Post-stroke cognitive impairment remains highly prevalent and disabling despite state-of-the-art stroke treatment.

BACKGROUND: State-of-the-art stroke treatment significantly reduces lesion size and stroke severity, but it remains unclear whether these therapeutic advances have diminished the burden of post-stroke cognitive impairment (PSCI). AIMS: In a cohort of patients receiving modern state-of-the-art stroke care including endovascular therapy, we assessed the frequency of PSCI and the pattern of domain-specific cognitive deficits, identified risk factors for PSCI, and determined the impact of acute PSCI on stroke outcome. METHODS: In this prospective monocentric cohort study, we examined patients with first-ever anterior circulation ischemic stroke without pre-stroke cognitive decline, using a comprehensive neuropsychological assessment ⩽10 days after symptom onset. Normative data were stratified by demographic variables. We defined PSCI as at least moderate (<1.5 standard deviation) deficits in ⩾2 cognitive domains. Multivariable regression analysis was applied to define risk factors for PSCI. RESULTS: We analyzed 329 non-aphasic patients admitted from December 2020 to July 2023 (67.2 ± 14.4 years old, 41.3% female, 13.1 ± 2.7 years of education). Although most patients had mild stroke (median National Institutes of Health Stroke Scale (NIHSS) 24 h = 1.00 (0.00; 3.00); 87.5% with NIHSS ⩽ 5), 69.3% of them presented with PSCI 2.7 ± 2.0 days post-stroke. The most severely and often affected cognitive domains were verbal learning, episodic memory, executive functions, selective attention, and constructive abilities (39.1%-51.2% of patients), whereas spatial neglect was less frequent (18.5%). The risk of PSCI was reduced with more years of education (odds ratio (OR) = 0.47, 95% confidence interval (CI) = 0.23-0.99) and right hemisphere lesions (OR = 0.47, 95% CI = 0.26-0.84), and increased with stroke severity (NIHSS 24 h, OR = 4.19, 95% CI = 2.72-6.45), presence of hyperlipidemia (OR = 1.93, 95% CI = 1.01-3.68), but was not influenced by age. After adjusting for stroke severity and depressive symptoms, acute PSCI was associated with poor functional outcome (modified Rankin Scale > 2, F = 13.695, p < 0.001) and worse global cognition (Montreal Cognitive Assessment (MoCA) score, F = 20.069, p < 0.001) at 3 months post-stroke. CONCLUSION: Despite modern stroke therapy and many strokes having mild severity, PSCI in the acute stroke phase remains frequent and associated with worse outcome. The most prevalent were learning and memory deficits. Cognitive reserve operationalized as years of education independently protects post-stroke cognition.

The analysis of association between single features of small vessel disease and stroke outcome shows the independent impact of the number of microbleeds and presence of lacunes.

The impact of small vessel disease (SVD) on stroke outcome was investigated either separately for its single features in isolation or for SVD sum score measuring a qualitative (binary) assessment of SVD-lesions. We aimed to investigate which SVD feature independently impacts the most on stroke outcome and to compare the continuous versus binary SVD assessment that reflects pronouncement and presence correspondingly. Patients with a first-ever anterior circulation ischemic stroke were retrospectively investigated. We performed an ordered logistic regression analysis to predict stroke outcome (mRS 3 months, 0-6) using age, stroke severity, and pre-stroke disability as baseline input variables and adding SVD-features (lacunes, microbleeds, enlarged perivascular spaces, white matter hyperintensities) assessed either continuously (model 1) or binary (model 2). The data of 873 patients (age 67.9 ± 15.4, NIHSS 24 h 4.1 ± 4.8) was analyzed. In model 1 with continuous SVD-features, the number of microbleeds was the only independent predictor of stroke outcome in addition to clinical parameters (OR 1.21; 95% CI 1.07-1.37). In model 2 with the binary SVD assessment, only the presence of lacunes independently improved the prediction of stroke outcome (OR 1.48, 1.1-1.99). In a post hoc analysis, both the continuous number of microbleeds and the presence of lacunes were independent significant predictors. Thus, the number of microbleeds evaluated continuously and the presence of lacunes are associated with stroke outcome independent from age, stroke severity, pre-stroke disability and other SVD-features. Whereas the presence of lacunes is adequately represented in SVD sum score, the microbleeds assessment might require another cutoff and/or gradual scoring, when prediction of stroke outcome is needed.

