Skip to main content
NCBI home page
NIHPA Author Manuscripts logoLink to NIHPA Author Manuscripts
. Author manuscript; available in PMC: 2020 Nov 1.
Published in final edited form as: Neurobiol Aging. 2019 Aug 29;83:63–72. doi: 10.1016/j.neurobiolaging.2019.08.021

Association of white matter microstructural integrity with cognition and dementia

Melinda C Power a, Dan Su b, Aozhou Wu c, Robert I Reid d, Clifford R Jack Jr e, David S Knopman f, Joe Coresh c, Juebin Huang g, Kejal Kantarci e, A Richey Sharrett c, Rebecca G Gottesman c,i, Mike E Griswold b, Thomas H Mosley g,h
PMCID: PMC6914220  NIHMSID: NIHMS1545638  PMID: 31585368

Abstract

Late-life measures of white matter microstructural integrity may predict cognitive status, cognitive decline, and incident MCI or dementia. We considered participants of the Atherosclerosis Risk in Communities (ARIC) study who underwent cognitive assessment and neuroimaging in 2011–2013 and were followed through 2016–2017 (n=1775 for analyses of prevalent MCI and dementia, baseline cognitive performance, and longitudinal cognitive change, n=889 for analyses of incident MCI, dementia, or death). Cross-sectionally, both overall white matter fractional anisotropy and overall white matter mean diffusivity were strongly associated with baseline cognitive performance and risk of prevalent MCI or dementia. Longitudinally, greater overall white matter mean diffusivity was associated with accelerated cognitive decline, as well as incident MCI, incident dementia, and mortality, but white matter fractional anisotropy was not robustly associated with cognitive change or incident cognitive impairment. Both cross-sectional and longitudinal associations were attenuated after additionally adjusting for likely downstream pathologic changes. Increased white matter mean diffusivity may provide an early indication of dementia pathogenesis.

Keywords: Dementia, White Matter Microstructural Integrity, Cognitive Impairment, DTI, MRI, Cognitive Decline

1. Introduction

Diffusion tensor imaging (DTI) quantifies the microstructural integrity of white matter.(Alexander et al., 2007) Commonly used DTI measures include fractional anisotropy (FA) and mean diffusivity (MD). FA measures the directional constraint of water diffusion, and MD measures the average rate of diffusion in any direction. As white matter (WM) is generally anisotropic (i.e., the direction of diffusion is highly constrained), lower WM FA and higher WM MD are generally thought to reflect worse white matter microstructural integrity.

DTI-based measures of WM microstructural integrity appear to provide an assessment of pathologic changes that precede and predict the development of white matter hyperintensities (WMH) and WM loss.(Ly et al., 2014; Maillard et al., 2013; Maillard et al., 2011; Maillard et al., 2014; Salat et al., 2005) Given this link and the recognized association of WMH and WM loss with cognition,(Ikram et al., 2010; Prins and Scheltens, 2015; Stout et al., 1996) DTI-based measures of WM microstructural integrity may provide an early indication of future cognitive decline, cognitive impairment, and dementia. Despite heterogeneity in the specific findings across studies (Stebbins and Murphy, 2009), substantial cross-sectional evidence suggests a link between DTI-based measures and current cognitive status, (Bagepally et al., 2012; Charlton et al., 2006; Chua et al., 2008; Fellgiebel et al., 2004; Huang and Auchus, 2007; Huang et al., 2007; Kantarci et al., 2014; Kantarci et al., 2011; Medina et al., 2006; Naggara et al., 2006; Oishi et al., 2011; Sala et al., 2015; Stebbins and Murphy, 2009; Tuladhar et al., 2015; Ukmar et al., 2008; van Norden et al., 2012; Vernooij et al., 2009). However, few studies have considered whether WM microstructural integrity is associated with future cognition. Thus, the purpose of this study is to examine both the cross-sectional and longitudinal associations of late-life measures of WM microstructural integrity with cognition and dementia, using data from the Atherosclerosis Risk in Communities Study (ARIC).

2. Material and Methods

2.1. Study sample

The ARIC study is a longitudinal cohort study that recruited 15,792 persons in 1987–1989 (Visit 1) ages 45–65 from four U.S. communities: Forsyth County, NC; Washington County, MD; Minneapolis suburbs, MN; and Jackson, MS. At the fifth full-cohort study visit in 2011–2013 (ARIC Visit 5), the ARIC cohort added dementia ascertainment and an expanded cognitive test battery to the study protocol. In addition, a stratified random sample (stratified on cognitive status, prior sub-study participation, age, and study site) of approximately 2,000 ARIC participants were recruited into a brain MRI sub-study as part of the ARIC Neurocognitive Study (ARIC-NCS). All living ARIC participants were subsequently invited to Visit 6 in 2016–2017, which included dementia ascertainment and repeat administration of the expanded cognitive test battery from Visit 5.

Our study sample includes all participants who completed the Visit 5 ARIC-NCS MRI (n=1951), excluding non-black and non-white participants (n=6) and black participants from MD or MN (n=9) as the small numbers made meaningful adjustment for race/ethnicity including these participants untenable, persons disallowing use of genetic data (n=11), and persons with documented stroke (n=67), history of brain tumor, surgery or radiation to the head (n=14), lacking valid DTI data (n=8), missing data on primary covariates or Visit 5 cognitive data (n=61). Thus, 1775 persons met our eligibility criteria for inclusion in our primary analyses of WM microstructural integrity with baseline cognitive status (normal, MCI, or dementia) as well as analyses of WM microstructural integrity and cognition, including associations with baseline cognition and cognitive change (Figure 1). Primary analyses of the association between WM microstructural integrity and of incident cognitive impairment or mortality were additionally restricted to those who were cognitively normal at Visit 5 and who we could categorize as having dementia, having MCI, cognitively normal, or deceased at the time of Visit 6 in 2016–2017 (n=889, Figure 1).

Figure 1.

Figure 1.

Open in a new tab

ARIC data collection and analytical sample derivation

2.2. Standard protocol approvals

The institutional review boards of all participating institutions approved this study, and participants provided written informed consent prior to participation.

2.3. Neuroimaging

Participants of the MRI sub-study at Visit 5/ARIC-NCS completed 3T brain MRI, including sagittal T1-weighted MPRAGE, axial T2 FLAIR, and axial DTI pulse sequences. Imaging and image analysis followed identical protocols across sites. The ARIC MRI Reading Center (Mayo Clinic, Rochester, MN) conducted all image analysis.

They used in-house algorithms to quantify WMH and estimated intracranial volume (eTIV),(Jack et al., 2014; Raz et al., 2013) which was used to normalize other volumetric measures as a covariate in our statistical analyses, and used Freesurfer (Version 5.1) to measure total brain volume and gray matter volumes including hippocampal volume and used an in-house atlas of lobar and deep WM regions, derived from the STAND400 template,(Vemuri et al., 2008), intersected with GM and WM segmentation from the T1 and FLAIR scans, to define regions of interest (ROIs) for measuring WM FA and WM MD.

The DTI scans had 1 diffusion unweighted volume (b = 0) followed by 64 volumes with diffusion weighting (b) = 1000 s/mm2 in distinct directions evenly distributed over the whole sphere. The voxel size was 2.7mm isotropic. Head motion and eddy current distortion were corrected using FSL’s eddy_correct, and each subject’s DTI scan was then deformably registered to their T1-weighted image using BrainSuite (Bhushan et al., 2015), which also corrects for susceptibility distortion and fits diffusion tensors to the data using a weighted least squares algorithm. The lobar and deep WM atlas was deformably registered to each subject’s T1-weighted image using ANTS (Avants et al., 2014).

