Impact of long-term white matter hyperintensity changes on mobility and dexterity.

White matter hyperintensities (WMH), a common feature of cerebral small vessel disease, are related to worse clinical outcomes after stroke. We assessed the impact of white matter hyperintensity changes over 1 year after minor stroke on change in mobility and dexterity, including differences between the dominant and non-dominant hands and objective in-person assessment versus patient-reported experience. We recruited participants with lacunar or minor cortical ischaemic stroke, performed medical and cognitive assessments and brain MRI at presentation and at 1 year. At both time points, we used the timed-up and go test and the 9-hole peg test to assess mobility and dexterity. At 1 year, participants completed the Stroke Impact Scale. We ran two linear mixed models to assess change in timed-up and go and 9-hole peg test, adjusted for age, sex, stroke severity (National Institutes of Health Stroke Scale), dependency (modified Rankin Score), vascular risk factor score, white matter hyperintensity volume (as % intracranial volume) and additionally for 9-hole peg test: Montreal cognitive assessment, hand (dominant/non-dominant), National Adult Reading Test (premorbid IQ), index lesion side. We performed ordinal logistic regression, corrected for age and sex, to assess relations between timed-up and go and Stroke Impact Scale mobility, and 9-hole peg test and Stroke Impact Scale hand function. We included 229 participants, mean age 65.9 (standard deviation = 11.13); 66% male. 215/229 attended 1-year follow-up. Over 1 year, timed-up and go time increased with aging (standardized ß [standardized 95% Confidence Interval]: 0.124[0.011, 0.238]), increasing National Institutes of Health Stroke Scale (0.106[0.032, 0.180]), increasing modified Rankin Score (0.152[0.073, 0.231]) and increasing white matter hyperintensity volume (0.176[0.061, 0.291]). Men were faster than women (-0.306[0.011, 0.238]). Over 1 year, slower 9-hole peg test was related to use of non-dominant hand (0.290[0.155, 0.424]), aging (0.102[0.012, 0.192]), male sex (0.182[0.008, 0.356]), increasing National Institutes of Health Stroke Scale (0.160 [0.094, 0.226]), increasing modified Rankin Score (0.100[0.032, 0.169]), decreasing Montreal cognitive assessment score (-0.090[-0.167, -0.014]) and increasing white matter hyperintensity volume (0.104[0.015, 0.193]). One year post-stroke, Stroke Impact Scale mobility worsened per second increase on timed-up and go, odds ratio 0.67 [95% confidence interval 0.60, 0.75]. Stroke Impact Scale hand function worsened per second increase on the 9-hole peg test for the dominant hand (odds ratio 0.79 [0.71, 0.86]) and for the non-dominant hand (odds ratio 0.88 [0.83, 0.93]). Decline in mobility and dexterity is associated with white matter hyperintensity volume increase, independently of stroke severity. Mobility and dexterity declined more gradually for stable and regressing white matter hyperintensity volume. Dominant and non-dominant hands might be affected differently. In-person measures of dexterity and mobility are associated with self-reported experience 1-year post-stroke.

Statin Therapy for Secondary Prevention in Ischemic Stroke Patients With Cerebral Microbleeds.

