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Chronic Cortical Cerebral Microinfarcts Slow Down Cognitive Recovery After Acute Ischemic Stroke

Originally published 2019;50:1430–1436


Background and Purpose—

Cortical cerebral microinfarcts (CMIs) have been associated with vascular dementia and Alzheimer disease. The aim of the present study was to evaluate the role of cortical CMI detected on 3T magnetic resonance imaging, on the evolution of cognition during the year following an acute ischemic stroke.


We conducted a prospective and monocentric study, including patients diagnosed for a supratentorial ischemic stroke with a National Institutes of Health Stroke Scale score ≥1, without prestroke dementia or neurological disability. Cortical CMIs were assessed on a brain 3T magnetic resonance imaging realized at baseline, as well as markers of small vessel disease, stroke characteristics, and hippocampal atrophy. Cognitive assessment was performed at 3 time points (baseline, 3 months, and 1 year) using the Montreal Cognitive Assessment, the Isaacs set test, and the Zazzo’s cancellation task. Generalized linear mixed models were performed to evaluate the relationships between the number of cortical CMI and changes in cognitive scores over 1 year.


Among 199 patients (65±13 years old, 68% men), 88 (44%) had at least one cortical CMI. Hypertension was the main predictor of a higher cortical CMI load (B=0.58, P=0.005). The number of cortical CMI was associated with an increase time at the Zazzo’s cancellation task over 1 year (B=3.84, P=0.01), regardless of the other magnetic resonance imaging markers, stroke severity, and demographic factors.


Cortical CMIs are additional magnetic resonance imaging markers of poorer processing speed after ischemic stroke. These results indicate that a high load of cortical CMI in patients with stroke can be considered as a cerebral frailty condition which counteracts to the recovery process, suggesting a reduced brain plasticity among these patients.


In addition to the size and location of the acute ischemic lesion, the structural integrity of the brain parenchyma surrounding the acute lesion significantly contribute to the poststroke cognitive outcome.1 While extensive white matter hyperintensities (WMH) and lower cortical volume were repeatedly reported as determinants of poststroke cognitive outcome,1,2 the role of additional structural brain lesions such as the presence of cortical cerebral microinfarcts (CMIs) remains uncertain.

Cortical CMIs were first reported on postmortem anatomopathological brain examination, then subsequently described using high resolution 7 Tesla (T) magnetic resonance imaging (MRI). More recently, they were demonstrated in vivo using 3T MRI scans, therefore, allowing the evaluation of their clinical relevance in clinical practice.3 Recent studies have suggested that the presence of cortical CMI is associated with cognitive impairment regardless of other markers of cerebrovascular disease.4–6

However, the contribution of cortical CMI on poststroke cognitive outcome following stroke is still unknown. The aim of the present study was to evaluate the role of chronic cortical CMI, identified on a 3T MRI at the acute phase of an ischemic stroke, on the cognitive outcome over 1 year after stroke.


All supporting data are available within the article and its in the online-only Data Supplement.

Study Population

Participants were prospectively recruited from the Brain Before Stroke study, a biomedical research study promoted by the Bordeaux University hospital. The study was approved by the regional French Human Protection Committee (CPP 2012/19 2012-A00190-43). All patients or their legal representative gave written informed consent to participate. The recruitment period went down between June 2012 and February 2015. The inclusion criteria were a supratentorial ischemic stroke with a National Institutes of Health Stroke Scale (NIHSS) score comprised between 1 and 25 at 24 to 72 hours following symptoms onset (baseline) in patients aged ≥18 years old. The exclusion criteria were a modified Rankin scale score of ≥1 because of prestroke dementia or prestroke neurological disability, the inability to perform cognitive assessment or MRI due to severe aphasia or hemiplegia, history of psychiatric disorder matching to axis 1 DSM-IV criteria (Diagnostic and Statistical Manual of Mental Disorders IV; schizophrenia, suicide, panic disorder, excessive alcohol, or drug consumption), MRI contraindication, history of chronic disease compromising the patient’s follow-up, agitation, coma, emergency conditions, pregnancy or breast-feeding women, and patients under protection of justice.