We averaged left and right WM FA and WM MD measures to calculate WM FA and WM MD in six ROIs (frontal lobe, temporal lobe, occipital lobe, parietal lobe, anterior corpus callosum, and posterior corpus callosum); we then took a weighted average of these six regions, with weights proportional to the number of voxels in each WM ROI, to calculate measures of overall WM FA and overall WM MD. Additional details of the neuroimaging protocol and image analysis are available elsewhere.(Power et al., 2016; Power et al., 2017)

2.4. Assessment of cognitive decline

All ARIC participants were asked to complete a cognitive battery of 11 neuropsychological tests at Visits 5 and 6. Prior examination of the factor structure(Gross et al., 2015; Rawlings et al., 2016) indicates these tests broadly assess cognition in three domains: (i) memory, (ii) language and verbal fluency, and (iii) sustained attention/processing speed. Tests contributing to the memory domain include the Delayed Word Recall Test (DWRT), Logical Memory Test (LMT), and a test of incidental learning (IL). Tests contributing to the language and verbal fluency domain include the animal naming test (ANT), the Boston Naming Test (BNT), and a word fluency test (WFT). Tests contributing to the sustained attention and processing speed domain include the Trail Making Test, Parts A and B (TMT), the Digit-Symbol Substitution Test (DSST), and Digit Span Backwards Test (DSB). The overall cognitive function domain incorporated all tests used in the three sub-domains, as well as the item-level data from the Mini Mental State Exam (MMSE); note that information from the MMSE did not contribute to any of the domain-specific factor scores, as derived below.

Prior to computing factor scores to quantify domain-specific and overall cognitive function, we calculated standardized Z scores scaled to Visit 5 by subtracting the Visit 5 test mean from each participant’s test score and dividing by the Visit 5 test standard deviation for those tests with approximately Gaussian distributions (LMT, ANT, BNT, WFT, TMTA time, TMTB time, and DSST). Both Visit 5 and Visit 6 scores were standardized to Visit 5 to examine cognitive decline. TMTA and TMTB times were both skewed and were natural-log transformed prior to standardizing. TMTB times additionally displayed a “spike” at the maximum time of TMTB=240 seconds, which we handled with censored distribution methods as described below. Other tests were considered to be categorical variables (DWRT, IL, TMTA errors, TMTB errors, DSB) or binary (MMSE item-level responses). Missing test scores for participants who were unable to complete a test due to difficulty were assigned to the minimum observed test score, similar to previous ARIC work.

Factor Analysis examines covariation among sets of observed variables to estimate a smaller number of underling latent variables. Generalized Structural Equation Model (GSEM) methods include Factor Analysis as a special case and allow expanded distributions, latent linkages, and other extensions. We fit GSEMs to sets of the ARIC cognitive instruments to construct measures of latent overall cognitive function and the a-priori specified cognitive sub-domains. Cognitive sub-domain GSEMs and an overall cognitive function GSEM were fit separately using full maximum likelihood estimation methods, allowing inclusion of all participants who had at least one test score. We assumed Gaussian distributions where scores were approximately normally distributed and assumed probit or ordered probit distributions for binary and categorical scores, respectively.

GSEMs for cognitive scores were fit using only the Visit 5 data. Parameters from the Visit 5 GSEMs were then applied to the Visit 6 data such that latent cognitive scores at Visit 6 were scaled to Visit 5, enabling examination of cognitive decline in further longitudinal models. This technique is similar conceptually to longitudinal analyses that use standardized z-scores across time, where the baseline mean and standard deviation are used to standardize across all visits instead of the visit-specific means and standard deviations; this allows tracking of changes over time.

2.5. Assessment of incident MCI and dementia and vital status

At Visit 5, all participants with prior MRI, suspected cognitive impairment (defined by poor cognitive test scores or evidence of substantial cognitive decline from a prior visit), and a random sample of those presumed to be cognitively normal were invited to in person clinical assessment and informant interview. Diagnoses of cognitively normal, mild cognitive impairment (MCI), and dementia were assigned algorithmically based on cognitive tests scores at Visit 5 (MMSE and 10 additional tests), change in cognitive test scores from earlier assessments on three consistently administered cognitive tests (Digit Symbol Substitution, Delayed Word Recall, and Word Fluency Tests), clinical dementia rating (CDR) interview scores, and functional activities questionnaire (FAQ) scores. Algorithmic diagnoses were reviewed and confirmed or revised by expert adjudicators.(Knopman et al., 2016) Participants who attended Visit 6 were classified as having normal cognition, MCI, or dementia at the time of the study visit according to a similar protocol based on data collected at the in-person study visit. Primary analyses only consider cognitive status based on adjudicated diagnoses referencing data from these in-person study visits. Vital status at the time of Visit 6 was assessed through follow-up calls and supplemented through cross-reference with the national death index.

As we were concerned that those who did not attend Visit 6 may not do so because of their dementia status, which could lead to bias, we conducted secondary analyses where Visit 6 non-participants who were known to be alive were classified as demented if they were suspected of having dementia based on cohort surveillance efforts (include telephone based cognitive screening, informant interview, interview with informants when poor function was suspected, and review of hospitalization discharge codes).

2.6. Other covariates

We defined age at each study visit using self-reported date of birth and visit date. We ascertained education (<high school, high school or vocational school, and >high school), smoking status at the time of visit 5 (current, former, or never), sex, and race by self-report. Information on race and study site was combined to create a site-race variable (Washington County/white, Minneapolis/white, Forsyth County/white, Forsyth County/black, Jackson/black). We calculated body mass index as the mass in kg at Visit 5 divided by the square of height in meters. We considered diabetes status in midlife (at Visit 1) and hypertension status in late life (at Visit 5), given previously described time-dependent relationships between these risk factors with both DTI measures and cognition.(Power et al., 2017; Qiu et al., 2005) Participants were classified as diabetic if they had self-reported diagnosis by a physician, ≥126 mg/dL fasting glucose, ≥200 mg/dL non-fasting glucose, or diabetes medication use. Participants were classified as hypertensive if they had measured systolic blood pressure ≥140 mmHg, measured diastolic blood pressure ≥90 mmHg or antihypertensive medication use. We determined presence or absence of an apolipoprotein (APOE) ε4 allele via genotyping for use as a covariate, given evidence of accelerated cognitive change in APOE ε4 carriers(Caselli et al., 2007; Caselli et al., 2004; Cosentino et al., 2008; Nilsson et al., 2006) and the potential for a link between APOE ε4 and WM microstructural integrity (Heise et al., 2011; Kanchibhotla et al., 2013; Nyberg and Salami, 2014; Westlye et al., 2012).

2.7. Statistical methods

We standardized WM FA and WM MD measures by subtracting the sample mean and dividing by the sample standard deviation (SD). We report associations with a 1SD unit worsening in WM FA (1 SD decrease) or WM MD (1 SD increase).

We used linear mixed models with random intercepts and Huber-White robust variance estimates to assess associations of Visit 5 WM microstructural integrity measures with overall and domain-specific cognitive status at Visit 5 and cognitive decline from Visit 5 to Visit 6. We used multinomial logistic regression models to examine associations of WM microstructural integrity with MCI and dementia at Visit 5 and with incident MCI, dementia and death outcomes at Visit 6 among those with normal cognition at Visit 5.

Primary adjustors for all models included age, sex, education, APOE e4 carrier status, and site-race. We included both main effects and interactions with time for all covariates in our linear mixed models. All models were weighted to account for the stratified random sampling approach used to select ARIC participants for Visit 5 MRI.

Primary analysis models considered overall WM MD and WM FA. In secondary analyses, we considered (i) associations with ROI-specific WM MD and WM FA, (ii) models additionally adjusted for white matter hyperintensity (WMH) and estimated intracranial volumes, (iii) models additionally adjusted for WMH, hippocampal, total brain, and estimated intracranial volumes. For models of cognitive change, we also considered whether the association differed based on baseline cognitive status (cognitively normal versus MCI or dementia at Visit 5). For models of incident MCI, dementia, and death, we also conducted analyses using an expanded sample including Visit 6 non-participants who remained alive and who were suspected of having dementia through surveillance efforts as having incident dementia at Visit 6. Analyses exploring ROI-specific effects were designed to look for regional effects. Analyses adjusting for additional brain features were designed to explore whether the associations were potentially mediated by markers of pathology thought to follow changes to WM microstructural integrity. Analyses using the expanded sample of dementia cases identified via surveillance were designed to address potential issues related to loss-to-follow-up.

We also ran sensitivity analyses incorporating additional adjustments for comorbidities (hypertension, smoking, BMI, and midlife diabetes), omitting weighting for selection into the MRI sub-sample, and implementing inverse probability weighting to account for missingness due to loss to follow-up for analyses considering incident outcomes or cognitive change. For analyses of global cognitive change only, we also tried an average z-score, a longitudinal GSEM approach, as well as alternate summary scores consisting of subsets of the potential components. All analyses were completed with STATA, Version 14 (StataCorp. 2015. College Station, TX: StataCorp LP).