BACKGROUND AND OBJECTIVES: The association between statin use and the risk of intracranial hemorrhage (ICrH) following ischemic stroke (IS) or transient ischemic attack (TIA) in patients with cerebral microbleeds (CMBs) remains uncertain. This study investigated the risk of recurrent IS and ICrH in patients receiving statins based on the presence of CMBs. METHODS: We conducted a pooled analysis of individual patient data from the Microbleeds International Collaborative Network, comprising 32 hospital-based prospective studies fulfilling the following criteria: adult patients with IS or TIA, availability of appropriate baseline MRI for CMB quantification and distribution, registration of statin use after the index stroke, and collection of stroke event data during a follow-up period of ≥3 months. The primary endpoint was the occurrence of recurrent symptomatic stroke (IS or ICrH), while secondary endpoints included IS alone or ICrH alone. We calculated incidence rates and performed Cox regression analyses adjusting for age, sex, hypertension, atrial fibrillation, previous stroke, and use of antiplatelet or anticoagulant drugs to explore the association between statin use and stroke events during follow-up in patients with CMBs. RESULTS: In total, 16,373 patients were included (mean age 70.5 ± 12.8 years; 42.5% female). Among them, 10,812 received statins at discharge, and 4,668 had 1 or more CMBs. The median follow-up duration was 1.34 years (interquartile range: 0.32-2.44). In patients with CMBs, statin users were compared with nonusers. Compared with nonusers, statin therapy was associated with a reduced risk of any stroke (incidence rate [IR] 53 vs 79 per 1,000 patient-years, adjusted hazard ratio [aHR] 0.68 [95% CI 0.56-0.84]), a reduced risk of IS (IR 39 vs 65 per 1,000 patient-years, aHR 0.65 [95% CI 0.51-0.82]), and no association with the risk of ICrH (IR 11 vs 16 per 1,000 patient-years, aHR 0.73 [95% CI 0.46-1.15]). The results in aHR remained consistent when considering anatomical distribution and high burden (≥5) of CMBs. DISCUSSION: These observational data suggest that secondary stroke prevention with statins in patients with IS or TIA and CMBs is associated with a lower risk of any stroke or IS without an increased risk of ICrH. CLASSIFICATION OF EVIDENCE: This study provides Class III evidence that for patients with IS or TIA and CMBs, statins lower the risk of any stroke or IS without increasing the risk of ICrH.

Magnetic Resonance Imaging Tissue Signatures Associated With White Matter Changes Due to Sporadic Cerebral Small Vessel Disease Indicate That White Matter Hyperintensities Can Regress.

BACKGROUND: White matter hyperintensities (WMHs) might regress and progress contemporaneously, but we know little about underlying mechanisms. We examined WMH change and underlying quantitative magnetic resonance imaging tissue measures over 1 year in patients with minor ischemic stroke with sporadic cerebral small vessel disease. METHODS AND RESULTS: We defined areas of stable normal-appearing white matter, stable WMHs, progressing and regressing WMHs based on baseline and 1-year brain magnetic resonance imaging. In these areas we assessed tissue characteristics with quantitative T1, fractional anisotropy (FA), mean diffusivity (MD), and neurite orientation dispersion and density imaging (baseline only). We compared tissue signatures cross-sectionally between areas, and longitudinally within each area. WMH change masks were available for N=197. Participants' mean age was 65.61 years (SD, 11.10), 59% had a lacunar infarct, and 68% were men. FA and MD were available for N=195, quantitative T1 for N=182, and neurite orientation dispersion and density imaging for N=174. Cross-sectionally, all 4 tissue classes differed for FA, MD, T1, and Neurite Density Index. Longitudinally, in regressing WMHs, FA increased with little change in MD and T1 (difference estimate, 0.011 [95% CI, 0.006-0.017]; -0.002 [95% CI, -0.008 to 0.003] and -0.003 [95% CI, -0.009 to 0.004]); in progressing and stable WMHs, FA decreased (-0.022 [95% CI, -0.027 to -0.017] and -0.009 [95% CI, -0.011 to -0.006]), whereas MD and T1 increased (progressing WMHs, 0.057 [95% CI, 0.050-0.063], 0.058 [95% CI, 0.050 -0.066]; stable WMHs, 0.054 [95% CI, 0.045-0.063], 0.049 [95% CI, 0.039-0.058]); and in stable normal-appearing white matter, MD increased (0.004 [95% CI, 0.003-0.005]), whereas FA and T1 slightly decreased and increased (-0.002 [95% CI, -0.004 to -0.000] and 0.005 [95% CI, 0.001-0.009]). CONCLUSIONS: Quantitative magnetic resonance imaging shows that WMHs that regress have less abnormal microstructure at baseline than stable WMHs and follow trajectories indicating tissue improvement compared with stable and progressing WMHs.

Evaluating artificial intelligence software for delineating hemorrhage extent on CT brain imaging in stroke: AI delineation of ICH on CT.