Demographic and Clinical Assessment

Demographic data including vascular risk factors (history of hypertension, dyslipidemia, diabetes mellitus, and current smoking) and educational level were recorded. Stroke severity at baseline was assessed by the NIHSS. Prestroke cognitive status was evaluated with the Informant Questionnaire in Cognitive Decline in the Elderly.7 Cognitive assessment was performed at baseline (M0), 3 months (M3), and 1 year (M12) following stroke during a medical visit. Cognitive assessment included a Montreal Cognitive Assessment for the evaluation of global cognition on 30 points,8 an Isaacs set test (IST) for the evaluation of executive functions through categorical verbal fluency, on 40 points,9 and a Zazzo’s cancellation task (ZCT, completion time and number of errors) for the evaluation of processing speed and attention.10 Contrary to Montreal Cognitive Assessment and IST scores, higher scores in ZCT indicated lower performances. Mood disorders were measured at the 3 time points using the Hospital Anxiety and Depression scale.11 Functional autonomy at 1 year was defined by a modified Rankin scale score of ≤2.

Imaging Protocol

A brain 3T MRI was performed at baseline (General Electrics Medical Systems Discovery MR750W) and included the following sequences: diffusion-weighted imaging (echo time/repetition time 82/9000, field of view 24×24 cm2, matrix 128×128, slice thickness 4 mm, gap between slices 0.5 mm), 3-dimensional T1-wi (196 slices, echo time/repetition time/inversion time 3.3/8.6/450, 12° flip angle, field of view 24×24 cm2, matrix 256×256, slice thickness 1 mm, reconstructed voxel size of 0.5 mm through zero-padding interpolation), 3-dimensional Fluid Attenuated Inversion Recovery sequences (FLAIR; 224 slices, echo time/repetition time/inversion time 142.8/9000/2358, field of view 24×24 cm2, matrix 288×224, slice thickness 1.8 mm), and susceptibility weighted imaging (echo time/repetition time 24.3/56, 15° flip angle, field of view 22×22 cm2, matrix 320×224).

Imaging Analysis

Cortical CMI

Cortical CMIs were identified according to the criteria described in van Dalen et al12 by 2 trained readers, a neuroradiologist and a stroke neurologist with >5 years’ experience, blinded to clinical data. Cortical CMIs detected by each reader separately were finally selected if the 2 readers agreed in a consensus reading. Cortical CMIs were defined as only intracortical lesions <5 mm in diameter, appearing in hyposignal on T1-wi and hyper or isosignal on FLAIR sequences. Lesions in hyposignal on FLAIR sequences, suggesting hemorrhagic lesions or vessels (and confirmed on susceptibility weighted imaging) were not accepted, as well as dilated perivascular spaces extended from the white matter to the cortex. Cortical CMIs were classified according to their distribution in frontal, temporal, parietal, and occipital regions.

Stroke Characteristics

Ischemic stroke lesions were segmented in a semiautomatic way on diffusion-weighted imaging using 3-dimensional Slicer 4.3.1,13 to achieve stroke volumes and stroke lesion masks. They were characterized according to their laterality (right side hemisphere, left side, or bilateral) and their location (cortical ischemic strokes in the middle, anterior, and posterior cerebral artery territories, or lacunar).

Small Vessel Disease Markers

Periventricular and deep WMH were rated on FLAIR sequences using the Fazekas score from 0 to 3. The presence of previous small deep infarcts and deep cerebral microbleeds (ie, in the basal ganglia, thalamus, internal, or external capsules) was evaluated on T1-wi, FLAIR and susceptibility weighted imaging according to the Standards for Reporting Vascular Changes on Neuroimaging recommendations.14 Dilated perivascular spaces in basal ganglia were evaluated according to the Standards for Reporting Vascular Changes on Neuroimaging recommendations and were graded in 4 degrees of severity as defined in Zhu et al.15 The burden of small vessel disease (SVD) was estimated using the total SVD score from 0 to 4.16 This score assigned one point for periventricular WMH rated 3 and deep WMH rated 2 or 3, one point for one or more small deep infarcts, one point for dilated perivascular spaces in basal ganglia rated 3 or 4, and one point for one or more deep microbleeds.

Cortical Volume

Cortical volume was obtained using a voxel-based morphometry approach on Statistical Parametric Mapping 12, MATLAB R2012b.17 Tissue class segmentations were performed on the basis of T1-wi and FLAIR sequences after correction for magnetic field inhomogeneities. To avoid improper segmentation of ischemic stroke lesions in gray matter masks, we created a new tissue probability map formed from stroke lesions masks coregistered on T1-wi. The segmentations resulted in masks of gray matter, white matter, cerebral spinal fluid, with ischemic stroke lesions being segmented in a separate tissue class. Cortical volume was generated from gray matter masks. Total intracranial volume was obtained by adding the volumes of gray matter, white matter, cerebral spinal fluid, and ischemic stroke lesions. Total intracranial volume was added as covariate in the statistical models including cortical and ischemic stroke lesion volumes for correction of head size variation.