2.8. Data availability

ARIC makes individual-level data available to qualified research investigators who collaborate with an ARIC investigator, use publicly available date accessed through the website https://biolincc.nhlbi.nih.gov/studies/aric, or successfully propose ancillary studies. ARIC’s data sharing policies are provided on the https://www2.cscc.unc.edu/aric/node/10303 website.

3. Results

On average, participants in our primary analytical sample were 76 years old at baseline (Visit 5), 40% were male, and 28% were black. Participant characteristics were generally similar in the sample used for analyses of incident cognitive impairment or mortality. Average follow-up time for in both samples was 4.9 years. Additional participant characteristics are provided in Table 1. Correlations between cognitive factor scores at Visit 5 and change in cognitive factor scores from Visit 5 to Visit 6 are available in Supplemental Tables S1 and S2.

Table 1.

Characteristics of eligible ARIC participants at ARIC Visit 5/ARIC-NCS unless otherwise noted

Characteristic Primary Analysis Sample (N=1775) Sample for Analysis of Incident Cognitive Impairment or Mortality (N=889)
No. (%) or mean(SD) No. (%) or mean(SD)
Age 76.3 (5.3) 75.6 (5.2)
Male 704 (40%) 332 (37%)
APOE e4 522 (29%) 236 (27%)
Education
 <High school 252 (14%) 116 (13%)
 High school 725 (41%) 333 (37%)
 >High school 798 (45%) 440 (49%)
Site Race
 Washington County whites 468 (26%) 226 (25%)
 Minneapolis whites 387 (22%) 175 (20%)
 Jackson blacks 468 (26%) 275 (31%)
 Forsyth County blacks 32 (2%) 18 (2%)
 Forsyth County whites 420 (24%) 195 (22%)
Total intracranial (cm^3) 1381.1 (155.8) 1372.01 (144.97)
White matter hyperintensities (cm^3) 17.2 (16.9) 15.4 (16.3)
Hippocampal volume (cm^3) 6.9 (1.0) i 7.1 (0.9)
Total brain volume (cm^3) 1013.0 (107.4) 1019.5 (104.4)
Cognitive Status at Visit 5
 Normal 1081 (61.0%) t 889 (100%)
 MCI 598 (33.8%) 0 (0%)
 Dementia 92 (5.2%) 0 (0%)
Cognitive/Vital Status at Visit 6
 Normal 849 (47.8%) 662 (74.5%)
 MCI 210 (11.8%) 116 (13.1%)
 Dementia 100 (5.6%) 25 (2.8%)
 Dead 227 (12.8%) 86 (9.7%)
 Lost to follow-up 389 (21.9%) N/A
Fractional anisotropy, by region (unitless)
 Frontal 0.28 (0.02) 0.28 (0.02)
 Temporal 0.28 (0.02) 0.28 (0.02)
 Parietal 0.30 (0.02) 0.30 (0.02)
 Occipital 0.22 (0.02) 0.22 (0.02)
 Anterior corpus callosum 0.42 (0.06) 0.43 (0.06)
 Posterior corpus callosum 0.57 (0.06) 0.58 (0.06)
 Overall 0.28 (0.02) 0.28 (0.02)
Mean diffusivity, by region (10−4 mm2/s)
 Frontal 8.60 (0.54) 8.52 (0.51)
 Temporal 8.88 (0.59) 8.76 (0.55)
 Parietal 8.79 (0.61) 8.70 (0.57)
 Occipital 8.74 (0.63) 8.67 (0.60)
 Anterior corpus callosum 11.62 (1.12) 11.42 (1.09)
 Posterior corpus callosum 11.20 (1.03) 11.05 (0.99)
 Overall 8.78 (0.55) 8.68 (0.51)
Cognitive Domain Factor Scores at Visit 5
 Overall −0.06 (0.76) 0.27 (0.65)
 Memory −0.05 (0.62) 0.22 (0.55)
 Language & verbal −0.00 (0.12) 0.03 (0.11)
 Sustained attention & processing speed −0.03 (0.33) 0.08 (0.31)
Cognitive Domain Factor Scores at Visit 6
 Overall −0.10 (0.82) 0.12 (0.72)
 Memory −0.01 (0.64) 0.15 (0.59)
 Language & verbal −0.01 (0.12) 0.01 (0.12)
 Sustained attention & processing speed −0.07 (0.35) −0.01 (0.34)
Open in a new tab

Abbreviations: AD = Alzheimer’s disease; APOE e4 = apolipoprotein E e4 allele; MCI = mild cognitive impairment.

With the exception of no evidence to support an association between WM FA and memory, both WM FA and WM MD were strongly associated with baseline cognitive performance overall and with each domain (Table 2). Generally, these associations weakened, but remained statistically significant after adjustment for WHM and/or brain volumes.

Table 2.

Primary and sensitivity analyses for the association quantifying difference in the annual rate of cognitive change per 1SD unit decrease (worsening) in overall WM FA or 1SD unit increase (worsening) in overall WM MD.

WM FAa WM MDa
Baseline differenceb Excess annual rate of changeb Baseline differenceb Excess annual rate of changeb
Beta (95% CI) p-value Beta (95% CI) p-value Beta (95% CI) p-value Beta (95% CI) p-value
Primary Analyses
Overall −0.081 (−0.114,−0.049) p<0.001 −0.004 (−0.010,0.002) p=0.23 −0.173 (−0.213,−0.133) p<0.001 −0.013 (−0.021,−0.005) p=0.002
Memory −0.005 (−0.035,0.025) p=0.75 −0.003 (−0.011,0.004) p=0.36 −0.055 (−0.090,−0.020) p=0.002 −0.007 (−0.016,0.002) p=0.13
Language −0.017 (−0.022,−0.011) p<0.001 −0.000 (−0.001,0.001) p=0.69 −0.023 (−0.029,−0.017) p<0.001 −0.001 (−0.002,0.000) p=0.25
Attention/Processing Speed −0.046 (−0.059,−0.033) p<0.001 0.000 (−0.003,0.003) p=0.85 −0.082 (−0.097,−0.067) p<0.001 −0.003 (−0.007,0.000) p=0.09
Additional adjustment for WMH and intracranial volume
Overall −0.053 (−0.090,−0.016) p=0.005 −0.001 (−0.009,0.006) p=0.70 −0.177 (−0.224,−0.131) p<0.001 −0.010 (−0.019,−0.001) p=0.03
Memory 0.015 (−0.023,0.052) p=0.44 −0.002 (−0.011,0.006) p=0.61 −0.059 (−0.102,−0.017) p=0.007 −0.005 (−0.015,0.006) p=0.39
Language −0.014 (−0.020,−0.007) p<0.001 −0.000 (−0.001,0.001) p=0.71 −0.022 (−0.030,−0.015) p<0.001 −0.001 (−0.002,0.000) p=0.22
Attention/Processing Speed −0.042 (−0.058,−0.026) p<0.001 0.002 (−0.002,0.006) p=0.28 −0.089 (−0.107,−0.071) p<0.001 −0.002 (−0.006,0.003) p=0.46
Additional adjustment for WMH, intracranial, hippocampal, and total brain volume
Overall −0.045 (−0.081,−0.010) p=0.01 −0.001 (−0.008,0.006) p=0.70 −0.116 (−0.164,−0.068) p<0.001 −0.007 (−0.017,0.002) p=0.13
Memory 0.014 (−0.023,0.050) p=0.46 −0.003 (−0.011,0.006) p=0.53 −0.026 (−0.072,0.019) p=0.26 −0.004 (−0.015,0.007) p=0.51
Language −0.012 (−0.018,−0.005) p<0.001 −0.000 (−0.001,0.001) p=0.83 −0.014 (−0.023,−0.006) p=0.001 −0.001 (−0.002,0.001) p=0.37
Attention/Processing Speed −0.036 (−0.052,−0.021) p<0.001 0.002 (−0.002,0.006) p=0.29 −0.064 (−0.085,−0.044) p<0.001 −0.000 (−0.005,0.004) p=0.87
Open in a new tab

Abbreviations: WM FA = white matter fractional anisotropy; WM MD = white matter mean diffusivity.

a

Lower FA and higher MD are indicative of worse brain microstructural integrity.

b

Adjusted for age, sex, education, race-center, and APOE e4 carrier status and additional variables as noted (main effects and interactions with time) and weighted to account for the stratified random sampling approach to select participants for Visit 5 MRI unless otherwise specified.

