Abstract
Cortical cerebral microinfarcts (CMIs) – a novel MRI marker of cerebral vascular pathology have been linked with dementia and impaired cognition in cross-sectional studies. However, it is unknown if cortical CMIs are an indicator of further cognitive decline. We sought to examine whether baseline cortical CMIs predict cognitive decline in a prospective memory-clinic setting. A total of 313 patients with baseline 3T MRI scans and at least two neuropsychological assessments obtained a minimum of one year apart were recruited. Cortical CMIs were graded on baseline MRI according to a validated protocol. The Montreal Cognitive Assessment (MoCA) and a detailed neuropsychological battery were used to assess cognition. Patients with increased cortical CMIs showed greater decline in MoCA and global cognition per year. Patients with > 2 cortical CMIs decline on average by 2 scores on MoCA and 0.5 on global cognition at year two which corresponds to 109.8% and 184.5% greater decline when compared to those without CMIs. Furthermore, cortical CMIs at baseline were associated with accelerated decline in memory and language domains. Similar associations were observed when analysis was restricted to demented patients. Cortical CMIs together with other cerebrovascular disease markers can be used to design clinical trials in vascular cognitive impairment.
Keywords: Cortical cerebral microinfarcts, cognitive decline, dementia, magnetic resonance imaging, memory clinic
Introduction
Cortical cerebral microinfarcts (CMIs) are small ischemic lesions which were first identified in autopsy studies.1 These lesions are more prevalent among persons with dementia and vascular cognitive impairment compared to cognitively normal individuals. Post-mortem studies have suggested that CMIs may number in hundreds or thousands in the brains of affected individuals likely affecting structural and functional brain connections.2,3 Previously considered as invisible lesions during life, cortical CMIs can now be detected in vivo on high resolution structural MRI making it possible to investigate longitudinal changes and the potential clinical implications of these lesions.4
Recently, cortical CMIs detected on 3T MRI have been associated with dementia and poorer performance on visuoconstruction and language in a memory clinic population.5 These findings were corroborated in a subsample of population-based study where cortical CMIs were linked with severe cognitive impairment independent of other cerebrovascular disease markers.6 Furthermore, persons with cortical CMIs performed worse in tasks assessing executive function, verbal and visual memory. However, these data have been restricted to cross-sectional studies which limit assessment of the temporal relationship between cortical CMIs and cognitive impairment. There is a growing need to clarify whether presence of cortical CMIs indicates an increased risk of cognitive decline as this may provide new insights in the link between subclinical cerebrovascular disease and cognitive dysfunction.
Thus far, only one study has examined the association of cortical CMIs with cognition longitudinally which reported that cortical CMIs at baseline were associated with worse performance in the visuospatial domain over 28 months as assessed by Montreal Cognitive Assessment (MoCA). However, the major limitations of this study were a small sample size and lack of detailed neuropsychological assessment.7 Thus, in the present study, we aimed to examine whether baseline cortical CMIs predict cognitive decline over a two year follow-up in a memory clinic setting. We further explored this association in patients with dementia as well as those without dementia.
