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. 2024 May 3;102(10):e209387. doi: 10.1212/WNL.0000000000209387

Modulation of the Association Between Corticospinal Tract Damage and Outcome After Stroke by White Matter Hyperintensities

Jennifer K Ferris 1, Bethany P Lo 1, Giuseppe Barisano 1, Amy Brodtmann 1, Cathrin M Buetefisch 1, Adriana B Conforto 1, Miranda R Donnelly 1, Natalia Egorova-Brumley 1, Kathryn S Hayward 1, Mohamed Salah Khlif 1, Kate P Revill 1, Artemis Zavaliangos-Petropulu 1, Lara Boyd 1, Sook-Lei Liew 1,✉
PMCID: PMC11196095  PMID: 38701386

Abstract

Background and Objectives

Motor outcomes after stroke relate to corticospinal tract (CST) damage. The brain leverages surviving neural pathways to compensate for CST damage and mediate motor recovery. Thus, concurrent age-related damage from white matter hyperintensities (WMHs) might affect neurologic capacity for recovery after CST injury. The role of WMHs in post-stroke motor outcomes is unclear. In this study, we evaluated whether WMHs modulate the relationship between CST damage and post-stroke motor outcomes.

Methods

We used data from the multisite ENIGMA Stroke Recovery Working Group with T1 and T2/fluid-attenuated inversion recovery imaging. CST damage was indexed with weighted CST lesion load (CST-LL). WMH volumes were extracted with Freesurfer's SAMSEG. Mixed-effects beta-regression models were fit to test the impact of CST-LL, WMH volume, and their interaction on motor impairment, controlling for age, days after stroke, and stroke volume.

Results

A total of 223 individuals were included. WMH volume related to motor impairment above and beyond CST-LL (β = 0.178, 95% CI 0.025–0.331, p = 0.022). Relationships varied by WMH severity (mild vs moderate-severe). In individuals with mild WMHs, motor impairment related to CST-LL (β = 0.888, 95% CI 0.604–1.172, p < 0.001) with a CST-LL × WMH interaction (β = −0.211, 95% CI −0.340 to −0.026, p = 0.026). In individuals with moderate-severe WMHs, motor impairment related to WMH volume (β = 0.299, 95% CI 0.008–0.590, p = 0.044), but did not significantly relate to CST-LL or a CST-LL × WMH interaction.

Discussion

WMHs relate to motor outcomes after stroke and modify relationships between motor impairment and CST damage. WMH-related damage may be under-recognized in stroke research as a factor contributing to variability in motor outcomes. Our findings emphasize the importance of brain structural reserve in motor outcomes after brain injury.

Introduction

Upper extremity motor impairment is one of the most common consequences of stroke1 and typically results in long-term disability.2 The degree of damage to the corticospinal tract (CST) relates strongly to motor impairment after stroke,3,4 indicating a primary insult to the motor system. However, motor recovery after stroke is variable even after accounting for CST damage.5 Recovery after stroke is likely mediated by compensation of surviving neural substrate.6 This suggests that the integrity of structures beyond the CST might be prognostic of motor recovery7,8 because overall brain health may be important in explaining why 2 individuals with similar stroke lesions can experience very different trajectories of recovery.9

White matter hyperintensities (WMHs) of presumed vascular origin are the most common form of age-related cerebrovascular damage.10 They are present in more than half of people older than 60 years.11 Individuals with WMHs are more likely to experience a stroke12 in part because of common cardiometabolic risk factors between WMHs and stroke.13 There is growing evidence that WMHs can also affect functional outcomes after stroke.14 The relationship between WMHs and post-stroke cognitive impairment has been well established14; however, there have been few investigations of the specific impact of WMHs on motor outcomes after stroke. WMHs modulate relationships between stroke lesion volume and overall functional outcome.15,16 Motor outcomes after stroke may similarly be modulated by concurrent WMHs because of the widespread impacts of WMHs on cerebral networks,17,18 which may create preexisting damage in compensatory pathways and, therefore, decrease the brain's capacity for motor recovery.

We tested whether the relationship between post-stroke motor impairment and CST damage is affected by concurrent WMH damage, controlling for age, time after stroke, and stroke lesion volume. We hypothesized that the relationship between motor impairment and CST damage would be attenuated in individuals with higher WMH volumes, indicating a greater influence of concurrent WMHs on motor outcomes after stroke.

