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Normal-Appearing White Matter Integrity Is a Predictor of Outcome After Ischemic Stroke

Originally published 2020;51:449–456


Background and Purpose—

The aim of the present study was to evaluate the relationship between normal-appearing white matter (NAWM) integrity and postischemic stroke recovery in 4 main domains including cognition, mood, gait, and dependency.


A prospective study was conducted, including patients diagnosed for an ischemic supratentorial stroke on a 3T brain MRI performed 24 to 72 hours after symptom onset. Clinical assessment 1 year after stroke included a Montreal Cognitive Assessment, an Isaacs set test, a Zazzo cancelation task, a Hospital Anxiety and Depression scale, a 10-meter walking test, and a modified Rankin Scale (mRS). Diffusion tensor imaging parameters in the NAWM were computed using FMRIB (Functional Magnetic Resonance Imaging of the Brain) Diffusion Toolbox. The relationships between mean NAWM diffusion tensor imaging parameters and the clinical scores were assessed using linear and ordinal regression analyses, including the volumes of white matter hyperintensities, gray matter, and ischemic stroke as radiological covariates.


Two hundred seven subjects were included (66±13 years old; 67% men; median National Institutes of Health Stroke Scale score, 3; interquartile range, 2–6). In the models including only radiological variables, NAWM fractional anisotropy was associated with the mRS and the cognitive scores. After adjusting for demographic confounders, NAWM fractional anisotropy remained a significant predictor of mRS (β=−0.24; P=0.04). Additional path analysis showed that NAWM fractional anisotropy had a direct effect on mRS (β=−0.241; P=0.001) and a less important indirect effect mediating white matter hyperintensity burden. Similar results were found with mean diffusivity, axial diffusivity, and radial diffusivity. In further subgroup analyses, a relationship between NAWM integrity in widespread white matter tracts, mRS, and Isaacs set test was found in right hemispheric strokes.


NAWM diffusion tensor imaging parameters measured early after an ischemic stroke are independent predictors of functional outcome and may be additional markers to include in studies evaluating poststroke recovery.


See related article, p 369

Stroke is a major source of cognitive impairment and functional disability. The severity of white matter hyperintensities (WMHs) and brain atrophy are the main brain imaging markers reported to be associated with poststroke outcome.1–6 Besides WMH, it is well known that normal-appearing white matter (NAWM) can be affected by microstructural changes that might contribute to impair the normal brain functioning. However, the effect of NAWM has been sparsely investigated in poststroke recovery studies,7–9 while NAWM might be a relevant area of cerebral plasticity. Indeed, the relationship between NAWM integrity, cognition, and motor function has been largely reported during aging.10–12 It has been described that altered NAWM integrity was associated with physical and leisure activities, together with better efficiency in information processing10,11 and gait performances.13 The alteration of NAWM integrity can be investigated using diffusion tensor imaging (DTI), by the detection of a decreases fractional anisotropy (FA) and increased mean diffusivity (MD).14 Maillard et al15 reported that lower FA in NAWM was an independent marker for increased risk of conversion from normal white matter to WMH, suggesting a continuous process of white matter degradation including demyelination and axonal loss due to chronic ischemic vascular processes. In patients with stroke, a relationship between diffusivity parameters of NAWM and 90-day functional recovery, along with cognitive recovery after 1 year, has been suggested.7–9 Similarly, in subjects with cerebral small vessel disease, a loss of NAWM integrity has been associated with lower gait performances.16 All this suggests a specific role of NAWM integrity in neuropsychological, gait, and functional processes after stroke, but data remain scarce in the field of stroke.

The aim of the present study was to evaluate the predictive value of NAWM integrity, assessed on a brain MRI performed 24 to 72 hours after an ischemic stroke, on the main domains of poststroke recovery, that is, cognition, mood, gait, and dependency, regardless of the other radiological markers of vascular and degenerative processes, along with the new ischemic lesion.


The authors declare that all supporting data are available within the article and the online-only Data Supplement.

Study Population and Ethics Statement

Study population covered participants from the Brain Before Stroke study—a biomedical research protocol promoted by the Bordeaux University Hospital and aiming at evaluating the influence of the cerebral parenchyma surrounding the new ischemic lesion on outcome. All patients or their legal representative provided a written informed consent. The study was approved by the regional French Human Protection Committee (CPP 2012/19 2012-A00190-43). Patients were prospectively and consecutively recruited from June 2012 to February 2015 at the Bordeaux University Hospital. Inclusion criteria were an age >18 years, an ischemic supratentorial stroke diagnosed within 24 to 72 hours after symptom onset (baseline), a National Institutes of Health Stroke Scale (NIHSS) score comprised between 1 and 25, and the absence of prestroke dementia or prestroke disability due to a neurological disease resulting in a modified Rankin Scale (mRS) score ≥1. Exclusion criteria are listed in the online-only Data Supplement.

