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. Author manuscript; available in PMC: 2025 Mar 1.
Published in final edited form as: Neurobiol Aging. 2023 Dec 12;135:79–90. doi: 10.1016/j.neurobiolaging.2023.10.012

Impact of white matter hyperintensities on structural connectivity and cognition in cognitively intact ADNI participants

Mohammad Taghvaei a, Dawn J Mechanic-Hamilton a, Shokufeh Sadaghiani a, Banafsheh Shakibajahromi a, Sudipto Dolui b, Sandhitsu Das a, Christopher Brown a, William Tackett a, Pulkit Khandelwal b, Philip Cook b, Russell T Shinohara c, Paul Yushkevich b, Danielle S Bassett d, David A Wolk a, John A Detre a, Alzheimer’s Disease Neuroimaging Initiative
PMCID: PMC10872454  NIHMSID: NIHMS1954621  PMID: 38262221

Abstract

We used indirect brain mapping with virtual lesion tractography to test the hypothesis that the extent of white matter tract disconnection due to white matter hyperintensities (WMH) is associated with corresponding tract-specific cognitive performance decrements. To estimate tract disconnection, WMH masks were extracted from FLAIR MRI data of 481 cognitively intact participants in the Alzheimer’s Disease Neuroimaging Initiative (ADNI) and used as regions of avoidance for fiber tracking in diffusion MRI data from 50 healthy young participants from the Human Connectome Project. Estimated tract disconnection in the right inferior fronto-occipital fasciculus, right frontal aslant tract, and right superior longitudinal fasciculus mediated the effects of WMH volume on executive function. Estimated tract disconnection in the left uncinate fasciculus mediated the effects of WMH volume on memory and in the right frontal aslant tract on language. In a subset of ADNI control participants with amyloid data, positive status increased the probability of periventricular WMH and moderated the relationship between WMH burden and tract disconnection in executive function performance.

Keywords: Cognition, Structural connectivity, White matter hyperintensity (WMH), White matter fiber tract

1. Introduction

White matter hyperintensities (WMH) on T2-weighted fluid-attenuated inversion recovery (FLAIR) magnetic resonance imaging (MRI) are nearly ubiquitous in older adults (Prins and Scheltens, 2015), including asymptomatic individuals (Brant-Zawadzki et al., 1985), and currently provide the most widely accepted biomarker of cerebral small vessel disease (SVD) (Wardlaw et al., 2013). WMH lesions are also linked to clinical progression in mild cognitive impairment (MCI) and Alzheimer’s disease (AD) (Coutu et al., 2017; Zhang et al., 2011). While mounting evidence describes the association between global WMH lesion burden and cognitive decline (Prins and Scheltens, 2015; Yoshita et al., 2005), studies also suggest that spatial distribution of WMH and tract-specific lesions may be more predictive of cognitive performance than the overall volume of WMH lesions (Biesbroek et al., 2013; Cremers et al., 2016; Lampe et al., 2019; Langen et al., 2018; Smith et al., 2011).

White matter (WM) infarcts typically cause disconnection syndromes by interrupting WM tracts connecting gray matter structures (Duering et al., 2012; Duering et al., 2015). Brain structural connectivity can be studied noninvasively using MRI diffusion tensor imaging (DTI) and tractography methods (Bullmore and Sporns, 2009) and recent evidence demonstrates that network properties derived from diffusion tractography have significant explanatory power for cognitive performance decrements in older participants (Bergamino et al., 2022; Lombardi et al., 2020). To date, limited data are available regarding the effect of WMH lesions on tract specific connectivity and corresponding cognitive performance. A study on individuals with SVD including both normal and cognitively abnormal participants found that WMH lesion burden in the anterior thalamic radiation and forceps minor are strongly associated with deficits in executive function, visuomotor speed, and memory performance, independent of global WMH lesion volume (Biesbroek et al., 2016). Another study aimed to investigate the association of WMH burden within three tract categories and memory function concluded that macrostructural damage in the form of WMH in association and projection tracts may contribute to memory decline in older adults (Rizvi et al., 2020).

However, the effects of WMH are challenging to study using diffusion tractography because they are diffuse and often occur in regions of crossing fiber tracts. In addition, WMH lesions can alter WM diffusion parameters and interfere with computerized tract-tracing algorithms, leading to inaccurate estimation of structural connectivity (Muñoz Maniega et al., 2019; Reginold et al., 2016; Reginold et al., 2018; Seiler et al., 2018; Wardlaw et al., 2015). An alternative approach for estimating the effects of WMH on the structural connectome is to use FLAIR-based WMH data as regions of avoidance for indirect fiber tracking in high-quality DTI data acquired from normal participants. This indirect ‘virtual lesion connectome’ approach alleviates the difficulties related to conducting tractography using participants’ diffusion MRI data containing WMH lesions (Li et al., 2021). In this study, we applied the virtual lesion approach to MRI and cognitive performance data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) to test the hypothesis that the spatial distribution and extent of WM tract disconnection due to WMH is associated with tract-specific cognitive performance decrements. Cognitively intact participants from the ADNI were included in this study in an effort to minimize the effect of other potential mechanisms (e.g. neurodegeneration) associated with AD or other types of dementia on cognitive function.

A fundamental interaction between vascular disease and AD pathogenesis has been proposed, sometimes referred as the vascular hypotheses of AD (Casado et al., 2008), whereby vascular dysfunction causes neuronal dysfunction through a complex process, including blood brain barrier compromise (Bell and Zlokovic, 2009; Van De Haar et al., 2016), diminished capillary perfusion causing focal ischemic or hypoxic micro-injuries, reduced Aβ clearance, and formation of neurotoxic oligomers (Canobbio et al., 2015; Janota et al., 2016; Kapasi and Schneider, 2016). Consistent with these hypotheses, studies have shown that baseline WMH lesion burden has been associated with both the severity and progression rate of amyloid burden (Gordon et al., 2015; Soldan et al., 2020; Wong et al., 2021). Additionally, a positive interaction between WMH burden and Aβ deposition on the fractional anisotropy of WM tracts has been identified (Bendlin et al., 2012; Chao et al., 2013). In this context, we also examined the effects of amyloid status on the relationship between structural connectivity and cognition to test the hypothesis that amyloid status impacts both the overall and tract-specific association between WMH and cognition in cognitively intact older adults.