Hippocampal Atrophy

Hippocampal atrophy was visually evaluated using the Scheltens scale18 on T1-wi.

Statistical Analysis

Quantitative variables were expressed as means and SD, or medians and interquartile ratios. Qualitative variables were expressed as percentages. We separated one group for patients presenting at least one cortical CMI, and a second group for patients without any cortical CMI. Demographic, radiological, and clinical variables were compared between these 2 groups using a χ2 test for categorical variables, an unpaired 2-sample Wilcoxon test for nonparametric data, and an independent Student t test for parametric data, after verification of normality by a Shapiro-Wilk test. Cognitive scores were compared in each group between the 3 time points using a Friedman rank-sum test for repeated measures. Then, to look for potential demographic (age, sex) and cardiovascular (hypertension, current smoking, dyslipidemia, and diabetes mellitus) predictors of the number of cortical CMI, we performed univariate followed by multivariate linear regressions.

Subsequently, to evaluate the role of the burden of cortical CMI on changes in cognitive scores between baseline and 1 year, we performed generalized linear mixed models with random slopes and intercepts, using maximum likelihood estimation. Mixed models have the advantage to include data measured at different time points, by calculating slopes of evolution. We first performed univariate analyses with each cognitive score (ie, Montreal Cognitive Assessment, IST, ZCT completion time, and a number of errors) as dependent variables, and the number of cortical CMI as independent variable, along with the other radiological, clinical, and demographic data. Multivariate analyses were secondly performed with the variables having a P<0.1. The first models were called radiological models and included only the MRI markers (ie, number of cortical CMI, ischemic stroke volume, laterality and location, SVD score, and hippocampal atrophy). To avoid collinearity, we did not include the cortical volume in these models. Then, clinicoradiological models were built, adding demographic (ie, age, sex, and educational level) and clinical confounders (ie, NIHSS at baseline, hospital anxiety and depression) to the previous radiological models. To avoid collinearity, we removed the ischemic stroke volume from models including NIHSS at baseline. The mixed models were built using the package lmerTest available on the R software and were validated by a visual inspection of the residuals, the random slopes, and intercepts which approximated a normal distribution.

Statistical significance was set at 0.05 for all tests. Statistical analyses were performed with the R software 3.2.4.


Subjects and Cortical CMI

A total of 199 patients were included in the analyses (mean age 65±SD 13 years old, 68% men, Figure I in the online-only Data Supplement). Despite data acquired at 3T, the readers have been able to identify cortical CMI corresponding to the classical description of the literature (Figure 1). Eighty-eight patients (44%) had at least one cortical CMI (mean number per patient 0.87, range 1–6). CMIs were randomly distributed in the different lobes: 97 of them (56%, min-max 1–3) were localized in the frontal lobe, 51 (29%, min-max 1–3) in the temporal lobe, 19 (11%, min-max 1–2) in the parietal lobe, and 7 (4%, max 1) in the occipital lobe. Compared with patients without cortical CMI, those with at least one cortical CMI were older (P=0.03) and had a history of hypertension more frequently (60% versus 41%, P=0.008; Table 1). Informant Questionnaire in Cognitive Decline in the Elderly was not different between the 2 groups at baseline (Table 1). The SVD score tended to be higher in patients with cortical CMI (23% of the patients with cortical CMI had an SVD score rated 3 and 4, versus 11% in the group without cortical CMI), but this did not reach significance.

