Abstract
Background & Purpose
Cerebral infarcts increase the risk for cognitive impairment. The relevance of location and number of infarcts with respect to cognitive function is less clear.
Methods
We studied the cross-sectional association between number and location of infarcts and cognitive performance in 4030 non-demented participants of the Age Gene/Environment Susceptibility-Reykjavik Study. Composite scores for memory (MEM), processing speed (SP) and executive function (EF) were created from a neuropsychological battery. Subcortical, cortical, and cerebellar infarcts were identified on brain MRI. We performed linear-regression analyses adjusted for demographic and vascular risk factors, depression, white matter lesions, and atrophy.
Results
Compared to participants with no infarcts, those with infarcts in multiple locations (n=287, 7%) had slower SP (β=-0.19, p<.001) and poorer MEM (β=-0.16, p<.001) and EF (β=-0.12, p=.003). Compared to no infarcts, the presence of either subcortical infarcts only (n=275) (β=-0.12, p=.016) or cortical infarcts only (n=215) (β=-0.17, p=.001) was associated with poorer MEM performance. Compared to no infarcts, a combination of cortical and subcortical infarcts (n=45) was associated with slower SP (β=-0.38, p<.001) and poorer EF (β=-0.22, p=.02), while a combination of cerebellar and subcortical infarcts (n=89) was associated with slower SP (β=-0.15, p=.04). Infarcts in all three locations was associated with slower SP (β=-0.33, p=.002).
Conclusions
Having infarcts in more than one location is associated with poor performance in memory, processing speed, and executive function, independent of cardiovascular comorbidities, white matter lesions and brain atrophy, suggesting that both the number and the distribution of infarcts jointly contribute to cognitive impairment.
Cerebral infarcts, common in older adults, increase the risk for cognitive impairment and dementia.1, 2 Depending on where they are located, infarcts may disrupt cerebral circuitry and impact function on specific cognitive abilities while sparing others. Frontal-subcortical circuits are associated with memory, information processing, and executive function.3-5 Cerebro-cerebellar circuits are associated with motor control and complex higher cognitive functions including executive function and memory.6, 7 In addition to location, number of infarcts may also be related to cognitive dysfunction.1, 2 Previous reports have found that, compared to having a single infarct, having two or more infarcts is associated with worse cognitive performance. 1, 2 While there is evidence suggesting both number and location of infarcts are independently clinically meaningful with respect to cognitive dysfunction, number and location of infarcts have not been examined together. It is possible that the combination of multiple infarcts in multiple locations is a stronger predictor of cognitive impairment than either infarct alone. Characterizing the cognitive effects of these two infarct parameters can contribute to our understanding of brain structure-function associations, and improve our identification of persons at particularly high risk for cognitive impairment.
Here we examine the role of infarct number and location on performance in three cognitive domains: processing speed, memory, and executive function. Data are from the population-based Age Gene-Environment Susceptibility - Reykjavik Study (AGES-Reykjavik).
Methods
The AGES-Reykjavik study is aimed at investigating the contributions of environmental factors, genetic susceptibility, and gene-environment interactions to aging of the neurocognitive, cardiovascular, musculoskeletal, body composition, and metabolic systems. Details on the study design and the baseline AGES-Reykjavik assessments have been described elsewhere.8 Briefly, participants are from the cohort of men and women born in 1907–1935, living in Reykjavik and who were followed as a part of the Reykjavik Study (RS) initiated in 1967 by the Icelandic Heart Association.9 In 2002, cohort members were re-invited to participate in AGES-Reykjavik. Here we report on 5764 participants who completed the AGES-Reykjavik exam, which included a structured survey instrument, cognitive testing, and brain MRI.
AGES-Reykjavik was approved by the Icelandic National Bioethics Committee (VSN 00-063), the Icelandic Data Protection Authority, and by the Institutional Review Board of the US National Institute on Aging, National Institutes of Health. Informed consent was signed by all participants.