Clinical Assessment

Prestroke cognitive state was evaluated with the Informant Questionnaire in Cognitive Decline in the Elderly17 submitted to a relative of the patient. Clinical assessment was achieved at 1 year poststroke during a medical visit. A Montreal Cognitive Assessment (MoCA) was performed to evaluate global cognition on 30 points.18 An Isaacs set test (IST) was performed for the evaluation of executive functions19 and a Zazzo cancelation task (ZCT) for the evaluation of processing speed and attention.20 The time to perform the task (in seconds) and the number of errors were recorded. Mood was evaluated using the Hospital Anxiety and Depression (HAD) scale. Gait was evaluated by walking speed, using the 10-meter walk test (10-MWT).21 Contrary to MoCA and IST, where higher scores meant better performances, higher scores in ZCT, HAD, and 10-MWT meant lower performances. Functional outcome at 1 year was assessed by the mRS.

Imaging Acquisition and Processing

A 3T brain MRI was performed at baseline (General Electric Medical Systems Discovery MR750W, Milwaukee, WI). The sequences acquired are detailed in the online-only Data Supplement, as imaging pre- and post-processing. Briefly, a mask of ischemic stroke lesions and WMH was obtained for each subject using the 3D Slicer 4.3.1 software (Figure 1). Maps of gray matter (GM), white matter, and cerebrospinal fluid were generated based on a voxel-by-voxel intensity analysis (SPM12, MATLAB R2012b). The sum of these 3 tissue classes provided the total intracranial volume. In the following analyses, GM, ischemic stroke lesion, and WMH volumes were expressed as the ratio of total intracranial volume. Subsequently, the masks of ischemic stroke lesions and WMH were applied on the maps of white matter using the FMRIB (Functional Magnetic Resonance Imaging of the Brain) Software Library maths function, to get a mask of NAWM, free of ischemic stroke lesions and WMH. FA, MD, axial diffusivity (AD), and radial diffusivity (RD) maps were computed using FMRIB Diffusion Toolbox from FMRIB Software Library (FSL 5.0.2; The NAWM masks were coregistered to the diffusion maps to extract mean DTI parameters values for each subject. NAWM microstructural alteration was defined by low FA values and high MD, AD, and RD values.22

Figure 1.

Figure 1. Example of tissue segmentation. Masks of normal-appearing white matter (cyan) were obtained by subtracting the masks of ischemic stroke (yellow) and white matter hyperintensity (red) from the masks of white matter. Masks of gray matter (blue) were obtained using a voxel-based morphometry approach, segmenting ischemic stroke in an additional tissue class. The 4 different masks are represented on a fluid attenuated inversion recovery sequence.

Statistical Analysis

Regression Analyses

To test the relationship between NAWM DTI parameters and the clinical scores, we performed either linear regressions (MoCA, IST, ZCT completion time and number of errors, HAD, and 10-MWT) or ordinal logistic regressions (mRS). Each clinical score (ie, MoCA, IST, ZCT completion time and number of errors, HAD, 10-MWT, and mRS) was inputted as a dependent variable, whereas radiological markers, and demographic confounders (age, sex, and educational level), were inputted as independent variables. Univariate analyses were first conducted. All variables having a P<0.1 in the univariate analyses were included in multivariate analyses that were conducted in 2 steps. First, we built a radiological model to evaluate the impact of mean NAWM DTI parameters against the other radiological markers (ie, WMH, GM and ischemic stroke volumes, and lacunar stroke defined according to the STRIVE recommendations [Standards for Reporting Vascular Changes on Neuroimaging]).23 Second, these models were adjusted for demographic confounders in a clinicoradiological model. As the ischemic stroke lesion volume and the NIHSS at baseline were significantly correlated (Table I in the online-only Data Supplement), we did not include the baseline NIHSS in the analyses, to avoid collinearity with the ischemic stroke lesion volume. Results were expressed as standardized regression coefficients β and SE and were corrected for multiple comparisons using the Holm-Bonferroni method. As DTI parameters were highly correlated with each other (Table I in the online-only Data Supplement), we included each NAWM DTI parameter in separate models.

Path Analyses

Path analyses were performed to evaluate whether mean NAWM DTI parameters had a direct or indirect effect on outcome, passing through the other radiological variables. NAWM DTI parameters were included as predictors and GM, WMH, and ischemic stroke lesion volumes as mediators. A direct path between NAWM DTI parameters and the clinical score measured the direct effect of the NAWM DTI parameter. Three indirect pathways passing through either GM, WMH, or ischemic stroke volumes measured the indirect effect of the NAWM DTI parameter on the outcome variable. The lavaan package available on R software was used to perform the path analyses, and maximum likelihood estimators were produced. The adequacy of models fit was determined by the following parameters: low χ2 relative to df with P for χ2 goodness-of-fit statistic >0.05, comparative fit index ≥0.95, root mean square error of approximation ≤0.06, and standardized root mean square residual ≤0.08.24

All statistical analyses were performed using the R software 3.5.1. Statistical significance was set at P<0.05.