Adaptation of the Concept of Brain Reserve for the Prediction of Stroke Outcome: Proxies, Neural Mechanisms, and Significance for Research.

The prediction of stroke outcome is challenging due to the high inter-individual variability in stroke patients. We recently suggested the adaptation of the concept of brain reserve (BR) to improve the prediction of stroke outcome. This concept was initially developed alongside the one for the cognitive reserve for neurodegeneration and forms a valuable theoretical framework to capture high inter-individual variability in stroke patients. In the present work, we suggest and discuss (i) BR-proxies-quantitative brain characteristics at the time stroke occurs (e.g., brain volume, hippocampus volume), and (ii) proxies of brain pathology reducing BR (e.g., brain atrophy, severity of white matter hyperintensities), parameters easily available from a routine MRI examination that might improve the prediction of stroke outcome. Though the influence of these parameters on stroke outcome has been partly reported individually, their independent and combined impact is yet to be determined. Conceptually, BR is a continuous measure determining the amount of brain structure available to mitigate and compensate for stroke damage, thus reflecting individual differences in neural resources and a capacity to maintain performance and recover after stroke. We suggest that stroke outcome might be defined as an interaction between BR at the time stroke occurs and lesion load. BR in stroke can potentially be influenced, e.g., by modifying cardiovascular risk factors. In addition to the potential power of the BR concept in a mechanistic understanding of inter-individual variability in stroke outcome and establishing individualized therapeutic approaches, it might help to strengthen the synergy of preventive measures in stroke, neurodegeneration, and healthy aging.

Cerebral small vessel disease and stroke: Linked by stroke aetiology, but not stroke lesion location or size.

BACKGROUND: Cerebral small vessel disease (SVD) has previously been associated with worse stroke outcome, vascular dementia, and specific post-stroke cognitive deficits. The underlying causal mechanisms of these associations are not yet fully understood. We investigated whether a relationship between SVD and certain stroke aetiologies or a specific stroke lesion anatomy provides a potential explanation. METHODS: In a retrospective observational study, we examined 859 patients with first-ever, non-SVD anterior circulation ischemic stroke (age = 69.0±15.2). We evaluated MRI imaging markers to assess an SVD burden score and mapped stroke lesions on diffusion-weighted MRI. We investigated the association of SVD burden with i) stroke aetiology, and ii) lesion anatomy using topographical statistical mapping. RESULTS: With increasing SVD burden, stroke of cardioembolic aetiology was more frequent (ρ = 0.175; 95 %-CI = 0.103;0.244), whereas cervical artery dissection (ρ = -0.143; 95 %-CI = -0.198;-0.087) and a patent foramen ovale (ρ = -0.165; 95 %-CI = -0.220;-0.104) were less frequent stroke etiologies. However, no significant associations between SVD burden and stroke aetiology remained after additionally controlling for age (all p>0.125). Lesion-symptom-mapping and Bayesian statistics showed that SVD burden was not associated with a specific stroke lesion anatomy or size. CONCLUSIONS: In patients with a high burden of SVD, non-SVD stroke is more likely to be caused by cardioembolic aetiology. The common risk factor of advanced age may link both pathologies and explain some of the existing associations between SVD and stroke. The SVD burden is not related to a specific stroke lesion location.

Stroke lesion size - Still a useful biomarker for stroke severity and outcome in times of high-dimensional models.