MRI Scanning & Reading Protocol
MR Image acquisition
High resolution MR images were acquired on a 1.5T Signa Twinspeed system (General Electric Medical Systems, Waukesha, WI). The image protocol consisted of the following pulse sequences: a proton density (PD)/T2 - weighted fast spin echo (FSE) sequence (time to echo (TE)1, 22 ms; TE2, 90 ms; repetition time (TR), 3220 ms; echo train length, 8; flip angle (FA), 90°; field of view (FOV), 220 mm; matrix 256 × 256), a fluid attenuated inversion recovery (FLAIR) sequence (TE, 100 ms; TR, 8000 ms, Inversion time, 2000 ms, FA, 90°; FOV, 220 mm; matrix 256 × 256), a T2*-weighted gradient echo type echo planar (GRE-EPI) sequence (TE, 50 ms; TR, 3050 ms; FA, 90°; FOV, 220 mm; matrix, 256 × 256). The acquisition of these sequences was performed with 3-mm thick interleaved slices. Additionally, images were acquired with a T1-weighted three dimensional spoiled gradient echo (3D-SPGR) sequence (TE, 8 ms; TR, 21 ms; FA, 30; FOV 240 mm; matrix 256 × 256, slice thickness 1.5 mm). All images were acquired to give full brain coverage and slices were angled parallel to the anterior commissure - posterior commissure line in order to give reproducible image views in the oblique-axial plane.
Parenchymal defects [infarct-like lesions]
A parenchymal defect (infarct) was defined as a defect of the brain parenchyma with a signal intensity that is isointense to that of cerebrospinal fluid (CSF) on all pulse sequences (i.e. FLAIR, T2-weighted, PD-weighted). Cortical infarct-like lesions were defined as parenchymal defects involving or limited to the cortical ribbon and surrounded by an area of high signal intensity on FLAIR images. Subcortical infarct-like lesions were defined as parenchymal defects not extending into the cortex that are surrounded by an area of high signal intensity on FLAIR images with a minimal size diameter of 4mm. Defects in the subcortical area without a rim or area of high signal intensity on FLAIR, and without evidence of hemosiderin on the T2*-weighted GRE-EPI scan were labeled as large Virchow-Robin Spaces (VRS). Large VRS were excluded from the definition of subcortical infarcts for this analysis. There was no size criteria for defects in the cerebellum. Infarcts that spanned two areas were assigned to the location with the largest measured (mm) diameter of the defect regardless of orientation.
Image analyses were performed in a two step procedure. An experienced neuro-radiologist (OK) examined the scan for clinical abnormalities that needed immediate attention. At the same time, the neuro-radiologist recorded directly into a shared data base, the slice location of observed cortical and cerebellar infarcts. Trained raters with access to the shared data base identified subcortical infarcts and characterized all of the infarcts in more radiologic detail.
WML rating scale
WMLs are considered present in the case of signal intensity higher than normal white and grey matter on both T2-weighted and FLAIR images. The load of WMLs in the subcortical and periventricular regions is separately rated according to a scale with known properties.10 Briefly, the size of the lesion is measured at the largest diameter and categorized into small (≤3 mm), medium (4–10 mm), and large (>10 mm) lesions. The total load of subcortical WMLs of the whole brain was calculated as the weighted sum of the number and size of lesions.11 Periventricular WMLs are graded in the frontal caps, occipital-parietal caps, and bands based on size of the lesions: 0 (absent), 1 (>0 - 5 mm), 2 (6–9 mm), and 3 (≥ 10 mm). A total load of periventricular WMLs was calculated as the sum of lesion scores.
Atrophy
Total brain volume and volumes of gray and white matter, CSF, and white matter hyperintensities were computed automatically with an algorithm based on the Montreal Neurological Institute (MNI) pipeline.12 The AGES-Reykjavik/MNI pipeline has been modified to accommodate full brain coverage including the cerebellum and brain stem, multi-spectral images (T1-weighted 3D SPGR, FLAIR and PD/T2-weighted FSE sequences), high throughput, and minimal editing. A parameter of global brain atrophy was derived from the ratio of the total brain volume and the intra-cranial volume.
Quality control procedures
Every six months the intra-observer variability for each observer and every 3 months the inter-observer variability for the whole group of observers were assessed. The intra-observer weighted κ statistics were 0.89 for global WMLs and 0.92 for parenchymal defects; the inter-observer weighted κ statistics were 0.71 for global WMLs and 0.66 for parenchymal defects.
