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Multicenter Study
. 2025 Aug;82(8):825-836.
doi: 10.1001/jamaneurol.2025.1601. Epub 2025 Jun 9.

White Matter Abnormalities and Cognition in Aging and Alzheimer Disease

Christopher Peter  1 , Aditi Sathe  1 , Niranjana Shashikumar  1 , Kimberly R Pechman  1 , Abigail W Workmeister  1 , T Bryan Jackson  1 , Yuankai Huo  2   3 , Shubhabrata Mukherjee  4 , Jesse Mez  5 , Logan C Dumitrescu  1   6   7   8 , Katherine A Gifford  1   5 , Corey J Bolton  1 , Leslie S Gaynor  1 , Shannon L Risacher  9   10 , Lori L Beason-Held  11 , Yang An  11 , Konstantinos Arfanakis  12   13   14 , Guray Erus  15 , Christos Davatzikos  15 , Duygu Tosun-Turgut  16 , Mohamad Habes  17 , Di Wang  18 , Arthur W Toga  19 , Paul M Thompson  20 , Panpan Zhang  1   21 , Kurt G Schilling  22   23 , Marilyn Albert  24 , Walter Kukull  25 , Sarah A Biber  25 , Bennett A Landman  1   2   3   8   22   23   26 , Barbara B Bendlin  27 , Sterling C Johnson  27   28 , Julie Schneider  13 , Lisa L Barnes  13 , David A Bennett  13 , Angela L Jefferson  1   6   8 , Susan M Resnick  11 , Andrew J Saykin  9   10 , Paul K Crane  4 , Michael L Cuccaro  29   30 , Timothy J Hohman  1   6   7   8 , Derek B Archer  1   6   7   8 ; and the Alzheimer’s Disease Sequencing Project Phenotype Harmonization Consortium (ADSP-PHC) Analyst Team; Dimitrios Zaras  1 , Yisu Yang  1 , Alaina Durant  1 , Praitayini Kanakaraj  2 , Michael E Kim  2 , Chenyu Gao  3 , Nancy R Newlin  2 , Karthik Ramadass  2   3 , Nazirah Mohd Khairi  3 , Zhiyuan Li  3 , Tianyuan Yao  2 , Seo-Eun Choi  4 , Brandon Klinedinst  4 , Michael L Lee  4 , Phoebe Scollard  4 , Emily H Trittschuh  31   32 , Elizabeth A Sanders  4
Affiliations
Multicenter Study

White Matter Abnormalities and Cognition in Aging and Alzheimer Disease

Christopher Peter et al. JAMA Neurol. 2025 Aug.

Abstract

Importance: There has yet to be a large-scale study quantifying the association between white matter microstructure and cognitive performance and decline in aging and Alzheimer disease (AD).

Objective: To investigate the associations between tract-specific white matter microstructure and cognitive performance and decline in aging and AD-related cognitive impairment.

Design setting and participants: This prognostic study of aging and AD, a secondary data analysis of multisite cohort studies, acquired data from 9 cohorts between September 2002 and November 2022. Participants were eligible if they had diffusion-weighted magnetic resonance imaging (dMRI) data, domain-specific cognitive composite z scores, demographic and clinical data, were aged 50 years or older, and passed neuroimaging quality control. Demographic and clinical covariates included age, sex, education, race and ethnicity, APOE haplotype status (ε2, ε3, ε4), and clinical status. The present study was conducted from June 2024 to February 2025.

Exposures: White matter microstructure and cognitive performance and decline.

Main outcomes and measures: Clinical diagnosis, imaging measures (dMRI, T1-weighted MRI, and amyloid and tau positron emission tomography), and cognitive tests.

Results: Of 4467 participants who underwent 9208 longitudinal cognitive sessions, 2698 (60.4%) were female, and the mean age (SD) was 74.3 (9.2) years; 3213 were cognitively unimpaired, 972 had mild cognitive impairment, and 282 had AD dementia. White matter free water (FW) showed the strongest associations with cross-sectional cognitive performance and longitudinal cognitive decline across all domains, particularly memory. FW in limbic tracts, such as the cingulum, presented the strongest associations with both memory performance (cingulum: β = -0.718; P < .001; fornix: β = -1.069; P < .001) and decline (cingulum: β = -0.115; P < .001; fornix: β = -0.153; P < .001). White matter FW measures interacted with baseline diagnosis, gray matter atrophy, APOE ε4 status, and amyloid positivity to predict poorer cognitive performance and accelerated cognitive decline. Noteworthy interactions include fornix FW and hippocampal volume (β = 10.598; P < .001), cingulum FW and SPARE-AD index (β = -0.532; P < .001), and inferior temporal gyrus transcallosal tract FW and baseline diagnosis (β = -0.537; P < .001), all predicting poorer memory performance.

Conclusions and relevance: White matter microstructural changes, particularly FW, play a critical role in cognitive decline in aging and AD-related cognitive impairment. These findings highlight the importance of FW correction in dMRI studies and highlight the limbic system, especially the cingulum and fornix, as key regions associated with cognitive decline; the interaction models highlight that integrating FW-corrected metrics with other AD biomarkers may further elucidate the biological mechanisms of neurodegeneration in aging.