BACKGROUND: The volumetric size of a brain lesion is a frequently used stroke biomarker. It stands out among most imaging biomarkers for being a one-dimensional variable that is applicable in simple statistical models. In times of machine learning algorithms, the question arises of whether such a simple variable is still useful, or whether high-dimensional models on spatial lesion information are superior. METHODS: We included 753 first-ever anterior circulation ischemic stroke patients (age 68.4±15.2 years; NIHSS at 24 h 4.4±5.1; modified Rankin Scale (mRS) at 3-months median[IQR] 1[0.75;3]) and traced lesions on diffusion-weighted MRI. In an out-of-sample model validation scheme, we predicted stroke severity as measured by NIHSS 24 h and functional stroke outcome as measured by mRS at 3 months either from spatial lesion features or lesion size. RESULTS: For stroke severity, the best regression model based on lesion size performed significantly above chance (p < 0.0001) with R2 = 0.322, but models with spatial lesion features performed significantly better with R2 = 0.363 (t(752) = 2.889; p = 0.004). For stroke outcome, the best classification model based on lesion size again performed significantly above chance (p < 0.0001) with an accuracy of 62.8%, which was not different from the best model with spatial lesion features (62.6%, p = 0.80). With smaller training data sets of only 150 or 50 patients, the performance of high-dimensional models with spatial lesion features decreased up to the point of being equivalent or even inferior to models trained on lesion size. The combination of lesion size and spatial lesion features in one model did not improve predictions. CONCLUSIONS: Lesion size is a decent biomarker for stroke outcome and severity that is slightly inferior to spatial lesion features but is particularly suited in studies with small samples. When low-dimensional models are desired, lesion size provides a viable proxy biomarker for spatial lesion features, whereas high-precision prediction models in personalised prognostic medicine should operate with high-dimensional spatial imaging features in large samples.

A typology of cerebral small vessel disease based on imaging markers.

BACKGROUND: Lacunes, microbleeds, enlarged perivascular spaces (EPVS), and white matter hyperintensities (WMH) are brain imaging features of cerebral small vessel disease (SVD). Based on these imaging markers, we aimed to identify subtypes of SVD and to evaluate the validity of these markers as part of clinical ratings and as biomarkers for stroke outcome. METHODS: In a cross-sectional study, we examined 1207 first-ever anterior circulation ischemic stroke patients (mean age 69.1 ± 15.4 years; mean NIHSS 5.3 ± 6.8). On acute stroke MRI, we assessed the numbers of lacunes and microbleeds and rated EPVS and deep and periventricular WMH. We used unsupervised learning to cluster patients based on these variables. RESULTS: We identified five clusters, of which the last three appeared to represent distinct late stages of SVD. The two largest clusters had no to only mild or moderate WMH and EPVS, respectively, and favorable stroke outcome. The third cluster was characterized by the largest number of lacunes and a likewise favorable outcome. The fourth cluster had the highest age, most pronounced WMH, and poor outcome. Showing the worst outcome, the fifth cluster presented pronounced microbleeds and the most severe SVD burden. CONCLUSION: The study confirmed the existence of different SVD types with different relationships to stroke outcome. EPVS and WMH were identified as imaging features of presumably early progression. The number of microbleeds and WMH severity appear to be promising biomarkers for distinguishing clinical subgroups. Further understanding of SVD progression might require consideration of refined SVD features, e.g., for EPVS and type of lacunes.

Bayesian lesion-deficit inference with Bayes factor mapping: Key advantages, limitations, and a toolbox.