Materials and methods
Study population
This study was conducted as part of an ongoing prospective memory clinic study which recruits individuals from two study sites in Singapore (National University Hospital and St. Luke's Hospital). Four diagnostic categories at baseline were eligible for inclusion in this study:8 (1) No cognitive impairment (NCI): individuals who had no objective cognitive impairment on neuropsychological tests, or functional loss, (2) Cognitive impairment no dementia (CIND) was diagnosed in patients who were impaired in at least one cognitive domain on a neuropsychological test battery without loss of daily functions. (3) Vascular CIND was defined as a history of ischemic stroke within the past 6–24 months and neuroimaging evidence of cerebral infarction, with objective evidence of neuropsychological deficits.9 (4) Dementia was diagnosed according to Diagnostic and Statistical Manual of Mental Disorders-Fourth Edition (DSM-IV) criteria. The etiological diagnoses of dementia were based on internationally accepted criteria: Alzheimer's Disease (AD) was diagnosed using the National Institute of Neurological and Communicative Disorders and Stroke and the Alzheimer's Disease and Related Disorders Association (NINCDS-ADRDA)10; Vascular dementia (VaD) was defined using the National Institute of Neurological Disorders and Stroke and Association Internationale pour la Recherché et l' Enseignement en Neurosciences (NINDS-AIREN) criteria.11
From 12 August 2010 till 30 May 2015, a total of 367 patients were recruited. All enrolled patients were invited to undergo clinical interviews, physical and laboratory examinations, neuroimaging and neuropsychological assessments at the National University of Singapore. These assessments were performed annually except for neuroimaging which was offered biennially. Of the 367 patients recruited, 29 had MRI scans ungradable for cortical CMIs and 9 had incomplete neuropsychological assessments at baseline leaving 338 patients eligible for this study. Of these 338 patients, seven died before the year 1 and nine before year 2 visits giving a final sample size of 313 patients. To be included in the cognitive decline analysis, patients had to have a minimum of two cognitive scores, i.e. baseline and one of the follow-up scores. Of the 313 patients, 220 had cognitive testing available at both year 1 and year 2, while 93 patients had at least one neuropsychological assessment on either of the follow-up visits (62 at year 1 and 31 at year 2). There were 39 cases of NCI, 54 CIND, 67 VCIND, 123 AD and 30 VaD. Due to small numbers, we combined NCI, CIND and VCIND into one group i.e. ‘without dementia’ and AD and VaD as ‘dementia’ in further analyses.
Ethics approval for this study was obtained from National Healthcare Group Domain-Specific Review Board. The study was conducted in accordance with the Declaration of Helsinki. Written informed consent was obtained, in the preferred language of the participants, by bilingual study coordinators prior to recruitment.
Neuroimaging
MRI was performed at baseline on a 3T Siemens Magnetom Trio Tim scanner, using a 32-channel head coil, at the Clinical Imaging Research Centre of the National University of Singapore. For each participant at baseline, the following MRI markers were determined:
Cortical CMIs grading
Cortical CMIs were graded on T1, T2-weighted and fluid-attenuated inversion recovery (FLAIR) sequences and were defined as hypointense lesions on T1-weighted images, < 5 mm in diameter, restricted to the cortex, and perpendicular to the cortical surface. The location of a hypointense cortical lesion found on T1 was confirmed on FLAIR and T2-weighted images. The lesion was rated as a definite cortical CMI if it was hyperintense or isointense (with the surrounding tissue) on FLAIR and T2 (Figure 1). The lesion was discarded as a cortical CMI if at the same location a hypointense signal was found on FLAIR or T2, indicating the T1 hypointense lesion was either due to a hemorrhagic lesion (confirmed on susceptibility-weighted imaging sequence), a vessel, or an artifact.5 Dilated perivascular spaces which extended from the white matter into the cortex, producing similar cortical signal intensities to those of cortical CMIs were not considered.6 Similarly, cortical CMIs in areas affected by large cortical infarcts were also discarded.6 MRI ratings for cortical CMIs were independently performed by two trained graders (SH, SvV) blinded to subject's characteristics. A subset of 100 scans was randomly selected among the scans which were graded by both raters. All the identified cortical CMIs were discussed in the weekly consensus meetings. The inter-rater reliability showed good to excellent agreement (kappa = 0.88).
Figure 1.
Cortical cerebral microinfarcts on 3Tesla MRI. Cortical cerebral microinfarcts (CMIs) were first explored on T1-weighted image (a) as hypointense lesion and further confirmed on T2-weighted image (b) and fluid-attenuated inversion recovery (FLAIR) (c) as hyperintense lesions. White boxes indicate area with CMIs as shown in the magnified view in insets. White arrowheads show additional CMIs other than the one shown in white boxes. This patient had 28 CMIs throughout the brain.
