We aimed to investigate the effect of sex on clinical outcomes after endovascular treatment (EVT) in ischemic stroke patients and whether this effect is age dependent. We used data from EVA-TRISP, a multicentre registry of consecutive EVT patients. Primary outcome was the 90-day modified Rankin Scale (mRS) score, analysed with unadjusted and adjusted mixed model ordinal regression. We compared women with men and assessed the interaction between age and sex. We assessed age both as a continuous variable and dichotomized at the population median. In total, 12,915 EVT patients were included for the current study (median age 74 [IQR 64–82] years, 6,251 [48.4

Inferential analysis of normal or pathological brain imaging data—as in brain mapping or the identification of neurological imaging markers—is often controlled for secondary variables. However, a rationale for covariate control is rarely given and formal criteria to identify appropriate covariates in such complex data are lacking. We investigated the impact and adequacy of covariate control in large-scale imaging data using the example of stroke lesion-deficit mapping. In 183 stroke patients, we evaluated control for age, sex, hypertension, or lesion volume when mapping real or simulated deficits. We examined (i) the impact of covariate control when mapping cognitive poststroke deficits, (ii) the association of covariates and brain pathology (iii) the impact of covariate control when mapping simulated deficits, and (iv) the impact of covariate control under a verified null hypothesis. We found that the impact of covariate control varies across covariates and deficits in both real and simulated data. However, simulations showed that covariate control does not necessarily improve the precision of lesion–deficit inference. Instead, it systematically reshapes statistical maps according to the associations between covariates and lesion anatomy. As a result, covariate control can bias statistical inference and, under a verified null hypothesis, may even generate spurious associations. These findings suggest that the widespread use of covariate control in clinical brain imaging—and likely other biological high-dimensional data—should be reconsidered, as it may introduce substantial analytical flexibility without necessarily improving inference.

BACKGROUND:For neurodegenerative diseases, the inter-individual variability in the functional response to pathology is explained by the construct of cognitive reserve (CR). We aimed to evaluate the association of CR with stroke outcome to improve the understanding of its inter-individual variability and prediction. METHODS:The peer-reviewed protocol was preregistered on PROSPERO (CRD42021256175). The systematic review and meta-analysis followed Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA), Meta-analysis of Observational Studies in Epidemiology (MOOSE), and CHecklist for critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies (CHARMS) reporting guidelines. Original studies reporting the association between CR-proxies (e.g. level or years of education, occupational attainment) and measures of non-cognitive stroke outcome (e.g. National Institute of Health Stroke Scale (NIHSS), modified Rankin Scale, Barthel Index, Functional Independence Measure) were selected. Risk of bias was assessed using Quality In Prognosis Studies (QUIPS). Estimates were pooled using a random-effects model. RESULTS:Of 4129 studies identified, 17 were included in the systematic review. Based on the quality check, 10 of them involving 19,308 patients were included in the meta-analysis, whereby only five studies directly addressed the association of CR-proxies with stroke outcome. Pooled standardized mean differences (SMDs) showed evidence for the association of low CR with poor stroke outcome (SMD = 0.23; 95% confidence interval (CI) = 0.04 to 0.42 corresponding to odds ratio (OR) = 1.52; 95% CI = 1.08 to 2.14). Subgroup analysis showed a greater association of level of education (SMD = 0.37; 95% CI = 0.12 to 0.62) and occupational attainment (SMD = 0.34; 95% CI = 0.10 to 0.57) with stroke outcome, as compared to years of education (SMD = 0.01; 95% CI = -0.06 to 0.08). The effect of CR was greater in the acute-subacute stroke phase (⩽3 months post-stroke, SMD = 0.28; 95% CI = 0.04 to 0.52) than in the chronic phase (SMD = 0.01; 95% CI = -0.06 to 0.08). CONCLUSION:We found evidence that CR explains inter-individual variability in stroke outcome and thus may improve its prediction. Low CR increases the risk of poor stroke outcome, and its proxies should be considered in both clinical and research settings. However, we observed high heterogeneity across studies, and further research with specific focus on this topic and CR-proxies extending beyond educational and occupational attainment is needed.