2. Materials and methods

2.1. Human participant data

This study used data from two publicly available datasets. The first sample consisted of 481 individual healthy control older adults from the ADNI database (adni.loni.usc.edu). The ADNI was launched in 2003 as a public-private partnership, led by Principal Investigator Michael W. Weiner, MD. The primary goal of ADNI has been to test whether serial MRI, positron emission tomography (PET), other biological markers, and clinical and neuropsychological assessment can be combined to measure the progression of MCI and early AD. For up-to-date information, see www.adni-info.org. Demographic information, structural MRI data, PET imaging, neuropsychological test scores, and cerebrospinal fluid (CSF) analysis were obtained for these participants. All selected participants scored greater than 24 on the Mini-Mental Status Examination (MMSE). Participants who were cognitively impaired or were diagnosed with MCI, AD, or other types of dementia were excluded from the study. The second dataset included high-resolution DTI data of a cohort of 50 healthy young (27 female, mean (SD) age of 39.2 (1.6) years) participants randomly selected from the publicly available Human Connectome Project-Aging lifespan 2.0 release (Bookheimer et al., 2019). In both studies, participants’ data were acquired using protocols approved by the Institutional Review Board of each center.

2.2. Neuropsychological assessment of ADNI controls

All ADNI participants underwent comprehensive neuropsychological evaluation based on a standardized battery of tests. In this study, cognitive data collected closest to the time of the MRI data acquisition for each participant were used for analyses. The selected ADNI cognitive data set included the following cognitive domains: Measures of global cognition (Mini-Mental Status Exam; MMSE, and Montreal Cognitive Assessment; MoCA, Alzheimer’s Disease Assessment Scale-Cognitive; ADAS13), episodic memory (Rey Auditory Verbal Learning Test immediate recall; RAVLT), language (Verbal Category Fluency test; VCF, Verbal Phonemic Fluency; VPF), visuospatial construction (Clock Drawing Test; CDT), processing speed (Trial Making Test A; TMT A) and executive function (Trail Making Test B; TMT B). The difference between the time taken to complete TMT B and the time taken to complete TMT A, results in a relatively pure measurement of executive function in part B, while reducing the visual search and psychomotor functioning requirements (Vazzana et al., 2010). This provides a better indication of an individual’s executive control abilities (Sánchez-Cubillo et al., 2009). Cognitive data were transformed into z-scores for each domain.

2.3. Beta Amyloid status of ADNI controls

We used Aβ-PET or CSF Aβ data to classify ADNI participants into two groups based on cerebral amyloid beta deposition status. For ADNI images PET tracer [18F] florbetapir was used to visualize Aβ deposition. PET data were available for 300 participants enrolled in this study. A florbetapir cortical summary measurement (SUVR) cut-off of 1.11 using the whole cerebellum reference region was considered for the beta-amyloid positive group (Landau et al., 2012). For the participants without Aβ-PET, beta-amyloid status was determined by CSF assay of Aβ42 concentration using the Elecsys assay (Roche Diagnostics GmbH). Those below the cut-off of 980 pg/ml were considered beta-amyloid positive (McCollum et al., 2021). Aβ data for 60 participants was not available.

2.4. MRI acquisition in the ADNI controls and HCP Lifespan cohort

A standardized MRI protocol for image acquisition was implemented across ADNI sites and validated across platforms (Jack Jr et al., 2008). A 3T MRI scanner was used to acquire FLAIR MRI scans with the following parameters: TR/TE/TI=4800/119/1655 ms, resolution 1.2x1x1 mm, and acquisition matrix=256x256x160 slices.

The diffusion MRI images of the HCP participants were acquired using a Siemens 3T Prisma scanner and the following parameters were used: TR/TE=3230/89.20 ms, resolution 1.5 mm isotropic, 92 slices, and multiband acceleration factor of 4. A total of 185 diffusion-weighting directions of 2 shells of b=1500 and 3000 s/mm2 in addition to 28 b=0 s/mm2 images were acquired (Harms et al., 2018).

2.5. WMH Lesion segmentation of FLAIR MRI images

FLAIR images were skull-stripped using automated brain extraction HD-BET (Isensee et al., 2019). The nnU-Net, a deep learning-based method developed for automatic medical image segmentation, was adopted to segment WMH lesions on FLAIR MRI of ADNI participants for the present study (Isensee et al., 2021). PyTorch 1.5.1 and Nvidia Quadro RTX 5000 GPUs were used to train the models using 100 manually segmented images. WMH lesions were defined as hyperintense lesions in the periventricular and deep WM compared to NAWM on FLAIR MRI and were annotated manually using ITK-SNAP (Yushkevich et al., 2006). We implemented nnU-Net with the default settings for 500 epochs. The model was tested on a total of 100 participants. The Dice coefficient for the five-fold cross-validation was 86.06 ± 1.48%. Then, the model was employed to segment the remaining 381 FLAIR images. These generated binary FLAIR-based WMH masks were manually inspected and revised, if required. All masks co-registered to the T1 images and non-linearly transformed to Montreal Neurological Institute (MNI) space using Advanced Normalization Tools (ANTs) (Avants et al., 2011).

Convert3D software was used to calculate the volume of segmented lesions for each mask (Yushkevich et al., 2006). WMH lesion volume (units of cubic millimeters) was measured as the number of detected voxels multiplied by voxel dimensions. The lesion volume was normalized by the intracranial volume provided in the ADNI dataset to adjust for the difference in brain size (DeCarli et al., 2005). These relative volume measures were log-transformed to normalize population variance. WMH masks were averaged using FSL (v6.0.6) to create WMH Lesion frequency maps across all or subsets of participants (Jenkinson et al., 2012).