Tests of cognitive Function
The cognitive test battery included multiple tests of three cognitive domains. Similar to other population-based studies,13, 14 composites scores for memory (MEM), processing speed (SP) and executive function (EF) were constructed based on a theoretical grouping of tests. The MEM composite includes: California Verbal Learning Test (CVLT)15 immediate and delayed recall. The SP composite includes: the Digit Symbol Substitution Test,16 Figure Comparison17 and the Stroop Test Parts 1&2.18 The EF composite includes: Digits Backward,16 the CANTAB spatial working memory test19 and the Stroop Test Part III.18
All tests were normally distributed in the cohort and inter-rater reliability was excellent (Spearman correlations for specific cognitive tests range from 0.96 - 0.99). Composite measures were computed for each test by converting raw scores to standardized z-scores and averaging them across the tests in each composite. A confirmatory factor analysis, previously reported, showed that the fit of the composites was adequate.20
Diagnosis of Dementia
Dementia case ascertainment was a 3-step process. The Mini-Mental State Examination21 and the DSST16 were administered to all participants. Individuals who screened-positive based on a combination of these tests [<24 on the MMSE or <18 on the DSST] were administered a second, diagnostic test battery. Based on performance on the Trails B 22 and the Rey Auditory Verbal Learning test (AVLT) 4 a subset of these individuals (AVLT ≤ 18 or Trails B ≥ 8 for the ratio of time taken for Trails B / Trails A corrected for the number correct: [(time Trails B/number correct Trails B) / (time Trails A /number correct Trails A)]) went on to a third step. This step included a neurologic exam and a proxy interview about medical history and social, cognitive, and daily functioning relevant to the diagnosis. A consensus diagnosis of dementia based on the DSM-IV guidelines23 was made by a panel that included a geriatrician, neurologist, neuropsychologist, and neuroradiologist. There were 316 cases of dementia diagnosed in the first 5764 AGES-Reykjavik participants.
Potential Confounders
Based on previous reports, we controlled for a number of demographic (age, sex, and education) and health related confounding variables associated with infarcts and cognitive impairment. High depressive symptomatology was classified as a score of 6 or greater on the 15-item Geriatric Depression Scale.24 We adjusted for the following vascular risk factors: hypertension [self-reported doctor’s diagnosis of hypertension, use of hypertensive medications, systolic blood pressure ≥140 or diastolic blood pressure ≥90]; myocardial infarction (MI) defined as a self-reported doctor’s diagnosis or detected on ECG, and smoking status assessed via questionnaire (ever smoker/never smoker). We also adjusted for periventricular and subcortical WMLs and brain atrophy.
Analytic Sample and Strategy
Of the 5764 participants, 281 were excluded based on standard MRI contraindications and an additional 834 had incomplete MRI scans (due to claustrophobia, equipment failure, refusal or choosing only to participate in an in-home exam) or had insufficient scans for post-processing data on brain atrophy, giving a sample of 4614 MRI scans. Dementia likely confounds the relationship between infarcts and cognitive function since stroke is a risk factor for dementia 2 and demented subjects by definition have lower cognitive performance. Therefore, we excluded demented subjects from the analysis. Of the 4614 with MRI scans, 4030 were non-demented and had complete cognitive data. Compared to those included in the current analysis, those excluded (n=1734) were older (79.3 vs. 76.2; p<0.001), more likely to be female (58.7 vs. 55.4, age-adjusted p=0.02), had a higher prevalence of diabetes (17.5 vs. 11.2, age-adjusted p=<0.001) and, expectedly, had lower median MMSE scores (26 vs. 27).
Subsequently for the analysis, infarcts were summarized by number and location into four ‘frequency’ groups: 1. No infarcts, 2. Single infarct, 3. Multiple infarcts in the same location (cortical, subcortical or cerebellar), and 4. Multiple infarcts in multiple locations. To estimate the effect of the specific infarct locations we further classified the sample into eight ‘location’ groups: 1. No infarcts, 2. Cortical infarcts only, 3.Subcortical infarcts only, 4. Cerebellar infarcts only, 5. Cortical and subcortical infarcts, 6. Cortical and cerebellar infarcts, 7. Subcortical and cerebellar infarcts, 8. Infarcts in all three locations.
Age-adjusted linear models for continuous variables and age-adjusted logistic regression for dichotomous outcomes were used to compare demographic and health characteristics of the four infarct frequency groups. Multiple general linear regression models were used to examine differences in cognitive composite scores among the four infarct frequency groups, and then the eight infarct location groups; the group with no infarcts was the reference in both analyses. Three models were estimated: Model 1, adjusted for demographic factors and depression, Model 2 was additionally adjusted for subcortical and periventricular WML and atrophy, and Model 3 was fully adjusted for previously described health and vascular risk factors. Since the results of Models 2 and 3 were very similar, we present results from Models 1 and 3 only (Tables 2&3); results from Model 2 are presented in Supplemental Table 2. We present standardized beta coefficients in the text and in the tables at a significance level of p<0.05. Results can also be considered with Bonferroni adjustment for multiple comparisons at p<0.008 [(0.05/6) = 0.008].