Statistical lesion-symptom mapping is largely dominated by frequentist approaches with null hypothesis significance testing. They are popular for mapping functional brain anatomy but are accompanied by some challenges and limitations. The typical analysis design and the structure of clinical lesion data are linked to the multiple comparison problem, an association problem, limitations to statistical power, and a lack of insights into evidence for the null hypothesis. Bayesian lesion deficit inference (BLDI) could be an improvement as it collects evidence for the null hypothesis, i.e. the absence of effects, and does not accumulate α-errors with repeated testing. We implemented BLDI by Bayes factor mapping with Bayesian t-tests and general linear models and evaluated its performance in comparison to frequentist lesion-symptom mapping with a permutation-based family-wise error correction. We mapped the voxel-wise neural correlates of simulated deficits in an in-silico-study with 300 stroke patients, and the voxel-wise and disconnection-wise neural correlates of phonemic verbal fluency and constructive ability in 137 stroke patients. Both the performance of frequentist and Bayesian lesion-deficit inference varied largely across analyses. In general, BLDI could find areas with evidence for the null hypothesis and was statistically more liberal in providing evidence for the alternative hypothesis, i.e. the identification of lesion-deficit associations. BLDI performed better in situations in which the frequentist method is typically strongly limited, for example with on average small lesions and in situations with low power, where BLDI also provided unprecedented transparency in terms of the informative value of the data. On the other hand, BLDI suffered more from the association problem, which led to a pronounced overshoot of lesion-deficit associations in analyses with high statistical power. We further implemented a new approach to lesion size control, adaptive lesion size control, that, in many situations, was able to counter the limitations imposed by the association problem, and increased true evidence both for the null and the alternative hypothesis. In summary, our results suggest that BLDI is a valuable addition to the method portfolio of lesion-deficit inference with some specific and exclusive advantages: it deals better with smaller lesions and low statistical power (i.e. small samples and effect sizes) and identifies regions with absent lesion-deficit associations. However, it is not superior to established frequentist approaches in all respects and therefore not to be seen as a general replacement. To make Bayesian lesion-deficit inference widely accessible, we published an R toolkit for the analysis of voxel-wise and disconnection-wise data.

The low dimensionality of post-stroke cognitive deficits: it's the lesion anatomy!

For years, dissociation studies on neurological single-case patients with brain lesions were the dominant method to infer fundamental cognitive functions in neuropsychology. In contrast, the association between deficits was considered to be of less epistemological value. Still, associational computational methods for dimensionality reduction-such as principal component analysis or factor analysis-became popular for the identification of fundamental cognitive functions and to understand human cognitive brain architecture from post-stroke neuropsychological profiles. In the present in silico study with lesion imaging of 300 stroke patients, we investigated the dimensionality of artificial simulated neuropsychological profiles that exclusively contained independent fundamental cognitive functions without any underlying low-dimensional cognitive architecture. Still, the anatomy of stroke lesions alone was sufficient to create a dependence between variables that allowed a low-dimensional description of the data with principal component analysis. All criteria that we used to estimate the dimensionality of data, including the Kaiser criterion, were strongly affected by lesion anatomy, while the Joliffe criterion provided the least affected estimates. The dimensionality of profiles was reduced by 62-70% for the Kaiser criterion, up to the degree that is commonly found in neuropsychological studies on actual cognitive measures. The interpretability of such low-dimensional factors as deficits of fundamental cognitive functions and their provided insights into human cognitive architecture thus seem to be severely limited, and the heavy focus of current cognitive neuroscience on group studies and associations calls for improvements. We suggest that qualitative criteria and dissociation patterns could be used to refine estimates for the dimensionality of the cognitive architecture behind post-stroke deficits. Further, given the strong impact of lesion anatomy on the associational structure of data, we see the need for further optimization of interpretation strategies of computational factors in post-stroke lesion studies of cognitive deficits.

Association of cognitive reserve with stroke outcome: a protocol for a systematic review.

INTRODUCTION: The concept of cognitive reserve (CR) was introduced to account for individual differences in the clinical manifestation of neurodegenerative diseases. Though several mechanisms and risk factors are shared between neurodegeneration and stroke, the effect of CR on poststroke functional outcome has been poorly addressed. This systematic review aims to synthesise the available research evidence on the association of CR with stroke outcome, in order to implement the understanding of interindividual variability in stroke outcome and to improve its prediction. METHODS AND ANALYSIS: Cochrane Library, Embase, PubMed, Web of Science and reference lists of relevant literature will be searched for publications on CR proxies (eg, education, years of education, occupational attainment, premorbid intelligence) and stroke outcome, published between 1 January 1980 and 10 March 2022. Two reviewers will independently perform the study selection, data extraction and quality assessment. Disagreements between reviewers will be resolved by a third independent reviewer. The Quality In Prognosis Studies tool will be used to assess the quality of each included study. The primary outcome will be functional outcome after stroke assessed with modified Rankin Scale, activities of daily living (eg, Barthel Index), National Institute of Health Stroke Scale, dichotomised as favourable versus not favourable as well as reported as continuous or ordinal variables. Qualitative and quantitative findings will be summarised and, if possible, data will be synthesised using appropriate meta-analytical methods. The quality of evidence will be assessed using the Grading of Recommendations Assessment, Development and Evaluation framework. ETHICS AND DISSEMINATION: No ethical approval is required as it is a protocol for a systematic review and the data used will be extracted from published studies. The findings from this systematic review will be disseminated in a peer-reviewed scientific journal and presented at conferences. The data will be made freely available. PROSPERO REGISTRATION NUMBER: CRD42021256175.