2.6. DTI structural connectivity analysis

HCP Diffusion MRI data of young participants were preprocessed using the standard HCP pipelines including eddy current distortion and head motion correction (Glasser et al., 2013). Then the images were reconstructed using q-space diffeomorphic reconstruction (QSDR) with a diffusion sampling length ratio of 1.25 to calculate the spin orientation distribution (SDF), and were aligned to the MNI quantitative anisotropy map using DSI Studio (Yeh and Tseng, 2011). Individual reconstructed images were visually inspected and data with artifacts, co-registration errors, and incorrect tensor calculations were excluded.

To mimic the effect of WMH lesions on brain structural connectivity, WMH lesion masks were used as ROA for indirect fiber tracking in the 50 HCP participants’ DTI data. Deterministic streamline tractography was conducted to extract 8 WM bundles using the HCP1065 white matter tractography atlas in right and left brain hemispheres (Yeh et al., 2018). For each WM pathway, 106 seeds were placed within the atlas tract volume, and streamlines were generated using the following tracking parameters: tracking index quantitative anisotropy (QA), angular threshold 0, minimum length 20 mm, maximum length 300 mm, termination of the process after 106 seeds, 0 iterations of topology-informed pruning, and an autotrack tolerance of 16. The Extracted WM pathways included eight tracts that were in proximity to high-frequency WMH lesions and had established relations with distinct cognitive domains (Barbeau et al., 2023; Conner et al., 2018; Dick et al., 2019; Janelle et al., 2022; Voineskos et al., 2012; Von Der Heide et al., 2013): arcuate fasciculus (AF), corticospinal tract (CST), fornix, frontal aslant tract (FAT), inferior fronto-occipital fasciculus (IFOF), inferior longitudinal fasciculus (ILF), superior longitudinal fasciculus (SLF), and uncinate fasciculus (UF) (Figure 1). Tract disconnection for each WM pathway was defined as the ratio of the number of the affecting streamline in the presence of simulated WMH lesion masks (streamlines passing through the ROA) to the number of streamlines without the presence of any lesion. 50 sets of DTI fiber tracking were carried out for each WMH lesion mask to determine a mean virtual lesion tract disconnection that is not biased by individual biological variations in anatomy or coregistration errors between the FLAIR MRI data and the diffusion MRI data (Figure 2).

Figure 1.

Figure 1.

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The spatial proximity of different white matter tracts with WMH lesions. The WMH lesion mask extracted from WMH lesion frequency map containing voxels with greater than 10% probability of lesion in our study participants is shown in red in each rendering. A: Arcuate fasciculus, B: Corticospinal tract, C: Frontal aslant tract, D: Fornix, E: Inferior fronto-occipital fasciculus, F: Inferior longitudinal fasciculus, G: Superior longitudinal fasciculus, H: Uncinate fasciculus.

Figure 2.

Figure 2.

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Virtual lesion tact-based disconnectome pipeline.

2.7. Statistical analysis

The statistical analyses were conducted in Statistical Package for the Social Sciences (SPSS v28). Data were presented using mean and standard deviation (SD) for the continuous variable, and frequencies for categorical variables. independent t-test or Wilcoxon rank-sum test for the continuous variable and Chi-square test or Fisher’s exact test for categorical variables were used to compare the difference between the two groups.

Logarithmic (log) transformation was performed to achieve a normal distribution for the WMH volume ratio. Z scores of cognitive measures, WMH volume ratio (log-transformed), and tract disconnection were used in all analyses described below. Linear regression models were used to examine the relationship between the global WMH volume or tract disconnection and cognitive functions considering age, sex, and level of education as covariates. The interaction effect of beta-amyloid status on these relationships was investigated by a general linear model. The false discovery rate (FDR) method was utilized to account for multiple comparisons. Further, we assessed the mediation effect of global WMH lesion burden and tract disconnection on the relationship between age and cognitive performance using PROCESS for SPSS v4.1 framework (Hayes, 2017). To adjust the confidence intervals in the mediation analysis for multiple comparisons, we used the fdrci package in R (version 4.1.2) (Millstein et al., 2022).

Voxel-based analysis was performed using Statistical non-Parametric Mapping (SnPM13.1.09, http://nisox.org/Software/SnPM13/) toolbox for the SPM12 framework to compare the voxel-wise WMH lesion probability between the beta-amyloid positive and negative groups (Holmes et al., 1996; Nichols and Holmes, 2002; Penny et al., 2001). The significance threshold was set at FDR corrected p<0.05.

3. Results

3.1. Characteristics of ADNI controls

Table 1 provides the demographic characteristics, neuroimaging, and cognitive variables of the control participants from the ADNI database. There were 481 ADNI participants, 40.3% females, with a mean age of 72.3 ± 6.3 years. Age was significantly correlated to the severity of WMH lesion burden, as expected (p < 0.001). Data for beta-amyloid status was available for 421 (87.5%) participants. Age was significantly higher in the beta-amyloid positive group than in the negative group (P<0.001). Both groups were of similar sex ratio and education level. In terms of cognitive measures, beta-amyloid positive participants had worse performance on MoCA, ADAS13, TMT A, TMT B, RAVLT, and VCF tests but showed no differences in MMSE, VPF, and clock drawing test scores.

Table 1.

Characteristics of the ADNI participants included in this study and comparison of variables across different beta-amyloid status groups.