To the contrary, there was little evidence to support an association between WM FA and the subsequent rate of overall or domain-specific cognitive change or between WM MD and domain-specific cognitive change (Table 2). However, despite no clear association with domain-specific change, worse WM MD was associated with faster overall cognitive decline. For context, the excess annual decline per 1SD increase in WM MD we observed (−0.013, 95% CI: −0.021, −0.005; p-value: 0.002, Table 2), is approximately 77% of the excess annual overall rate of cognitive decline we observe when comparing APOE e4 carriers to non-carriers in this model (excess annual decline in APOE e4 carriers −0.017, 95%CI: −0.028, −0.005). Sensitivity analyses suggest the presence of the effect for the summary measure but no the domain specific measures is driven by the additional information inherent in the overall measure relative to the individual measures (Supplemental Table S2) which was not wholly dependent upon any single global cognitive measure definition (Supplemental Table S3). Adjustment for WMH volumes attenuated these estimates slightly (Table 2); nonetheless, the association between greater WM MD and overall cognitive decline persisted (excess annual decline per 1SD increase in WM MD −0.010, 95% CI: −0.019, −0.001). Simultaneous adjustment for WMH, hippocampal, and total brain volumes further attenuated this association (excess annual decline per 1SD increase in WM MD −0.007, 95% CI: −0.017, 0.002). Findings were generally consistent across additional sensitivity analyses (data not shown) and analyses considering ROI-specific WM MD or WM FA (data not shown, Figure 2). There was no evidence to support a difference in the association with cognitive change by whether the participant was cognitively normal or diagnosed with MCI/dementia at baseline (all p-values for interaction >0.35).

Figure 2. Difference in the annual cognitive change per 1SD unit worsening in ROI-specific WM FA or WM MD.

Figure 2.

Open in a new tab

Lower FA and higher MD are indicative of worse brain microstructural integrity.

All models adjusted for age, sex, education, race-center, and APOE e4 carrier status (main effects and interactions with time) and weighted to account for the stratified random sampling approach to select participants for Visit 5 MRI

WM FA and WM MD were strongly associated with prevalent cognitive status, although these associations were attenuated, and most became non-significant after adjustment for WMH, hippocampal and total brain volumes (Table 3).

Table 3.

Association quantifying change in odds of prevalent MCI or dementia at Visit 5 per 1SD unit decrease (worsening) in overall WM FA or increase (worsening) in WM MD.

WM FAa WM MDa
ORb (95%CI) p-value ORb (95%CI) p-value
Primary Analyses
MCI 1.16 (1.03,1.30) p=0.01 1.37 (1.18,1.59) p<0.001
Dementia 1.47 (1.17,1.86) p=0.001 2.79 (2.16,3.59) p<0.001
Additional adjustment for WMH and intracranial volume
MCI 1.05 (0.91,1.22) p=0.48 1.29 (1.08,1.53) p=0.005
Dementia 1.40 (1.03,1.89) p=0.03 3.17 (2.37,4.24) p<0.001
Additional adjustment for WMH, intracranial, hippocampal, and total brain volume
MCI 1.05 (0.90,1.22) p=0.56 1.13 (0.93,1.37) p=0.21
Dementia 1.34 (0.96,1.86) p=0.08 2.00 (1.41,2.83) p<0.001
Open in a new tab

Abbreviations: MCI = mild cognitive impairment; WM FA = white matter fractional anisotropy; WM MD = white matter mean diffusivity

a

Lower FA and higher MD are indicative of worse brain microstructural integrity.

b

Adjusted for age, sex, education, race-center, and APOE e4 carrier status and weighted to account for the stratified random sampling approach to select participants for Visit 5 MRI.

When examining associations with incident outcomes, there was little evidence to support an association between WM FA and incident MCI or mortality in those who were cognitively normal at baseline (Table 4). While WM FA appeared associated with incident dementia in our primary analysis this association was not robust to sensitivity analyses (data not shown). In contrast, we observed strong associations of higher WM MD with incident MCI, dementia, and mortality (Table 4). These associations were slightly attenuated after adjustment for WMH volumes (Table 4) and were further attenuated after additionally adjusting for WMH, hippocampal and total brain volume (OR per 1 SD increase in WM MD for incident dementia: 1.82, 95% CI: 0.95, 3.49, for incident MCI: 1.30, 95% CI: 0.87, 1.94, for death: 1.63, 95%CI: 0.97, 2.73). Results were similar when we included additional dementia cases identified via surveillance (Table 4) and across additional sensitivity analyses (data not shown). Analyses of ROI-specific WM FA and MD were generally consistent with our primary analyses (Figure 3 and Figure 4), although it is notable that the association between WM MD and incident MCI, dementia, and death was generally strongest for WM MD in the frontal, temporal, and parietal lobes (Figure 4).

Table 4.

Association quantifying change in odds of incident MCI or dementia among persons who were cognitively normal at Visit 5 per 1SD unit decrease (worsening) in overall WM FA or increase (worsening) in WM MD.

WM FAa WM MDa
ORb (95%CI) p-value ORb (95%CI) p-value
Primary Analyses
MCI 1.16 (0.86, 1.55) p=0.34 1.54 (1.09, 2.18) p=0.02
Dementia 1.87 (1.17, 3.01) p=0.009 2.51 (1.62, 3.88) p<0.001
Death 1.12 (0.76, 1.65) p=0.57 1.89 (1.26, 2.85) p=0.002
Additional adjustment for WMH and intracranial volume
MCI 1.09 (0.80, 1.50) p=0.59 1.48 (1.01, 2.18) p=0.04
Dementia 1.66 (0.86, 3.18) p=0.13 2.22 (1.35, 3.68) p=0.002
Death 0.90 (0.55, 1.46) p=0.66 1.76 (1.10, 2.83) p=0.02
Additional adjustment for WMH, intracranial, hippocampal, and total brain volume
MCI 1.07 (0.77, 1.48) p=0.71 1.30 (0.87, 1.94) p=0.20
Dementia 1.42 (0.76, 2.67) p=0.27 1.82 (0.95, 3.49) p=0.07
Death 0.86 (0.52, 1.42) p=0.55 1.63 (0.97, 2.73) p=0.06
Secondary Analyses, including additional dementia cases identified by cohort surveillance
MCI 1.16 (0.87, 1.55) p=0.32 1.55 (1.09, 2.20) p=0.01
Dementia 1.40 (0.94, 2.09) p=0.10 2.17 (1.49, 3.17) p<0.001
Death 1.11 (0.76, 1.62) p=0.59 1.89 (1.26, 2.83) p=0.002
Open in a new tab

Abbreviations: MCI = mild cognitive impairment; WM FA = white matter fractional anisotropy; WM MD = white matter mean diffusivity

a

Lower FA and higher MD are indicative of worse brain microstructural integrity.

b

Adjusted for age, sex, education, race-center, and APOE e4 carrier status and weighted to account for the stratified random sampling approach to select participants for Visit 5 MRI.

Figure 3. OR (95%CI) for incident MCI or dementia per 1SD unit worsening in ROI-specific WM FA.

Figure 3.

Open in a new tab

Adjusted association quantifying change in odds of incident MCI or dementia were quantified among persons who were cognitively normal at Visit 5. All analyses were adjusted for age, sex, education, race-center, and APOE e4 carrier status. Lower WM FA reflects worse WM FA.

Abbreviations: ACC, Anterior corpus callosum; MCI, mild cognitive impairment; PCC, posterior corpus callosum; ROI, region of interest; WM MD, white matter mean diffusivity.

Figure 4. OR (95%CI) for incident MCI or dementia per 1SD unit worsening in ROI-specific WM MD.

Figure 4.

Open in a new tab

Adjusted association quantifying change in odds of incident MCI or dementia were quantified among persons who were cognitively normal at Visit 5. All analyses were adjusted for age, sex, education, race-center, and APOE e4 carrier status. Higher WM MD reflects worsening WM MD.

Abbreviations: ACC, Anterior corpus callosum; MCI, mild cognitive impairment; PCC, posterior corpus callosum; ROI, region of interest; WM MD, white matter mean diffusivity.