Methods

Study Data

This study used cross-sectional multisite data from the ENIGMA Stroke Recovery Working Group.19 The ENIGMA Stroke Recovery Working Group is an international consortium that pools and harmonizes retrospective stroke data for large-scale analyses of brain-behavior relationships after stroke. The core ENIGMA Center is based at the University of Southern California. To be eligible for inclusion in the ENIGMA Stroke Recovery Database, contributing sites need to provide T1-weighted anatomic scans and at least 1 behavioral measure of post-stroke outcomes for their cohort. Data for this analysis were frozen on March 17, 2023. The inclusion criteria for selection of participants from the ENIGMA database for this analysis were (1) individuals with stroke where imaging and behavioral assessments occurred >7 days after stroke, (2) availability of T1-weighted MRI scans to index stroke lesions and either fluid-attenuated inversion recovery (FLAIR) or T2-weighted scans to index WMHs, and (3) availability of a sensorimotor outcome measure (e.g., Fugl-Meyer Assessment, Action Research Arm Test, and Wolf Motor Function Test). We extracted the following demographic information: age, sex, and days after stroke. This study comprised a cohort of individuals in the subacute and chronic phases of recovery after stroke. Stroke chronicity was defined as early subacute phase of recovery >7 and ≤90 days after stroke, late subacute phase >90 and ≤180 days after stroke, and chronic phase >180 days after stroke.20

Standard Protocol Approvals, Registrations, and Patient Consents

Ethics approval was obtained from the local research ethics board of each contributing site. Written informed consent was obtained from all study participants in accordance with the Declaration of Helsinki. This study protocol was reviewed and approved by members of the ENIGMA Stroke Recovery Working Group.

Motor Impairment Score

Sensorimotor outcome measures were harmonized across study cohorts with a motor outcome score, consistent with previous ENIGMA publications.9,21 Harmonized motor outcome scores were calculated as a proportion of the total possible score for each sensorimotor scale. In this analysis, 0% indicates no sensorimotor impairment and 100% indicates maximum sensorimotor impairment. For example, if someone scored a 33 on the Fugl-Meyer Upper-Extremity Assessment (where the maximum value is 66, and higher numbers indicate less motor impairment), their harmonized motor score would be 50%. For simplicity, we refer to this harmonized score as “motor impairment.”

Lesion Analysis

MRI processing was conducted with tools from FSL (v.6.0.5) and Freesurfer (v.7.2). Stroke lesions were manually traced on T1-weighted scans by trained researchers following established ENIGMA lesion tracing protocols.22,23 WMH damage was operationalized by whole-brain WMH volume. WMH volumes were segmented with Freesurfer's SAMSEG, which we previously established has robust performance in multisite data from individuals with stroke.24 Linearly co-registered T1 and FLAIR/T2 scans were used as inputs into SAMSEG, and a 0.1 probability threshold was applied to tissue segmentation. We subtracted stroke lesion masks from segmented WMHs to prevent any misclassification of stroke lesions as WMHs. Fazekas scores were visually rated for each scan by a neuroradiologist.25

CST damage was operationalized by CST lesion load (CST-LL), an index of the degree of overlap between the CST and the stroke lesion.3 CST-LL is a biomarker of post-stroke motor outcomes.4 We calculated weighted CST-LL from stroke lesion masks. T1 images were nonlinearly registered to MNI152 space using FSL's FNIRT.26 To improve nonlinear registration, we performed enantiomorphic normalization of the stroke lesion, by filling the stroke lesion with a copy of intact cerebral tissue from the opposite cerebral hemisphere.27,28 WMH masks were incorporated as a cost-function mask, down-weighting their influence on nonlinear registration.28 Stroke lesion masks were transformed to atlas space and overlaid onto CST derived from the JHU white matter atlas. Weighted CST-LL was calculated as a sum of the cross-sectional area of overlap between the stroke lesion and the CST, weighted by the maximum cross-sectional area of the CST.3 This metric quantifies the degree of injury to the CST and accounts for the narrowing of the CST at the internal capsule, with higher CST-LL indicating more extensive overlap of the stroke lesion with the CST.