Amyloid − (N=272) Amyloid + (N=149) p-value Total (N=481)
Sex (Female%) 86 (57.7%) 63 (42.3%) 0.543 194 (40.3%)
Age (Mean±SD) 70.0 ± 6.3 72.3 ± 6.3 <0.001 70.7 ± 6.3
Race (%) White 241 (88.6%) 133 (89.3%) 0.561 426 (88.6%)
Black 15 (5.5%) 12 (8.1%) 31 (6.4)
Asian 6 (2.2%) 2 (1.3%) 11 (2.3)
American Indian/Alaskan 2 (0.7%) 0 (0.0%) 2 (0.4%)
More than one 6 (2.2%) 2 (1.3%) 9 (1.9%)
Unknown 2 (0.7%) 0 (0.0%) 2 (0.4%)
Education year (Mean±SD) 16.9 ± 2.3 16.6 ± 2.5 0.245 16.8 ± 2.3
MMSE (Mean±SD) 29.1 ± 1.2 28.9 ± 1.3 0.086 29.0 ± 1.2
MoCA (Mean±SD) 26.5 ± 2.5 25.7 ± 2.8 0.002 26.2 ± 2.6
ADAS13 (Mean±SD) 8.0 ± 4.2 9.0 ± 5.0 0.040 8.3 ± 4.5
Trail A (Mean±SD) 30.2 ± 8.5 31.7 ± 9.0 0.075 30.4 ± 8.6
Trail B (Mean±SD) 72.8 ± 33.8 81.7 ± 42.0 0.019 75.4 ± 36.4
Trail B – A (Mean±SD) 40.2 ± 29.3 49.9 ± 39.6 0.009 43.6 ± 32.8
RAVLT immediate (Mean±SD) 47.8 ± 10.8 45.5 ± 10.2 0.035 46.9 ± 10.6
Verbal category fluency (Mean±SD) 22.3 ± 5.3 20.9 ± 5.4 0.008 21.8 ± 5.4
Verbal phonemic fluency (Mean±SD) 15.1 ± 4.9 14.7 ± 4.2 0.405 14.9 ± 4.6
Clock drawing test (Mean±SD) 4.75 ± 0.54 4.67 ± 0.59 0.152 4.74 ± 0.55
WMH Volume (log-transformed) (Mean±SD) −2.81 ± 0.60 −2.58 ± 0.65 <0.001 −2.74 ± 0.62
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3.2. Quantification and distribution of WMH across different amyloid status

The overall volume of WMH lesion burden was significantly higher in the beta-amyloid positive group compared to the negative group (p<0.001). This difference between the two groups remained significant after controlling for age (p=0.027). Figure 3 illustrates the lesion frequency maps for 110 age-matched beta-amyloid negative and positive participants along with the voxel-wise difference map between the two groups (See also Supplementary Figure 1). Using a previously defined periventricular region mask as a region of interest (Dolui et al., 2019), the mean frequency of WMH lesions in the periventricular area was significantly higher in the beta-amyloid positive group compared to the beta-amyloid negative group (26.4 ± 11.3% vs 20.8 ± 12.3%, p<0.001).

Figure 3.

Figure 3.

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Lesion frequency distribution maps for 110 age-matched beta-amyloid negative (A) and positive (B) participants. C. Voxel-wise difference map between two groups. WMH lesions in highlighted areas are significantly more frequent in beta-amyloid positive participants compared to the negative group (FDR corrected p<0.05).

3.3. Associations between overall WMH lesion burden and cognitive performance

Two linear regression models were used to examine the association between normalized WMH volume and cognition. Model 1 included sex and level of education as covariates, and Model 2 added age to Model 1 (Table 2). WMH was significantly associated with global cognitive measures (ADAS13, p=0.025), episodic memory (RAVLT immediate, p=0.016), and executive function and processing speed (TMT B, p=0.019; TMT B minus A, p=0.027), even after controlling for age. We did not find an association between WMH lesion volume and visuospatial construction abilities or language performance in our cohort of cognitively normal older participants. The scatter plots in Figure 4 illustrate the association between WMH lesion burden and various cognitive tests. Additionally, mediation analysis was conducted to investigate the mediating role of WMH lesion burden on age-related cognitive decrement while controlling for sex and level of education. The global volume of WMH lesions significantly mediated the association between age and scores on the ADAS13, TMT B minus A, and RAVLT immediate recall tests (Supplementary Figure 2).

Table 2.

Linear regression model of the association between WMH volume and cognitive tests before and after adding age to the model.

MOCA ADAS13 TMT B-A RAVLT VCF VPF CDT
Model 1 B (p-value)
sex 1.30 (<0.001) −2.76 (<0.001) −0.27 (0.934) 7.51 (<0.001) 1.08 (0.038) 1.24 (0.006) 0.10 (0.069)
Level of education 0.8 (<0.001) −0.25 (0.004) −1.44 (0.035) 0.89 (<0.001) 0.53 (<0.001) 0.27 (0.005) 0.004 (0.693)
WMH Volume −0.68 (0.001) 4.31 (<0.001) 10.89 (<0.001) −3.32 (<0.001) −1.32 (0.002) −0.97 (0.008) −0.08 (0.054)
Model 2 B (p-value)
sex 1.12 (<0.001) −2.46 (<0.001) 2.11 (0.516) 6.81 (<0.001) 0.72 (0.163) 1.15 (0.012) 0.08 (0.163)
Level of education 0.29 (<0.001) −0.28 (0.001) −1.65 (0.015) 0.93 (<0.001) 0.56 (<0.001) 0.29 (0.003) 0.01 (0.585)
Age −0.08 (<0.001) 0.14 (<0.001) 1.05 (<0.001) −0.30 (<0.001) −0.16 (<0.001) −0.05 (0.173) −0.01 (0.040)
WMH Volume −0.30 (0.173) 0.82 (0.025) 6.30 (0.027) −2.03 (0.016) −0.63 (0.166) −0.73 (0.071) −0.04 (0.368)
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Figure 4.

Figure 4.

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Scatter plot of cognitive tests’ Z scores on tract disconnection Z score controlling for age, sex, and level of education.

The interaction of amyloid status on WMH lesion related cognitive decline was also assessed for each cognitive task. A significant interaction was seen for the relation between WMH lesion volume and amyloid status for TMT B minus A times (p=0.023) (Figure 5).

Figure 5.

Figure 5.

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Scatter plot of TMT B minus A times residual on WMH volume Z scores across beta-amyloid positive and negative groups.

3.4. Association between estimated WMH tract disconnection and cognitive performance

A higher WMH burden was associated with higher disconnection for all examined WM tracts (Supplementary Tables 1). After adjusting for age, sex, and level of education, the relationships between WM tract disconnection and cognitive scores, both before and after adding WMH lesion volume as a covariate are presented in Supplementary Tables 2 and 3. Scatter plots illustrating the Z scores of cognitive tests in relation to tract disconnection Z scores, while controlling for age, sex, and level of education, can be found in Supplementary Figures 3 and 4. Table 3 provides a summary of WM tract-specific disconnections that exhibited significant associations with each cognitive domain.