4. Discussion

Our study found that the cross-sectional and longitudinal associations between WM microstructural integrity and cognitive outcomes differ. While both WM FA and WM MD were strongly associated with baseline cognitive performance and prevalent cognitive status (i.e., normal, MCI, dementia), associations of WM FA and WM MD with cognitive change or incident MCI, dementia, or death were mixed. While we did not find strong evidence of associations between WM FA and cognitive change or incident outcomes, our study found strong, consistent associations between higher overall WM MD and subsequent late life cognitive decline, incident dementia, and mortality over a period of approximately 5 years. Results linking WM MD to global cognitive decline were stronger than those considering domain-specific cognitive decline, reflecting the additional information captured by the global measure, and associations between WM MD and dementia appear strongest when considering WM MD in the frontal, temporal, and parietal lobes, consistent with the demonstrated importance of these regions in cognitive function commonly affected by dementia, particularly executive function and memory. All observed associations were attenuated after simultaneous adjustment for WMH, hippocampal, and total brain volumes, which are thought to be downstream pathologic changes.

Consistent with our findings, most cross-sectional studies suggest an association between WM microstructural integrity and cognitive function. However, there remains heterogeneity in the specific findings. Several, but not all, studies report that age-related differences in processing speed or other aspects of cognitive performance are partially mediated by white matter microstructural integrity.(Burgmans et al., 2011; Charlton et al., 2008; Madden et al., 2012; Madden et al., 2017; Madden et al., 2009; Madden et al., 2004; O’Sullivan et al., 2001; Salami et al., 2012) In a sample of older adults, FA and MD were associated with perceptual speed, but other cognitive domains (Laukka et al., 2013). In the Rotterdam Study, lower WM FA and higher WM MD were associated with decreased cognitive test performance (Vernooij et al., 2009) and greater risk of mortality.(Sedaghat et al., 2016) In the Mayo Clinic Study of Aging, specific cognitive domain functions are associated with distinct patterns of cortical and white matter diffusivity in elderly with no dementia(Kantarci et al., 2011), and these changes were in-part explained by gray matter neurodegeneration but not β-amyloid.(Kantarci et al., 2014) Similar associations between DTI-based measures and performance on tests of cognition and reaction time were frequently observed in other samples.(Charlton et al., 2006; Sala et al., 2015; Tuladhar et al., 2015; Ukmar et al., 2008; van Norden et al., 2012) Finally, in related cross-sectional work, persons with a diagnosis of Alzheimer’s disease (AD) or mild cognitive impairment (MCI) frequently exhibit worse regional WM microstructural integrity compared to cognitively intact controls, despite some heterogeneity within sets of analyses considering specific regions.(Bagepally et al., 2012; Chua et al., 2008; Fellgiebel et al., 2004; Huang and Auchus, 2007; Huang et al., 2007; Medina et al., 2006; Naggara et al., 2006; Oishi et al., 2011; Stebbins and Murphy, 2009; Ukmar et al., 2008)

In comparison, relatively few studies have assessed the longitudinal relationship between WM microstructural integrity and cognition in older adults, and the results are mixed. In a small sample of 77 persons over age 81 selected from the Swedish National Study of Aging and Care in Kungsholmen (SNAC-K, mean(SD): MMSE 28.6 (1.4)), of whom 40 had a repeat MRI, change in FA and MD in the corticospinal tract was associated with change in perceptual speed but not other domains of cognitive function, over 2 years of follow-up (Lovden et al., 2014). This study did not consider the influence of other neuroimaging parameters on their analyses. In Participants of the Lothian Birth Cohort 1936, worse baseline FA did not predict subsequent cognitive decline, but change in FA from approximately age 73 to 76 was correlated with change in general cognition in analyses accounting for potential confounding by age and sex (Ritchie et al., 2015). In a sample of 265 cognitively normal individuals from the Harvard Aging Brain Study, lower global FA, but not tract-specific FA, predicted accelerated decline in processing speed and episodic memory in analyses adjusted for demographics (Rabin et al., 2019). The RunDMC study considered a sample of older non-demented adults with cerebral small vessel disease, defined as the presence of lacunes or WMH. Reports from RunDMC conclude no association between WM FA or WM MD in normal appearing white matter and subsequent cognitive decline. However, prior to Bonferroni correction for multiple testing, worse white matter microstructural integrity was associated with greater decline on tests of verbal memory and fluency at standard cut-offs for statistical significance.(van Uden et al., 2015) In related work in RunDMC, WM microstructural integrity was not associated with incident dementia after adjusting for WMH volumes, WM volumes, and hippocampal volumes.(van Uden et al., 2016) To the contrary, in the LADIS study, which enrolled older adults with mild or no IADL disability and evidence of WMH on MRI (mean (SD) MMSE score 27 (2.3)), MD was associated with accelerated decline in processing speed, executive function, and memory; these associations persisted after adjustment for WMH volumes, lacunes, and brain atrophy.(Jokinen et al., 2013) Similarly, in the GENIE study, which enrolled healthy individuals without known neurological conditions from a local physician practice, change in white matter microstructural integrity was correlated with change in working memory after adjusting for age and WMH volumes.(Charlton et al., 2010) Differences in the severity of existing brain pathologies, baseline cognitive status, or other sample characteristics may contribute to the observed heterogeneity across studies. However, our analysis suggests differences across studies are partially attributable to differences in adjustment for other MRI indicators of brain pathology. As changes to white matter microstructural integrity appear to precede white matter hyperintensities (WMH), which themselves are thought to precede loss of brain volume,(Appelman et al., 2009; Ly et al., 2014; Maillard et al., 2013; Maillard et al., 2011; Maillard et al., 2014; Salat et al., 2005) we may expect that estimates of the impact of WM microstructural integrity on late-life cognition be attenuated when we adjust for these proposed markers of intermediaries on the causal pathway.

Strengths of this study include the relatively large number of well-characterized participants originally recruited from the community with MRI, longitudinal follow-up, and in-person dementia ascertainment. In addition, given the community-based nature of the original enrollment, we expect our results to be broadly generalizable. However, this study also has limitations. While the association between WM MD and late life cognitive change may not be homogenous, our study was not large enough to justify consideration of effect modification. We focused on regional summaries of WM FA and WM MD, as this allowed us to cover the entire WM, which may be important because vascular lesions in the WM tend to involve vascular watershed zones that may not follow specific tracts. Measures based on tractography or analyses using voxel-wise comparisons would better identify how the health of specific WM tracts relate to cognition. While we did not find robust associations between WM FA and either cognitive change or incident cognitive impairment, this may be related to the fact that FA can be influenced by crossing-fibers more than MD, limiting the power of FA to detect WM degeneration (Jeurissen et al., 2013) Similarly, we quantify WM FA and WM MD in total white matter, rather than normal appearing white matter. This choice allowed us to adjust for WMH in our sensitivity analyses, demonstrating that associations with WM MD or WM FA were not driven by correlation between these measures and WMH burden.(Svard et al., 2017) We did not include alternate measures such as axial or radial diffusivity in our imaging analysis protocol given concerns about interpretability(Wheeler-Kingshott and Cercignani, 2009), limiting synthesis with other investigations using such methods. In addition, using track-based approaches may be a better approach to explore tract-specific integrity. Finally, bias is always a potential issue in observational studies. However, our sensitivity analyses addressing residual confounding and potential selection bias were consistent with our primary analyses. Moreover, we anticipate only non-differential measurement error, which may account for our negative findings but is unlikely to account for our positive findings.

5. Conclusions

DTI-based measures of WM microstructural integrity have been shown to precede and predict the development of white matter hyperintensities (WMH) and WM loss.(Ly et al., 2014; Maillard et al., 2013; Maillard et al., 2011; Maillard et al., 2014; Salat et al., 2005) Thus, the findings of our study suggest that WM microstructural integrity, as measured by WM MD, may be a useful early indicator of pathologic changes that lead to future cognitive decline and incident dementia. Future work will need to better understand how WM MD contributes to declining cognition, as our work suggests this relationship may be mediated by accumulation of other brain pathologies.

Supplementary Material

1

Acknowledgements

The authors thank the staff and participants of the ARIC study for their important contributions.