Other MRI markers
Quantitative MRI analyses were performed at the Department of Medical Informatics, Erasmus University Medical Center, the Netherlands. Total intracranial volume, total brain volume and white matter hyperintensity volume were quantified by automatic segmentation using the T1 and FLAIR sequences.12 Cortical and lacunar infarcts were graded on FLAIR and T2 sequences using the STandards for ReportIng Vascular changes on nEuroimaging (STRIVE) criteria.13 Cerebral microbleeds were graded on susceptibility-weighted images using the Brain Observer Microbleed Scale.14 Intracranial stenosis was graded on magnetic resonance angiography and was defined as stenosis ≥50% in the vertebral, basilar, internal carotids, posterior cerebral, middle cerebral and/or anterior cerebral arteries.
Neuropsychological assessment
All patients completed brief cognitive testing with the MoCA as well as a detailed neuropsychological battery based on the recommendation of the National Institute of Neurological Disorders and Stroke and the Canadian Stroke Network at each follow-up visit.15 The complete battery includes a 60-min protocol, which assesses six cognitive domains: (1) executive function: verbal fluency, color trail test A&B; (2) language: 15-item modified Boston Naming Test; (3) visuomotor speed: Symbol Digit Modalities Test; (4) visuospatial function: Rey Complex Figure Test-copy, (5) working memory: digit span forward and backward and (6) memory: Rey Complex Figure Test-immediate/delayed recall and recognition, Hopkins Verbal Learning Test-immediate/delayed recall and recognition.
All individual test raw scores on the National Institute of Neurological Disorders and Stroke and the Canadian Stroke Network battery were transformed to standardized z-scores using the means and standard deviation (SD) of the control group, i.e. NCI. The score for each domain was created by averaging the z-scores of individual tests and standardized using the composite mean and SD of the control group. To obtain the global cognition score for each patient, the domain z-scores were averaged and standardized using the mean and SD of the control group. At follow-up, global and domain-based cognitive z-scores were obtained using the means and SDs of the control group. Cognitive decline was determined as the difference in the global and domain specific scores between baseline and follow-up visits.
Covariate assessment
A detailed questionnaire was administered to all participants to collect information on age, gender, race and education. A previous history of hypertension, hyperlipidemia, type-II diabetes mellitus was also noted and subsequently verified by medical records. Physical examination included height, weight and blood pressure. Systolic and diastolic blood pressures were measured using a digital automatic blood pressure monitor after the subject rested for five minutes. Blood pressure was measured twice, 5 min apart. The mean of two readings was considered as the relevant blood pressure. Hypertension was defined as systolic blood pressure ≥140 mmHg and/or diastolic blood pressure ≥90 mmHg, or prescription of antihypertensive medication. Mean arterial blood pressure was calculated as two-thirds of the diastolic blood pressure plus one-third of the systolic blood pressure. Type-II diabetes mellitus was defined as glycated hemoglobin ≥6.5%, or use of anti-diabetic medication. Hyperlipidemia was defined as total cholesterol levels ≥4.14 mmol/l, or use of lipid lowering medication. Heart disease was defined as the previous history of atrial fibrillation, congestive heart failure, and myocardial infarction.