Table 3.

Tract disconnection predicting each cognitive performance test score using linear regression.

Cognitive test WM tract (FDR corrected p-value)
ADAS13 Right SLF (0.021), right FAT (0.021), left fornix (0.019)
TMT B - A Right ILF (0.041), left ILF (0.021), right IFOF (0.011), left IFOF (0.013), right SLF (0.019), right FAT (0.006), right AF (0.019), left UF (0.025), right CS (0.006), left CS (0.006)
RAVLT Right SLF (0.041), left UF (0.021)
VCF Right FAT (0.025)
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In addition, the moderation effect of amyloid status on the relationship between tract disconnection and cognitive measures was assessed. Amyloid positivity significantly moderated the correlation of TMT B minus A with right IFOF (p=0.028) and right UF tract disconnection (p=0.005).

3.5. Mediation effect of tract disconnection and WMH lesion burden on the age-related cognitive decrement

Mediation analyses were used in a serial manner to assess the effect of WMH lesion burden and tract disconnection on the age-related cognitive decrement. Sex and level of education were included as covariates in the analysis. In this model, both global WMH lesion burden and tract disconnection were treated as mediator variables within the same model (Figure 6). For every cognitive domain, 16 separate mediation analyses were conducted for each tract disconnection, and the significant outcomes are presented in Table 4. For the relationship between age and TMT B minus A scores, the mediating effects of WMH volume through disconnection in the right IFOF, right FAT, and right SLF tracts were significant. We found that WMH volume via disconnection in the left UF tract mediated the effect of age on the RAVLT immediate test and WMH volume through disconnection in the right FAT mediated the effect of WMH lesion volume on VCF test scores. After applying FDR correction across various tract disconnections within each cognitive test, the mediation effect of WMH lesion volume through right FAT and right SLF remained significant for the relationship between age and TMT B minus A scores.

Figure 6.

Figure 6.

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Mediation model of global WMH lesions and tract disconnection on age-related cognitive decrement.

Table 4.

Mediation effect of WMH burden and tract disconnection on age-related cognitive decrement.

Cognitive test Tract Total effect (P value) Direct effect (P value) Indirect effect (BootLLCI-BootULCI) Indirect effect (BootLLCI-BootULCI) Indirect effect (BootLLCI-BootULCI)
Age->WMH->Cognition Age->Tract->Cognition Age->WMH->Tract->Cognition
ADAS13 Left Fornix 0.0394 (<0.001) 0.0300 (<0.001) 0.0044 (−0.0021, 0.0117) 0.0017 (−0.0002, 0.0049) 0.0033 (0.0004, 0.0068) (p=0.022)
Right SLF 0.0383 (<0.001) 0.0306 (<0.001) −0.0002 (−0.0083, 0.0081) 0.0000 (−0.0022, 0.0025) 0.0078 (0.0023, 0.0145) (p=0.004) *
Right FAT 0.0381 (<0.001) 0.0315 (<0.001) 0.0002 (−0.0081, 0.0084) −0.0010 (−0.0035, 0.0012) 0.0075 (0.0018, 0.0140) (p=0.008)
TMT B - A Right IFOF 0.0413 (<0.001) 0.0328 (<0.001) −0.0007 (−0.0119, 0.0106) 0.0011 (−0.0006, 0.0039) 0.0082 (0.0003, 0.0170) (p=0.025)
Right SLF 0.0416 (<0.001) 0.0340 (<0.001) 0.0005 (−0.0088, 0.0097) 0.0001 (−0.0019, 0.0025) 0.0070 (0.0019, 0.0131) (p=0.007) *
Right FAT 0.0412 (<0.001) 0.0349 (<0.001) −0.0026 (−0.0116, 0.0069) −0.0012 (−0.042, 0.0018) 0.0101 (0.0035, 0.0175) (p=0.002) *
RAVLT Left UF −0.0385 (<0.001) −0.0286 (<0.001) −0.0051 (−0.0121, 0.0018) −0.0017 (−0.0041, 0.0001) −0.0031 (−0.0065, −0.0003) (p=0.024)
VCF Right FAT −0.0352 (<0.001) −0.0306 (<0.001) −0.0005 (−0.096, 0.0084) 0.0007 (−0.0009, 0.0024) −0.0048 (−0.0103, −0.0009) (p=0.031)
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Statistically significant results are bolded.

*

Significant p value after FDR correction for multiple comparisons.

Furthermore, another mediation analysis was used to assess the extent to which associations between WMH volume and decrements in specific cognitive domains were mediated by corresponding tract-specific disconnection. In this model age, sex, and level of education were included as covariates. For the relationship between WMH volume and TMT B minus A scores, the mediating effects of right IFOF, right FAT, and right SLF tract disconnection, for the relationship between WMH volume and RVLT immediate the mediating effects of left UF tract disconnection, and for the relationship between WMH volume and VCF scores the mediating effects of right FAT tract disconnection were significant (Supplementary Figure 5).

4. Discussion

In this study, we utilized the virtual lesion approach, an indirect method of estimating structural connectivity changes, to examine the relationship between WM tract disconnection caused by WMH and cognitive performance in cognitively intact older individuals from the ADNI dataset. We found a direct effect of WMH lesion burden on cognitive performance, as well as an indirect effect of WMH-induced tract disconnection on specific cognitive domain tests. Additionally, we observed tract-specific associations between WM disconnection and cognitive function, as well as a moderating effect of amyloid status on the relationship between structural connectivity changes and performance in certain cognitive domains, particularly executive function. Taken together, our findings suggest that some of the correlation between WMH lesions and cognitive performance can be attributed to the effect of lesions on WM tracts, and provide new evidence regarding the underlying mechanisms of WMH-related cognitive decline.