Tract-Based Spatial Statistics Analyses

To identify the relevant white matter tracts implied in the association between NAWM microstructure and the outcome measures, the DTI maps were analyzed according to a conventional tract-based spatial statistics procedure25 using linear regressions. In these tract-based spatial statistics models, each 1-year clinical score was inputted as a dependent variable, adjusting for age, sex, educational level, ischemic stroke volume, WMH volume, and GM volume. To overcome the potential influence of ischemic stroke location, the analyses were performed separately in the subgroups of right and left hemispheric ischemic strokes, and left-handed patients were removed from the groups. Thus, we were able to analyze NAWM in lesional and contralesional hemispheres. Statistical threshold was set at P<0.05 corrected for multiple comparisons (threshold-free cluster enhancement and 5000 permutations). The Johns Hopkins University white matter atlas implemented in FMRIB Software Library was used to labelize the significant white matter tracts.



Two hundred and seven patients were included in the analyses (Figure 2; mean age±SD, 66±13; 67% men). Median NIHSS score at baseline was 3 (interquartile range, 2–6) and was correlated with the ischemic stroke volume (r=0.52, P<0.001). The other clinical scores are detailed in Table 1. The mRS was correlated with MoCA (r=−0.25, P=0.003), HAD (r=0.26, P=0.003), and 10-MWT (r=0.31, P<0.001; Table II in the online-only Data Supplement). Right and left hemispheric stroke subgroups were comparable for demographic, clinical, and radiological data (Table III in the online-only Data Supplement), including comparable NIHSS at baseline and infarct volume.

Table 1. Characteristics of All Participants

Demographic Data and Cardiovascular Risk Factors, n=207
Age, y; mean±SD66±13
Men, n (%)138 (67)
Right handed, n (%)189 (91)
Educational level, n/184 (%)
 None3 (1)
 Primary42 (23)
 Junior high school59 (32)
 Secondary high school/baccalaureate31 (17)
 Superior49 (27)
Cardiovascular risk factors, n (%)
 High blood pressure100 (48)
 Dyslipidemia85 (41)
 Current smoking52 (25)
 Diabetes mellitus33 (16)
 Atrial fibrillation23 (11)
Clinical assessment
 IQCODE, median (IQR)3 (3)
 NIHSS at baseline, median (IQR)3 (2–6)
 MoCA at 1 y, median (IQR)25 (23–28)
 IST at 1 y, median (IQR)32 (27–36)
 ZCT at 1 y: completion time (s), median (IQR)79 (61–104)
 ZCT at 1 y: No. of errors, median (IQR)1 (0–4)
 HAD at 1 y, median (IQR)9 (6–13)
 10-MWT at 1 y, s; median (IQR)8.87 (7.75–10.56)
 mRS at 1 y: 0–1–2, n (%)60 (29)–65 (31)–45 (22)
 mRS at 1 y: 3–4–5, n (%)25 (12)–11 (5)–1 (<1)
Radiological data
 Stroke hemispheric side, n (%)
  Left side98 (47)
  Right side97 (47)
  Left and right sides12 (6)
 Lacunar strokes, n (%)55 (27)
 Stroke volume, mL; median (IQR)8.5 (1.6–25.9)
 GM volume, mL; median (IQR)635 (581–688)
 WMH volume, mL; median (IQR)3.7 (1.4–10.1)
 NAWM FA, median (IQR)0.35 (0.33–0.37)
 NAWM MD, median·10−3 mm2/s (IQR)0.91 (0.87–0.98)
 NAWM AD, median·10−3 mm2/s (IQR)1.24 (1.2–1.31)
 NAWM RD, median·10−3 mm2/s (IQR)0.74 (0.7–0.81)

10-MWT indicates 10-m walk test; AD, axial diffusivity; FA, fractional anisotropy; GM, gray matter; HAD, Hospital Anxiety and Depression; IQCODE, Informant Questionnaire in Cognitive Decline in the Elderly; IQR, interquartile range; IST, Issaacs set test; MD, mean diffusivity; MoCA, Montreal Cognitive Assessment; mRS, modified Rankin Scale; NAWM, normal-appearing white matter; NIHSS, National Institutes of Health Stroke Scale; RD, radial diffusivity; WMH, white matter hyperintensity; and ZCT, Zazzo cancelation task.

Figure 2.

Figure 2. Flowchart. DTI indicates diffusion tensor imaging; MRI, magnetic resonance imaging; and VBM, voxel-based morphometry.

Relationship Between NAWM DTI Parameters and Clinical Scores at 1 Year

In univariate analyses, NAWM FA was associated with 1-year MoCA (β=0.23, P=0.001), IST (β=0.27, P<0.001), ZCT completion time (β=−0.34, P<0.001), ZCT number of errors (β=−0.25, P<0.001), 10-MWT (β=−0.31, P<0.001), and mRS (β=−0.29, P<0.001). NAWM FA was not associated with HAD scores at 1 year poststroke (Table IV in the online-only Data Supplement).

In multivariate analyses, the models including only radiological markers showed an association between NAWM FA and cognitive scores (Table 2), along with mRS (β=−0.24, P=0.005). After controlling for age, sex, and educational level, NAWM FA remained independently associated only with the mRS measured 1 year after stroke (β=−0.24, P=0.04). The models including the other DTI parameters showed a similar independent association between mRS and NAWM MD, AD, and RD, regardless of the other radiological markers and demographic confounders (Tables V through VII in the online-only Data Supplement). In our stroke sample, WMH and ischemic stroke volumes were also associated with functional outcome at 1 year (Table 2).