Table 1. Demographic, Radiological, and Clinical Characteristics of Patients With and Without Cortical CMI

Cortical CMI, N=88No Cortical CMI, N=111P Value
Demographic data
 Age, mean (SD)67 ± 1463 ± 130.03
 Sex male, n (%)54 (61)81 (73)0.08
 Educational level, n (%)*0.6
  None2 (3)1 (1)
  Primary17 (22)15 (15)
  Junior high school26 (33)33 (33)
  Secondary high school/baccalaureate14 (18)20 (20)
  Superior19 (24)31 (31)
 IQCODE, mean (SD)3.02±0.23.09±0.41
 Cardiovascular risk factors, n (%)
  Hypertension53 (60)46 (41)0.008
  Dyslipidemia37 (42)42 (38)0.5
  Current smoking17 (19)35 (32)0.051
  Diabetes mellitus15 (17)16 (14)0.6
Radiological data
 Stroke volume, mL, mean (SD)20.4 (37.4)21.7 (31.6)0.6
 Cortical volume, mL, mean (SD)632.6 (76.2)647.9 (68.4)0.2
 SVD score, n (%)0.1
  030 (34)53 (48)
  122 (25)24 (22)
  216 (18)22 (20)
  315 (17)9 (8)
  45 (6)3 (3)
 Stroke laterality, n (%)0.8
  Right hemisphere39 (20)49 (25)
  Left hemisphere42 (21)58 (29)
  Right and left hemisphere7 (4)4 (2)
 Stroke location, n (%)0.3
  Middle cerebral artery54 (24)59 (27)
  Anterior cerebral artery10 (5)12 (5)
  Posterior cerebral artery15 (7)19 (9)
  Lacunar21 (9)32 (14)
Baseline severity and 1-year functional outcome
 NIHSS at baseline, median (IQR)3 (2–6)3 (1.3–5)0.1
 Modified Rankin scale at 1 year ≤2, n (%)74 (84)97 (88)0.4
 HAD at baseline, median (IQR)9 (5–12)9 (5.3–14)0.9
  3 months10 (6–14)9 (4–13)0.4
  1 year9 (6–13)9 (5–15)0.9

CMI indicates cerebral microinfarct; HAD, Hospital Anxiety and Depression scale; IQCODE, Informant Questionnaire in Cognitive Decline in the Elderly; IQR, interquartile range; NIHSS, National Institutes of Health Stroke Scale; and SVD score, small vessel disease score.

*Educational level was available for 78 patients in the CMI group and in 100 patients in the no CMI group.

Figure 1.

Figure 1. Three examples of cortical cerebral microinfarct (CMI) detected on 3T brain magnetic resonance imaging (MRI). The cortical CMIs were detectable as a hyposignal on T1-wi, as a hypersignal on Fluid Attenuated Inversion Recovery (FLAIR) sequences, and as an isosignal on susceptibility weighted imaging.

Patients’ cognitive evaluations progressively improved in the whole population in all tests, mainly from baseline to 3 months poststroke (Figure 2; Table I in the online-only Data Supplement). Nevertheless, patients with at least one cortical CMI had lower cognitive performances than patients with no CMI over the year of follow-up (Figure 2). Hospital anxiety and depression were similar in the 2 groups (Table 1).

Figure 2.

Figure 2. Cognitive scores measured at the 3 time points according to the presence of cortical cerebral microinfarct (CMI). The group CMI (black curve) includes patients with at least one cortical CMI. Data are presented as means and standard errors of the means. IST indicates Isaacs set test; MoCA, Montreal Cognitive Assessment; and ZCT, Zazzo’s cancellation task. *P<0.05 and **P<0.001 (Friedman rank-sum test).

Determinants of the Number of Cortical CMI

Vascular risk factors associated with the number of cortical CMI are presented in Table 2. In multivariate analysis, history of hypertension was the only independent cardiovascular risk factor associated with the number of cortical CMI (B=0.58, P=0.005; Table 2).

Table 2. Demographic and Cardiovascular Predictors of the Number of Cortical CMI

Univariate AnalysisMultivariate Analysis
B (SE)P ValueB (SE)P Value
Age0.01 (0.007)0.070.01 (0.01)0.4
Male−0.37 (0.2)0.07−0.39 (0.2)0.06
Hypertension0.55 (0.2)0.0040.58 (0.2)0.005
Current smoking−0.06 (0.2)0.80.19 (0.2)0.4
Dyslipidemia−0.11 (0.2)0.6−0.25 (0.2)0.2
Diabetes mellitus0.19 (0.3)0.50.08 (0.3)0.8

CMI indicates cerebral microinfarct.

Relationships Between the Number of Cortical CMI and Changes in Poststroke Cognition

In univariate analyses (Table II in the online-only Data Supplement), the number of cortical CMI was significantly associated with changes in IST (B=−0.71, P=0.046) and ZCT completion time (B=6.53, P<0.001) over the year after stroke.