4.1. Advantages and limitations of the virtual lesion approach

In our prior study using the virtual lesion disconnectome approach, indirect mapping of structural connectivity showed a significantly stronger correlation with the functional connectivity of the older participants compared to the direct mapping of structural connectome using the older participants’ own DTI data (Li et al., 2021). This observation supports the assertion that the changes in diffusion properties within the WMH lesion and adjacent area can confound tracttracing algorithms (Ciccarelli et al., 2008; Muñoz Maniega et al., 2019; Reginold et al., 2016; Reginold et al., 2018).

Most clinical research studies of brain aging, and dementia include FLAIR MRI data that could be leveraged to elucidate brain-behavior relationships between WMH-based disconnection and cognitive performance, including interactions with clinical or demographic factors and comorbid pathologies such as Alzheimer’s pathophysiology. Although there is the potential for misregistration errors when applying virtual lesion masks onto normative data from younger individuals, we recently demonstrated, using DTI data from the HCP (Van Essen et al., 2013), that normative DTI data from young and older participants produce comparable disconnectome metrics. However, we concluded that the utilization of young, healthy participants’ diffusion MRI data for virtual lesion tractography was preferable over older participants due to their higher streamline counts and the absence of WMH lesions (Taghvaei et al., 2023).

A potential drawback of this method is that lesion masks serve as ROA in DTI tractography, meaning that any streamlines passing through the lesion are considered fully deleted. WMH lesions may partially and heterogeneously impact WM tracts that pass through them. Thus, this model likely results in an overestimation of true structural disconnection. However, most of the impacted WM pathways in our study were not fully disconnected by the lesion (ROA), and modeling partial instead of complete disconnection for streamlines passing through the lesion should only cause a scaling effect, rather than alter the spatial connectivity pattern (Wang et al., 2017). Nevertheless, this partial model fails to account for the non-uniform effects of WMH lesions on WM tracts.

4.2. Association of WMH lesion burden, tract disconnection, and cognitive performance

Multiple studies have suggested that WMH lesions as a marker of small vessel disease related to cognitive decline. In line with them, we found WMH lesions are particularly associated with performance decrements in processing speed, executive function, and episodic memory (Banerjee et al., 2018; Dong et al., 2015; Kloppenborg et al., 2014; Lampe et al., 2019; Staals et al., 2015; Sun et al., 2014; Van Den Berg et al., 2018; Zeng et al., 2020). We did not find an association between WMH lesions volume and verbal fluency or visuospatial construction in our cohort, which is consistent with the findings of a previous study (Kim et al., 2022). These findings might be due to the low variance of visuospatial construction and verbal fluency tests scores in our cohort of cognitively intact participants or the tests’ insufficient sensitivity to detect differences in these cognitive domains.

The correlation between WMH lesions’ location and specific cognitive performance was previously investigated in cognitively normal and impaired participants (Biesbroek et al., 2013; Lampe et al., 2019; Smith et al., 2011). Those studies found that WMH lesion burden in the frontal lobe was associated with a decline in executive function, while parieto-temporal lesion burden was associated with poor episodic memory performance (Biesbroek et al., 2013; Lampe et al., 2019; Zeng et al., 2020). More recent studies have demonstrated that connectivity and microstructural integrity of specific tract depends on the burden of WMH lesion within the tract (Hilal et al., 2021; Seiler et al., 2018; Vernooij et al., 2009). Together, these findings emphasize the importance of the spatial distribution of WMH regarding their effects on cognitive performance and suggest that the relationship between regional WMH lesions and different cognitive domains may be tract specific. Our current study confirms and extends these observations.

We found a significant correlation between disconnection in different tracts and performance on specific cognitive tests (Table 2). In general, right-side association tracts had a relation with processing speed and executive function, and left-side tracts were associated with language, verbal working, and episodic memory, with the exception of the right FAT. After correction for multiple comparisons, the correlation between left-side association tracts and language did not survive. While verbal working memory did not show a correlation with global WMH burden, right FAT tract disconnection was related to alterations in this domain (Supplementary Table 2). This tract-specific effect suggests that the specific spatial distribution of WMH lesion is more predictive of language performance decrement than global WMH lesion burden.

In the domain of executive function, disconnections in the right ILF and right IFOF were the strongest predictors of TMT A and TMT B functions. ILF supports visuomotor dexterity and fast visual processing (Voineskos et al., 2012), and IFOF supports goal-oriented behavior, visual switching tasks, and semantic language processing (Conner et al., 2018). The performance on TMT A is primarily influenced by visual processing speed. Conversely, TMT B is a more complex task, which involves alternating between numbers and letters in ascending order, necessitating the integration of additional cognitive abilities, including cognitive flexibility and visual switching ability. Right IFOF, right SLF, and right FAT disconnections all significantly mediated the global WMH lesion burden effect on TMT B minus A, which more specifically measures executive function. Our findings suggest that executive function decrements are linked to disconnections in multiple tracts rather than to a specific WM pathway, which is in line with previous studies (Haász et al., 2013; Kuznetsova et al., 2016). Timed task function and processing speed depend on the coordination of multiple brain regions and WMH lesions potentially can interfere with WM association fibers, causing a slowdown in neural transmissions (Kandiah et al., 2009; Vannorsdall et al., 2009). The roles of Right SLF and FAT on executive performance were previously described (Dick et al., 2019; Janelle et al., 2022).

In the language domain, prior to adjustments for multiple comparisons, we observed significant relationships between the VPF performance and left ILF and left IFOF. The involvement of these tracts in the processing of semantic language has been established (Conner et al., 2018; Mandonnet et al., 2007; Sierpowska et al., 2019). Of note, right FAT disconnection mediated the effect of WMH lesion on the verbal working memory test. While in general, left FAT supports speech production and verbal fluency, and right FAT supports executive function in right-handed individuals (Dick et al., 2019), there is some evidence suggesting the involvement of the right FAT in language function. One study found a relationship between speech fluency and the fractional anisotropy of the right FAT in children with autism (Chenausky et al., 2017), and impairments in white matter integrity of the right FAT have been observed in adults with stuttering (Neef et al., 2018). Furthermore, the right FAT has been implicated in recovery from speech deficits associated with left FAT lesions (Berthier et al., 2017; Young et al., 2020).

