Brain iron deposits and lifespan cognitive ability

Several studies have reported associations between brain iron deposits and cognitive status, and cardiovascular and neurodegenerative diseases in older individuals, but the mechanisms underlying these associations remain unclear. We explored the associations between regional brain iron deposits and different factors of cognitive ability (fluid intelligence, speed and memory) in a large sample (n = 662) of individuals with a mean age of 73 years. Brain iron deposits in the corpus striatum were extracted automatically. Iron deposits in other parts of the brain (i.e., white matter, thalamus, brainstem and cortex), brain tissue volume and white matter hyperintensities (WMH) were assessed separately and semi-automatically. Overall, 72.8 % of the sample had iron deposits. The total volume of iron deposits had a small but significant negative association with all three cognitive ability factors in later life (mean r = −0.165), but no relation to intelligence in childhood (r = 0.043, p = 0.282). Regression models showed that these iron deposit associations were still present after control for a variety of vascular health factors, and were separable from the association of WMH with cognitive ability. Iron deposits were also associated with cognition across the lifespan, indicating that they are relevant for cognitive ability only at older ages. Iron deposits might be an indicator of small vessel disease that affects the neuronal networks underlying higher cognitive functioning. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s11357-015-9837-2) contains supplementary material, which is available to authorized users

The Brain Health Index: Towards a combined measure of neurovascular and neurodegenerative structural brain injury

Background: A structural magnetic resonance imaging measure of combined neurovascular and neurodegenerative burden may be useful as these features often coexist in older people, stroke and dementia. Aim: We aimed to develop a new automated approach for quantifying visible brain injury from small vessel disease and brain atrophy in a single measure, the brain health index. Materials and methods: We computed brain health index in N = 288 participants using voxel-based Gaussian mixture model cluster analysis of T1, T2, T2*, and FLAIR magnetic resonance imaging. We tested brain health index against a validated total small vessel disease visual score and white matter hyperintensity volumes in two patient groups (minor stroke, N = 157; lupus, N = 51) and against measures of brain atrophy in healthy participants (N = 80) using multiple regression. We evaluated associations with Addenbrooke’s Cognitive Exam Revised in patients and with reaction time in healthy participants. Results: The brain health index (standard beta = 0.20–0.59, P < 0.05) was significantly and more strongly associated with Addenbrooke’s Cognitive Exam Revised, including at one year follow-up, than white matter hyperintensity volume (standard beta = 0.04–0.08, P > 0.05) and small vessel disease score (standard beta = 0.02–0.27, P > 0.05) alone in both patient groups. Further, the brain health index (standard beta = 0.57–0.59, P < 0.05) was more strongly associated with reaction time than measures of brain atrophy alone (standard beta = 0.04–0.13, P > 0.05) in healthy participants. Conclusions: The brain health index is a new image analysis approach that may usefully capture combined visible brain damage in large-scale studies of ageing, neurovascular and neurodegenerative disease

Exploratory analysis of dietary intake and brain iron accumulation detected using magnetic resonance imaging in older individuals: the Lothian Birth Cohort 1936

Peer reviewedPublisher PD

Diffusion-weighted imaging lesions and risk of recurrent stroke after intracerebral haemorrhage

OBJECTIVE: To determine whether the presence of diffusion-weighted imaging-positive (DWI+) lesions is associated with recurrent stroke after intracerebral haemorrhage (ICH). METHODS: The REstart or STop Antithrombotics Randomised Trial (RESTART) assessed the effect of restarting versus avoiding antiplatelet therapy after ICH on major vascular events for up to 5 years. We rated DWI sequences of MRI done before randomisation for DWI+ lesion presence, masked to outcome and antiplatelet use. Cox proportional hazards regression models were used to quantify associations. RESULTS: Of 537 participants in RESTART, 247 (median (IQR) age 75.7 (69.6-81.1) years; 170 men (68.8%); 120 started vs 127 avoided antiplatelet therapy) had DWI sequences on brain MRI at a median of 57 days (IQR 19-103) after ICH, of whom 73 (30%) had one or more DWI+ lesion. During a median follow-up of 2 years (1-3), 18 participants had recurrent ICH and 21 had ischaemic stroke. DWI+ lesion presence was associated with all stroke, (adjusted HR 2.2 (95% CI 1.1 to 4.2)) and recurrent ICH (4.8 (95% CI 1.8 to 13.2)), but not ischaemic stroke (0.9 (95% CI 0.3 to 2.5)). DWI+ lesion presence (0.5 (95% CI 0.2 to 1.3)) vs absence (0.6 (95% CI 0.3 to 1.5), pinteraction=0.66) did not modify the effect of antiplatelet therapy on a composite outcome of recurrent stroke. CONCLUSIONS: DWI+ lesion presence in ICH survivors is associated with recurrent ICH, but not with ischaemic stroke. We found no evidence of modification of effects of antiplatelet therapy on recurrent stroke after ICH by DWI+ lesion presence. These findings provide a new perspective on the significance of DWI+ lesions, which may be markers of microvascular mechanisms associated with recurrent ICH. TRIAL REGISTRATION NUMBER: ISRCTN71907627