Statistical Analysis

Statistical analyses were conducted in R (v.4.3.1). For our primary analysis, we tested the effects of WMH volume, CST-LL, and their interaction on motor impairment with a beta regression model. Beta regression was well suited to our data because motor impairment scores were heavily right skewed and are a bounded proportion (from [0, 1]).29,30 Beta regression is designed to handle data that follows a beta distribution (heavily right-skewed),29,30 and therefore, we did not need to transform our data to fit a normal distribution as with traditional linear regression. We tested assumptions of beta distribution fit with the “fitdistrplus” package. We fit mixed-effects beta regression models with the “glmmTMB” package.31 We tested the effects of WMH volume, CST-LL, and their interaction on motor impairment, with age, days after stroke, and whole-brain stroke lesion volume as covariates and random slopes to control for study site. In line with recommendations from the Canadian Institutes for Health Research Sex and Gender-based Analysis policy,32 we conducted a supplementary analysis to explore whether relationships with motor impairment varied by sex. In this supplementary analysis, we included sex as an interaction term with CST-LL and WMH volume.

For our secondary analysis, we tested whether WMH severity modifies relationships between motor impairment, CST-LL, and WMH volume. We stratified the sample into “mild” and “moderate-severe” WMH subgroups with a cutoff for moderate-severe of ≥10 mL WMH volume. This cutoff corresponds to a moderate Fazekas severity rating (periventricular or deep WMH Fazekas score of 2)33 and is a posited threshold for WMH-related neurologic changes.34 We ran beta regression models within each subgroup. The subgroups were underpowered to fit random effects by site; therefore, these beta regression models were fit without random effects.

Data Availability

Anonymized data not published within this article will be made available by request from any qualified investigator.

Results

Data from 223 individuals with stroke (82 female and 141 male patients) from 7 contributing research sites across 4 countries met our study inclusion criteria (median [interquartile range] age: 67 [58–75]; days post-stroke: 147 [92–1,300]; motor impairment: 5% [0%–27%]; stroke volume [in milliliters]: 2.2 [0.6–16.5]; WMH volume [in milliliters]: 5.3 [1.8–11.7]). In terms of chronicity, 24% of the sample (n = 54) were in the early subacute phase of recovery (>7 and ≤90 days after stroke), 27% (n = 60) in the late subacute phase (>90 and ≤180 days after stroke), and 49% (n = 109) in the chronic phase of recovery (>180 days after stroke).20 The primary sensorimotor assessment used by each study site is included in eTable 1, along with a summary of participant characteristics by site.

WMH volumes were significantly related to Fazekas scores (β = 0.106, p < 0.001; eFigure 1), indicating that SAMSEG accurately estimated WMH volumes.

Higher CST-LL and WMH Volume Relate to Worse Motor Impairment After Stroke

We tested our hypothesis that CST-LL, WMH volume, and their interaction relate to motor impairment with mixed-effects beta regression, controlling for age, days after stroke, stroke lesion volume, and research site (as a random effect). Greater CST-LL and larger WMH volumes were related to more severe motor impairment, holding all other factors constant (CST-LL: β = 0.812, p < 0.001; WMH volume: β = 0.178, p = 0.022; Table 1, Figure 1). Age, days after stroke, and stroke volume also significantly related to motor impairment (Table 1). The interaction term between CST-LL and WMH volume was not significant (β = −0.115, p = 0.265). Variance inflation factors (VIFs) in our model were all <1.5, indicating no evidence of significant predictor collinearity. There was no significant interaction effects with sex, indicating that relationships between motor impairment and CST-LL or WMH volume did not vary by sex (eTable 2).

Table 1.

Relationships Between Motor Impairment and Stroke/WMHs

Predictor Estimate SE p Value
Fixed
 Age −0.332 0.076 <0.001
 Days after stroke 0.335 0.084 <0.001
 Stroke volume −0.228 0.103 0.027
 CST lesion load 0.812 0.241 0.001
 WMH volume 0.178 0.078 0.022
 CST lesion load:WMH volume −0.115 0.103 0.265
Random
 σ2 0.469
 Nsite 7
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Abbreviations: CST = corticospinal tract; WMH = white matter hyperintensity.

Summary statistics and standardized parameter estimates from mixed-effects beta regression models, with harmonized motor impairment score as the outcome measure.

Figure 1. Relationships Between Motor Impairment and Stroke/WMHs.