Table 2.
Infarct number and location and cognitive performance in non-demented subjects adjusted for demographic and cardiovascular factors,, location of infarcts and WML: AGES-Reykjavik. N=4030.
| Memory | Processing Speed | Executive Function | |||||
|---|---|---|---|---|---|---|---|
| Number of Infarcts | N | Model 1† β (95 % CI) | Model 2‡ β (95 % CI) | Model 1†β (95 % CI) | Model 2‡ β (95 % CI) | Model 1† β (95 % CI) | Model 2‡ β (95 % CI) |
| No Infarcts | 2818 | reference | reference | reference | reference | reference | Reference |
| Single Infarct | 623 | -0.10** (-0.17, -0.04) | -0.07* (-0.14, -0.01) | -0.04 (-0.10, 0.02) | -0.01 (-0.07, 0.04) | -0.05 (-0.11, 0.01) | -0.03 (-0.08, 0.02) |
| Multiple Infarcts, single location | 302 | -0.07 (-0.16, 0.02) | -0.04 (-0.13, 0.05) | -0.10* (-0.18, -0.02) | -0.07 (-0.15, 0.01) | -0.06 (-0.13, 0.02) | -0.03 (-0.11, 0.04) |
| Multiple Infarcts, multiple locations | 287 | -0.22*** (-0.32, -0.13) | -0.16*** (-0.26, -0.07) | -0.26*** (-0.34, -0.17) | -0.19*** (-0.28, -0.11) | -0.17*** (-0.25, -0.09) | -0.12*** (-0.20, -0.04) |
Note.
Adjusted for age, education, sex, and depression.
Additional adjustments for subcortical and periventricular white matter lesions, brain atrophy, diabetes, smoking status, hypertension, MI, and total cholesterol
p<.05
p< .01
p<.001
Table 3.
Infarct location and cognitive performance in non-demented subjects adjusted for demographic and cardiovascular factors, cerebral infarcts and WML: AGES-Reykjavik. (N=4030)
| Memory | Processing Speed | Executive Function | |||||
|---|---|---|---|---|---|---|---|
| Number of Infarcts | N | Model 1† β (95 % CI) |
Model 2‡ β (95 % CI) |
Model 1† β (95 % CI) |
Model 2‡ β (95 % CI) |
Model 1† β (95 % CI) |
Model 2‡ β (95 % CI) |
| No Infarcts | 2818 | reference | reference | reference | reference | reference | reference |
| Cortical infarcts only | 215 | -0.22*** (-0.32, -0.11) | -0.17** (-0.28, -0.07) | -0.09* (-0.19, -0.01) | -0.05 (-0.14, 0.04) | -0.07 (-0.15, 0.02) | -0.04 (-0.12, 0.05) |
| Subcortical infarcts only | 275 | -0.14** (-0.24, -0.05) | -0.12* (-0.21, -0.02) | -0.10* (-0.18, -0.01) | -0.07 (-0.16, 0.01) | -0.06 (-0.14, 0.02) | -0.04 (-0.12, 0.04) |
| Cerebellar infarcts only | 435 | 0.01 (-0.08, 0.08) | 0.02 (-0.05, 0.10) | -0.02 (-0.09, 0.04) | -0.01 (-0.07, 0.07) | -0.04 (-0.11, 0.02) | -0.02 (-0.09, 0.04) |
| Cortical & Subcortical | 45 | -0.29* (-0.52, -0.07) | -0.22 (-0.44, 0.01) | 0.46*** (-0.66, -0.26) | -0.38*** (-0.57, -0.18) | -0.27** (-0.46, -0.07) | -0.22* (-0.41, -0.03) |
| Cortical & Cerebellar | 115 | -0.18* (-0.32, -0.04) | -0.13 (-0.28, 0.01) | -0.17** (-0.30, -0.05) | -0.11 (-0.24, 0.01) | -0.11 (-0.23, 0.01) | -0.07 (-0.19, 0.05) |
| Subcortical & Cerebellar | 89 | -0.21* (-0.37, -0.05) | -0.14 (-0.31, 0.02) | -0.21** (-0.35, -0.07) | -0.15* (-0.29, -0.01) | -0.17* (-0.31, -0.03) | -0.11 (-0.25, 0.02) |
| Infarcts in three locations | 38 | -0.31* (-0.55, -0.06) | -0.23 (-0.47, 0.01) | -0.39*** (-0.61, -0.18) | -0.33** (-0.54, -0.12) | -0.23* (-0.44, -0.03) | -0.17 (-0.38, 0.02) |
Note.