Stroke remains a leading cause of mortality and long-term disability worldwide, with variable recovery trajectories posing substantial challenges in anticipating post-event care and rehabilitation planning. To address these challenges, we established the NeuralCup consortium to benchmark predictive models of stroke outcome through a collaborative, data-driven approach. This study presents findings from 15 international teams who used a comprehensive dataset including clinical and imaging data, to identify and compare predictors of motor, cognitive, and emotional outcomes one year post-stroke. Our analyses integrated traditional statistical approaches and novel machine learning algorithms to uncover 'optimal recipes' for predicting each domain. The differences in these 'optimal recipes' reflect distinct brain mechanisms in response to different tasks. Key predictors across all domains included infarct characteristics, T1-weighted MRI sequences, and demographic factors. Additionally, integrating FLAIR imaging and white matter tract analysis significantly improved the prediction of cognitive and motor outcomes, respectively. These findings support a multifaceted approach to stroke outcome prediction, underscoring the potential of collaborative data science to develop personalized care strategies that enhance recovery and quality of life for stroke survivors. To encourage further model development and validation, we provide access to the training dataset at http://neuralcup.bcblab.com.

Personalized prediction of stroke outcome using lesion imaging markers is still too imprecise to make a breakthrough in clinical practice. We performed a combined prediction and brain mapping study on topographic and connectomic lesion imaging data to evaluate (i) the relationship between lesion-deficit associations and their predictive value and (ii) the influence of time since stroke. In patients with first-ever ischaemic stroke, we first applied high-dimensional machine learning models on lesion topographies or structural disconnection data to model stroke severity (National Institutes of Health Stroke Scale 24 h/3 months) and functional outcome (modified Rankin Scale 3 months) in cross-validation. Second, we mapped the topographic and connectomic lesion impact on both clinical measures. We retrospectively included 685 patients [age 67.4 +/- 15.1, National Institutes of Health Stroke Scale 24 h median(IQR) = 3(1; 6), modified Rankin Scale 3 months = 1(0; 2), National Institutes of Health Stroke Scale 3 months = 0(0; 2)]. Predictions for acute stroke severity (National Institutes of Health Stroke Scale 24 h) were better with topographic lesion imaging (R-2 = 0.41) than with disconnection data (R-2 = 0.29, P = 0.0015), whereas predictions at 3 months (National Institutes of Health Stroke Scale/modified Rankin Scale) were generally close to chance level. In the analysis of lesion-deficit associations, the correlates of more severe acute stroke (National Institutes of Health Stroke Scale 24 h > 4) and poor functional outcome (modified Rankin Scale 3 months >= 2) were left-lateralized. The lesion location impact of both variables corresponded in right-hemisphere stroke with peaks in primary motor regions, but it markedly differed in left-hemisphere stroke. Topographic and disconnection lesion features predict acute stroke severity better than the 3-months outcome. This suggests a likely higher impact of lesion-independent factors in the longer term and highlights challenges in the prediction of global functional outcome. Prediction and brain mapping diverge, and the existence of statistically significant associations-as here for 3-months outcomes-does not imply predictive value. Routine neurological scores better capture left- than right-hemispheric lesions, further complicating the challenge of outcome prediction.

Stroke lesion imaging alone is not sufficient to predict stroke severity and outcome at a clinically meaningful level, and non-lesional factors are to be defined to enable stroke prognosis and individually tailored therapy. While brain health concept is mainly discussed in the context of primary prevention of neurological diseases, quantitative parameters of brain health like brain parenchymal fraction (BPF) might be associated with better maintenance of function and compensation in (acute) brain pathology. We aimed to investigate whether BPF independently mediates neurological impairment and functional outcome after stroke. We retrospectively analysed patients with first-ever middle cerebral artery stroke. We used generalized linear models with gamma distribution and log link to model neurological impairment [NIH Stroke Scale (NIHSS) 24 h and 3 months] and ordinal logistic regression to model functional outcome at 3 months (modified Rankin scale, 0-6) with the independent variables age, sex, BPF and lesion size. We analysed data of 832 patients (mean age: 67.7 ± 15.3 years, female: 43.5%, median NIHSS 24 h: 3 (1-6)]. A higher BPF was associated with lower neurological impairment: 10% higher BPF was associated with a 16% reduction of NIHSS 24 h (mean ratio 0.840, 95% confidence interval [CI] 0.751-0.940) and a 15% reduction of NIHSS 3 months (mean ratio 0.845, 95% CI 0.745-0.957) independent of age, sex and lesion size. Similarly, BPF had an independent protective effect on functional disability 3 months post stroke (10% higher BPF decreased the odds of worse outcome for 1 modified Rankin scale point by odds ratio [OR] 0.593; 95% CI 0.407-0.864). Brain health, operationalized as BPF, is independently associated with lower neurological impairment both 24 h and 3 months post stroke and better functional stroke outcome. BPF might improve the prediction of stroke outcome and explain interindividual variability in lesion-outcome associations: the same lesion load leads to higher neurological impairment in the presence of brain atrophy, whereas the lesion burden might be clinically less apparent in healthier brains. The data might improve personalized treatment prospects and suggest that efforts on brain health improvement might represent both the primary and secondary reduction of burden of stroke.