Figure 1

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Motor impairment by CST lesion load (A) or WMH volumes (in milliliters; B). Plots present beta regression line (solid), SE (shaded), and parameter estimates (text). CST = corticospinal tract; WMH = white matter hyperintensity.

WMH Severity Modifies Relationships Between Motor Impairment and CST-LL/WMHs

There were 162 individuals with mild WMHs and 61 individuals with moderate-severe WMHs in our sample. Wilcoxon rank-sum tests revealed that individuals with mild WMHs were younger than individuals with moderate-severe WMHs (W = 2,824, p < 0.001), but there were no differences between groups in CST-LL (W = 4,805, p = 0.748), stroke lesion volume (W = 5,097, p = 0.717), or severity of motor impairment (W = 4,729, p = 0.618) (Figure 2).

Figure 2. Differences in Stroke Characteristics Between Mild and Moderate-Severe WMH Subgroups.

Figure 2

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Group differences in WMH subgroup stroke characteristics (light blue = mild WMHs, dark blue = moderate-severe WMHs). Individual data points are plotted against the median and IQR of the data range (solid circles and line) and the density of the data distribution (half-violin plot). CST = corticospinal tract; IQR = interquartile range; WMH = white matter hyperintensity.

In individuals with mild WMHs, motor impairment was significantly related to CST-LL (β = 0.888, p < 0.001), with a significant CST-LL × WMH volume interaction (β = −0.211, 0.026) indicating individuals with smaller WMH volumes had a stronger relationship between CST-LL and motor impairment (Table 2 and eFigures 2 and 3). In individuals with moderate-severe WMHs, motor impairment related to WMH volume (β = 0.299, p = 0.044), but not CST-LL (β = 0.332, p = 0.120), with no significant CST-LL × WMH volume interaction (Table 2). VIFs were all <1.8 across both WMH severity models, indicating no evidence of significant predictor collinearity. Figure 3 plots relationships between motor impairment and CST-LL for each WMH severity subgroup.

Table 2.

Relationships Between Motor Impairment and Stroke/WMHs, Stratified by WMH Severity

Predictor Mild WMHs Moderate-severe WMHs
Estimate SE p Value Estimate SE p Value
Age −0.296 0.084 <0.001 −0.278 0.153 0.070
Days after stroke 0.405 0.090 <0.001 0.567 0.165 0.001
Stroke volume −0.194 0.111 0.081 −0.088 0.184 0.633
CST lesion load 0.888 0.145 <0.001 0.332 0.213 0.120
WMH volume 0.057 0.086 0.503 0.299 0.149 0.044
CST lesion load:WMH volume −0.211 0.095 0.026 0.079 0.200 0.692
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Abbreviations: CST = corticospinal tract; WMH = white matter hyperintensity.

Summary statistics and standardized parameter estimates from beta regression models, with harmonized motor impairment score as the outcome measure.

Figure 3. Relationships Between Motor Impairment and CST Lesion Load Stratified by WMH Severity.

Figure 3

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Motor impairment by CST lesion load, stratified by WMH severity (light blue = mild WMHs, dark blue = moderate-severe WMHs). Plots present beta regression line (solid), SE (shaded), and parameter estimates (text) for stratified models. CST = corticospinal tract; WMH = white matter hyperintensity.

Discussion

In this study, WMH volume related to post-stroke motor impairment over and above CST-LL and stroke volume. WMH severity was an effect modifier35 of CST-LL and motor impairment relationships, meaning the relationship between motor impairment and CST-LL varies in subgroups stratified by WMH severity. In individuals with mild WMHs, there was a significant interaction between CST-LL and WMH volume, such that the relationship between motor impairment and CST-LL was attenuated with larger WMH volumes. In individuals with moderate-severe WMHs, motor impairment related to WMH volume and did not significantly relate to CST-LL. Our findings provide preliminary cross-sectional evidence that WMHs may be an important consideration in building prognostic neurologic models of stroke recovery, especially for individuals with existing moderate-to-severe WMHs.