Adjusted for age, education, sex, and depression.
Additional adjustments for subcortical and periventricular white matter lesions, brain atrophy, diabetes, smoking status, hypertension, MI, and total cholesterol
p<.05
p< .01
p<.001
Results
Of the 4030 non-demented subjects with full cognitive and MRI data, 69.9% (n=2818) had no infarcts, 15.5% (n=623) had a single infarct, 7.5% (n=302) had multiple infarcts in the same location, and 7.1% (n=287) had multiple infarcts in multiple locations. Compared to participants with no infarcts, the other three infarct groups were older, less likely to be female, more likely to have a self-reported stroke, and lower average brain parenchymal volume. (Table 1) In addition, participants with multiple infarcts, were more likely to be hypertensive compared to those with no infarcts and their largest infarct was, on average, larger than the largest infarct of those with a single infarct. (Table 1) Performance on the individual cognitive tests varied by infarct group; results are provided in Supplemental Table 1.
Table 1.
Characteristics of non-demented subjects by infarct number and number of locations. AGES-Reykjavik (n=4030)
| No Infarcts (N=2818) | Single Infarct (N=623) | Multiple Infarcts, Single Location (N=302) | Multiple Infarcts, Multiple Locations (N=287) | |
|---|---|---|---|---|
| Sociodemographic factors | ||||
| Age, years | 75.5 (5.2) | 76.7 (5.5)‡ | 77.5 (5.3)† | 78.3 (5.3)‡ |
| Education,% primary | 22.5 | 20.7 | 22.9 | 21.4 |
| Sex, % Female | 63.3 | 52.6‡ | 44.6§ | 38.0§ |
| Depression | 5.7 | 5.3 | 8.1 | 9.2† |
| Cardiovascular risk factors, (%) | ||||
| Self reported stroke | 3.0 | 9.3§ | 10.6§ | 22.0§ |
| Hypertension | 77.9 | 83.1 | 86.8† | 89.2† |
| Ever Smoke | 54.2 | 61.5 | 64.2 | 60.3† |
| Infarct Characteristics | ||||
| Cortical, (%) | 0 | 23.4 | 22.8 | 69.0 |
| Subcortical, (%) | 0 | 33.7 | 21.5 | 59.9 |
| Cerebellar, (%) | 0 | 42.9 | 55.6 | 84.3 |
| Subcortical WML load, % top quartile | 21.6 | 32.3§ | 34.5§ | 43.4§ |
| Periventricular WML load, % top quartile | 18.4 | 26.1‡ | 31.9§ | 33.2§ |
| Brain parenchymal fraction | 0.73 (0.04) | 0.72 (0.04)§ | 71 (0.04)§ | 0.70 (0.04)§ |
| Number of infarcts, median (25th, 75th) | - | 1.0 (1.0, 1.0) | 2.0 (2.0, 3.0) | 2.0 (2.0, 5.0) |
| Size of largest infarct, (mm) median (25th, 75th) | - | 5.0 (6.0, 12.0) | 9.0 (6.0, 15.0) | 15.0 (6.0, 24.0) |
Data are shown as mean (SD) for continuous variables and % for categorical variables.
p ≤ 0.05,
p ≤ 0.01
p ≤ 0.001 for age-adjusted comparison with no infarct group.
The prevalence of cortical infarcts in the AGES-Reykjavik population was 10% (n=413), of subcortical infarcts was 11% (n=447), and of cerebellar infarcts was 17% (n=677). Among participants with infarcts in a single location (n=925; includes multiple infarcts in the same location), 23% (n=215) had cortical infarcts only, 30% (n=275) had subcortical infarcts only and 47% (n=435) had cerebellar infarcts only.
Among those with multiple infarcts (n=589), 51% (n=302) had multiple infarcts in a single location, 8% (n=45) had cortical and subcortical infarcts, 20% (n=115) had cortical and cerebellar infarcts, 15% (n=89) had subcortical and cerebellar infarcts and 6% (n=38) had infarcts in all three areas.