In multivariate analyses, the number of cortical CMI remained significantly associated with changes in ZCT completion time (B=3.84, P=0.01) after controlling for radiological markers, stroke severity at baseline, hospital anxiety and depression, and demographic factors (Table 3). Although NIHSS at baseline was also associated with changes in ZCT completion time, interaction analysis between NIHSS at baseline and the number of cortical CMI was not significant (B=−0.66, P=0.1).

Table 3. Predictors of Changes in Poststroke Cognitive Scores Over 1 Year (Generalized Linear Mixed Models)

N=199MoCA, B (SE)Isaacs Set Test, B (SE)ZCT: Time, B (SE)ZCT: Errors, B (SE)
Radiological model
 Number of cortical CMI−0.24 (0.22)−0.51 (0.32)5.68 (1.63)0.26 (0.21)
P value0.30.10.0010.2
 SVD score−0.59 (0.27)−0.85 (0.4)1.43 (2.04)0.48 (0.26)
P value0.
 Stroke volume−0.04 (0.01)−0.06 (0.02)0.25 (0.07)0.03 (0.009)
P value<0.001<0.001<0.001<0.001
 Stroke laterality−1.3 (0.49)−1.87 (0.73)9.78 (3.65)−0.86 (0.47)
P value0.0080.010.0080.07
 Stroke location0.2 (0.23)0.41 (0.35)−1.34 (1.76)−0.28 (0.23)
P value0.
 Hippocampal atrophy−0.92 (0.32)−1.36 (0.48)11.5 (2.42)0.9 (0.31)
P value0.0050.005<0.0010.004
Clinicoradiological model
 Age−0.07 (0.02)−0.11 (0.04)0.98 (0.18)0.05 (0.02)
P value0.0090.002<0.0010.06
 Sex−0.19 (0.61)0.11 (0.9)−9.15 (4.42)−1.55 (0.6)
P value0.
 Educational level−0.15 (0.12)−0.04 (0.18)−0.47 (0.87)−0.27 (0.12)
P value0.
 NIHSS at baseline−0.35 (0.07)−0.56 (0.1)1.72 (0.48)0.27 (0.07)
P value<0.001<0.001<0.001<0.001
 HAD−0.04 (0.03)−0.1 (0.04)0.5 (0.27)−0.02 (0.04)
P value0.20.0080.070.7
 No. of cortical CMI−0.12 (0.21)−0.25 (0.31)3.84 (1.51)0.03 (0.21)
P value0.
 SVD score−0.28 (0.26)−0.33 (0.39)−1.74 (1.9)0.28 (0.26)
P value0.
 Stroke laterality−1.08 (0.47)−1.62 (0.68)9.06 (3.34)−0.8 (0.46)
P value0.020.020.0070.08
 Stroke location0.21 (0.22)0.33 (0.32)0.21 (1.59)−0.31 (0.22)
P value0.
 Hippocampal atrophy−0.48 (0.34)−0.57 (0.5)5.57 (2.44)0.6 (0.33)
P value0.

Data represent estimate coefficients (SE). Models including stroke volume were adjusted for TIV. CMI indicates cerebral microinfarct; HAD, Hospital Anxiety and Depression scale; MoCA, Montreal Cognitive Assessment; NIHSS, National Institutes of Health Stroke Scale; SVD, small vessel disease; TIV, Total intracranial volume; and ZCT, Zazzo’s cancellation task.

Moreover, right side hemisphere lesions were associated with lower performances in Montreal Cognitive Assessment, IST, and ZCT completion time (Table 3). Hippocampal atrophy was associated with an increase time at the ZCT.


The main results of the present study are that (1) the frequency of cortical CMI detected in a stroke population free from prestroke cognitive impairment is high and mainly related to hypertension and (2) cortical CMI is a radiological marker associated with worse performances and slowed down improvement of psychomotor speed following ischemic stroke.

Cortical CMIs were observed in 44% of our stroke population, which is relatively high compared with the prevalence of 6% to 29% reported by Hilal et al.6,19 However, in this population-based study, only 65% of the participants with cortical CMI had a history of ischemic stroke. Besides, the presence of a previous ischemic stroke was identified as an independent risk factor of cortical CMI.6 Reinforcing this observation, cortical CMIs have been reported in 52.3% of stroke patients with intracranial atheroma,20 and in 62% of patients diagnosed with vascular dementia in neuropathological studies,21 which are close to our results.