BACKGROUND: The extent and distribution of intracranial hemorrhage (ICH) directly affects clinical management. Artificial intelligence (AI) software can detect and may delineate ICH extent on brain CT. We evaluated e-ASPECTS software (Brainomix Ltd.) performance for ICH delineation. METHODS: We qualitatively assessed software delineation of ICH on CT using patients from six stroke trials. We assessed hemorrhage delineation in five compartments: lobar, deep, posterior fossa, intraventricular, extra-axial. We categorized delineation as excellent, good, moderate, or poor. We assessed quality of software delineation with number of affected compartments in univariate analysis (Kruskall-Wallis test) and ICH location using logistic regression (dependent variable: dichotomous delineation categories 'excellent-good' versus 'moderate-poor'), and report odds ratios (OR) and 95 % confidence intervals (95 %CI). RESULTS: From 651 patients with ICH (median age 75 years, 53 % male), we included 628 with assessable CTs. Software delineation of ICH extent was 'excellent' in 189/628 (30 %), 'good' in 255/628 (41 %), 'moderate' in 127/628 (20 %), and 'poor' in 57/628 cases (9 %). The quality of software delineation of ICH was better when fewer compartments were affected (Z = 3.61-6.27; p = 0.0063). Software delineation of ICH extent was more likely to be 'excellent-good' quality when lobar alone (OR = 1.56, 95 %CI = 0.97-2.53) but 'moderate-poor' with any intraventricular (OR = 0.56, 95 %CI = 0.39-0.81, p = 0.002) or any extra-axial (OR = 0.41, 95 %CI = 0.27-0.62, p<0.001) extension. CONCLUSIONS: Delineation of ICH extent on stroke CT scans by AI software was excellent or good in 71 % of cases but was more likely to over- or under-estimate extent when ICH was either more extensive, intraventricular, or extra-axial.

Detectability and accuracy of computational measurements of in-silico and physical representations of enlarged perivascular spaces from magnetic resonance images.

BACKGROUND: Magnetic Resonance Imaging (MRI) visible perivascular spaces (PVS) have been associated with age, decline in cognitive abilities, interrupted sleep, and markers of small vessel disease. But the limits of validity of their quantification have not been established. NEW METHOD: We use a purpose-built digital reference object to construct an in-silico phantom for addressing this need, and validate it using a physical phantom. We use cylinders of different sizes as models for PVS. We also evaluate the influence of 'PVS' orientation, and different sets of parameters of the two vesselness filters that have been used for enhancing tubular structures, namely Frangi and RORPO filters, in the measurements' accuracy. RESULTS: PVS measurements in MRI are only a proxy of their true dimensions, as the boundaries of their representation are consistently overestimated. The success in the use of the Frangi filter relies on a careful tuning of several parameters. Alpha= 0.5, beta= 0.5 and c= 500 yielded the best results. RORPO does not have these requirements and allows detecting smaller cylinders in their entirety more consistently in the absence of noise and confounding artefacts. The Frangi filter seems to be best suited for voxel sizes equal or larger than 0.4 mm-isotropic and cylinders larger than 1 mm diameter and 2 mm length. 'PVS' orientation did not affect measurements in data with isotropic voxels. COMPARISON WITH EXISTENT METHODS: Does not apply. CONCLUSIONS: The in-silico and physical phantoms presented are useful for establishing the validity of quantification methods of tubular small structures.

Influence of threshold selection and image sequence in in-vivo segmentation of enlarged perivascular spaces.

BACKGROUND: Growing interest surrounds perivascular spaces (PVS) as a clinical biomarker of brain dysfunction given their association with cerebrovascular risk factors and disease. Neuroimaging techniques allowing quick and reliable quantification are being developed, but, in practice, they require optimisation as their limits of validity are usually unspecified. NEW METHOD: We evaluate modifications and alternatives to a state-of-the-art (SOTA) PVS segmentation method that uses a vesselness filter to enhance PVS discrimination, followed by thresholding of its response, applied to brain magnetic resonance images (MRI) from patients with sporadic small vessel disease acquired at 3 T. RESULTS: The method is robust against inter-observer differences in threshold selection, but separate thresholds for each region of interest (i.e., basal ganglia, centrum semiovale, and midbrain) are required. Noise needs to be assessed prior to selecting these thresholds, as effect of noise and imaging artefacts can be mitigated with a careful optimisation of these thresholds. PVS segmentation from T1-weighted images alone, misses small PVS, therefore, underestimates PVS count, may overestimate individual PVS volume especially in the basal ganglia, and is susceptible to the inclusion of calcified vessels and mineral deposits. Visual analyses indicated the incomplete and fragmented detection of long and thin PVS as the primary cause of errors, with the Frangi filter coping better than the Jerman filter. COMPARISON WITH EXISTING METHODS: Limits of validity to a SOTA PVS segmentation method applied to 3 T MRI with confounding pathology are given. CONCLUSIONS: Evidence presented reinforces the STRIVE-2 recommendation of using T2-weighted images for PVS assessment wherever possible. The Frangi filter is recommended for PVS segmentation from MRI, offering robust output against variations in threshold selection and pathology presentation.