Statistical analysis
Baseline differences between patients with dementia and those without dementia were studied using the Student t-test, Mann–Whitney U test, or Chi-square test where applicable. We used a linear regression model with generalized estimating equations (GEE) to assess associations between the number of cortical CMIs and cognitive decline as measured by MoCA and detailed neuropsychological assessment. GEE was used because it allows us to account for the correlation of repeated measurements and obtain population mean estimates for the effects of cortical CMIs on cognition at each time point including baseline. We specified the correlation structure to be first-order autoregressive and robust variance estimators were used. To investigate whether the effect of baseline cortical CMIs on cognitive scores are different at each time point, we included an interaction term ‘cortical CMIs x time’. The first model included number of cortical CMIs, time and the interaction between cortical CMIs and time with age, sex and education as covariates and cognitive scores as the dependent variable. The second model was additionally adjusted for cardiovascular risk factors which included hyperlipidemia, hypertension, type-II diabetes mellitus and heart disease. The final model was adjusted for other MRI markers which included total brain volume, intracranial stenosis, cerebral microbleeds, white matter hyperintensities volume, lacunar infarcts, and cortical infarcts. All the analyses were performed for MoCA, global cognition and specific cognitive domains as outcomes in all patients and separately for patients with and without dementia. Because of the multiple testing performed within six cognitive domains, Bonferroni correction was applied to obtain a revised significance level of 0.05/6∼0.0083. This was applied to overall p value from the interaction term and subsequently for each time points of different cognitive domain. The estimated marginal mean of each cognition score at baseline, year 1 and year 2 was used to plot the trajectory of the expected cognition score among three groups of individuals: those without cortical CMI, with one to two cortical CMIs and with more than two cortical CMIs in order to investigate the potential dose-response effect. We modelled baseline cognition in longitudinal cognitive trajectories in order to appreciate the cognitive decline over follow-up visits compared to baseline scores. The “average” member in each group across each time-point was represented by the distribution of individuals across the levels within each categorical predictors, mean values of the continuous predictors, and the mean value of cortical CMIs in all patients as well as stratified by dementia status. The level of significance was set to 5% and all tests were two-sided. All statistical analysis was performed using standard statistical softwares (SPSS Version24 SPSS Inc., USA) and R.
Results
Baseline characteristics
In total, 313 participants [mean (SD) age, 72.8 (8.8) years, 170 women (54.3%)] underwent baseline and follow-up cognitive testing. Of 313 patients, 160 were categorized as without dementia and 153 as dementia. The prevalence of cortical CMIs at baseline in patients without dementia was significantly lower (15.6%) than in the dementia (21.6%) (p = 0.012). A higher prevalence of 1–2 and >2 CMIs were observed in patients with dementia compared to those without dementia. Compared to the non-demented group, persons with dementia were more likely to be older, often males, less educated and had a higher frequency of hypertension, type-II diabetes mellitus and other markers of cerebrovascular disease (intracranial stenosis and WMH volume) (Table 1).
Table 1.
Baseline characteristics of the study population (n = 313).
| Without dementia (n = 160) | Dementia (n = 153) | p | |
|---|---|---|---|
| Age, years, mean (SD) | 69.67 (8.53) | 76.20 (7.89) | <0.001 |
| Males, no. (%) | 83 (51.9) | 60 (39.2) | 0.025 |
| Education, number of years, mean (SD) | 7.49 (4.98) | 4.84 (4.57) | <0.001 |
| Hypertension, no. (%) | 110 (68.8) | 126 (82.4) | 0.005 |
| Hyperlipidemia, no. (%) | 123 (76.9) | 111 (72.5) | 0.378 |
| Type-II diabetes mellitus, no. (%) | 48 (30) | 63 (41.2) | 0.039 |
| Heart disease, no. (%) | 30 (18.8) | 28 (18.3) | 0.919 |
| Total brain volume, ml, mean (SD) | 888.38 (160.45) | 849.12 (187.61) | 0.051 |
| Intracranial stenosis, no (%) | 28 (17.8) | 40 (27.8) | 0.039 |
| Cerebral microbleeds, no. (%) | 81 (51.3) | 90 (61.6) | 0.068 |
| WMH volume, ml, median (IQR) | 2.61 (8.56) | 8.47 (21.39) | <0.001 |
| Lacunar infarcts, no. (%) | 63 (39.4) | 53 (34.6) | 0.386 |
| Cortical infarcts, no. (%) | 25 (15.6) | 33 (21.6) | 0.176 |
| Cortical cerebral microinfarcts, no. (%) | 36 (22.5) | 54 (35.3) | 0.012 |
| 1–2 microinfarcts | 26 (16.3) | 34 (22.2) | |
| >2 microinfarcts | 10 (6.3) | 20 (13.1) |
CMI: cortical cerebral microinfarcts; SD: standard deviation; no.: number; ml: milliliters; WMH: white matter hyperintensities; IQR: interquartile range. Bold values represent p < 0.05.