Cognitive Status Predicts Return to Functional Independence After Minor Stroke: A Decision Tree Analysis.

About two-thirds of patients with minor strokes are discharged home. However, these patients may have difficulties returning to their usual living activities. To investigate the factors associated with successful home discharge, our aim was to provide a decision tree (based on clinical data) that could identify if a patient discharged home could return to pre-stroke activities and to perform an external validation of this decision tree on an independent cohort. Two cohorts of patients with minor strokes gathered from stroke registries at the Hôpital Pitié-Salpêtrière and University Hospital Bern were included in this study (n = 105 for the construction cohort coming from France; n = 100 for the second cohort coming from Switzerland). The decision tree was built using the classification and regression tree (CART) analysis on the construction cohort. It was then applied to the validation cohort. Accuracy, sensitivity, specificity, false positive, and false-negative rates were reported for both cohorts. In the construction cohort, 60 patients (57%) returned to their usual, pre-stroke level of independence. The CART analysis produced a decision tree with the Montreal Cognitive Assessment (MoCA) as the first decision point, followed by discharge NIHSS score or age, and then by the occupational status. The overall prediction accuracy to the favorable outcome was 80% in the construction cohort and reached 72% accuracy in the validation cohort. This decision tree highlighted the role of cognitive function as a crucial factor for patients to return to their usual activities after a minor stroke. The algorithm may help clinicians to tailor planning of patients' discharge.

[Post-stroke cognitive deficits and dementia]. / Kognitive Defizite und Demenz nach Schlaganfall.

Post-stroke cognitive deficits and dementia Abstract. Prediction of stroke outcome remains challenging due to a large inter-individual variability. For a long time, research on stroke outcome has been mainly confined to post-stroke motor deficits, whereas post-stroke cognitive decline has been less investigated though being an often reason for dependency and disability. Post-stroke cognitive impairment demonstrate high inter-individual variability, which is expected to increase further due to the increasing life expectancy and number of patients with pre-stroke brain pathology and cognitive deficits. There exist different types and patterns of post-stroke cognitive impairment: i) the deficits in one or several cognitive domains meaning the variability in neuropsychological profiles; ii) the decline might vary from mild to manifested dementia comprising a wide spectrum in severity; iii) with occurrence immediately after stroke or with delayed manifestation several months later without obvious reasons. Patients at risk for post-stroke cognitive impairment cannot be reliably identified. Many factors have been shown to worsen post-stroke cognitive outcome, but their effects have been only investigated in isolation by ignoring their potential interactions. An overall model sufficiently predicting post-stroke cognitive outcome was therefore missing until now. We recently suggested that the concepts of brain reserve and cognitive reserve, which are established for neurodegeneration, may represent a valuable theoretical framework to predict stroke-induced cognitive decline and disability. Cognitive stroke outcome can be defined as a result of an interaction between brain reserve (e. g. brain volume), cognitive reserve (e. g. level of education, cognitive-stimulation leisure activities) and lesion load. Our recent findings supported this hypothesis also for functional stroke outcome. By representing a valuable model comprehensively incorporating an individual's characteristics, the concepts of brain and cognitive reserve might help in screening of risk patients, establishment of individualized therapeutic approaches, and enable knowledge transfer.