Declarations of Interest: Drs. Coresh, Griswold, Huang, Mosley, Power, Reid, Sharrett, and Wu, and Ms. Su have nothing to declare. Dr. Clifford R. Jack Jr. consults for Lily and serves on an independent data monitoring board for Roche but he receives no personal compensation from any commercial entity. He receives research support from NIH and the Alexander Family Alzheimer’s Disease Research Professorship of the Mayo Clinic. Dr. David Knopman serves on a Data Safety Monitoring Board for the DIAN study; is an investigator in clinical trials sponsored by Biogen, Lilly Pharmaceuticals and the University of Southern California; and receives research support from the NIH. Kejal Kantarci serves on the Data Safety Monitoring Board for Takeda Global Research and Development Center, Inc.; receives research support from Avid Radiopharmaceuticals and Eli Lilly, and receives funding from NIH and Alzheimer’s Drug Discovery Foundation. Dr. Rebecca G. Gottesman is an associate editor for Neurology.

Funding: This work was supported by the NIH (The Atherosclerosis Risk in Communities Study is carried out as a collaborative study supported by National Heart, Lung, and Blood Institute contracts HHSN268201700001I, HHSN268201700002I, HHSN268201700003I, HHSN268201700005I, HHSN268201700004I. Neurocognitive data is collected by U01 2U01HL096812, 2U01HL096814, 2U01HL096899, 2U01HL096902, 2U01HL096917 from the NHLBI, NINDS, NIA and NIDCD, and with previous brain MRI examinations funded by R01-HL70825 from the NHLBI.) The sponsors had no role in the study design; collection, analysis, or interpretation of the data; in the writing of the report; or in the decision to submit the article for publication.