Table 2. Predictors of Clinical Scores Measured at 1 Year (Multiple Linear Regressions)

β (SE)P Valueβ (SE)P Valueβ (SE)P Valueβ (SE)P Valueβ (SE)P Valueβ (SE)P Value
Radiological model
 NAWM FA0.14 (0.11)0.20.18 (0.16)0.04−0.26 (0.97)0.002−0.2 (0.12)0.03−0.18 (0.14)0.1−0.26 (0.05)0.004
 WMH volume−0.22 (0.34)0.01−0.21 (0.49)0.020.17 (3.08)0.0450.09 (0.37)0.40.27 (0.48)0.0050.17 (0.15)0.2
 GM volume0.11 (0.1)0.20.06 (0.14)0.7−0.12 (0.91)0.1−0.1 (0.11)0.4−0.06 (0.12)0.90.01 (0.04)0.6
 Stroke volume−0.32 (0.12)<0.001−0.23 (0.2)0.0050.18 (1.11)0.030.22 (0.13)0.010.06 (0.22)0.90.37 (0.06)<0.001
 Lacunar stroke0.05 (0.74)0.50.03 (1.06)0.7−0.01 (6.61)0.8−0.07 (0.8)0.40.08 (0.87)0.90.1 (0.31)0.2
Clinicoradiological model
 NAWM FA−0.05 (0.11)1−0.01 (0.16)1−0.09 (1.06)0.5−0.02 (0.13)0.9−0.1 (0.15)1−0.24 (0.05)0.04
 WMH volume−0.29 (0.31)<0.001−0.26 (0.46)0.0050.1 (3.04)0.50.11 (0.38)0.60.28 (0.53)0.010.18 (0.16)0.2
 GM volume0.02 (0.1)1−0.05 (0.15)1−0.02 (1.01)0.8−0.07 (0.13)0.8−0.05 (0.14)1−0.05 (0.05)1
 Stroke volume−0.37 (0.1)<0.001−0.2 (0.18)0.020.13 (1.01)0.30.22 (0.13)0.01−0.005 (0.22)10.35 (0.06)<0.001
 Age, y−0.15 (0.03)0.3−0.19 (0.04)0.10.3 (0.26)0.0030.1 (0.03)0.80.08 (0.04)1−0.05 (0.01)1
 Sex: male−0.05 (0.63)1−0.04 (0.95)1−0.13 (6.16)0.3−0.16 (0.76)0.2−0.07 (0.88)1−0.01 (0.31)1
 Educational level0.21 (0.25)0.010.19 (0.38)0.04−0.12 (2.47)0.3−0.17 (0.31)0.1−0.06 (0.35)10.01 (0.12)1

The results were corrected for multiple comparisons (Holm-Bonferroni method). 10-MWT indicates 10-m walk test; FA, fractional anisotropy; GM, gray matter; IST, Isaacs set test; MoCA, Montreal Cognitive Assessment; mRS, modified Rankin Scale; NAWM, normal-appearing white matter; WMH, white matter hyperintensity; and ZCT, Zazzo cancelation task.

Path Analyses

Path analyses were completed using WMH, GM, and ischemic stroke volumes as mediators. The model including NAWM FA showed good fitting: χ2=7.4; df =6; P=0.286; comparative fit index, 0.993; root mean square error of approximation, 0.034; and standardized root mean square residual, 0.041. High NAWM FA value was associated with a good recovery directly (β=−0.241, P=0.001) and indirectly with the mediation of WMH volume (Figure 3). To determine which pathway was more important in the prediction, we compared the β coefficients of the direct and indirect pathways. The β coefficient of the indirect pathway was computed as followed (−0.43 for pathway between NAWM FA and WMH volume×0.181 for pathway between WMH volume and mRS). β-coefficient of the indirect pathway was of −0.078, whereas the β-coefficient of the direct pathway was of −0.241, highlighting the relevance of the direct pathway.

Figure 3.

Figure 3. Path analysis for the effect of normal-appearing white matter (NAWM) fractional anisotropy (FA) and mean diffusivity (MD) on 1-y modified Rankin Scale (mRS). Numbers represent standardized β-coefficient values. Continuous line arrows represent the statistically significant paths (P<0.05), and dotted arrows represent nonsignificant paths. Age and sex were entered as covariates. GM indicates gray matter; and WMH, white matter hyperintensity.

The model including NAWM MD (χ2=11.2; df=6; P=0.081; comparative fit index, 0.974; root mean square error of approximation, 0.06; and standardized root mean square residual, 0.048) showed similar results. NAWM MD had a direct effect on mRS (β=0.247, P<0.001) and an indirect effect mediated by WMH volume. The β-coefficient of the direct pathway was more important than the β-coefficient of the indirect one (β=0.354×0.194=0.069). The models including NAWM AD and RD showed also similar results (Figure I in the online-only Data Supplement).