Cerebrovascular Reactivity in Patients With Small Vessel Disease: A Cross-Sectional Study.

BACKGROUND: Cerebrovascular reactivity (CVR) is inversely related to white matter hyperintensity severity, a marker of cerebral small vessel disease (SVD). Less is known about the relationship between CVR and other SVD imaging features or cognition. We aimed to investigate these cross-sectional relationships. METHODS: Between 2018 and 2021 in Edinburgh, we recruited patients presenting with lacunar or cortical ischemic stroke, whom we characterized for SVD features. We measured CVR in subcortical gray matter, normal-appearing white matter, and white matter hyperintensity using 3T magnetic resonance imaging. We assessed cognition using Montreal Cognitive Assessment. Statistical analyses included linear regression models with CVR as outcome, adjusted for age, sex, and vascular risk factors. We reported regression coefficients with 95% CIs. RESULTS: Of 208 patients, 182 had processable CVR data sets (median age, 68.2 years; 68% men). Although the strength of association depended on tissue type, lower CVR in normal-appearing tissues and white matter hyperintensity was associated with larger white matter hyperintensity volume (BNAWM=-0.0073 [95% CI, -0.0133 to -0.0014] %/mm Hg per 10-fold increase in percentage intracranial volume), more lacunes (BNAWM=-0.00129 [95% CI, -0.00215 to -0.00043] %/mm Hg per lacune), more microbleeds (BNAWM=-0.00083 [95% CI, -0.00130 to -0.00036] %/mm Hg per microbleed), higher deep atrophy score (BNAWM=-0.00218 [95% CI, -0.00417 to -0.00020] %/mm Hg per score point increase), higher perivascular space score (BNAWM=-0.0034 [95% CI, -0.0066 to -0.0002] %/mm Hg per score point increase in basal ganglia), and higher SVD score (BNAWM=-0.0048 [95% CI, -0.0075 to -0.0021] %/mm Hg per score point increase). Lower CVR in normal-appearing tissues was related to lower Montreal Cognitive Assessment without reaching convention statistical significance (BNAWM=0.00065 [95% CI, -0.00007 to 0.00137] %/mm Hg per score point increase). CONCLUSIONS: Lower CVR in patients with SVD was related to more severe SVD burden and worse cognition in this cross-sectional analysis. Longitudinal analysis will help determine whether lower CVR predicts worsening SVD severity or vice versa. REGISTRATION: URL: https://www.isrctn.com; Unique identifier: ISRCTN12113543.

Predicting incident dementia in cerebral small vessel disease: comparison of machine learning and traditional statistical models.

Background: Cerebral small vessel disease (SVD) contributes to 45% of dementia cases worldwide, yet we lack a reliable model for predicting dementia in SVD. Past attempts largely relied on traditional statistical approaches. Here, we investigated whether machine learning (ML) methods improved prediction of incident dementia in SVD from baseline SVD-related features over traditional statistical methods. Methods: We included three cohorts with varying SVD severity (RUN DMC, n = 503; SCANS, n = 121; HARMONISATION, n = 265). Baseline demographics, vascular risk factors, cognitive scores, and magnetic resonance imaging (MRI) features of SVD were used for prediction. We conducted both survival analysis and classification analysis predicting 3-year dementia risk. For each analysis, several ML methods were evaluated against standard Cox or logistic regression. Finally, we compared the feature importance ranked by different models. Results: We included 789 participants without missing data in the survival analysis, amongst whom 108 (13.7%) developed dementia during a median follow-up of 5.4 years. Excluding those censored before three years, we included 750 participants in the classification analysis, amongst whom 48 (6.4%) developed dementia by year 3. Comparing statistical and ML models, only regularised Cox/logistic regression outperformed their statistical counterparts overall, but not significantly so in survival analysis. Baseline cognition was highly predictive, and global cognition was the most important feature. Conclusions: When using baseline SVD-related features to predict dementia in SVD, the ML survival or classification models we evaluated brought little improvement over traditional statistical approaches. The benefits of ML should be evaluated with caution, especially given limited sample size and features.