How Much Do Focal Infarcts Distort White Matter Lesions and Global Cerebral Atrophy Measures?

BACKGROUND: White matter lesions (WML) and brain atrophy are important biomarkers in stroke and dementia. Stroke lesions, either acute or old, symptomatic or silent, are common in older people. Such stroke lesions can have similar signals to WML and cerebrospinal fluid (CSF) on magnetic resonance (MR) images, and may be classified accidentally as WML or CSF by MR image processing algorithms, distorting WML and brain atrophy volume from the true volume. We evaluated the effect that acute or old stroke lesions at baseline, and new stroke lesions occurring during follow-up, could have on measurement of WML volume, cerebral atrophy and their longitudinal progression. METHODS: We used MR imaging data from patients who had originally presented with acute lacunar or minor cortical ischaemic stroke symptoms, recruited prospectively, who were scanned at baseline and about 3 years later. We measured WML and CSF volumes (ml) semi-automatically. We manually outlined the acute index stroke lesion (ISL), any old stroke lesions present at baseline, and new lesions appearing de novo during follow-up. We compared baseline and follow-up WML volume, cerebral atrophy and their longitudinal progression excluding and including the acute ISL, old and de novo stroke lesions. A non-parametric test (Wilcoxon's signed rank test) was used to compare the effects. RESULTS: Among 46 patients (mean age 72 years), 33 had an ISL visible on MR imaging (median volume 2.05 ml, IQR 0.88–8.88) and 7 of the 33 had old lacunes at baseline: WML volume was 8.54 ml (IQR 5.86–15.80) excluding versus 10.98 ml (IQR 6.91–24.86) including ISL (p < 0.001). At follow-up, median 39 months later (IQR 30–45), 3 patients had a de novo stroke lesion; total stroke lesion volume had decreased in 11 and increased in 22 patients: WML volume was 12.17 ml (IQR 8.54–19.86) excluding versus 14.79 ml (IQR 10.02–38.03) including total stroke lesions (p < 0.001). Including/excluding lacunes at baseline or follow-up also made small differences. Twenty-two of the 33 patients had tissue loss due to stroke lesions between baseline and follow-up, resulting in a net median brain tissue volume loss (i.e. atrophy) during follow-up of 24.49 ml (IQR 12.87–54.01) excluding versus 24.61 ml (IQR 15.54–54.04) including tissue loss due to stroke lesions (p < 0.001). Including stroke lesions in the WML volume added substantial noise, reduced statistical power, and thus increased sample size estimated for a clinical trial. CONCLUSIONS: Failure to exclude even small stroke lesions distorts WML volume, cerebral atrophy and their longitudinal progression measurements. This has important implications for design and sample size calculations for observational studies and randomised trials using WML volume, WML progression or brain atrophy as outcome measures. Improved methods of discriminating between stroke lesions and WML, and between tissue loss due to stroke lesions and true brain atrophy are required

Sample size considerations for trials using cerebral white matter hyperintensity progression as an intermediate outcome at 1 year after mild stroke: Results of a prospective cohort study

Background: White matter hyperintensities (WMHs) are commonly seen on in brain imaging and are associated with stroke and cognitive decline. Therefore, they may provide a relevant intermediate outcome in clinical trials. WMH can be measured as a volume or visually on the Fazekas scale. We investigated predictors of WMH progression and design of efficient studies using WMH volume and Fazekas score as an intermediate outcome. Methods: We prospectively recruited 264 patients with mild ischaemic stroke and measured WMH volume, Fazekas score, age and cardiovascular risk factors at baseline and 1 year. We modelled predictors of WMH burden at 1 year and used the results in sample size calculations for hypothetical randomised controlled trials with different analysis plans and lengths of follow-up. Results: Follow-up WMH volume was predicted by baseline WMH: a 0.73-ml (95% CI 0.65-0.80, p < 0.0001) increase per 1-ml baseline volume increment, and a 2.93-ml increase (95% CI 1.76-4.10, p < 0.0001) per point on the Fazekas scale. Using a mean difference of 1 ml in WMH volume between treatment groups, 80% power and 5% alpha, adjusting for all predictors and 2-year follow-up produced the smallest sample size (n = 642). Other study designs produced samples sizes from 2054 to 21,270. Sample size calculations using Fazekas score as an outcome with the same power and alpha, as well as an OR corresponding to a 1-ml difference, were sensitive to assumptions and ranged from 2504 to 18,886. Conclusions: Baseline WMH volume and Fazekas score predicted follow-up WMH volume. Study size was smallest using volumes and longer-term follow-up, but this must be balanced against resources required to measure volumes versus Fazekas scores, bias due to dropout and scanner drift. Samples sizes based on Fazekas scores may be best estimated with simulation studies