Tract-Based Spatial Statistics Analyses

In right hemispheric strokes, high mRS (ie, poor prognosis) was associated with low FA, high MD, AD, and RD, in widespread regions of white matter (Table VIII in the online-only Data Supplement), regardless of ischemic stroke, WMH and GM volumes, age, sex and educational level (Figure 4). Lower performances in IST were associated with lower FA in the corpus callosum and higher MD and RD in widespread white matter regions. No additional association was found with MoCA, ZCT, 10-MWT, and HAD.

Figure 4.

Figure 4. White matter tracts associated with modified Rankin Scale (mRS) and Isaacs set test (IST) measured at 1 y in the subgroup of right hemispheric stroke patients. Tract-Based Spatial Statistics analyses in the subgroup of right-side hemispheric strokes showed a negative association between mRS and fractional anisotropy (FA) and a positive association between mRS, mean diffusivity (MD), axial diffusivity (AD), and radial diffusivity (RD). A positive association was found between IST and FA in the forceps minor (corpus callosum), and a negative association was found between IST, MD, and RD. The results were adjusted for ischemic stroke volume, white matter hyperintensity volume, gray matter volume, age, male sex, and educational level (P<0.05 corrected for multiple comparisons threshold-free cluster enhancement and 5000 permutations).

In the left hemispheric strokes subgroup, no significant association was found.


The main results of the present study are that (1) NAWM integrity is an independent predictor of dependency state 1 year after an ischemic stroke; (2) NAWM integrity is an additional radiological marker associated with cognitive outcome; (3) the influence of NAWM integrity in the domain of executive functions is dependent of stroke location, being only observed in right hemispheric strokes.

Similar to these results, Etherton et al8 demonstrated that microstructural alterations of NAWM in the contralateral hemisphere of stroke were associated with a worse functional outcome at 3 months, whereas WMH volume was not, and Kliper et al7 demonstrated a relevant role of NAWM integrity on global cognitive outcome regardless of the ischemic lesion’s volume. In the present study, we used a mean value of NAWM in both lesional and contralesional hemispheres. However, we can assume that the mean FA values extracted from the NAWM are related to the number and location of the voxels included in the NAWM masks, which are tightly related to the location and volume of the infarct and WMH. To overcome this issue, we added the NAWM volume in our models, which did not change the results, suggesting a predominant role played by the severity of the microstructural disorganization. Considering the association between NAWM integrity and mRS, we found an involvement of white matter tracts in widespread regions. The mRS is a global evaluation of functional recovery, encompassing motor and cognitive functions. Thus, it was not surprising to identify white matter tracts reported to be involved in motor and cognitive outcome after stroke,26,27 as corpus callosum, corticospinal tract, superior longitudinal fasciculus, thalamic radiations, cingulum, or inferior fronto-occipital fasciculus.

Furthermore, tract-based spatial statistics analyses were based on the hemispheric side of the acute ischemic lesion, and these results were observed only for right hemispheric lesions. Similarly, additional subgroup path analyses also identified a sustained effect of NAWM integrity on mRS only in right hemispheric strokes (data not shown). On top of the infarct size, stroke location has been repeatedly identified as a major determinant of stroke outcome, with left hemispheric lesions being associated with a worse prognosis.28,29 The absence of influence of NAWM integrity on functional and cognitive outcome following left hemispheric strokes might reflect the influence of language disturbances, even when they do not seem to be clinically relevant. Indeed, language disturbances might impair the sensitivity of nonspecific cognitive evaluations in the identification of cognitive disability and worsen functional outcome. Overall, this result highlights the relevance of NAWM integrity in the recovery of some cognitive functions, especially executive domains, when strategic locations are spared.

Regarding mood scores, neither NAWM DTI parameters nor other MRI markers were associated with HAD scores, suggesting that rather than the brain lesions, acute stress and difficulties to deal with the new handicap might be the main determinants of poststroke mood disorders.

NAWM Integrity and Poststroke Outcome—the Hypothesis of an Invisible Cerebrovascular Disease

The role of NAWM integrity in outcome has been reported in large elderly populations.30–32 Vernooij et al32 reported an association between altered diffusivity parameters in NAWM and lower performances in processing speed, executive functions, global cognition, and motor speed. It has been suggested that DTI parameter changes in NAWM were the early stage of a stepwise white matter disease, including degenerative and vascular processes.15,33 Supporting this hypothesis, the path analyses showed not only a direct effect of NAWM integrity on mRS but also an indirect effect passing through the volume of WMH that was associated with NAWM integrity. Therefore, the association between DTI parameters measured shortly after the ischemic stroke in NAWM might be related to the presence of an underlying and invisible cerebrovascular disease on conventional MRI sequences, reflecting the first step toward macrostructural white matter abnormalities.