The higher frequency of cortical CMI observed in the population with a cerebrovascular disease5,21 and the strong association between the load of cortical CMI and a history of chronic hypertension, which is the main cerebrovascular risk factor,22 strongly suggest that cortical CMI might be considered as a marker of brain SVD. Reinforcing this hypothesis, the total SVD score measured in our population tended to be higher among patients with cortical CMI. Arteriolosclerosis and lipohyalinosis of the small penetrating arterioles related to chronic hypertension might underlie the progressive development of cortical CMI.23

Similarly to other MRI biomarkers such as WMH, the relationship between cortical CMI and cognitive impairment has already been addressed in population-based studies.6,24 However, to the best of our knowledge, data are scarce in stroke patients and included both acute and chronic cortical CMI.25 Hilal et al6 reported an association between the presence of chronic cortical CMI, and moderate to severe cognitive impairment, regardless of the presence of usual cerebrovascular MRI markers. Patients included in the present study were free of prestroke cognitive disability and had a progressive improvement of their cognitive evaluations from baseline to 1-year poststroke. The transient impairment in cognitive functions early after stroke might be a marker of the cognitive vulnerability of the patients. Herein, we found that the presence of cortical CMI was associated with worse cognitive performances and cognitive recovery slowdown, suggesting that it might increases the cognitive vulnerability observed in the acute phase of stroke. Moreover, the recovery of cognitive processes seems to be slowdown among patients with CMI, suggesting that CMI may contribute to a lower capacity to recover (lower plasticity) after an acute ischemic stroke. According with this hypothesis, Coban et al26 described a loss of axons and axoglial contacts in autopsy brain tissue adjacent to cortical CMI, impairing neural transmission, a dysfunction that could be emphasized following an acute ischemic stroke.

The role of cortical CMI in cognitive processes is still a matter of debate. Among patients with cerebral SVD, Ferro et al24 observed an association between the presence of cortical CMI and worse cognitive performances in multiple domains, including perception and reconstruction, attention, executive functions, and processing speed. Conversely, in patients with stroke, cortical CMIs were associated with decline in visuospatial functions,25 which differs from our results where processing speed was the main cognitive domain to be altered. These discrepancies might be explained by the limited cognitive workup used in our study. This topic will deserve more extensive investigations, including large population sample.

Noteworthy, in our sample, >50% of the CMI were located in the frontal lobe, which can explain their specific effects on processing speed. Therefore, suggesting that, in addition to the burden, the location of cortical CMI might determine the domain of cognitive disability. These data are in accordance with De Reuck et al,27 who described in a postmortem study, a significantly higher number of cortical CMI in the frontal lobe of patients with vascular dementia than a neurodegenerative disease.

Although the present study included a large sample of patients with prospective data, our results have some limitations. First, the number of cortical CMI has probably been underestimated by the use of a 3T MRI compared with a 7T MRI, because of lower spatial and contrast resolutions.3 Indeed, van Veluw et al3 reported the detection of only 27% of 7T MRI-cortical CMI on 3T MRI. Although we were able to detect a significant association between cortical CMI and some cognitive dysfunctions, we probably underestimated the absolute number of CMI and their true contribution to the cognitive outcome. The difficulty of detecting cortical lesions on in vivo brain imaging has also been reported in multiple sclerosis, but what is captured in vivo is considered as the tip of the iceberg and is strongly related to the real pathologically defined count.28 However, the use of a 3T MRI is more available than 7T MRI in clinical routine. Second, the cognitive assessment was limited to a few number of tests, which did not make possible the evaluation of all cognitive domains. In addition, prestroke cognitive status was assessed using the Informant Questionnaire in Cognitive Decline in the Elderly, but this questionnaire might not detected slight prestroke cognitive dysfunction related to cortical CMI, that did not come to the attention of the informant. A self-assessment would be more relevant. Third, the most severe patients were not able to stay in the magnet to get MRI sequences of sufficient quality to be analyzed. This could have resulted in a selection bias, impeding the generalizability of the results in severe patients. Finally, beyond the detection of strictly intracortical lesions, the impact of juxtacortical lesions might be the purpose of further studies, as it was shown in multiple sclerosis that this lesion’s location was a determinant of neuropsychological impairment.29


Chronic cortical CMIs are independent radiological markers associated with worse performances in psychomotor speed after an ischemic stroke and with a slowed down the improvement of psychomotor speed over the year following stroke. Further studies are needed to understand the pathophysiological mechanisms of these associations.