Footnotes

Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

References

  1. Alexander AL, Lee JE, Lazar M, Field AS, 2007. Diffusion tensor imaging of the brain. Neurotherapeutics 4(3), 316–329. [DOI] [PMC free article] [PubMed] [Google Scholar]
  2. Appelman AP, Exalto LG, van der Graaf Y, Biessels GJ, Mali WP, Geerlings MI, 2009. White matter lesions and brain atrophy: more than shared risk factors? A systematic review. Cerebrovasc Dis 28(3), 227–242. [DOI] [PubMed] [Google Scholar]
  3. Avants BB, Tustison NJ, Stauffer M, Song G, Wu B, Gee JC, 2014. The Insight ToolKit image registration framework. Frontiers in neuroinformatics 8, 44. [DOI] [PMC free article] [PubMed] [Google Scholar]
  4. Bagepally BS, Halahalli HN, John JP, Kota L, Purushottam M, Mukherjee O, Sivakumar PT, Bharath S, Jain S, Varghese M, 2012. Apolipoprotein E4 and brain white matter integrity in Alzheimer’s disease: tract-based spatial statistics study under 3-Tesla MRI. Neurodegener Dis 10(1–4), 145–148. [DOI] [PubMed] [Google Scholar]
  5. Bhushan C, Haldar JP, Choi S, Joshi AA, Shattuck DW, Leahy RM, 2015. Co-registration and distortion correction of diffusion and anatomical images based on inverse contrast normalization. Neuroimage 115, 269–280. [DOI] [PMC free article] [PubMed] [Google Scholar]
  6. Burgmans S, Gronenschild EH, Fandakova Y, Shing YL, van Boxtel MP, Vuurman EF, Uylings HB, Jolles J, Raz N, 2011. Age differences in speed of processing are partially mediated by differences in axonal integrity. Neuroimage 55(3), 1287–1297. [DOI] [PMC free article] [PubMed] [Google Scholar]
  7. Caselli RJ, Reiman EM, Locke DE, Hutton ML, Hentz JG, Hoffman-Snyder C, Woodruff BK, Alexander GE, Osborne D, 2007. Cognitive domain decline in healthy apolipoprotein E epsilon4 homozygotes before the diagnosis of mild cognitive impairment. Arch Neurol 64(9), 1306–1311. [DOI] [PubMed] [Google Scholar]
  8. Caselli RJ, Reiman EM, Osborne D, Hentz JG, Baxter LC, Hernandez JL, Alexander GG, 2004. Longitudinal changes in cognition and behavior in asymptomatic carriers of the APOE e4 allele. Neurology 62(11), 1990–1995. [DOI] [PubMed] [Google Scholar]
  9. Charlton RA, Barrick TR, McIntyre DJ, Shen Y, O’Sullivan M, Howe FA, Clark CA, Morris RG, Markus HS, 2006. White matter damage on diffusion tensor imaging correlates with age-related cognitive decline. Neurology 66(2), 217–222. [DOI] [PubMed] [Google Scholar]
  10. Charlton RA, Landau S, Schiavone F, Barrick TR, Clark CA, Markus HS, Morris RG, 2008. A structural equation modeling investigation of age-related variance in executive function and DTI measured white matter damage. Neurobiol Aging 29(10), 1547–1555. [DOI] [PubMed] [Google Scholar]
  11. Charlton RA, Schiavone F, Barrick TR, Morris RG, Markus HS, 2010. Diffusion tensor imaging detects age related white matter change over a 2 year follow-up which is associated with working memory decline. J Neurol Neurosurg Psychiatry 81(1), 13–19. [DOI] [PubMed] [Google Scholar]
  12. Chua TC, Wen W, Slavin MJ, Sachdev PS, 2008. Diffusion tensor imaging in mild cognitive impairment and Alzheimer’s disease: a review. Current opinion in neurology 21(1), 83–92. [DOI] [PubMed] [Google Scholar]
  13. Cosentino S, Scarmeas N, Helzner E, Glymour MM, Brandt J, Albert M, Blacker D, Stern Y, 2008. APOE epsilon 4 allele predicts faster cognitive decline in mild Alzheimer disease. Neurology 70(19 Pt 2), 1842–1849. [DOI] [PMC free article] [PubMed] [Google Scholar]
  14. Fellgiebel A, Wille P, Muller MJ, Winterer G, Scheurich A, Vucurevic G, Schmidt LG, Stoeter P, 2004. Ultrastructural hippocampal and white matter alterations in mild cognitive impairment: a diffusion tensor imaging study. Dement Geriatr Cogn Disord 18(1), 101–108. [DOI] [PubMed] [Google Scholar]
  15. Gross AL, Power MC, Albert MS, Deal JA, Gottesman RF, Griswold M, Wruck LM, Mosley TH Jr., Coresh J, Sharrett AR, Bandeen-Roche K, 2015. Application of Latent Variable Methods to the Study of Cognitive Decline When Tests Change over Time. Epidemiology 26(6), 878–887. [DOI] [PMC free article] [PubMed] [Google Scholar]
  16. Heise V, Filippini N, Ebmeier KP, Mackay CE, 2011. The APOE varepsilon4 allele modulates brain white matter integrity in healthy adults. Mol Psychiatry 16(9), 908–916. [DOI] [PubMed] [Google Scholar]
  17. Huang J, Auchus AP, 2007. Diffusion tensor imaging of normal appearing white matter and its correlation with cognitive functioning in mild cognitive impairment and Alzheimer’s disease. Ann N Y Acad Sci 1097, 259–264. [DOI] [PubMed] [Google Scholar]
  18. Huang J, Friedland RP, Auchus AP, 2007. Diffusion tensor imaging of normal-appearing white matter in mild cognitive impairment and early Alzheimer disease: preliminary evidence of axonal degeneration in the temporal lobe. AJNR Am J Neuroradiol 28(10), 1943–1948. [DOI] [PMC free article] [PubMed] [Google Scholar]
  19. Ikram MA, Vrooman HA, Vernooij MW, den Heijer T, Hofman A, Niessen WJ, van der Lugt A, Koudstaal PJ, Breteler MM, 2010. Brain tissue volumes in relation to cognitive function and risk of dementia. Neurobiol Aging 31(3), 378–386. [DOI] [PubMed] [Google Scholar]
  20. Jack CR Jr., Wiste HJ, Weigand SD, Rocca WA, Knopman DS, Mielke MM, Lowe VJ, Senjem ML, Gunter JL, Preboske GM, Pankratz VS, Vemuri P, Petersen RC, 2014. Age-specific population frequencies of cerebral beta-amyloidosis and neurodegeneration among people with normal cognitive function aged 50–89 years: a cross-sectional study. Lancet Neurol 13(10), 997–1005. [DOI] [PMC free article] [PubMed] [Google Scholar]
  21. Jeurissen B, Leemans A, Tournier JD, Jones DK, Sijbers J, 2013. Investigating the prevalence of complex fiber configurations in white matter tissue with diffusion magnetic resonance imaging. Hum Brain Mapp 34(11), 2747–2766. [DOI] [PMC free article] [PubMed] [Google Scholar]
  22. Jokinen H, Schmidt R, Ropele S, Fazekas F, Gouw AA, Barkhof F, Scheltens P, Madureira S, Verdelho A, Ferro JM, Wallin A, Poggesi A, Inzitari D, Pantoni L, Erkinjuntti T, 2013. Diffusion changes predict cognitive and functional outcome: the LADIS study. Ann Neurol 73(5), 576–583. [DOI] [PubMed] [Google Scholar]
  23. Kanchibhotla SC, Mather KA, Wen W, Schofield PR, Kwok JB, Sachdev PS, 2013. Genetics of ageing-related changes in brain white matter integrity - a review. Ageing Res Rev 12(1), 391–401. [DOI] [PubMed] [Google Scholar]
  24. Kantarci K, Schwarz CG, Reid RI, Przybelski SA, Lesnick TG, Zuk SM, Senjem ML, Gunter JL, Lowe V, Machulda MM, Knopman DS, Petersen RC, Jack CR Jr., 2014. White matter integrity determined with diffusion tensor imaging in older adults without dementia: influence of amyloid load and neurodegeneration. JAMA neurology 71(12), 1547–1554. [DOI] [PMC free article] [PubMed] [Google Scholar]
  25. Kantarci K, Senjem ML, Avula R, Zhang B, Samikoglu AR, Weigand SD, Przybelski SA, Edmonson HA, Vemuri P, Knopman DS, Boeve BF, Ivnik RJ, Smith GE, Petersen RC, Jack CR Jr., 2011. Diffusion tensor imaging and cognitive function in older adults with no dementia. Neurology 77(1), 26–34. [DOI] [PMC free article] [PubMed] [Google Scholar]
  26. Knopman DS, Gottesman RF, Sharrett AR, Wruck LM, Windham BG, Coker L, Schneider AL, Hengrui S, Alonso A, Coresh J, Albert MS, Mosley TH Jr., 2016. Mild Cognitive Impairment and Dementia Prevalence: The Atherosclerosis Risk in Communities Neurocognitive Study (ARIC-NCS). Alzheimer’s & dementia (Amsterdam, Netherlands) 2, 1–11. [DOI] [PMC free article] [PubMed] [Google Scholar]
  27. Laukka EJ, Lovden M, Kalpouzos G, Li TQ, Jonsson T, Wahlund LO, Fratiglioni L, Backman L, 2013. Associations between white matter microstructure and cognitive performance in old and very old age. PLoS One 8(11), e81419. [DOI] [PMC free article] [PubMed] [Google Scholar]
  28. Lovden M, Kohncke Y, Laukka EJ, Kalpouzos G, Salami A, Li TQ, Fratiglioni L, Backman L, 2014. Changes in perceptual speed and white matter microstructure in the corticospinal tract are associated in very old age. Neuroimage 102 Pt 2, 520–530. [DOI] [PubMed] [Google Scholar]
  29. Ly M, Canu E, Xu G, Oh J, McLaren DG, Dowling NM, Alexander AL, Sager MA, Johnson SC, Bendlin BB, 2014. Midlife measurements of white matter microstructure predict subsequent regional white matter atrophy in healthy adults. Hum Brain Mapp 35(5), 2044–2054. [DOI] [PMC free article] [PubMed] [Google Scholar]
  30. Madden DJ, Bennett IJ, Burzynska A, Potter GG, Chen NK, Song AW, 2012. Diffusion tensor imaging of cerebral white matter integrity in cognitive aging. Biochimica et biophysica acta 1822(3), 386–400. [DOI] [PMC free article] [PubMed] [Google Scholar]
  31. Madden DJ, Parks EL, Tallman CW, Boylan MA, Hoagey DA, Cocjin SB, Packard LE, Johnson MA, Chou YH, Potter GG, Chen NK, Siciliano RE, Monge ZA, Honig JA, Diaz MT, 2017. Sources of disconnection in neurocognitive aging: cerebral white-matter integrity, resting-state functional connectivity, and white-matter hyperintensity volume. Neurobiol Aging 54, 199–213. [DOI] [PMC free article] [PubMed] [Google Scholar]
  32. Madden DJ, Spaniol J, Costello MC, Bucur B, White LE, Cabeza R, Davis SW, Dennis NA, Provenzale JM, Huettel SA, 2009. Cerebral white matter integrity mediates adult age differences in cognitive performance. J Cogn Neurosci 21(2), 289–302. [DOI] [PMC free article] [PubMed] [Google Scholar]
  33. Madden DJ, Whiting WL, Huettel SA, White LE, MacFall JR, Provenzale JM, 2004. Diffusion tensor imaging of adult age differences in cerebral white matter: relation to response time. Neuroimage 21(3), 1174–1181. [DOI] [PubMed] [Google Scholar]