Cortical CMIs and cognitive decline in MoCA and global cognition
Table 2 shows the association of cortical CMIs with MoCA and global cognition at each follow-up visit in all patients and stratified by dementia status. Compared to patients with no cortical CMIs, patients with increased numbers of cortical CMIs at baseline showed decline in MoCA scores and global cognition at year 1 and year 2 after controlling for cardiovascular risk factors and other MRI markers. The interaction term between cortical CMIs and time was significant for MoCA (p ≤ 0.001) and global cognition (p ≤ 0.05). Similarly, in patients with and without dementia, the association between cortical CMIs and decline in MoCA scores was again observed at year 1 and year 2 (interaction between CMI and time, p ≤ 0.001). Additionally, in patients with dementia, increased number of cortical CMIs at baseline was associated with accelerated decline in global cognition at year 2 (interaction between cortical CMIs and time, p ≤ 0.001).
Table 2.
Association between cortical cerebral microinfarcts and cognitive decline.
| Study population | Time pointsa | MoCA Mean difference in scores per CMI increase (95%CI)b, p | p* | Global z-scores Mean difference in scores per CMI increase (95%CI)**, p | p* |
|---|---|---|---|---|---|
| All patients | Baseline | −0.18 (−0.32; −0.05), p = 0.009 | <0.001 | −0.09 (−0.13; −0.05), p ≤ 0.001 | 0.044 |
| Year 1 | −0.22 (−0.36; −0.08), p = 0.002 | −0.09 (−0.14; −0.04), p ≤ 0.001 | |||
| Year 2 | −0.30 (−0.44; −0.16), p ≤ 0.001 | −0.13 (−0.19; −0.07), p ≤ 0.001 | |||
| Without dementia | Baseline | −0.08 (−0.22; 0.05), p = 0.209 | <0.001 | −0.06 (−0.12; −0.01), p = 0.013 | 0.132 |
| Year 1 | −0.17 (−0.29; −0.05), p = 0.005 | −0.09 (−0.16; −0.02), p = 0.013 | |||
| Year 2 | −0.22 (−0.36; −0.08), p = 0.002 | −0.08 (−0.14; −0.02), p = 0.008 | |||
| Dementia | Baseline | −0.09 (−0.20; 0.03), p = 0.139 | <0.001 | −0.06 (−0.12; −0.01), p = 0.017 | <0.001 |
| Year 1 | −0.07 (−0.19; 0.05), p = 0.230 | −0.03 (−0.09; 0.03), p = 0.316 | |||
| Year 2 | −0.21 (−0.32; −0.09), p ≤ 0.001 | −0.12 (−0.17; −0.07), p ≤ 0.001 |
MoCA: Montreal cognitive assessment; CI: confidence interval.
Cortical cerebral microinfarcts treated as count data in the models.
Adjusted for age, sex, education, hyperlipidemia, hypertension, type-II diabetes mellitus, heart disease, lacunes, white matter hyperintensities, cerebral microbleeds, intracranial stenosis and total brain volume.
p value for the interaction between CMI and time. Bold values represent p < 0.05.
Cortical CMIs and domain-specific cognitive decline
In terms of the cognitive domain analysis, though a negative association was observed between increased number of cortical CMIs at baseline and cognitive performance, the effects were homogenous across all cognitive domains (non-significant interaction between cortical CMIs and time) except for memory (p < 0.001) and language (p = 0.036). These results were unaltered after controlling for all covariates in the models. The cognitive decline in tasks assessing language was more obvious at year 2, whereas in the case of memory domain, this was observed at both years (Table 3).
Table 3.