With regard to memory, left UF disconnection showed the strongest relationship with episodic memory (RAVLT immediate) and mediated the WMH-related memory decline. The UF supports associative and episodic memory functions (Von Der Heide et al., 2013) and changes in UF microstructure were found to predict cognitive performance in the memory (Metzler-Baddeley et al., 2011; Tariq et al., 2020). The ADAS13 cognitive test assesses multiple cognitive domains including memory, language, praxis, and orientation (Skinner et al., 2012). We found that the effect of WMH lesions burden on ADAS13 was mediated by the left fornix, right SLF, and right FAT disconnection. Fornix is well known to support episodic memory (Douet and Chang, 2015) and SLF plays a major role in language, memory, and visuospatial functioning (Janelle et al., 2022).

However, the mechanisms underlying the relationship between WMHs and cognitive decline remain to be fully elucidated. One proposed theory is that WMH lesions may promote neurodegeneration, resulting in a decline in cognitive function. Rizvi et al. found evidence to support this theory through their examination of cortical thinning as a mediator of the relationship between WMHs and cognition. They also demonstrated that medial temporal lobe atrophy volume mediates the relationship between WMH volume and memory functioning (Rizvi et al., 2018). Other research has shown that WMH-associated tract-specific injury is related to specific lobar gray matter atrophy, with the integrity of the associated WM tracts and gray matter volumes having a differential impact on episodic memory and processing speed (Seiler et al., 2018).

By demonstrating that specific tract disconnection mediated the effect of global WMH lesions on associated cognitive domains even in cognitively intact participants where the extent of cognitive decrements was small, our study provides additional support for the “disconnection hypothesis” that WMH lesions have a tract-specific effect on distinct cognitive domains. An alteration to a specific tract may result in the deterioration of a specific functional domain related to the regions that were connected by the tract. To ensure the robustness of our methodology we conducted mediation analysis across various cognitive domains for all tracts and did not find any unanticipated associations between tract disconnection and cognitive performance, such as the relation between the corticospinal tract disconnection and memory function. In future studies, it could be beneficial to use resting-state functional MRI (fMRI) to assess how region-specific cortico-cortical connectivity changes are influenced by subcortical structural connectivity changes. In addition, future work assessing connectivity changes due to WMH in data from participants with vascular cognitive impairment might be expected to yield even stronger correlations.

Studies by Boes et al. (Boes et al., 2015), Griffis et al. (Griffis et al., 2019), and Thiebaut de Schotten et al. (Thiebaut de Schotten et al., 2020) used resting-state functional MRI to examine connectivity changes due to focal infarcts. While this differs considerably from our focus on age associated diffuse WMH, some comparisons can still be made. Because functional connectivity considers connections between gray matter parcels, it captures second-order connections that may be important for functional recovery and are not assessed in our approach. However, Griffis et al. demonstrated that subcortical disconnections significantly mediated functional connectivity changes, which supports the validity of examining direct structural connectivity changes (Griffis et al., 2019). In our prior study using indirect tractography, we found a significant correlation between functional and structural connectivity due to diffuse WMH (Li et al., 2021). In contrast with the atlas-based approach used by Griffis et al., in the current study, we used 50 sets of individual diffusion MRI to capture individual variability in the structural connectome. Thiebaut de Shotten et al. developed statistical WM function maps for each cognitive task based on functional connectivity changes in a large dataset of patients with stroke lesions (Thiebaut de Schotten et al., 2020). Although they did not assess the function of each white matter tract separately, they provided statistical probability maps of the WM function for each cognitive task that are in agreement with our findings. As in our study, deficits in verbal fluency, verbal working memory, and executive function were associated with a white matter region compatible with the FAT while visuomotor function was associated with the SLF region.

Future research on the significance of SVD in dementia should encompass not only the total volume of WMH, but also consider their spatial distribution and the tract-wise effects of WMH. In this study we focused on connectivity changes in tracts intersecting with high-frequency WMH, but lower frequency WMH may also cause tract disconnection and connectivity may also be affected by microstructural changes in WM that occur prior to the manifestation of WMH on FLAIR MRI (Muñoz Maniega et al., 2019). Abnormalities in WM are believed to be among the initial changes in AD in its pre-dementia stages (Gunning-Dixon et al., 2009; Nasrabady et al., 2018). Hence, our findings may also provide insight into the early processes of cognitive decline associated with AD.

4.3. Amyloid effect

The primary pathologies of AD include Aβ plaques and neurofibrillary tangles that accumulate years before the onset of dementia (Thal et al., 2014). VCID and Alzheimer’s disease (AD) have traditionally been conceptualized as having different mechanisms, but there is mounting evidence of overlap between these categories (Iadecola, 2010; Sahathevan et al., 2012). Both VCI and AD share common risk factors such as hypertension, hyperlipidemia, and ApoE genotype, and there are well-documented mechanistic interactions between VCID and AD (Helzner et al., 2009; Petrovitch et al., 2005). WMH lesions have been found to be more strongly linked with cerebral Aβ accumulation than any other established biomarker or cognitive examination (Kandel et al., 2016). In this study, we observed that the frequency of periventricular WMH lesions was higher in Aβ positive participants and Aβ positive status significantly moderated the relationship of WMH lesion burden and tract disconnection with executive function performance. These findings provide further evidence for the synergistic effect of WMH lesions and Aβ deposition on the decrement of cognitive performance (Vemuri et al., 2015). A previous study on cognitively healthy populations also reported a significant relationship between Aβ with memory and executive function (Harrington et al., 2013; Hedden et al., 2013; Insel et al., 2020). A recent study on cognitively unimpaired adults showed that Aβ accumulation independently of tau pathology and cortical thickness was associated with worse performance on the executive tests, but not memory function (Tideman et al., 2022). They suggested that the underlying cause of the effect of Aβ on executive function could be due to either a direct regional effect of Aβ accumulation on functional networks such as the central executive network, or a disruption of the cholinergic neurotransmission system (Lim et al., 2015; Tideman et al., 2022). Our findings provide indirect support for the first of these potential mechanisms.