The prediction of stroke outcome from imaging markers could be used to guide individualized therapeutic approaches. We aimed to find the best imaging marker to predict the global functional impact of a stroke lesion among low- to high-level connectomic measures—indirect estimations of structural connectivity, graph representations, or brain modes—as well as spatial lesion features. This observational study retrospectively analysed clinical routine data from patients with acute first-ever ischaemic stroke. We traced lesions in diffusion-weighted MRI and computed 21 topographic or connectomic measures, including (i) tract-wise, voxel-wise and interregional white matter disconnection that were indirectly estimated by reference to healthy connectome data; (ii) interregional network structure by graph measures; and (iii) brain modes, which represent elementary interactions between grey matter regions. We used all features to predict stroke severity [National Institutes of Health Stroke Scale (NIHSS) 24 h] or classify poor functional outcome (mRS 3 months ≥ 2) in a nested cross-validation with high-dimensional machine-learning models. For comparison to specific, granular post-stroke cognitive deficits, we replicated the modelling procedures in another sample for selective attention and phonemic word fluency. The study included 755 patients [mean age = 66.9 ± 15.3 years; NIHSS 24 h median (IQR) = 2 (1; 5); mRS 3 months = 1 (0; 2)]. For both measures, simple spatial lesion features (NIHSS 24 h: R² = 0.395 ± 0.059; mRS: accuracy = 65.62% ± 3.45, positive predictive value = 0.72 ± 0.13; negative predictive value = 0.64 ± 0.04) outperformed connectomic measures (all P < 0.0007), even though the predictions of the best measures in each category were numerically close. Control analyses on specific cognitive deficits in a sample of 182 patients found connectomic measures to be equal or even superior to spatial lesion features. Connectomic stroke imaging markers provide no benefit in the prediction of acute stroke severity and functional outcome at 3 months. Spatial lesion imaging features seem to effectively capture the global neurological perturbation caused by a stroke lesion and could provide a basis for personalized prediction algorithms. On the other hand, connectomic stroke imaging markers may be warranted when modelling specific post-stroke cognitive deficits.

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.

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.

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 (rho = 0.175; 95 %-CI = 0.103;0.244), whereas cervical artery dissection (rho = -0.143; 95 %-CI = -0.198;-0.087) and a patent foramen ovale (rho = -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.

Dissecting aneurysms in patients with spontaneous cervical artery dissections have, so far, been reported as “benign”, but more specific information is scarce. We aimed to elucidate (1) vascular risk factors, (2) local and ischemic symptoms, and (3) long-term prognosis compared to non-aneurysmal dissections. This case–control study included consecutive patients with spontaneous cervical artery dissection from three university hospitals in Switzerland and France, evaluated at baseline and at 3 months. In addition, further follow-ups were performed at the discretion of the treating physician. Dissecting aneurysms were diagnosed with duplex sonography, magnetic resonance angiography, and/or digital subtraction angiography. Of 1012 patients, 151 (14.9%) presented with 167 dissecting aneurysms at baseline (n = 103) or follow-up (n = 64). The median follow-up was 24.9 months (IQR: 6.8–60.8). Compared to patients without a dissecting aneurysm there were no significant differences in the vascular risk factors or local symptoms (91.4 vs. 89.8%). Ischemic strokes at baseline were less common (29.1% vs. 54.4%; OR: 0.41; 95% CI: 0.28–0.60) in patients with a dissecting aneurysm, even after correction for the degree of stenosis of the dissected arteries (OR: 0.53; 95% CI: 0.34–0.81). Patients with a dissecting aneurysm more often had a favorable clinical outcome (modified Rankin Scale Score of 0–1) at 3 months (80.6% vs. 54.5%). There was no significant difference in recurrent cerebrovascular events at 3 months or overall. The lower rate of ischemic strokes at baseline may reflect a different pathogenic mechanism, such as a smaller initial tear in the vessel wall or an increased vessel caliber from an early or primary intramural hematoma with a different shape.

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.

This scientific commentary refers to 'Ground-truth validation of uni- and multivariate lesion inference approaches', by Zavaglia et al. (https://doi.org/10.1093/braincomms/fcae251).