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

Correspondence to Sharmila Sagnier, MD, INCIA-UMR 5287-Université Bordeaux 2, 146 rue Léo Saignat Zone Nord, Bâtiment 2A 2e étage, 33076 Bordeaux Cedex, 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. Sagnier S, Catheline G, Dilharreguy B, Munsch F, Bigourdan A, Poli M, et al. Admission brain cortical volume: an independent determinant of poststroke cognitive vulnerability.Stroke. 2017; 48:2113–2120. doi: 10.1161/STROKEAHA.117.017646LinkGoogle Scholar
  • 3. van Veluw SJ, Zwanenburg JJ, Engelen-Lee J, Spliet WG, Hendrikse J, Luijten PR, et al. In vivo detection of cerebral cortical microinfarcts with high-resolution 7T MRI.J Cereb Blood Flow Metab. 2013; 33:322–329. doi: 10.1038/jcbfm.2012.196CrossrefMedlineGoogle Scholar
  • 4. Arvanitakis Z, Leurgans SE, Barnes LL, Bennett DA, Schneider JA. Microinfarct pathology, dementia, and cognitive systems.Stroke. 2011; 42:722–727. doi: 10.1161/STROKEAHA.110.595082LinkGoogle Scholar
  • 5. Smith EE, Schneider JA, Wardlaw JM, Greenberg SM. Cerebral microinfarcts: the invisible lesions.Lancet Neurol. 2012; 11:272–282. doi: 10.1016/S1474-4422(11)70307-6CrossrefMedlineGoogle Scholar
  • 6. Hilal S, Sikking E, Shaik MA, Chan QL, van Veluw SJ, Vrooman H, et al. Cortical cerebral microinfarcts on 3T MRI: a novel marker of cerebrovascular disease.Neurology. 2016; 87:1583–1590. doi: 10.1212/WNL.0000000000003110CrossrefMedlineGoogle Scholar
  • 7. Jorm AF. The Informant Questionnaire on cognitive decline in the elderly (IQCODE): a review.Int Psychogeriatr. 2004; 16:275–293.CrossrefMedlineGoogle Scholar
  • 8. 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
  • 9. 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.CrossrefMedlineGoogle Scholar
  • 10. Zazzo R. Manuel pour l’examen psychologique de l’enfant. Neuchâtel: Delachaux et Niestlé; 1969.Google Scholar
  • 11. Zigmond AS, Snaith RP. The hospital anxiety and depression scale.Acta Psychiatr Scand. 1983; 67:361–370.CrossrefMedlineGoogle Scholar
  • 12. van Dalen JW, Scuric EE, van Veluw SJ, Caan MW, Nederveen AJ, Biessels GJ, et al. Cortical microinfarcts detected in vivo on 3 Tesla MRI: clinical and radiological correlates.Stroke. 2015; 46:255–257. doi: 10.1161/STROKEAHA.114.007568LinkGoogle Scholar
  • 13. Fedorov A, Beichel R, Kalpathy-Cramer J, Finet J, Fillion-Robin JC, Pujol S, et al. 3D Slicer as an image computing platform for the Quantitative Imaging Network.Magn Reson Imaging. 2012; 30:1323–1341. doi: 10.1016/j.mri.2012.05.001CrossrefMedlineGoogle Scholar
  • 14. 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
  • 15. Zhu YC, Dufouil C, Mazoyer B, Soumaré A, Ricolfi F, Tzourio C, et al. Frequency and location of dilated Virchow-Robin spaces in elderly people: a population-based 3D MR imaging study.AJNR Am J Neuroradiol. 2011; 32:709–713. doi: 10.3174/ajnr.A2366CrossrefMedlineGoogle Scholar
  • 16. Staals J, Makin SD, Doubal FN, Dennis MS, Wardlaw JM. Stroke subtype, vascular risk factors, and total MRI brain small-vessel disease burden.Neurology. 2014; 83:1228–1234. doi: 10.1212/WNL.0000000000000837CrossrefMedlineGoogle Scholar
  • 17. Ashburner J, Friston KJ. Voxel-based morphometry–the methods.Neuroimage. 2000; 11(6 pt 1):805–821. doi: 10.1006/nimg.2000.0582CrossrefMedlineGoogle Scholar