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.

Are neuropsychiatric symptoms a marker of small vessel disease progression in older adults? Evidence from the Lothian Birth Cohort 1936.

BACKGROUND: Neuropsychiatric symptoms could form part of an early cerebral small vessel disease prodrome that is detectable before stroke or dementia onset. We aimed to identify whether apathy, depression, anxiety, and subjective memory complaints associate with longitudinal white matter hyperintensity (WMH) progression. METHODS: Community-dwelling older adults from the observational Lothian Birth Cohort 1936 attended three visits at mean ages 73, 76, and 79 years, repeating MRI, Mini-Mental State Examination, neuropsychiatric (Dimensional Apathy Scale, Hospital Anxiety and Depression Scale), and subjective memory symptoms. We ran regression and mixed-effects models for symptoms and normalised WMH volumes (cube root of WMH:ICV × 10). RESULTS: At age 73, 76, and 79, m = 672, n = 476, and n = 382 participants attended MRI respectively. Worse apathy at age 79 was associated with WMH volume increase (ß = 0.27, p = 0.04) in the preceding 6 years. A 1SD increase in apathy score at age 79 associated with a 0.17 increase in WMH (ß = 0.17 normalised WMH percent ICV, p = 0.009). In apathy subscales, executive (ß = 0.13, p = 0.05) and emotional (ß = 0.13, p = 0.04) scores associated with increasing WMH more than initiation scores (ß = 0.11, p = 0.08). Increasing WMH also associated with age (ß = 0.40, p = 0.002) but not higher depression (ß = -0.01, p = 0.78), anxiety (ß = 0.05, p = 0.13) scores, or subjective memory complaints (ß = 1.12, p = 0.75). CONCLUSIONS: Apathy independently associates with preceding longitudinal WMH progression, while depression, anxiety, and subjective memory complaints do not. Patients with apathy should be considered for enrolment to small vessel disease trials.

Neuropsychiatric symptoms as a sign of small vessel disease progression in cognitive impairment.

Background: Neuropsychiatric symptoms associate cross-sectionally with cerebral small vessel disease but it is not clear whether these symptoms could act as early clinical markers of small vessel disease progression. We investigated whether longitudinal change in Neuropsychiatric Inventory (NPI) scores associated with white matter hyperintensity (WMH) progression in a memory clinic population. Material and methods: We included participants from the prospective Sunnybrook Dementia Study with Alzheimer's disease and vascular subtypes of mild cognitive impairment and dementia with two MRI and ≥ 1 NPI. We conducted linear mixed-effects analyses, adjusting for age, atrophy, vascular risk factors, cognition, function, and interscan interval. Results: At baseline (n=124), greater atrophy, age, vascular risk factors and total NPI score were associated with higher baseline WMH volume, while longitudinally, all but vascular risk factors were associated. Change in total NPI score was associated with change in WMH volume, χ2 = 7.18, p = 0.007, whereby a one-point change in NPI score from baseline to follow-up was associated with a 0.0017 change in normalized WMH volume [expressed as cube root of (WMH volume cm³ as % intracranial volume)], after adjusting for age, atrophy, vascular risk factors and interscan interval. Conclusions: In memory clinic patients, WMH progression over 1-2 years associated with worsening neuropsychiatric symptoms, while WMH volume remained unchanged in those with stable NPI scores in this population with low background WMH burden.

External Validation of e-ASPECTS Software for Interpreting Brain CT in Stroke.