Furthermore, although NAWM might be the location of early degenerative and vascular processes, the proposal of lower GM volume because of altered NAWM integrity is not clearly established. Our path analyses showed a direct effect of NAWM integrity on GM volume, suggesting a relationship between altered integrity in NAWM and cortical atrophy, but longitudinal studies are required to confirm a causal effect. Also, we found that NAWM FA was associated with mRS without being mediated by GM volume. This observation has already been raised in patients with subcortical and neurodegenerative cognitive impairment.4,34 Indeed, Kim et al4,34 demonstrated an association between decreased FA in specific regions and gait performances or executive functions, which was not mediated by brain atrophy, suggesting that white matter microstructural integrity explained a substantial part of clinical performances on top of brain atrophy.

Moreover, contrary to mRS, we observed that the association between NAWM DTI parameters in the whole population and cognitive scores did not remain after controlling for demographic factors. Aside from the potential influence of stroke location discussed previously, this might also be due to the role of age and educational level, which are known factors associated with white matter integrity in healthy adults.35 Thus, our results suggest that cognitive reserve and age-related microstructural white matter alterations could constitute a factor of vulnerability, impairing the cognitive recovery.

Hypothesis of Altered NAWM Integrity as a Consequence of the Acute Ischemic Stroke

Besides the hypothesis of an underlying invisible white matter pathology, it has been suggested that DTI parameters could be altered near and remote the ischemic lesion through the diaschisis.36 The disruption of white matter microstructural integrity remote from stroke has consequences on cognitive and motor performances.26,36,37 In line with this hypothesis, we observed similar results in both lesional and contralesional hemispheres for mRS and IST, suggesting an altered microstructural integrity near and remote from the lesion. Hence, the new poststroke NAWM changes might inhibit compensatory mechanisms, impeding the reactivation of deafferented regions and brain plasticity, which contribute to a poorer outcome. However, to confirm the hypothesis of an association between altered NAWM integrity triggered by stroke and recovery, a longitudinal evaluation of NAWM and recovery scores is needed.


Several limitations of this study should be considered before the generalization of the results. First, although inclusion criteria allowed to include patients with various degrees of stroke severity, our population study covered subjects with mild-to-moderate stroke because of the inability of more severe patients to perform the clinical assessment or MRI protocol. Second, our cognitive assessment was relatively short compared with existing neuropsychological battery of tests. However, we chose simple tasks that could be replicated in routine, and the addition of gait and mood assessment in the same time did not allow us to extend the time of the neuropsychological evaluation.


NAWM integrity is part of the early neuroimaging markers that could help to predict poststroke functional outcome and could be added as potential target to follow in therapeutic trials aimed at developing neuroprotective agents. NAWM integrity is also a radiological marker of poststroke cognitive recovery but with a nonsignificant weight facing age, educational level, or stroke location. Further longitudinal studies are needed, to follow the changes occurring in NAWM after stroke and the evolution of WMH.


Guest Editor for this article was Gregory W. Albers, MD.

The online-only Data Supplement is available with this article at

Correspondence to Sharmila Sagnier, MD, INCIA-Université Bordeaux 2, 146 rue Léo Saignat Zone Nord, Bâtiment 2A-2e étage, 33076 Bordeaux, France. Email