4.4. Limitation

There are additional limitations in our study that must be acknowledged, beyond the limitation of modeling virtual WMH lesions as causing complete disconnection mentioned previously. First, neuropsychological tests are not completely specific for each cognitive domain and some of our cognitive function tests employed in this study, particularly the clock drawing test, may not be sensitive enough to detect subtle variability in cognitively intact participants. Second, the findings derived from the ADNI database may not be generalized to the broader population, as the participants are typically healthy, predominantly white, and well-educated, which might not represent the full diversity of individuals found in the general population (Gianattasio et al., 2021).

Finally, the design of the current study, being cross-sectional, cannot establish causal relationships of the observed associations, though it stands to reason that WMH lesions are probably not caused by decrements in cognitive performance. To better understand these issues, further larger studies with longitudinal data are required to confirm our findings and establish the causal relationships between WMH-induced WM structural disconnections and corresponding cognitive function.

5. Conclusion

Using a virtual lesion approach to estimate disconnections in major white matter tracts due to white matter hyperintensities, we demonstrated that tract-specific disconnections mediate cognitive domain-specific decrements in cognitive performance in cognitively intact older adults. Amyloid status further moderated the effects of WMH lesions or tract disconnection on executive function.

Supplementary Material

1

Highlights:

  • Global WMH burden was associated with episodic memory and executive function.

  • Estimated disconnections due to WMH correlated to cognitive performance decrements.

  • The probability of periventricular WMH was higher in amyloid-positive subjects.

  • Amyloid status moderated the effect of tract disconnection on executive function.

6. Acknowledgment

This study was supported by NIH grants R21AG070434, P30AG072979, and U01AG052943. Dr. Shinohara receives consulting income from Octave Bioscience and compensation for scientific reviewing from the American Medical Association. His work is partially supported by NIH R01NS112274, R01MH112847, R01NS060910, and P41EB029460.

Data collection and sharing for this project was funded by the Alzheimer’s Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Department of Defense award number W81XWH-12-2-0012). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: AbbVie, Alzheimer’s Association; Alzheimer’s Drug Discovery Foundation; Araclon Biotech; BioClinica, Inc.; Biogen; Bristol-Myers Squibb Company; CereSpir, Inc.; Cogstate; Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; EuroImmun; F. Hoffmann-La Roche Ltd and its affiliated company Genentech, Inc.; Fujirebio; GE Healthcare; IXICO Ltd.; Janssen Alzheimer Immunotherapy Research & Development, LLC.; Johnson & Johnson Pharmaceutical Research & Development LLC.; Lumosity; Lundbeck; Merck & Co., Inc.; Meso Scale Diagnostics, LLC.; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging; Servier; Takeda Pharmaceutical Company; and Transition Therapeutics. The Canadian Institutes of Health Research is providing funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health (www.fnih.org). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer’s Therapeutic Research Institute at the University of Southern California. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California.

Diffusion MRI data used in the preparation of this article were obtained from the Lifespan human connectome project in aging. The HCP Lifespan project was supported by the National Institute on Aging of the National Institutes of Health under Award Number U01AG052564 and by funds provided by the McDonnell Center for Systems Neuroscience at Washington University in St. Louis. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.”

Abbreviations:

AD

Alzheimer’s Disease

ADAS13

Alzheimer’s Disease Assessment Scale-Cognitive 13

ADNI

Alzheimer’s Disease Neuroimaging Initiative

AF

Arcuate Fasciculus

ANTs

Advanced Normalization Tools

CDT

Clock Drawing Test

CSF

Cerebrospinal Fluid

CST

Corticospinal Tract

DTI

Diffusion Tensor Imaging

FAT

Frontal Aslant Tract

FDR

False Discovery Rate

FLAIR

Fluid-attenuated Inversion Recovery

HCP

Human Connectome Project

IFOF

Inferior Fronto-occipital Fasciculus

ILF

Inferior Longitudinal Fasciculus

MCI

Mild Cognitive Impairment

MMSE

Mini-Mental Status Examination

MNI

Montreal Neurological Institute

MoCA

Montreal Cognitive Assessment

MRI

Magnetic Resonance Imaging

PET

Positron Emission Tomography

QA

Quantitative Anisotropy

QSDR

Q-space Diffeomorphic Reconstruction

RAVLT

Rey Auditory Verbal Learning Test

ROA

Region of Avoidance

SD

Standard Deviation

SDF

Spin Distribution Function

SLF

Superior Longitudinal Fasciculus

SPSS

Statistical Package for the Social Sciences

SUVR

Standardized Uptake Value Ratio

SVD

Cerebral Small Vessel Disease

TE

Time to Echo

TI

Inversion Time

TMT A

Trial Making Test A

TMT B

Trial Making Test B

TR

Repetition Time

UF

Uncinate Fasciculus

VCF

Verbal Category Fluency

VCID

Vascular Cognitive Impairment and Dementia

VPF

Verbal Phonemic Fluency

WM

White Matter

WMH

White Matter Hyperintensity

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.

Credit author statement:

Mohammad Taghvaei: Formal analysis, Investigation, Data Curation, Writing - Original Draft, Visualization. Dawn J. Mechanic-Hamilton: Supervision, Resources, Writing - Review & Editing. Shokufeh Sadaghiani: Investigation, Data Curation. Banafsheh Shakibajahromi: Investigation, Data Curation. Sudipto Dolui: Methodology, Writing - Review & Editing. Sandhitsu Das: Validation, Resources, Writing - Review & Editing. Christopher Brown: Methodology, Data Curation, Writing - Review & Editing. William Tackett: Software. Pulkit Khandelwal: Software. Philip Cook: Methodology, Validation. Russell T. Shinohara: Formal analysis. Paul Yushkevich: Validation, Resources, Writing - Review & Editing. Danielle S. Bassett: Resources, Writing - Review & Editing. David A. Wolk: Supervision, Resources, Writing - Review & Editing. John A. Detre: Conceptualization, Resources, Writing - Review & Editing, Supervision, Project administration, Funding acquisition.

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