Number of Infarcts
Performance on the three cognitive composites varied both by the number of infarcts and whether or not infarcts were present in more than one location. Compared to participants with no infarcts, those with a single infarct performed significantly worse in MEM (Table 2; p=0.03) but performance on SP and EF tests was similar. Participants with multiple infarcts in a single location did not perform significantly different from those with no infarcts after adjustment (Table 2); significance was attenuated after adjustment for brain atrophy and white matter lesions (Supplemental Table 2). Participants with multiple infarcts in multiple locations had significantly lower MEM (p=0.0008) and EF (p=0.003) and significantly slower SP (p<.0001) compared to those with no infarcts (Table 2).
Location of Infarcts
In the fully adjusted models, participants with cerebellar infarcts only did not perform different from those with no infarcts on MEM, SP, or EF (Table 3). Participants with subcortical infarcts only and those with cortical infarcts only had significantly poorer MEM performance compared to those with no infarcts (Table 3; subcortical p=0.016; cortical p=0.001) but did not perform different on SP or EF.
Compared to participants with no infarcts, those who had both cortical and subcortical infarcts had significantly slower SP (p<0.001) and poorer EF (p = 0.024) performance (Table 3). The join effect on SP performance of having lesions in both the subcortical and cortical regions (-0.38) was greater than the additive effect of having infarcts in either the cortical or subcortical regions (i.e., -0.05 + -0.07 = -0.12; see Table 3), suggesting some synergism between cortical and subcortical infarcts on SP performance. Similar evidence for synergism of cortical and subcortical infarcts was observed for EF performance (see Table 3). Due to sample size, we did not formally test for interaction between cortical and subcortical infarcts.
Presence of both subcortical and cerebellar infarcts was associated with significantly slower SP (p=0.04). (Table 3) Participants with infarcts in all three areas had significantly slower SP (p=0.002) and marginally poorer MEM and EF (p=0.06).
Discussion
We examined the association of location and number of cerebral infarcts to the pattern of cognitive function in a population-based sample of non-demented older adults. We found single infarcts, specifically cortical infarcts, were associated with poor memory performance. We also found compared to participants with no infracts, those with multiple infarcts in the same location did not perform more poorly on tests of memory, processing speed or executive function. Importantly, we found that multiple infarcts in multiple locations were associated with poor performance in all three cognitive abilities. Specifically, the combination of subcortical and cortical infarcts was associated with slower processing speed and poorer executive function. These associations were independent of the presence of white matter lesions, brain atrophy, depressive symptomotology and cardiovascular risk factors. In addition, we found a higher prevalence of cerebellar infarcts than has been previously reported.2, 5, 25
This study has several strengths. Findings are based on a large population-based cohort, which is well characterized. This allowed us to adjust for several factors that have not been assessed in previous studies. We adjusted for other brain lesions, which could explain the association of infarcts to cognitive performance. Further, we could exclude those with clinical dementia, to reduce the potentially overwhelming effect of whatever pathology underlies the dementia and cannot be detected on MRI. A large sample reduces the impact of lower precision associated with good but not excellent inter-rater reliability. We derived composite cognitive test scores, which are more stable measures of cognitive domains than any one single test. Finally, we examined the association of cognitive performance to the occurrence of cerebral infarcts with a prevalence as low as 1% in the cohort.
However, results should be interpreted with caution. We did not have sufficient power to investigate thoroughly, the joint associations of multiple infarcts on multiple locations to cognitive performance. Both depression and cardiovascular risk factors may have an adverse effect on cognitive performance through multiple brain pathologies. Although we adjusted for these factors as well as other brain pathology visible on MRI, there may be residual confounding of these variables due to unmeasured risk factors or pathology not visible on our MR images. Finally, these results are cross-sectional and need to be repeated in longitudinal studies.
Previous studies have shown a higher risk of cognitive impairment and dementia in the presence of multiple infarcts compared to single or no infarcts.1, 2 We extend these findings to include not just the number of infarcts but the location of the infarcts. We find that those with multiple infarcts in a single location have slightly lower performance compared to those with no infarcts, but those with multiple infarcts in multiple locations had the lowest performance on all three abilities.