Dietary iodine exposure and brain structures and cognition in older people. Exploratory analysis in the Lothian Birth Cohort 1936

Background: Iodine deficiency is one of the three key micronutrient deficiencies highlighted as major public health issues by the World Health Organisation. Iodine deficiency is known to cause brain structural alterations likely to affect cognition. However, it is not known whether or how different (lifelong) levels of exposure to dietary iodine influences brain health and cognitive functions. Methods: From 1091 participants initially enrolled in The Lothian Birth Cohort Study 1936, we obtained whole diet data from 882. Three years later, from 866 participants (mean age 72 yrs, SD ±0.8), we obtained cognitive information and ventricular, hippocampal and normal and abnormal tissue volumes from brain structural magnetic resonance imaging scans (n=700). We studied the brain structure and cognitive abilities of iodine-rich food avoiders/low consumers versus those with a high intake in iodine-rich foods (namely dairy and fish). Results: We identified individuals (n=189) with contrasting diets, i) belonging to the lowest quintiles for dairy and fish consumption, ii) milk avoiders, iii) belonging to the middle quintiles for dairy and fish consumption, and iv) belonging to the middle quintiles for dairy and fish consumption. Iodine intake was secured mostly though the diet (n=10 supplement users) and was sufficient for most (75.1%, median 193 μg/day). In individuals from these groups, brain lateral ventricular volume was positively associated with fat, energy and protein intake. The associations between iodine intake and brain ventricular volume and between consumption of fish products (including fish cakes and fish-containing pasties) and white matter hyperintensities (p=0.03) the latest being compounded by sodium, proteins and saturated fats, disappeared after type 1 error correction. Conclusion: In this large Scottish older cohort, the proportion of individuals reporting extreme (low vs. high)/medium iodine consumption is small. In these individuals, low iodine-rich food intake was associated with increased brain volume shrinkage, raising an important hypothesis worth being explored for designing appropriate guidelines

On the computational assessment of white matter hyperintensity progression: difficulties in method selection and bias field correction performance on images with significant white matter pathology

Introduction Subtle inhomogeneities in the scanner’s magnetic fields (B0 and B1) alter the intensity levels of the structural magnetic resonance imaging (MRI) affecting the volumetric assessment of WMH changes. Here, we investigate the influence that (1) correcting the images for the B1 inhomogeneities (i.e. bias field correction (BFC)) and (2) selection of the WMH change assessment method can have on longitudinal analyses of WMH progression and discuss possible solutions. Methods We used brain structural MRI from 46 mild stroke patients scanned at stroke onset and 3 years later. We tested three BFC approaches: FSL-FAST, N4 and exponentially entropy-driven homomorphic unsharp masking (E2D-HUM) and analysed their effect on the measured WMH change. Separately, we tested two methods to assess WMH changes: measuring WMH volumes independently at both time points semi-automatically (MCMxxxVI) and subtracting intensity-normalised FLAIR images at both time points following image gamma correction. We then combined the BFC with the computational method that performed best across the whole sample to assess WMH changes. Results Analysis of the difference in the variance-to-mean intensity ratio in normal tissue between BFC and uncorrected images and visual inspection showed that all BFC methods altered the WMH appearance and distribution, but FSL-FAST in general performed more consistently across the sample and MRI modalities. The WMH volume change over 3 years obtained with MCMxxxVI with vs. without FSL-FAST BFC did not significantly differ (medians(IQR)(with BFC) = 3.2(6.3) vs. 2.9(7.4)ml (without BFC), p = 0.5), but both differed significantly from the WMH volume change obtained from subtracting post-processed FLAIR images (without BFC)(7.6(8.2)ml, p < 0.001). This latter method considerably inflated the WMH volume change as subtle WMH at baseline that became more intense at follow-up were counted as increase in the volumetric change. Conclusions Measurement of WMH volume change remains challenging. Although the overall volumetric change was not significantly affected by the application of BFC, these methods distorted the image intensity distribution affecting subtle WMH. Subtracting the FLAIR images at both time points following gamma correction seems a promising technique but is adversely affected by subtle WMH. It is important to take into account not only the changes in volume but also in the signal intensity