It is important to contextualize our findings with the existing literature on the CST damage and post-stroke motor outcomes. First, regardless of statistical significance, the effect sizes of CST-LL parameter estimates were greater than those for WMH volume across all tested models. This underscores the importance of stroke-related CST damage as a principle explanatory variable of motor impairment after stroke (see consensus statement from Boyd et al., 20174). Second, our mild and moderate-severe WMH groups did not vary in severity of motor impairment. Therefore, it was not the case that individuals with moderate-severe WMHs (exceeding 10 mL) had worse motor outcomes than individuals with mild WMHs. Rather, we saw that with increased WMH severity, WMH explained greater variability in motor impairment and CST-LL explained less variability in motor impairment. This suggests that WMH severity is an effect modifier of lesion-behavior relationships, not an interactive factor.35 In other words, while WMH volume related to motor impairment across the whole sample, CST-LL and WMH did not have joint synergistic effects on motor impairment. Instead, individuals with moderate-severe WMHs may represent a neurologic subgroup where concurrent age-related cerebrovascular damage has greater explanatory power in post-stroke motor outcomes.

Very few studies to date have examined the impact of WMHs on motor systems, despite the well-known impact of WMHs on widespread white matter networks and connected cortical regions (for review, see Ter Telgte et al., 201836). Research on the impact of WMHs on post-stroke motor outcomes has been equivocal. WMH volume in chronic stroke related to Wolf Motor Function Score in 2 previous reports.37,38 WMH severity in acute stroke consistently relates to total Functional Independence Measure (FIM) score, but the motor subscale of FIM related to WMH severity in some reports,39 but not in others.40,41 Our study considers the combined effects of WMHs and CST damage on post-stroke motor outcome. Our results align with a previous report that individuals with moderate-severe WMHs had an attenuated relationship between stroke lesion volume and overall stroke severity as measured by the NIH Stroke Scale.15

This study contributes important cross-sectional evidence for the impact of WMHs on motor outcomes after stroke. Future research should extend upon these findings using longitudinal designs to test the effects of concurrent WMHs on trajectories of motor recovery. The interaction between WMHs and stroke lesion location could also be evaluated in relationship to somatosensory deficits after stroke as stroke lesion location also relates to proprioceptive outcomes.42 The strengths of our study are the large heterogeneous sample of individuals with stroke and the use of robust methodologies to extract candidate neuroimaging predictors. Although data for this study came from different sites, manual stroke lesion drawings were performed at a single center with standardized protocols.23 Moreover, we have previously demonstrated that our automated WMH segmentation protocol is robust across multiple sites in individuals with stroke.24 Future work could make use of advanced imaging metrics such as diffusion tensor imaging to capture more sensitive quantitative information about the white matter structure in cerebrovascular disease43 and provide further insight into specific damage in patients with poor recovery.8 A limitation of the multisite and secondary nature of our study sample is that we had limited availability of additional covariates that may influence WMH severity and stroke outcomes, including cardiometabolic risk factors such as diabetes and hypertension13 and demographic characteristics such as socioeconomic status and race/ethnicity. Another limitation is that our sample had mild motor impairment overall (median impairment of 5%), which is typical of neuroimaging samples of motor impairment after stroke. However, this limited our ability to test for specific imaging markers of severe upper extremity impairment,44 a patient subgroup where neuroimaging biomarkers may have the greatest benefit for prognostication of recovery.8

In conclusion, our results suggest that WMHs are an under-recognized factor in stroke motor recovery research. WMHs explained variability in motor impairment over and above stroke lesion volume and CST damage. Furthermore, WMH severity might define neurologic subtypes, wherein structural brain reserve has more explanatory power and CST damage has less explanatory power for individuals with extensive preexisting damage to cerebral white matter. Our results will need to be replicated in longitudinal studies to assess the impact and causality of WMHs and CST damage on motor recovery after stroke. The structural reserve of the brain before a stroke injury is increasingly recognized as an important element predicting capacity for motor recovery.9 Including WMHs in motor recovery research could advance models of neurologic recovery by accounting for the full spectrum of cerebrovascular damage in the brain.