OBJECTIVE: The purpose of this study was to test e-ASPECTS software in patients with stroke. Marketed as a decision-support tool, e-ASPECTS may detect features of ischemia or hemorrhage on computed tomography (CT) imaging and quantify ischemic extent using Alberta Stroke Program Early CT Score (ASPECTS). METHODS: Using CT from 9 stroke studies, we compared software with masked experts. As per indications for software use, we assessed e-ASPECTS results for patients with/without middle cerebral artery (MCA) ischemia but no other cause of stroke. In an analysis outside the intended use of the software, we enriched our dataset with non-MCA ischemia, hemorrhage, and mimics to simulate a representative "front door" hospital population. With final diagnosis as the reference standard, we tested the diagnostic accuracy of e-ASPECTS for identifying stroke features (ischemia, hyperattenuated arteries, and hemorrhage) in the representative population. RESULTS: We included 4,100 patients (51% women, median age = 78 years, National Institutes of Health Stroke Scale [NIHSS] = 10, onset to scan = 2.5 hours). Final diagnosis was ischemia (78%), hemorrhage (14%), or mimic (8%). From 3,035 CTs with expert-rated ASPECTS, most (2084/3035, 69%) e-ASPECTS results were within one point of experts. In the representative population, the diagnostic accuracy of e-ASPECTS was 71% (95% confidence interval [CI] = 70-72%) for detecting ischemic features, 85% (83-86%) for hemorrhage. Software identified more false positive ischemia (12% vs 2%) and hemorrhage (14% vs <1%) than experts. INTERPRETATION: On independent testing, e-ASPECTS provided moderate agreement with experts and overcalled stroke features. Therefore, future prospective trials testing impacts of artificial intelligence (AI) software on patient care and outcome are required before widespread implementation of stroke decision-support software. ANN NEUROL 2022;92:943-957.

Topological relationships between perivascular spaces and progression of white matter hyperintensities: A pilot study in a sample of the Lothian Birth Cohort 1936.

Enlarged perivascular spaces (PVS) and white matter hyperintensities (WMH) are features of cerebral small vessel disease which can be seen in brain magnetic resonance imaging (MRI). Given the associations and proposed mechanistic link between PVS and WMH, they are hypothesized to also have topological proximity. However, this and the influence of their spatial proximity on WMH progression are unknown. We analyzed longitudinal MRI data from 29 out of 32 participants (mean age at baseline = 71.9 years) in a longitudinal study of cognitive aging, from three waves of data collection at 3-year intervals, alongside semi-automatic segmentation masks for PVS and WMH, to assess relationships. The majority of deep WMH clusters were found adjacent to or enclosing PVS (waves-1: 77%; 2: 76%; 3: 69%), especially in frontal, parietal, and temporal regions. Of the WMH clusters in the deep white matter that increased between waves, most increased around PVS (waves-1-2: 73%; 2-3: 72%). Formal statistical comparisons of severity of each of these two SVD markers yielded no associations between deep WMH progression and PVS proximity. These findings may suggest some deep WMH clusters may form and grow around PVS, possibly reflecting the consequences of impaired interstitial fluid drainage via PVS. The utility of these relationships as predictors of WMH progression remains unclear.

Associations of Peak-Width Skeletonized Mean Diffusivity and Post-Stroke Cognition.

Post-stroke cognitive impairment is common and can have major impact on life after stroke. Peak-width of Skeletonized Mean Diffusivity (PSMD) is a diffusion imaging marker of white matter microstructure and is also associated with cognition. Here, we examined associations between PSMD and post-stroke global cognition in an ongoing study of mild ischemic stroke patients. We studied cross-sectional associations between PSMD and cognition at both 3-months (N = 229) and 1-year (N = 173) post-stroke, adjusted for premorbid IQ, sex, age, stroke severity and disability, as well as the association between baseline PSMD and 1-year cognition. At baseline, (mean age = 65.9 years (SD = 11.1); 34% female), lower Montreal Cognitive Assessment (MoCA) scores were associated with older age, lower premorbid IQ and higher stroke severity, but not with PSMD (ßstandardized = −0.116, 95% CI −0.241, 0.009; p = 0.069). At 1-year, premorbid IQ, older age, higher stroke severity and higher PSMD (ßstandardized = −0.301, 95% CI −0.434, −0.168; p < 0.001) were associated with lower MoCA. Higher baseline PSMD was associated with lower 1-year MoCA (ßstandardized = −0.182, 95% CI −0.308, −0.056; p = 0.005). PSMD becomes more associated with global cognition at 1-year post-stroke, possibly once acute effects have settled. Additionally, PSMD in the subacute phase after a mild stroke could help predict long-term cognitive impairment.