There are several mechanisms by which multiple infarcts in multiple locations may impact cognitive performance to a greater extent than a single infarct or multiple infarcts in a single location. Compensatory brain responses that contribute to plasticity and repair following infarcts may be inefficient when multiple brain regions are infarcted.26 In the case of a single infarct or infarctions in a single location, function may be retained if neural networks, such as frontal-subcortical circuits,3 enlist secondary areas that are connected to the injured area, restoring function. This compensation may be disrupted with multiple infarcted areas in the brain.
There was a high prevalence of cerebellar infarcts in our cohort (17%), higher than that reported in many other population-based cohorts.2, 5, 25 This is potentially due to differences in the age of the sample5 or spatial resolution (slice thickness and matrix size) of the MR images acquired in our study compared to others.2, 25
In conclusion, we found that having multiple infarcts in multiple locations, but not multiple infarcts in a single location, was associated with poor performance in memory, processing speed and executive function. These findings suggest number or location alone may not be adequate to describe the functional impairments due to infarcts. To further clarify the association of infarct location and function, longitudinal studies are needed, as are additional studies with more detailed mapping of infarct location, functional MRI and tractology as delineated from Diffusion Tensor Imaging.
Supplementary Material
Acknowledgments
This study was funded by National Institutes of Health contract N01-AG-12100, the National Institute on Aging Intramural Research Program, Hjartavernd (the Icelandic Heart Association), and the Althingi (the Icelandic Parliament). Components of the study were also supported by the National Eye Institute, the National Institute on Deafness and Other Communication Disorders, and the National Heart, Lung and Blood Institute.
References
- 1.Schneider JA, Wilson RS, Cochran EJ, Bienias JL, Arnold SE, Evans DA, Bennett DA. Relation of cerebral infarctions to dementia and cognitive function in older persons. Neurology. 2003;60:1082–1088. doi: 10.1212/01.wnl.0000055863.87435.b2. [DOI] [PubMed] [Google Scholar]
- 2.Vermeer SE, Prins ND, den Heijer T, Hofman A, Koudstaal PJ, Breteler MMB. Silent brain infarcts and the risk of dementia and cognitive decline. N Engl J Med. 2003;348:1215–1222. doi: 10.1056/NEJMoa022066. [DOI] [PubMed] [Google Scholar]
- 3.Cummings JL. Anatomic and behavioral aspects of frontal-subcortical circuits. Ann N Y Acad Sci. 1995;769:1–13. doi: 10.1111/j.1749-6632.1995.tb38127.x. [DOI] [PubMed] [Google Scholar]
- 4.Tekin S, Cummings JL. Frontal-subcortical neuronal circuits and clinical neuropsychiatry: An update. Journal of Psychosomatic Research. 2002;53:647–654. doi: 10.1016/s0022-3999(02)00428-2. [DOI] [PubMed] [Google Scholar]
- 5.Wright CB, Festa JR, Paik MC, Schmiedigen A, Brown TR, Yoshita M, DeCarli C, Sacco R, Stern Y. White matter hyperintensities and subclinical infarction: Associations with psychomotor speed and cognitive flexibility. Stroke. 2008;39:800–805. doi: 10.1161/STROKEAHA.107.484147. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6.Leiner HC, Leiner AL, Dow RS. The human cerebro-cerebellar system: Its computing, cognitive, and language skills. Behav Brain Res. 1991;44:113–128. doi: 10.1016/s0166-4328(05)80016-6. [DOI] [PubMed] [Google Scholar]
- 7.Schmahmann J. From movement to thought: Anatomica substrates of the cerebellar contribution to cognitive processing. Human Brain Mapping. 1996;4:174–198. doi: 10.1002/(SICI)1097-0193(1996)4:3<174::AID-HBM3>3.0.CO;2-0. [DOI] [PubMed] [Google Scholar]
