset included 4,702 participants (age=73.06 ± 9.75) with 9,671 imaging sessions over time. FW and FW-corrected fractional anisotropy (FAFWcorr) were used to assess differences in white matter microstructure by sex, race, andAPOE-ε4 carrier status.RESULTSSex differences in FAFWcorrin association and projection tracts, racial differences in FAFWcorrin projection tracts, andAPOE-ε4 differences in FW limbic and occipital transcallosal tracts were most pronounced.DISCUSSIONThere are prominent differences in white matter microstructure by sex, race, andAPOE-ε4 carrier status. This work adds to our understanding of disparities in AD. Additional work to understand the etiology of these differences is warranted.HighlightsSex, race, andAPOE-ε4 carrier status relate to white matter microstructural integrityFemales generally have lower FAFWcorrcompared to malesNon-Hispanic Black adults generally have lower FAFWcorrthan non-Hispanic White adultsAPOE-ε4 carriers tended to have higher FW than non-carriersResearch in ContextSystematic ReviewThe authors used PubMed and Google Scholar to review literature that used conventional and free-water (FW)-corrected microstructural metrics to evaluate sex, race, andAPOE-ε4 differences in white matter microstructure. While studies have previously explored differences by sex andAPOE-ε4 status, less is known about racial differences and no large-scale FW-corrected analysis has been performed.InterpretationSex and race were more associated with FAFWcorrwhileAPOE-ε4 status was associated with FW metrics. Association, projection, limbic, and occipital transcallosal tracts showed the greatest differences.Future DirectionFuture studies to determine the biological and social pathways that lead to sex, racial, andAPOE-ε4 differences are warranted.Consent StatementAll participants provided informed consent in their respective cohort studies.

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Section: Discussionsupporting

confidence: 74%

Section: Discussionmentioning

confidence: 64%

Sex, racial, andAPOE-ε4 allele differences in longitudinal white matter microstructure in multiple cohorts of aging and Alzheimer’s disease

Peterson,

Sathe,

Zaras

et al. 2024

Preprint

Self Cite

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“…The cingulum bundle is critical for cognitive and emotional processing, and disruption of its microstructure can lead to impaired connectivity between the frontal lobe and other brain regions, contributing to the cognitive decline in AD patients. 67,68 The corpus callosum (CC), the largest white matter structure in the brain, facilitates inter-hemispheric communication. The impact on the corpus callosum (including forces major, forceps minor, and the middle sector of CC) is of great interest, as it plays a crucial role in coordinating cognitive and motor functions across the two brain hemispheres.…”

Section: Discussionmentioning

confidence: 99%

Amyloid, Tau, and APOE in Alzheimer’s Disease: Impact on White Matter Tracts

Chandio,

Villalon-Reina,

Nir

et al. 2024

Preprint

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Alzheimer’s disease (AD) is characterized by cognitive decline and memory loss due to the abnormal accumulation of amyloid-beta (Aβ) plaques and tau tangles in the brain; its onset and progression also depend on genetic factors such as the apolipoprotein E (APOE) genotype. Understanding how these factors affect the brain’s neural pathways is important for early diagnostics and interventions. Tractometry is an advanced technique for 3D quantitative assessment of white matter tracts, localizing microstructural abnormalities in diseased populationsin vivo. In this work, we applied BUAN (Bundle Analytics) tractometry to 3D diffusion MRI data from 730 participants in ADNI3 (phase 3 of the Alzheimer’s Disease Neuroimaging Initiative; age range: 55-95 years, 349M/381F, 214 with mild cognitive impairment, 69 with AD, and 447 cognitively healthy controls). Using along-tract statistical analysis, we assessed the localized impact of amyloid, tau, and APOE genetic variants on the brain’s neural pathways. BUAN quantifies microstructural properties of white matter tracts, supporting along-tract statistical analyses that identify factors associated with brain microstructure. We visualize the 3D profile of white matter tract associations with tau and amyloid burden in Alzheimer’s disease; strong associations near the cortex may support models of disease propagation along neural pathways. Relative to the neutral genotype, APOEɛ3/ɛ3, carriers of the AD-risk conferring APOEɛ4 genotype show microstructural abnormalities, while carriers of the protectiveɛ2 genotype also show subtle differences. Of all the microstructural metrics, mean diffusivity (MD) generally shows the strongest associations with AD pathology, followed by axial diffusivity (AxD) and radial diffusivity (RD), while fractional anisotropy (FA) is typically the least sensitive metric. Along-tract microstructural metrics are sensitive to tau and amyloid accumulation, showing the potential of diffusion MRI to track AD pathology and map its impact on neural pathways.

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“…We use (i) the mean FA values for 112 of the 118 regions of the EVE Type-III atlas for each participant; (ii) covariates of age, sex, cognitive status, race, education, APOE2 carrier status, and APOE4 carrier status, the covariates used in DTI harmonization by Yang et al; 36 and (iii) a covariate indicating the dataset the participant scan came from. Six regions are excluded from consideration because the registration process resulted in regions with zero volume, which could induce a large shift in the mean of the distribution of mean FA values for the cohort.…”

Section: Combat Harmonizationmentioning

confidence: 99%

Empirical assessment of the assumptions of ComBat with diffusion tensor imaging

Kim,

Gao,

Cai

et al. 2024

J. Med. Imag.

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Diffusion tensor imaging (DTI) is a magnetic resonance imaging technique that provides unique information about white matter microstructure in the brain but is susceptible to confounding effects introduced by scanner or acquisition differences. ComBat is a leading approach for addressing these site biases. However, despite its frequent use for harmonization, ComBat's robustness toward site dissimilarities and overall cohort size have not yet been evaluated in terms of DTI.Approach: As a baseline, we match N ¼ 358 participants from two sites to create a "silver standard" that simulates a cohort for multi-site harmonization. Across sites, we harmonize mean fractional anisotropy and mean diffusivity, calculated using participant DTI data, for the regions of interest defined by the JHU EVE-Type III atlas. We bootstrap 10 iterations at 19 levels of total sample size, 10 levels of sample size imbalance between sites, and 6 levels of mean age difference between sites to quantify (i) β AGE , the linear regression coefficient of the relationship between FA and age; (ii) γÃ sf , the ComBat-estimated site-shift; and (iii) δÃ sf , the ComBat-estimated sitescaling. We characterize the reliability of ComBat by evaluating the root mean squared error in these three metrics and examine if there is a correlation between the reliability of ComBat and a violation of assumptions.Results: ComBat remains well behaved for β AGE when N > 162 and when the mean age difference is less than 4 years. The assumptions of the ComBat model regarding the normality of residual distributions are not violated as the model becomes unstable. Conclusion:Prior to harmonization of DTI data with ComBat, the input cohort should be examined for size and covariate distributions of each site. Direct

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White matter microstructural metrics are sensitively associated with clinical staging in Alzheimer's disease

Cited by 7 publications

References 36 publications

Sex, racial, andAPOE-ε4 allele differences in longitudinal white matter microstructure in multiple cohorts of aging and Alzheimer’s disease

Sex, racial, andAPOE-ε4 allele differences in longitudinal white matter microstructure in multiple cohorts of aging and Alzheimer’s disease

Amyloid, Tau, and APOE in Alzheimer’s Disease: Impact on White Matter Tracts

Empirical assessment of the assumptions of ComBat with diffusion tensor imaging

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