A daily temperature rhythm in the human brain predicts survival after brain injury.

Patients undergo interventions to achieve a 'normal' brain temperature; a parameter that remains undefined for humans. The profound sensitivity of neuronal function to temperature implies the brain should be isothermal, but observations from patients and non-human primates suggest significant spatiotemporal variation. We aimed to determine the clinical relevance of brain temperature in patients by establishing how much it varies in healthy adults. We retrospectively screened data for all patients recruited to the Collaborative European NeuroTrauma Effectiveness Research in Traumatic Brain Injury (CENTER-TBI) High Resolution Intensive Care Unit Sub-Study. Only patients with direct brain temperature measurements and without targeted temperature management were included. To interpret patient analyses, we prospectively recruited 40 healthy adults (20 males, 20 females, 20-40 years) for brain thermometry using magnetic resonance spectroscopy. Participants were scanned in the morning, afternoon, and late evening of a single day. In patients (n = 114), brain temperature ranged from 32.6 to 42.3°C and mean brain temperature (38.5 ± 0.8°C) exceeded body temperature (37.5 ± 0.5°C, P < 0.0001). Of 100 patients eligible for brain temperature rhythm analysis, 25 displayed a daily rhythm, and the brain temperature range decreased in older patients (P = 0.018). In healthy participants, brain temperature ranged from 36.1 to 40.9°C; mean brain temperature (38.5 ± 0.4°C) exceeded oral temperature (36.0 ± 0.5°C) and was 0.36°C higher in luteal females relative to follicular females and males (P = 0.0006 and P < 0.0001, respectively). Temperature increased with age, most notably in deep brain regions (0.6°C over 20 years, P = 0.0002), and varied spatially by 2.41 ± 0.46°C with highest temperatures in the thalamus. Brain temperature varied by time of day, especially in deep regions (0.86°C, P = 0.0001), and was lowest at night. From the healthy data we built HEATWAVE-a 4D map of human brain temperature. Testing the clinical relevance of HEATWAVE in patients, we found that lack of a daily brain temperature rhythm increased the odds of death in intensive care 21-fold (P = 0.016), whilst absolute temperature maxima or minima did not predict outcome. A warmer mean brain temperature was associated with survival (P = 0.035), however, and ageing by 10 years increased the odds of death 11-fold (P = 0.0002). Human brain temperature is higher and varies more than previously assumed-by age, sex, menstrual cycle, brain region, and time of day. This has major implications for temperature monitoring and management, with daily brain temperature rhythmicity emerging as one of the strongest single predictors of survival after brain injury. We conclude that daily rhythmic brain temperature variation-not absolute brain temperature-is one way in which human brain physiology may be distinguished from pathophysiology.

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 < 3 months post-stroke to no PSCI at follow-up, and cognitive decline as conversion from no PSCI to PSCI. The network impact score was related to serial measures of PSCI using Generalized Estimating Equations (GEE) models, and to PSCI stratified according to post-stroke interval (<3, 3-12, 12-24, >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%) <3 months, 709/1640 (43%) at 3-12 months, 243/853 (28%) at 12-24 months, and 208/522 (40%) >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/.

Contribution of white matter hyperintensities to ventricular enlargement in older adults.