  • 1. Pendlebury ST, Rothwell PM. Prevalence, incidence, and factors associated with pre-stroke and post-stroke dementia: a systematic review and meta-analysis.Lancet Neurol. 2009; 8:1006–1018. doi: 10.1016/S1474-4422(09)70236-4CrossrefMedlineGoogle Scholar
  • 2. Debette S, Markus HS. The clinical importance of white matter hyperintensities on brain magnetic resonance imaging: systematic review and meta-analysis.BMJ. 2010; 341:c3666. doi: 10.1136/bmj.c3666CrossrefMedlineGoogle Scholar
  • 3. Firbank MJ, Burton EJ, Barber R, Stephens S, Kenny RA, Ballard C, et al. Medial temporal atrophy rather than white matter hyperintensities predict cognitive decline in stroke survivors.Neurobiol Aging. 2007; 28:1664–1669. doi: 10.1016/j.neurobiolaging.2006.07.009CrossrefMedlineGoogle Scholar
  • 4. Kim YJ, Kwon HK, Lee JM, Cho H, Kim HJ, Park HK, et al. Gray and white matter changes linking cerebral small vessel disease to gait disturbances.Neurology. 2016; 86:1199–1207. doi: 10.1212/WNL.0000000000002516CrossrefGoogle Scholar
  • 5. Dufouil C, Godin O, Chalmers J, Coskun O, MacMahon S, Tzourio-Mazoyer N, et al; PROGRESS MRI Substudy Investigators. Severe cerebral white matter hyperintensities predict severe cognitive decline in patients with cerebrovascular disease history.Stroke. 2009; 40:2219–2221. doi: 10.1161/STROKEAHA.108.540633LinkGoogle Scholar
  • 6. Tang WK, Chen YK, Lu JY, Chu WC, Mok VC, Ungvari GS, et al. White matter hyperintensities in post-stroke depression: a case control study.J Neurol Neurosurg Psychiatry. 2010; 81:1312–1315. doi: 10.1136/jnnp.2009.203141CrossrefGoogle Scholar
  • 7. Kliper E, Ben Assayag E, Tarrasch R, Artzi M, Korczyn AD, Shenhar-Tsarfaty S, et al. Cognitive state following stroke: the predominant role of preexisting white matter lesions.PLoS One. 2014; 9:e105461. doi: 10.1371/journal.pone.0105461CrossrefGoogle Scholar
  • 8. Etherton MR, Wu O, Cougo P, Giese AK, Cloonan L, Fitzpatrick KM, et al. Integrity of normal-appearing white matter and functional outcomes after acute ischemic stroke.Neurology. 2017; 88:1701–1708. doi: 10.1212/WNL.0000000000003890CrossrefMedlineGoogle Scholar
  • 9. Rost NS, Cougo P, Lorenzano S, Li H, Cloonan L, Bouts MJ, et al. Diffuse microvascular dysfunction and loss of white matter integrity predict poor outcomes in patients with acute ischemic stroke.J Cereb Blood Flow Metab. 2018; 38:75–86. doi: 10.1177/0271678X17706449CrossrefGoogle Scholar
  • 10. Gow AJ, Bastin ME, Muñoz Maniega S, Valdés Hernández MC, Morris Z, Murray C, et al. Neuroprotective lifestyles and the aging brain: activity, atrophy, and white matter integrity.Neurology. 2012; 79:1802–1808. doi: 10.1212/WNL.0b013e3182703fd2CrossrefMedlineGoogle Scholar
  • 11. Deary IJ, Bastin ME, Pattie A, Clayden JD, Whalley LJ, Starr JM, et al. White matter integrity and cognition in childhood and old age.Neurology. 2006; 66:505–512. doi: 10.1212/01.wnl.0000199954.81900.e2CrossrefMedlineGoogle Scholar
  • 12. Rosario BL, Rosso AL, Aizenstein HJ, Harris T, Newman AB, Satterfield S, et al; Health ABC Study. Cerebral white matter and slow gait: contribution of hyperintensities and normal-appearing Parenchyma.J Gerontol A Biol Sci Med Sci. 2016; 71:968–973. doi: 10.1093/gerona/glv224CrossrefGoogle Scholar
  • 13. Moscufo N, Wakefield DB, Meier DS, Cavallari M, Guttmann CRG, White WB, et al. Longitudinal microstructural changes of cerebral white matter and their association with mobility performance in older persons.PLoS One. 2018; 13:e0194051. doi: 10.1371/journal.pone.0194051CrossrefGoogle Scholar
  • 14. Maniega SM, Valdés Hernández MC, Clayden JD, Royle NA, Murray C, Morris Z, et al. White matter hyperintensities and normal-appearing white matter integrity in the aging brain.Neurobiol Aging. 2015; 36:909–918. doi: 10.1016/j.neurobiolaging.2014.07.048CrossrefMedlineGoogle Scholar
  • 15. Maillard P, Carmichael O, Harvey D, Fletcher E, Reed B, Mungas D, et al. FLAIR and diffusion MRI signals are independent predictors of white matter hyperintensities.AJNR Am J Neuroradiol. 2013; 34:54–61. doi: 10.3174/ajnr.A3146CrossrefMedlineGoogle Scholar
  • 16. de Laat KF, Tuladhar AM, van Norden AG, Norris DG, Zwiers MP, de Leeuw FE. Loss of white matter integrity is associated with gait disorders in cerebral small vessel disease.Brain. 2011; 134(pt 1):73–83. doi: 10.1093/brain/awq343CrossrefMedlineGoogle Scholar
  • 17. Jorm AF. The Informant Questionnaire on cognitive decline in the elderly (IQCODE): a review.Int Psychogeriatr. 2004; 16:275–293. doi: 10.1017/s1041610204000390CrossrefMedlineGoogle Scholar
  • 18. Nasreddine ZS, Phillips NA, Bédirian V, Charbonneau S, Whitehead V, Collin I, et al. The Montreal Cognitive Assessment, MoCA: a brief screening tool for mild cognitive impairment.J Am Geriatr Soc. 2005; 53:695–699. doi: 10.1111/j.1532-5415.2005.53221.xCrossrefMedlineGoogle Scholar