  • 18. Scheltens P, Leys D, Barkhof F, Huglo D, Weinstein HC, Vermersch P, et al. Atrophy of medial temporal lobes on MRI in “probable” Alzheimer’s disease and normal ageing: diagnostic value and neuropsychological correlates.J Neurol Neurosurg Psychiatry. 1992; 55:967–972.CrossrefMedlineGoogle Scholar
  • 19. Hilal S, Chai YL, van Veluw S, Shaik MA, Ikram MK, Venketasubramanian N, et al. Association between subclinical cardiac biomarkers and clinically manifest cardiac diseases with cortical cerebral microinfarcts.JAMA Neurol. 2017; 74:403–410. doi: 10.1001/jamaneurol.2016.5335CrossrefMedlineGoogle Scholar
  • 20. Leng X, Fang H, Pu Y, Zou X, Pan Y, Soo YOY, et al. Cortical microinfarcts in patients with middle cerebral artery stenosis.J Stroke Cerebrovasc Dis. 2017; 26:1760–1765. doi: 10.1016/j.jstrokecerebrovasdis.2017.04.002CrossrefGoogle Scholar
  • 21. Brundel M, de Bresser J, van Dillen JJ, Kappelle LJ, Biessels GJ. Cerebral microinfarcts: a systematic review of neuropathological studies.J Cereb Blood Flow Metab. 2012; 32:425–436. doi: 10.1038/jcbfm.2011.200CrossrefMedlineGoogle Scholar
  • 22. O’Donnell MJ, Xavier D, Liu L, Zhang H, Chin SL, Rao-Melacini P, et al; INTERSTROKE Investigators. Risk factors for ischaemic and intracerebral haemorrhagic stroke in 22 countries (the INTERSTROKE study): a case-control study.Lancet. 2010; 376:112–123. doi: 10.1016/S0140-6736(10)60834-3CrossrefMedlineGoogle Scholar
  • 23. Shih AY, Blinder P, Tsai PS, Friedman B, Stanley G, Lyden PD, et al. The smallest stroke: occlusion of one penetrating vessel leads to infarction and a cognitive deficit.Nat Neurosci. 2013; 16:55–63. doi: 10.1038/nn.3278CrossrefMedlineGoogle Scholar
  • 24. Ferro DA, van Veluw SJ, Koek HL, Exalto LG, Biessels GJ; Utrecht Vascular Cognitive Impairment (VCI) study group. Cortical cerebral microinfarcts on 3 Tesla MRI in Patients with Vascular Cognitive Impairment.J Alzheimers Dis. 2017; 60:1443–1450. doi: 10.3233/JAD-170481CrossrefMedlineGoogle Scholar
  • 25. Wang Z, van Veluw SJ, Wong A, Liu W, Shi L, Yang J, et al. Risk factors and cognitive relevance of cortical cerebral microinfarcts in patients with ischemic stroke or transient ischemic attack.Stroke. 2016; 47:2450–2455. doi: 10.1161/STROKEAHA.115.012278LinkGoogle Scholar
  • 26. Coban H, Tung S, Yoo B, Vinters HV, Hinman JD. Molecular disorganization of axons adjacent to human cortical microinfarcts.Front Neurol. 2017; 8:405. doi: 10.3389/fneur.2017.00405CrossrefMedlineGoogle Scholar
  • 27. De Reuck JL, Auger F, Durieux N, Cordonnier C, Deramecourt V, Pasquier F, et al. The topography of cortical microinfarcts in neurodegenerative diseases and in vascular dementia: a postmortem 7.0-Tesla Magnetic Resonance Imaging Study.Eur Neurol. 2016; 76:57–61. doi: 10.1159/000447297CrossrefGoogle Scholar
  • 28. Seewann A, Vrenken H, Kooi EJ, van der Valk P, Knol DL, Polman CH, et al. Imaging the tip of the iceberg: visualization of cortical lesions in multiple sclerosis.Mult Scler. 2011; 17:1202–1210. doi: 10.1177/1352458511406575CrossrefGoogle Scholar
  • 29. Lazeron RH, Langdon DW, Filippi M, van Waesberghe JH, Stevenson VL, Boringa JB, et al. Neuropsychological impairment in multiple sclerosis patients: the role of (juxta)cortical lesion on FLAIR.Mult Scler. 2000; 6:280–285. doi: 10.1177/135245850000600410CrossrefGoogle Scholar


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