  34. Maillard P, Carmichael O, Harvey D, Fletcher E, Reed B, Mungas D, DeCarli C, 2013. FLAIR and diffusion MRI signals are independent predictors of white matter hyperintensities. AJNR Am J Neuroradiol 34(1), 54–61. [DOI] [PMC free article] [PubMed] [Google Scholar]
  35. Maillard P, Fletcher E, Harvey D, Carmichael O, Reed B, Mungas D, DeCarli C, 2011. White matter hyperintensity penumbra. Stroke 42(7), 1917–1922. [DOI] [PMC free article] [PubMed] [Google Scholar]
  36. Maillard P, Fletcher E, Lockhart SN, Roach AE, Reed B, Mungas D, DeCarli C, Carmichael OT, 2014. White matter hyperintensities and their penumbra lie along a continuum of injury in the aging brain. Stroke 45(6), 1721–1726. [DOI] [PMC free article] [PubMed] [Google Scholar]
  37. Medina D, DeToledo-Morrell L, Urresta F, Gabrieli JD, Moseley M, Fleischman D, Bennett DA, Leurgans S, Turner DA, Stebbins GT, 2006. White matter changes in mild cognitive impairment and AD: A diffusion tensor imaging study. Neurobiol Aging 27(5), 663–672. [DOI] [PubMed] [Google Scholar]
  38. Naggara O, Oppenheim C, Rieu D, Raoux N, Rodrigo S, Dalla Barba G, Meder JF, 2006. Diffusion tensor imaging in early Alzheimer’s disease. Psychiatry Res 146(3), 243–249. [DOI] [PubMed] [Google Scholar]
  39. Nilsson LG, Adolfsson R, Backman L, Cruts M, Nyberg L, Small BJ, Van Broeckoven C, 2006. The influence of APOE status on episodic and semantic memory: data from a population-based study. Neuropsychology 20(6), 645–657. [DOI] [PubMed] [Google Scholar]
  40. Nyberg L, Salami A, 2014. The APOE epsilon4 allele in relation to brain white-matter microstructure in adulthood and aging. Scand J Psychol 55(3), 263–267. [DOI] [PubMed] [Google Scholar]
  41. O’Sullivan M, Jones DK, Summers PE, Morris RG, Williams SC, Markus HS, 2001. Evidence for cortical “disconnection” as a mechanism of age-related cognitive decline. Neurology 57(4), 632–638. [DOI] [PubMed] [Google Scholar]
  42. Oishi K, Mielke MM, Albert M, Lyketsos CG, Mori S, 2011. DTI analyses and clinical applications in Alzheimer’s disease. J Alzheimers Dis 26 Suppl 3, 287–296. [DOI] [PMC free article] [PubMed] [Google Scholar]
  43. Power MC, Schneider AL, Wruck L, Griswold M, Coker LH, Alonso A, Jack CR Jr., Knopman D, Mosley TH, Gottesman RF, 2016. Life-course blood pressure in relation to brain volumes. Alzheimers Dement 12(8), 890–899. [DOI] [PMC free article] [PubMed] [Google Scholar]
  44. Power MC, Tingle JV, Reid RI, Huang J, Sharrett AR, Coresh J, Griswold M, Kantarci K, Jack CR Jr., Knopman D, Gottesman RF, Mosley TH, 2017. Midlife and Late-Life Vascular Risk Factors and White Matter Microstructural Integrity: The Atherosclerosis Risk in Communities Neurocognitive Study. J Am Heart Assoc 6(5). [DOI] [PMC free article] [PubMed] [Google Scholar]
  45. Prins ND, Scheltens P, 2015. White matter hyperintensities, cognitive impairment and dementia: an update. Nat Rev Neurol 11(3), 157–165. [DOI] [PubMed] [Google Scholar]
  46. Qiu C, Winblad B, Fratiglioni L, 2005. The age-dependent relation of blood pressure to cognitive function and dementia. Lancet Neurol 4(8), 487–499. [DOI] [PubMed] [Google Scholar]
  47. Rabin JS, Perea RD, Buckley RF, Neal TE, Buckner RL, Johnson KA, Sperling RA, Hedden T, 2019. Global White Matter Diffusion Characteristics Predict Longitudinal Cognitive Change Independently of Amyloid Status in Clinically Normal Older Adults. Cereb Cortex 29(3), 1251–1262. [DOI] [PMC free article] [PubMed] [Google Scholar]
  48. Rawlings AM, Bandeen-Roche K, Gross AL, Gottesman RF, Coker LH, Penman AD, Sharrett AR, Mosley TH, 2016. Factor Structure of the ARIC-NCS Neuropsychological Battery: An Evaluation of Invariance Across Vascular Factors and Demographic Characteristics. Psychological assessment. [DOI] [PMC free article] [PubMed] [Google Scholar]
  49. Raz L, Jayachandran M, Tosakulwong N, Lesnick TG, Wille SM, Murphy MC, Senjem ML, Gunter JL, Vemuri P, Jack CR Jr., Miller VM, Kantarci K, 2013. Thrombogenic microvesicles and white matter hyperintensities in postmenopausal women. Neurology 80(10), 911–918. [DOI] [PMC free article] [PubMed] [Google Scholar]
  50. Ritchie SJ, Bastin ME, Tucker-Drob EM, Maniega SM, Engelhardt LE, Cox SR, Royle NA, Gow AJ, Corley J, Pattie A, Taylor AM, Valdes Hernandez Mdel C, Starr JM, Wardlaw JM, Deary IJ, 2015. Coupled changes in brain white matter microstructure and fluid intelligence in later life. J Neurosci 35(22), 8672–8682. [DOI] [PMC free article] [PubMed] [Google Scholar]
  51. Sala M, de Roos A, Blauw GJ, Middelkoop HA, Jukema JW, Mooijaart SP, van Buchem MA, de Craen AJ, van der Grond J, 2015. Association between changes in brain microstructure and cognition in older subjects at increased risk for vascular disease. BMC Neurol 15, 133. [DOI] [PMC free article] [PubMed] [Google Scholar]
  52. Salami A, Eriksson J, Nilsson LG, Nyberg L, 2012. Age-related white matter microstructural differences partly mediate age-related decline in processing speed but not cognition. Biochimica et biophysica acta 1822(3), 408–415. [DOI] [PubMed] [Google Scholar]
  53. Salat DH, Tuch DS, Hevelone ND, Fischl B, Corkin S, Rosas HD, Dale AM, 2005. Age-related changes in prefrontal white matter measured by diffusion tensor imaging. Ann N Y Acad Sci 1064, 37–49. [DOI] [PubMed] [Google Scholar]
  54. Sedaghat S, Cremers LGM, de Groot M, Hofman A, van der Lugt A, Niessen WJ, Franco OH, Dehghan A, Ikram MA, Vernooij MW, 2016. Lower microstructural integrity of brain white matter is related to higher mortality. Neurology 87(9), 927–934. [DOI] [PubMed] [Google Scholar]
  55. Stebbins GT, Murphy CM, 2009. Diffusion tensor imaging in Alzheimer’s disease and mild cognitive impairment. Behavioural neurology 21(1), 39–49. [DOI] [PMC free article] [PubMed] [Google Scholar]
  56. Stout JC, Jernigan TL, Archibald SL, Salmon DP, 1996. Association of dementia severity with cortical gray matter and abnormal white matter volumes in dementia of the Alzheimer type. Arch Neurol 53(8), 742–749. [DOI] [PubMed] [Google Scholar]
  57. Svard D, Nilsson M, Lampinen B, Latt J, Sundgren PC, Stomrud E, Minthon L, Hansson O, van Westen D, 2017. The effect of white matter hyperintensities on statistical analysis of diffusion tensor imaging in cognitively healthy elderly and prodromal Alzheimer’s disease. PLoS One 12(9), e0185239. [DOI] [PMC free article] [PubMed] [Google Scholar]
  58. Tuladhar AM, van Norden AG, de Laat KF, Zwiers MP, van Dijk EJ, Norris DG, de Leeuw FE, 2015. White matter integrity in small vessel disease is related to cognition. NeuroImage. Clinical 7, 518–524. [DOI] [PMC free article] [PubMed] [Google Scholar]
  59. Ukmar M, Makuc E, Onor ML, Garbin G, Trevisiol M, Cova MA, 2008. Evaluation of white matter damage in patients with Alzheimer’s disease and in patients with mild cognitive impairment by using diffusion tensor imaging. La Radiologia medica 113(6), 915–922. [DOI] [PubMed] [Google Scholar]
  60. van Norden AG, de Laat KF, van Dijk EJ, van Uden IW, van Oudheusden LJ, Gons RA, Norris DG, Zwiers MP, de Leeuw FE, 2012. Diffusion tensor imaging and cognition in cerebral small vessel disease: the RUN DMC study. Biochimica et biophysica acta 1822(3), 401–407. [DOI] [PubMed] [Google Scholar]
  61. van Uden IW, van der Holst HM, Tuladhar AM, van Norden AG, de Laat KF, Rutten-Jacobs LC, Norris DG, Claassen JA, van Dijk EJ, Kessels RP, de Leeuw FE, 2016. White Matter and Hippocampal Volume Predict the Risk of Dementia in Patients with Cerebral Small Vessel Disease: The RUN DMC Study. J Alzheimers Dis 49(3), 863–873. [DOI] [PubMed] [Google Scholar]
  62. van Uden IWM, van der Holst HM, Schaapsmeerders P, Tuladhar AM, van Norden AGW, de Laat KF, Norris DG, Claassen J, van Dijk EJ, Richard E, Kessels RPC, de Leeuw FE, 2015. Baseline white matter microstructural integrity is not related to cognitive decline after 5 years: The RUN DMC study. BBA Clinical 4, 108–114. [DOI] [PMC free article] [PubMed] [Google Scholar]
  63. Vemuri P, Gunter JL, Senjem ML, Whitwell JL, Kantarci K, Knopman DS, Boeve BF, Petersen RC, Jack CR Jr., 2008. Alzheimer’s disease diagnosis in individual subjects using structural MR images: validation studies. Neuroimage 39(3), 1186–1197. [DOI] [PMC free article] [PubMed] [Google Scholar]
  64. Vernooij MW, Ikram MA, Vrooman HA, Wielopolski PA, Krestin GP, Hofman A, Niessen WJ, Van der Lugt A, Breteler MM, 2009. White matter microstructural integrity and cognitive function in a general elderly population. Arch Gen Psychiatry 66(5), 545–553. [DOI] [PubMed] [Google Scholar]
  65. Westlye LT, Reinvang I, Rootwelt H, Espeseth T, 2012. Effects of APOE on brain white matter microstructure in healthy adults. Neurology 79(19), 1961–1969. [DOI] [PubMed] [Google Scholar]
  66. Wheeler-Kingshott CA, Cercignani M, 2009. About “axial” and “radial” diffusivities. Magnetic resonance in medicine : official journal of the Society of Magnetic Resonance in Medicine / Society of Magnetic Resonance in Medicine 61(5), 1255–1260. [DOI] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

1
NIHMS1545638-supplement-1.docx (32.9KB, docx)

Data Availability Statement

ARIC makes individual-level data available to qualified research investigators who collaborate with an ARIC investigator, use publicly available date accessed through the website https://biolincc.nhlbi.nih.gov/studies/aric, or successfully propose ancillary studies. ARIC’s data sharing policies are provided on the https://www2.cscc.unc.edu/aric/node/10303 website.

RESOURCES