  • 19. Isaacs B, Kennie AT. The Set test as an aid to the detection of dementia in old people.Br J Psychiatry. 1973; 123:467–470. doi: 10.1192/bjp.123.4.467CrossrefMedlineGoogle Scholar
  • 20. Zazzo R. Manuel pour l’examen psychologique de l’enfant. Neuchâtel: Delachaux et Niestlé; 1969.Google Scholar
  • 21. Graham JE, Ostir GV, Kuo YF, Fisher SR, Ottenbacher KJ. Relationship between test methodology and mean velocity in timed walk tests: a review.Arch Phys Med Rehabil. 2008; 89:865–872. doi: 10.1016/j.apmr.2007.11.029CrossrefMedlineGoogle Scholar
  • 22. Concha L. A macroscopic view of microstructure: using diffusion-weighted images to infer damage, repair, and plasticity of white matter.Neuroscience. 2014; 276:14–28. doi: 10.1016/j.neuroscience.2013.09.004CrossrefGoogle Scholar
  • 23. Wardlaw JM, Smith EE, Biessels GJ, Cordonnier C, Fazekas F, Frayne R, et al; Standards for Reporting Vascular Changes on Neuroimaging (STRIVE v1). Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration.Lancet Neurol. 2013; 12:822–838. doi: 10.1016/S1474-4422(13)70124-8CrossrefMedlineGoogle Scholar
  • 24. Hooper D, Coughlan J, Mullen M. Structural equation modelling: guidelines for determining model fit.Electronic Journal of Business Research Methods.2008; 6:53–60.Google Scholar
  • 25. Smith SM, Jenkinson M, Johansen-Berg H, Rueckert D, Nichols TE, Mackay CE, et al. Tract-based spatial statistics: voxelwise analysis of multi-subject diffusion data.Neuroimage. 2006; 31:1487–1505. doi: 10.1016/j.neuroimage.2006.02.024CrossrefMedlineGoogle Scholar
  • 26. Schaapsmeerders P, Tuladhar AM, Arntz RM, Franssen S, Maaijwee NAM, Rutten-Jacobs LCA, et al. Remote lower white matter integrity increases the risk of long-term cognitive impairment after ischemic stroke in young adults.Stroke. 2016; 47:2517–2525. doi: 10.1161/STROKEAHA.116.014356LinkGoogle Scholar
  • 27. Li Y, Wu P, Liang F, Huang W. The microstructural status of the corpus callosum is associated with the degree of motor function and neurological deficit in stroke patients.PLoS One. 2015; 10:e0122615. doi: 10.1371/journal.pone.0122615CrossrefMedlineGoogle Scholar
  • 28. Wu O, Cloonan L, Mocking SJ, Bouts MJ, Copen WA, Cougo-Pinto PT, et al. Role of acute lesion topography in initial ischemic stroke severity and long-term functional outcomes.Stroke. 2015; 46:2438–2444. doi: 10.1161/STROKEAHA.115.009643LinkGoogle Scholar
  • 29. Zhao L, Biesbroek JM, Shi L, Liu W, Kuijf HJ, Chu WW, et al. Strategic infarct location for post-stroke cognitive impairment: a multivariate lesion-symptom mapping study.J Cereb Blood Flow Metab. 2018; 38:1299–1311. doi: 10.1177/0271678X17728162CrossrefMedlineGoogle Scholar
  • 30. Verlinden VJ, van der Geest JN, de Groot M, Hofman A, Niessen WJ, van der Lugt A, et al. Structural and microstructural brain changes predict impairment in daily functioning.Am J Med. 2014; 127:1089–1096.e2. doi: 10.1016/j.amjmed.2014.06.037CrossrefMedlineGoogle Scholar
  • 31. Sedaghat S, Cremers LG, de Groot M, Hofman A, van der Lugt A, Niessen WJ, et al. Lower microstructural integrity of brain white matter is related to higher mortality.Neurology. 2016; 87:927–934. doi: 10.1212/WNL.0000000000003032CrossrefMedlineGoogle Scholar
  • 32. Vernooij MW, Ikram MA, Vrooman HA, Wielopolski PA, Krestin GP, Hofman A, et al. White matter microstructural integrity and cognitive function in a general elderly population.Arch Gen Psychiatry. 2009; 66:545–553. doi: 10.1001/archgenpsychiatry.2009.5CrossrefMedlineGoogle Scholar
  • 33. Pelletier A, Periot O, Dilharreguy B, Hiba B, Bordessoules M, Chanraud S, et al. Age-related modifications of diffusion tensor imaging parameters and white matter hyperintensities as inter-dependent processes.Front Aging Neurosci. 2015; 7:255. doi: 10.3389/fnagi.2015.00255CrossrefMedlineGoogle Scholar
  • 34. Kim HJ, Im K, Kwon H, Lee JM, Kim C, Kim YJ, et al. Clinical effect of white matter network disruption related to amyloid and small vessel disease.Neurology. 2015; 85:63–70. doi: 10.1212/WNL.0000000000001705CrossrefMedlineGoogle Scholar
  • 35. Vaqué-Alcázar L, Sala-Llonch R, Valls-Pedret C, Vidal-Piñeiro D, Fernández-Cabello S, Bargalló N, et al. Differential age-related gray and white matter impact mediates educational influence on elders’ cognition.Brain Imaging Behav.2017; 11:318–332. doi: 10.1007/s11682-016-9584-8CrossrefGoogle Scholar
  • 36. Dacosta-Aguayo R, Graña M, Fernández-Andújar M, López-Cancio E, Cáceres C, Bargalló N, et al. Structural integrity of the contralesional hemisphere predicts cognitive impairment in ischemic stroke at three months.PLoS One. 2014; 9:e86119. doi: 10.1371/journal.pone.0086119CrossrefMedlineGoogle Scholar
  • 37. Bigourdan A, Munsch F, Coupé P, Guttmann CRG, Sagnier S, Renou P, et al. Early fiber number ratio is a surrogate of corticospinal tract integrity and predicts motor recovery after stroke.Stroke.2016; 47:1053–1059. doi: 10.1161/STROKEAHA.115.011576LinkGoogle Scholar


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