Sex Differences in Poststroke Cognitive Impairment: A Multicenter Study in 2343 Patients With Acute Ischemic Stroke

BACKGROUND: Poststroke cognitive impairment (PSCI) occurs in about half of stroke survivors. Cumulative evidence indicates that functional outcomes of stroke are worse in women than men. Yet it is unknown whether the occurrence and characteristics of PSCI differ between men and women. METHODS: Individual patient data from 9 cohorts of patients with ischemic stroke were harmonized and pooled through the Meta-VCI-Map consortium (n=2343, 38% women). We included patients with visible symptomatic infarcts on computed tomography/magnetic resonance imaging and cognitive assessment within 15 months after stroke. PSCI was defined as impairment in ≥1 cognitive domains on neuropsychological assessment. Logistic regression analyses were performed to compare men to women, adjusted for study cohort, to obtain odds ratios for PSCI and individual cognitive domains. We also explored sensitivity and specificity of cognitive screening tools for detecting PSCI, according to sex (Mini-Mental State Examination, 4 cohorts, n=1814; Montreal Cognitive Assessment, 3 cohorts, n=278). RESULTS: PSCI was found in 51% of both women and men. Men had a lower risk of impairment of attention and executive functioning (men: odds ratio, 0.76 [95% CI, 0.61-0.96]), and language (men: odds ratio, 0.67 [95% CI, 0.45-0.85]), but a higher risk of verbal memory impairment (men: odds ratio, 1.43 [95% CI, 1.17-1.75]). The sensitivity of Mini-Mental State Examination (<25) for PSCI was higher for women (0.53) than for men (0.27; P=0.02), with a lower specificity for women (0.80) than men (0.96; P=0.01). Sensitivity and specificity of Montreal Cognitive Assessment (<26.) for PSCI was comparable between women and men (0.91 versus 0.86; P=0.62 and 0.29 versus 0.28; P=0.86, respectively). CONCLUSIONS: Sex was not associated with PSCI occurrence but affected domains differed between men and women. The latter may explain why sensitivity of the Mini-Mental State Examination for detecting PSCI was higher in women with a lower specificity compared with men. These sex differences need to be considered when screening for and diagnosing PSCI in clinical practice

Network impact score is an independent predictor of post-stroke cognitive impairment: A multicenter cohort study in 2341 patients with acute ischemic stroke

BACKGROUND: Post-stroke cognitive impairment (PSCI) is a common consequence of stroke. Accurate prediction of PSCI risk is challenging. The recently developed network impact score, which integrates information on infarct location and size with brain network topology, may improve PSCI risk prediction. AIMS: To determine if the network impact score is an independent predictor of PSCI, and of cognitive recovery or decline. METHODS: We pooled data from patients with acute ischemic stroke from 12 cohorts through the Meta VCI Map consortium. PSCI was defined as impairment in ≥ 1 cognitive domain on neuropsychological examination, or abnormal Montreal Cognitive Assessment. Cognitive recovery was defined as conversion from PSCI 24 months) and cognitive recovery or decline using logistic regression. Models were adjusted for age, sex, education, prior stroke, infarct volume, and study site. RESULTS: We included 2341 patients with 4657 cognitive assessments. PSCI was present in 398/844 patients (47%) 24 months. Cognitive recovery occurred in 64/181 (35%) patients and cognitive decline in 26/287 (9%). The network impact score predicted PSCI in the univariable (OR 1.50, 95%CI 1.34-1.68) and multivariable (OR 1.27, 95%CI 1.10-1.46) GEE model, with similar ORs in the logistic regression models for specified post-stroke intervals. The network impact score was not associated with cognitive recovery or decline. CONCLUSIONS: The network impact score is an independent predictor of PSCI. As such, the network impact score may contribute to a more precise and individualized cognitive prognostication in patients with ischemic stroke. Future studies should address if multimodal prediction models, combining the network impact score with demographics, clinical characteristics and other advanced brain imaging biomarkers, will provide accurate individualized prediction of PSCI. A tool for calculating the network impact score is freely available at https://metavcimap.org/features/software-tools/lsm-viewer/