Glossary

CST

corticospinal tract

CST-LL

CST lesion load

FIM

functional independence measure

FLAIR

fluid-attenuated inversion recovery

VIF

variance inflation factor

WMH

white matter hyperintensity

Appendix. Authors

Name Location Contribution
Jennifer K. Ferris, MSc, PhD Gerontology Research Centre, Simon Fraser University; Department of Physical Therapy and Djavad Mowafaghian Centre for Brain Health, University of British Columbia, Vancouver, Canada Drafting/revision of the manuscript for content, including medical writing for content; major role in the acquisition of data; study concept or design; analysis or interpretation of data
Bethany P. Lo, BSc Chan Division of Occupational Science and Occupational Therapy, University of Southern California, Los Angeles Drafting/revision of the manuscript for content, including medical writing for content; analysis or interpretation of data
Giuseppe Barisano, MD, PhD Department of Neurosurgery, Stanford School of Medicine, Stanford University, CA Drafting/revision of the manuscript for content, including medical writing for content; analysis or interpretation of data
Amy Brodtmann, MBBS, PhD, FRACP Central Clinical School, Monash University, Melbourne; Department of Medicine, Royal Melbourne Hospital, University of Melbourne, Victoria, Australia Drafting/revision of the manuscript for content, including medical writing for content; major role in the acquisition of data
Cathrin M. Buetefisch, MD, PhD Department of Neurology, Department of Rehabilitation Medicine, and Department of Radiology, Emory University, Atlanta, GA Drafting/revision of the manuscript for content, including medical writing for content; major role in the acquisition of data
Adriana B. Conforto Hospital das Clinicas HCFMUSP, Faculdade de Medicina, Universidade de São Paulo; Hospital Israelita Albert Einstein, São Paulo, Brazil Drafting/revision of the manuscript for content, including medical writing for content; major role in the acquisition of data
Miranda R. Donnelly, MS Chan Division of Occupational Science and Occupational Therapy, University of Southern California, Los Angeles Drafting/revision of the manuscript for content, including medical writing for content; analysis or interpretation of data
Natalia Egorova-Brumley, PhD Melbourne School of Psychological Sciences, University of Melbourne, Victoria, Australia Drafting/revision of the manuscript for content, including medical writing for content; major role in the acquisition of data
Kathryn S. Hayward Departments of Physiotherapy, Medicine (RMH) & The Florey Institute of Neuroscience and Mental Health, University of Melbourne, Victoria, Australia Drafting/revision of the manuscript for content, including medical writing for content; analysis or interpretation of data
Mohamed Salah Khlif, PhD Central Clinical School, Monash University, Melbourne, Victoria, Australia Drafting/revision of the manuscript for content, including medical writing for content; major role in the acquisition of data
Kate P. Revill, PhD Facility for Education and Research in Neuroscience, Emory University, Atlanta, GA Drafting/revision of the manuscript for content, including medical writing for content; major role in the acquisition of data
Artemis Zavaliangos-Petropulu, PhD Brain Mapping Center, Department of Neurology, Geffen School of Medicine, University of California Los Angeles Drafting/revision of the manuscript for content, including medical writing for content; analysis or interpretation of data
Lara Boyd, PT, PhD Mark and Mary Stevens Neuroimaging and Informatics Institute and Keck School of Medicine, University of Southern California, Los Angeles Drafting/revision of the manuscript for content, including medical writing for content; major role in the acquisition of data
Sook-Lei Liew, PhD, OTR/L, FAOTA Chan Division of Occupational Science and Occupational Therapy, and Mark and Mary Stevens Neuroimaging and Informatics Institute and Keck School of Medicine, University of Southern California, Los Angeles Drafting/revision of the manuscript for content, including medical writing for content; major role in the acquisition of data; study concept or design; analysis or interpretation of data
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Study Funding

J.K. Ferris receives salary support from the Canadian Institutes of Health Research (CIHR) and Michael Smith Health Research BC (HSIF-2022-2990). This research was funded by the following granting agencies: Australian Heart Foundation Future Leader Fellowships (PI Brodtmann: 104748 and 100784; PI Hayward: 106607), Canadian Institutes of Health Research (PI Boyd: PTJ-148535, MOP-130269, MOP-106651), Hospital Israelita Albert Einstein (PI Conforto: 2250-14), National Health and Medical Research Council (PI Brodtmann: GNT1020526 GNT1094974 GNT1045617; PI Hayward: 2016420), and NIH (PI Butefisch: R21HD067906; R01NS090677; PI Conforto: R01NS076348-01; PI Liew: R01NS115845; PI Revill: R01NS090677).

Disclosure

A. Conforto was a consultant for Boehringer Ingelheim in 2021. Go to Neurology.org/N for full disclosures.

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