- 8.Harris TB, Launer LJ, Eiriksdottir G, Kjartansson O, Jonsson PV, Sigurdsson G, Thorgeirsson G, Aspelund T, Garcia ME, Cotch MF, Hoffman HJ, Gudnason V. Age, gene/environment susceptibility-reykjavik study: Multidisciplinary applied phenomics. Am J Epidemiol. 2007;165:1076–1087. doi: 10.1093/aje/kwk115. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.Danesh J, Wheeler JG, Hirschfield GM, Eda S, Eiriksdottir G, Rumley A, Lowe GDO, Pepys MB, Gudnason V. C-reactive protein and other circulating markers of inflammation in the prediction of coronary heart disease. N Engl J Med. 2004;350:1387–1397. doi: 10.1056/NEJMoa032804. [DOI] [PubMed] [Google Scholar]
- 10.Achten E, Brenteler M, de Leeuw F, de Groot J, Scheltens P, Heyboer R, Oudekerk M. Rating scale fior age related brain changes. Imaging Decisions. 2002;4:10. [Google Scholar]
- 11.Launer LJ, Berger K, Breteler MM, Dufouil C, Fuhrer R, Giampaoli S, Nilsson LG, Pajak A, de Ridder M, van Dijk EJ, Sans S, Schmidt R, Hofman A. Regional variability in the prevalence of cerebral white matter lesions: An mri study in 9 european countries (cascade) Neuroepidemiology. 2006;26:23–29. doi: 10.1159/000089233. [DOI] [PubMed] [Google Scholar]
- 12.Zijdenbos AF, Evans A. Automatic “pipeline” analysis of 3-d mri data for clinical trials: Application to multiple sclerosis. IEEE Trans Med Imaging. 2002;21:1280–1292. doi: 10.1109/TMI.2002.806283. [DOI] [PubMed] [Google Scholar]
- 13.Wilson RS, Mendes de Leon CF, Barnes LL, Schneider JA, Bienias JL, Evans DA, Bennett DA. Participation in cognitively stimulating activities and risk of incident alzheimer disease. JAMA. 2002;287:742–748. doi: 10.1001/jama.287.6.742. [DOI] [PubMed] [Google Scholar]
- 14.de Groot JC, de Leeuw FE, Oudkerk M, van Gijn J, Hofman A, Jolles J, Breteler MM. Cerebral white matter lesions and cognitive function: The rotterdam scan study. Ann Neurol. 2000;47:145–151. doi: 10.1002/1531-8249(200002)47:2<145::aid-ana3>3.3.co;2-g. [DOI] [PubMed] [Google Scholar]
- 15.Kaplan E, Fein D, Morris R, Delis D. The wais-r as a neuropsychological instrument. San Antonio: Psychological Corporation; 1991. [Google Scholar]
- 16.Wechsler D. Wechsler adult intelligence scale. New York: Psychological Corporation; 1955. [Google Scholar]
- 17.Salthouse T, Babcock R. Decomposing adult age differences in executive function. Developmental Psychology. 1991;27:763–776. [Google Scholar]
- 18.Stroop J. Studies of interference in serial verbal reactions. Journal of Experimental Psychology. 1935;18:643–662. [Google Scholar]
- 19.Robbins T, James T, Owen A, et al. Cantab: A factor analytic study of a large sample of normal elderly volunteers. Dementia. 1994;5:266–281. doi: 10.1159/000106735. [DOI] [PubMed] [Google Scholar]
- 20.Saczynski J, Jonsdottir M, Sigurdsson S, Eiriksdottir G, Garcia M, Jonsson P, Kjartansson O, van Buchem M, Gudnason V, Launer L. White matter lesions and cognitive performance: The role of cognitively complex leisure activity. Journals of Gerontology: Medical Sciences. doi: 10.1093/gerona/63.8.848. in press. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21.Folstein MF, Folstein SE, McHugh PR. “Mini-mental state”. A practical method for grading the cognitive state of patients for the clinician. J Psychiatr Res. 1975;12:189–198. doi: 10.1016/0022-3956(75)90026-6. [DOI] [PubMed] [Google Scholar]
- 22.Reitan R. Validity of the trail making test as an indicator of organic brain damage. Perceptual and Motor Skills. 1958;8:271–276. [Google Scholar]
- 23.American Psychological Association. Diagnostic and statistical manual of mental disorders. Fourth edition. Wachington, DC: American Psychiatric Association; [Google Scholar]
- 24.Sheikh J, Yesavage J. Geriatric depression scale (gds): Recent evidence and development of a shorter version. New York: Hawthorne Press; 1986. [Google Scholar]
- 25.Bryan RN, Cai J, Burke G, Hutchinson RG, Liao D, Toole JF, Dagher AP, Cooper L. Prevalence and anatomic characteristics of infarct-like lesions on mr images of middle-aged adults: The atherosclerosis risk in communities study. AJNR Am J Neuroradiol. 1999;20:1273–1280. [PMC free article] [PubMed] [Google Scholar]
- 26.Steven CC. Repairing the human brain after stroke: I. Mechanisms of spontaneous recovery. Annals of Neurology. 2008;63:272–287. doi: 10.1002/ana.21393. [DOI] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.