Association of cortical cerebral microinfarcts with cognitive decline in specific cognitive domains.
| Study population | Time pointsa | Executive function Mean difference (95%CI)b | p* | Attention Mean difference (95%CI)** | p* | Memory Mean difference (95%CI)b | p* | Language mean difference (95%CI)** | p* | Visuospatial Mean difference (95%CI)** | p* | Visuomotor speed Mean difference (95%CI)** | p* |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| All patients | Baseline | −0.07(−0.11; −0.03)c | 0.291 | −0.04(−0.07; −0.02)b | 0.945 | −0.01 (−0.03; 0.02) | <0.001‡ | −0.16(−0.25; −0.07)c | 0.036 | −0.07(−0.12; −0.02)b | 0.105 | −0.03(−0.05; −0.02)c | 0.394 |
| Year 1 | −0.09(−0.14; −0.05)c | −0.04(−0.06; −0.02)c | −0.04(−0.07; −0.01)a | −0.07 (−0.16; 0.03) | −0.09(−0.15; −0.03)b | −0.04(−0.06; −0.02)c | |||||||
| Year 2 | −0.08(−0.12; −0.04)c | −0.05(−0.08; −0.02)b | −0.05(−0.09; −0.01)a | −0.25(−0.46; −0.04)a | −0.08(−0.15; −0.02)b | −0.04(−0.06; −0.02)c | |||||||
| Without dementia | Baseline | −0.04 (−0.09; 0.01) | 0.103 | −0.04 (−0.08; 0.01) | 0.760 | −0.01 (−0.04; 0.03) | <0.001‡ | −0.05(−0.09; −0.01)a | 0.327 | −0.10(−0.19; −0.01)a | 0.644 | −0.03(−0.07; −0.00)a | 0.002‡ |
| Year 1 | −0.09(−0.19; −0.00)a | −0.05(−0.09; −0.00)a | −0.01 (−0.05; 0.02) | −0.02 (−0.08; 0.04) | −0.12 (−0.24; 0.00) | −0.05(−0.09; −0.01)a | |||||||
| Year 2 | −0.07 (−0.15; 0.00) | −0.04(−0.08; −0.01)a | −0.02 (−0.06; 0.02) | −0.03 (−0.08; 0.03) | −0.12 (−0.25; 0.01) | −0.05(−0.09; −0.02)b,‡ | |||||||
| Dementia | Baseline | −0.04(−0.08; −0.01)a | 0.254 | −0.05(−0.07; −0.02)c,‡ | 0.001‡ | 0.02 (−0.00; 0.05) | <0.001‡ | −0.14(−0.28; 0.00)a | <0.001‡ | −0.04 (−0.07; 0.00) | 0.104 | −0.02(−0.03; −0.01)b | 0.967 |
| Year 1 | −0.05(−0.08; −0.01)b | −0.02 (−0.05; 0.00) | −0.01 (−0.04; 0.01) | 0.01 (−0.15; 0.17) | −0.05(−0.09; −0.02)b | −0.02(−0.03; −0.01)c | |||||||
| Year 2 | −0.03(−0.06; −0.00)c | −0.04(−0.06; −0.01)d,‡ | −0.03(−0.05; −0.01)d,‡ | −0.32(−0.46; −0.17)e‡ | −0.04(−0.08; −0.00)c | −0.02(−0.04; −0.01)d |
CI: confidence interval
Cortical cerebral microinfarcts treated as count data in the models.
Adjusted for age, sex, education, hyperlipidemia, hypertension, type-II diabetes mellitus, heart disease, lacunes, white matter hyperintensities, cerebral microbleeds, intracranial stenosis and total brain volume.
p value between 0.05–0.01.
p value between 0.01–0.001.
p value < 0.001.
p value for the interaction between CMI and time.
Significant after Bonferroni correction p < 0.0083 Bold values represent p < 0.05.