Lateral ventricles might increase due to generalized tissue loss related to brain atrophy. Alternatively, they may expand into areas of tissue loss related to white matter hyperintensities (WMH). We assessed longitudinal associations between lateral ventricle and WMH volumes, accounting for total brain volume, blood pressure, history of stroke, cardiovascular disease, diabetes and smoking at ages 73, 76 and 79, in participants from the Lothian Birth Cohort 1936, including MRI data from all available time points. Lateral ventricle volume increased steadily with age, WMH volume change was more variable. WMH volume decreased in 20% and increased in remaining subjects. Over 6 years, lateral ventricle volume increased by 3% per year of age, 0.1% per mm Hg increase in blood pressure, 3.2% per 1% decrease of total brain volume, and 4.5% per 1% increase of WMH volume. Over time, lateral ventricle volumes were 19% smaller in women than men. Ventricular and WMH volume changes are modestly associated and independent of general brain atrophy, suggesting that their underlying processes do not fully overlap.

Impact of Small Vessel Disease Progression on Long-term Cognitive and Functional Changes After Stroke.

BACKGROUND AND OBJECTIVES: The severity of white matter hyperintensities (WMH) at presentation with stroke is associated with poststroke dementia and dependency. However, WMH can decrease or increase after stroke; prediction of cognitive decline is imprecise; and there are few data assessing longitudinal interrelationships among changing WMH, cognition, and function after stroke, despite the clinical importance. METHODS: We recruited patients within 3 months of a minor ischemic stroke, defined as NIH Stroke Scale (NIHSS) score <8 and not expected to result in a modified Rankin Scale (mRS) score >2. Participants repeated MRI at 1 year and cognitive and mRS assessments at 1 and 3 years. We ran longitudinal mixed-effects models assessing change in Addenbrooke's Cognitive Examination-Revised (ACE-R) and mRS scores. For mRS score, we assessed longitudinal WMH volumes (cube root; percentage intracranial volume [ICV]), adjusting for age, NIHSS score, ACE-R, stroke subtype, and time to assessment. For ACE-R score, we additionally adjusted for ICV, mRS, premorbid IQ, and vascular risk factors. We then used a multivariate model to jointly assess changing cognition/mRS score, adjusted for prognostic variables, using all available data. RESULTS: We recruited 264 patients; mean age was 66.9 (SD 11.8) years; 41.7% were female; and median mRS score was 1 (interquartile range 1-2). One year after stroke, normalized WMH volumes were associated more strongly with 1-year ACE-R score (ß = -0.259, 95% CI -0.407 to -0.111 more WMH per 1-point ACE-R decrease, p = 0.001) compared to subacute WMH volumes and ACE-R score (ß = 0.105, 95% CI -0.265 to 0.054, p = 0.195). Three-year mRS score was associated with 3-year ACE-R score (ß = -0.272, 95% CI -0.429 to -0.115, p = 0.001). Combined change in baseline-1-year jointly assessed ACE-R/mRS scores was associated with fluctuating WMH volumes (F = 9.3, p = 0.03). DISCUSSION: After stroke, fluctuating WMH mean that 1-year, but not baseline, WMH volumes are associated strongly with contemporaneous cognitive scores. Covarying longitudinal decline in cognition and independence after stroke, central to dementia diagnosis, is associated with increasing WMH volumes.

Relationship between inferior frontal sulcal hyperintensities on brain MRI, ageing and cerebral small vessel disease.

Raised signal in cerebrospinal fluid (CSF) on fluid-attenuated inversion recovery (FLAIR) may indicate raised CSF protein or debris and is seen in inferior frontal sulci on routine MRI. To explore its clinical relevance, we assessed the association of inferior frontal sulcal hyperintensities (IFSH) on FLAIR with demographics, risk factors, and small vessel disease markers in three cohorts (healthy volunteers, n=44; mild stroke patients, n=105; older community-dwelling participants from Lothian birth cohort 1936, n=101). We collected detailed clinical data, scanned all subjects on the same 3T MRI scanner and 3-dimensional FLAIR sequence and developed a scale to rate IFSH. In adjusted analyses, the IFSH score increased with age (per 10-year increase; OR 1.69; 95% CI, 1.42-2.02), and perivascular spaces score in centrum semiovale in stroke patients (OR 1.73; 95% CI, 1.13-2.69). Since glymphatic CSF clearance declines with age and drains partially via the cribriform plate to the nasal lymphatics, IFSH on 3T MRI may be a non-invasive biomarker of altered CSF clearance and justifies further research in larger, more diverse samples.