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Conflict of interest statement

Conflict of Interest Disclosures: Ms Workmeister reported grants from the National Institutes of Health (NIH) during the conduct of the study. Dr Mez reported grants from NIH during the conduct of the study. Dr Risacher reported grants from NIH during the conduct of the study; and stock from Eli Lilly outside the submitted work. Dr Davatzikos reported grants from NIH during the conduct of the study. Dr Albert reported grants from the National Institute on Aging (NIA) during the conduct of the study. Dr Kukull reported grants from NIA during the conduct of the study. Dr Landman reported grants from NIH during the conduct of the study; and personal fees from Silver Maple LLC, SPIE, and Coursera outside the submitted work. Dr Bendlin reported grants from NIH during the conduct of the study; and personal fees from New Amsterdam, Cognito Therapeutics, Merry Life Biomedical, Rush Alzheimer’s Disease Center, and Emory Alzheimer’s Disease Research Center and an advisor grant from Weston outside the submitted work. Dr Johnson reported grants from NIH during the conduct of the study; and personal fees for serving on advisory boards for Enigma Biomedical and ALZPath outside the submitted work. Dr Schneider reported grants from NIA during the conduct of the study. Dr Jefferson reported grants from NIA, Alzheimer’s Association, and NIH during the conduct of the study. Dr Saykin reported grants from NIH during the conduct of the study. Dr Hohman reported grants from NIH during the conduct of the study; and personal fees from Vivid Genomics, serving as senior associate editor for Alzheimer’s & Dementia, and serving as deputy editor for Alzheimer’s & Dementia: Translational Research & Clinical Interventions outside the submitted work. Dr Li reported grants from NIA during the conduct of the study; and student stipends from the Rochester Institute of Technology outside the submitted work. No other disclosures were reported.

Figures

Figure 1.
Figure 1.. Cohort Characteristics and Data Harmonization
A, Participants were drawn from 9 well-established cohorts, including 3213 cognitively unimpaired (CU) individuals, 972 with mild cognitive impairment (MCI), and 282 with Alzheimer disease (AD) at baseline. B, The study also incorporated longitudinal data across 9208 cognitive sessions, spanning up to 13 years of follow-up. Longitudinal ComBat harmonization was applied to all imaging features to account for variability across imaging batches. C and D, Associations are shown between cingulum free-water (FW) and memory performance, using both raw (C) and harmonized (D) FW data, with points and lines color coded by imaging batch. Harmonized data were used across all analyses. ADNI indicates Alzheimer’s Disease Neuroimaging Initiative; BIOCARD, Biomarkers of Cognitive Decline Among Normal Adults; BLSA, Baltimore Longitudinal Study of Aging; MAP, Rush Memory and Aging Project; MARS, Minority Aging Research Study; NACC, National Alzheimer’s Coordinating Center; ROS, Religious Orders Study; VMAP, Vanderbilt Memory and Aging Project; WRAP, Wisconsin Registry for Alzheimer’s Prevention.
Figure 2.
Figure 2.. White Matter Association With Cognitive Performance
A, Associations are shown between free water (FW)–corrected metrics and baseline cognitive performance across the all-memory (top), executive function (middle), and language (bottom) domains. Linear regression models were conducted for each FW-corrected metric (FW, FW-corrected fractional anisotropy [FAFWcorr], FW-corrected mean diffusivity [MDFWcorr], FW-corrected axial diffusivity [AxDFWcorr], FW-corrected radial diffusivity [RDFWcorr]). Each heatmap is grouped by tract type and represents the individual z value for each independent model. The arrows represent the direction of β coefficients that reached significance following correction for multiple comparisons. B, The regression plots show the correlations between cognitive performance and the top microstructural association for each domain. Assoc indicates association; IFG, inferior frontal gyrus; IFOF, inferior frontal occipital fasciculus; ILF, inferior longitudinal fasciculus; IPL, inferior parietal lobule; PMd, dorsal premotor cortex; PMv, ventral premotor cortex; preSMA, pre–supplementary motor area; SLF, superior longitudinal fasciculus; SLF-TP, superior longitudinal fasciculus temporal parietal component; SMA, supplementary motor area; SPL, superior parietal lobule; TC, transcallosal; UF, uncinate fasciculus.
Figure 3.
Figure 3.. White Matter Association With Cognitive Decline
A, Associations are shown between free water (FW)–corrected metrics and longitudinal cognitive decline across the all-memory (top), executive function (middle), and language (bottom) domains. Linear mixed-effects regression models were conducted for each FW-corrected metric (FW, FW-corrected fractional anisotropy [FAFWcorr], FW-corrected mean diffusivity [MDFWcorr], FW-corrected axial diffusivity [AxDFWcorr], FW-corrected radial diffusivity [RDFWcorr]). Each heatmap is grouped by tract type and represents the individual z value for each independent model. The arrows represent the direction of β coefficients that reached significance following correction for multiple comparisons. B, The regression plots show the correlations between cognitive decline and the top microstructural association for each domain. Assoc indicates association; IFG, inferior frontal gyrus; IFOF, inferior frontal occipital fasciculus; ILF, inferior longitudinal fasciculus; IPL, inferior parietal lobule; PMd, dorsal premotor cortex; PMv, ventral premotor cortex; preSMA, pre–supplementary motor area; SLF, superior longitudinal fasciculus; SLF-TP, superior longitudinal fasciculus temporal parietal component; SMA, supplementary motor area; SPL, superior parietal lobule; TC, transcallosal; UF, uncinate fasciculus.
Figure 4.
Figure 4.. Bootstrapped Head-to-Head Analysis to Compare Microstructure, Tract Type, and Individual Tracts on Cognitive Decline
A, Bootstrapped linear mixed-effects regression (n = 1000) identified free water (FW) as the microstructural metric most associated with cognitive decline. B, Within FW, limbic tracts showed the strongest association with cognitive decline. C, Further analysis revealed specific limbic tracts driving this association. ΔR2 represents the contribution of white matter changes (microstructure and covariates) vs covariates alone. AxDFWcorr indicates FW-corrected axial diffusivity; FAFWcorr, FW-corrected fractional anisotropy; MDFWcorr, FW-corrected mean diffusivity; RDFWcorr, FW-corrected radial diffusivity; TC, transcallosal.
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