On stratified analysis, an association between cortical CMIs and cognitive decline in visuomotor speed was observed at both years (interaction between cortical CMIs and time was significant, p = 0.002) in patients without dementia. Similarly, in patients with dementia, a negative association was again observed between increased CMI numbers and cognitive decline; moreover, a greater decline with time was observed in the domains of attention, memory and language [interaction between cortical CMI and time for attention (p = 0.001), memory (p ≤ 0.001), language (p ≤ 0.001)] (Table 3). After applying Bonferroni correction, these associations remained significant.
Cortical CMIs and cognitive trajectories
The effects of cortical CMIs on different cognitive trajectories of all patients are shown in Figure 2. For MoCA and global cognition, the decline among patients with >2 cortical CMIs was steeper compared to those who had less than two CMIs and no cortical CMIs. Patients with > 2 cortical CMIs decline on average by 2 scores on MoCA and 0.5 on global cognition at year two which corresponds to 109.8% and 184.5% greater decline when compared to those without cortical CMIs. Similar findings were observed for memory and language scores where the decline persisted in both years 1 and 2 for memory but only in year 2 for language. On stratified analysis by dementia status, demented patients with >2 cortical CMIs decline on an average score of 2.7 and 0.9 on MoCA and global cognition at year 2, respectively, corresponding to 150.7% and 180.5% greater decline when compared to those without cortical CMIs. Similar findings were observed for attention, memory and language scores in demented patients. By contrast, non-demented patients with > 2 cortical CMIs decline on an average score of 0.8 on MoCA at year 2 which was consistently observed in the domain of visuomotor speed (Figure 2).
Figure 2.
Cortical cerebral microinfarcts and cognitive trajectories. Estimated marginal means of MoCA and cognitive z scores of all patients (a–d), patients without dementia (e–h)) and patients with dementia (i–l) over follow-up visits for cortical cerebral microinfarct (CMI) counts among patients with no CMIs (black line), 1–2 CMIs (gray line) and >2 CMIs (light gray line) after adjustment for age, gender, education, cardiovascular risk factors and MRI markers. Vertical lines represent 95% confidence interval.
Discussion
In this memory clinic population, we showed that increased cortical CMI numbers at baseline predicted accelerated cognitive decline in overall cognition as well as in memory and language domains over two years of follow-up. This cognitive decline was more steeper in patients with dementia compared to those without dementia even after modelling baseline cognition. The interaction effects between cortical CMIs and time in the association between cortical CMI numbers and worse performance in memory and language domains were significant. These findings support the hypothesis that cortical CMIs are an important biomarker of a microvascular contribution to cognitive decline.
Recently, cortical CMIs have gained increasing attention because of the feasibility of detecting these lesions on 3T MRI making it possible to study the clinical relevance and longitudinal consequences of these lesions in the clinical setting. To date, there is only one study exploring the effects of cortical CMIs on cognitive decline which was in a stroke population. It was reported that cortical CMIs at baseline were associated with poor visuospatial performance at baseline and predicted its decline over 28 months.7 However, the association of cortical CMI with visuospatial domain was tested using a brief cognitive test (i.e. MoCA) which is a limited measurement of cognitive function. Moreover, such associations were possibly due to higher burden of vascular risk factors in the stroke population. Our study adds further to the previous literature by showing that cortical CMIs, especially a higher number of cortical CMIs at baseline exert deleterious effects on cognitive functioning eventually leading to cognitive decline which increased with increasing severity of cognitive impairment. The significant interaction between cortical CMIs and time showed that the negative effects of cortical CMI on cognitive domains increased with length of follow-up. This deleterious effect in memory and language domains was more obvious in patients with ≥2 cortical CMIs. This finding is consistent with a previous autopsy study where a relationship was reported between cortical CMIs and worse performance on semantic memory, perceptual speed, and visuospatial abilities measured antemortem.16 A recent population-based study has further shown that cortical CMIs were associated with poorer memory and executive function in a cross-sectional analysis.6 In the current study, memory was defined as a compound of working memory and immediate, delayed and recognition recall scores. Therefore, a decline in memory together with attention and language could be ascribed not only to temporal lobe but also to frontal and parietal lobe (regions with high preponderance of CMIs) because memory may be affected via executive function and attention. In this study, we also showed association between increased number of cortical CMIs and worse performance in the domain of visuomotor speed (task that is known to be related to vascular damage in subcortical regions) which is consistent with previous finding.
The mechanisms by which cortical CMIs affect cognition remain unexplored and may be causal or non-causal.1 Cortical CMIs located strategically in brain regions may cause focal damage as well as concomitant microstructural damage of the surrounding tissue.17 It must be acknowledge that the presence of single cortical CMI reflects hundreds and thousands of CMIs in the rest of the brain leading to disruption of neuronal tracts and thus interference with specific cognitive domains.2 On the other hand, cortical CMIs are also suggested to be a proxy for underlying cerebrovascular disease, and their higher numbers may indicate more extensive and severe microvascular damage. It has been reported that these lesions often coexist with other cerebrovascular disease, share risk factors and possibly indicate a single pathologic continuum.3,5 However, our results showed that the association of cortical CMIs with accelerated cognitive decline was independent of other cardiovascular risk factors and MRI markers of cerebrovascular disease suggesting that these associations were not simply due to confounding by other vascular mechanisms. Our finding further suggest disruption of important cortical and subcortical tracts especially those located in the thalamic nuclei which are mainly involved in storage and short-term memory and is consistent with memory impairment, the hallmark of AD, also being present in vascular-related cognitive impairment.18 The association of cortical CMI with decline in cortical function domain such as language as well as the subcortical regions, i.e. attention and visuomotor speed are consistent with the hypothesis that cerebral microvascular damage affects both gray and white matter, with disruption of integrity of frontal-subcortical circuits.18,19
Our study has several strengths: first, detection of cortical CMIs on 3T has made it possible to link these lesions with clinical consequences in a longitudinal setting. Second, the availability of comprehensive data on neuroimaging and neuropsychological assessment made it possible to capture the full burden of cognitive impairment and hence correlate cortical CMIs with cognitive dysfunction. Moreover, the extensive characterization of the participants enabled us to adjust for series of potential confounders including other MRI markers of cerebrovascular disease. Limitations of the study include: first, only larger cortical CMIs (≥1mm) can be detected on 3T MRI, thus underestimating the true burden of these lesions.1 However, despite the fact that only a proportion of the total cortical CMI burden is captured with MRI, we were able to find significant association between cortical CMIs and cognitive decline suggesting that the true effect estimates may have been larger. Second, we did not study the relationship between incident cortical CMIs on follow up scan and cognitive decline but this would be of interest in future studies. Third, our results are not generalizable to population-based studies and non-memory clinic elderly patients who may have other risk factor profiles. Fourth, though we adjusted for all possible confounders, we cannot fully exclude the possibility of residual confounding. Fifth, though baseline cognition was lower in patients with cortical CMIs, the cognitive decline was independent of baseline scores and was present in both demented and non-demented patients. Finally, we did not take into account the presence of new cerebrovascular lesions which might have occurred on follow-up visits.
Future studies should elucidate how progression of cortical CMIs relates to cognitive impairment over time. From a clinical perspective, the association between cortical CMIs and accelerated cognitive decline is important as this suggests that cortical CMIs are a marker of steeper cognitive decline, and hence should be a target for treatment and prevention strategies in the elderly.
Funding
The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This study is funded by the Singapore National Medical Research Council (grants NMRC/CG/NUHS/2010 and NMRC/CG/013/2013).
Declaration of conflicting interests
The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
Authors' contributions
SH is responsible for study concept and design, participated in data acquisition, performed statistical analysis, drafting and revising the manuscript. CST participated in statistical analysis and revising the manuscript for intellectual content. SvV, XX, HV, TBY, NV and GJB participated in data acquisition and revising the manuscript for intellectual content. CC was responsible for study concept and design, obtaining funding and revising the manuscript.
References
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