Given advantages in reperfusion therapy leading to mild stroke, less apparent cognitive deficits can be overseen in a routine neurological examination. Despite the widespread use of the Montreal Cognitive Assessment (MoCA), age- and education-specific cutoffs for the detection of post-stroke cognitive impairment (PSCI) are not established, hampering its valid application in stroke. We aimed to establish age- and education-specific MoCA cutoffs to better discriminate patients with and without acute PSCI. Patients with acute ischemic stroke underwent the MoCA and a detailed neuropsychological assessment. PSCI was defined as a performance < - 1.5 SD in >= 2 cognitive domains. As secondary data analysis, the discriminant abilities of the MoCAraw-score (not adding + 1 as correction for <= 12 years of education, YoE) cutoffs were automatically derived based on Youden Index and evaluated by receiver operating characteristic analyses across age- (< 55, 55-70, > 70 years old) and education-specific (<= 12 and > 12 YoE) groups. 351 stroke patients (67.4 +/- 14.1 years old; 13.1 +/- 2.8 YoE) underwent the neuropsychological assessment 2.7 +/- 2.0 days post-stroke. The original MoCA cutoff < 26 falsely classified 26.2% of examined patients, with poor sensitivity in younger adults (34.8% in patients < 55 years > 12 YoE) and poor specificity in older adults (55.0%, in > 70 years <= 12 YoE). By maximizing both sensitivity and specificity, the optimal MoCAraw cutoffs were: (i) < 28 in patients aged < 55 with > 12 YoE (sensitivity = 69.6%, specificity = 77.8%); (ii) < 22 and < 25 in patients > 70 years with <= 12 and > 12 YoE (sensitivity = 61.6%, specificity = 90.0%; sensitivity = 63.3%, specificity = 84.0%, respectively). In other groups the optimal MoCAraw cutoff was < 26. Age and education level should be considered when interpreting MoCA-scores. Though new age- and education-specific cutoffs demonstrated higher discriminant ability for PSCI, their performance in young stroke and adults with higher education level was low due to ceiling effects and MoCA subtests structure, and cautious interpretation in these patients is warranted. Trial registration: ClinicalTrials.gov Identifier: NCT05653141.

Aim The aim of this study was to investigate baseline characteristics and outcome of patients after endovascular therapy (EVT) for acute large vessel occlusion (LVO) in relation to their history of symptomatic vascular disease and sex.Methods Consecutive EVT-eligible patients with LVO in the anterior circulation admitted to our stroke center between 04/2015 and 04/2020 were included in this observational cohort study. All patients were treated according to a standardized acute ischaemic stroke (AIS) protocol. Baseline characteristics and successful reperfusion, recurrent/progressive in-hospital ischaemic stroke, symptomatic in-hospital intracranial hemorrhage, death at discharge and at 3 months, and functional outcome at 3 months were analyzed according to previous symptomatic vascular disease and sex.Results 995 patients with LVO in the anterior circulation (49.4% women, median age 76 years, median admission NIHSS score 14) were included. Patients with multiple vs. no previous vascular events showed higher mortality at discharge (20% vs. 9.3%, age/sex - adjustedOR = 1.43, p = 0.030) and less independency at 3 months (28.8% vs. 48.8%, age/sex - adjustedOR = 0.72, p = 0.020). All patients and men alone with one or multiple vs. patients and men with no previous vascular events showed more recurrent/progressive in-hospital ischaemic strokes (19.9% vs. 6.4% in all patients, age/sex - adjustedOR = 1.76, p = 0.028) (16.7% vs. 5.8% in men, age-adjustedOR = 2.20, p = 0.035). Men vs. women showed more in-hospital symptomatic intracranial hemorrhage among patients with one or multiple vs. no previous vascular events (23.7% vs. 6.6% in men and 15.4% vs. 5.5% in women, OR = 2.32, p = 0.035/age - adjustedOR = 2.36, p = 0.035).Conclusions Previous vascular events increased the risk of in-hospital complications and poorer outcome in the analyzed patients with EVT-eligible LVO-AIS. Our findings may support risk assessment in these stroke patients and could contribute to the design of future studies.

Inferential analysis of normal or pathological brain imaging data – as in brain mapping or the identification of neurological imaging markers – is often controlled for secondary variables. However, a rationale for covariate control is rarely given and formal criteria to identify appropriate covariates in such complex data are lacking. We investigated the impact and adequacy of covariate control in large-scale imaging data using the example of stroke lesion-deficit mapping. In 183 stroke patients, we evaluated control for age, sex, hypertension, or lesion volume when mapping real or simulated deficits. We found that the impact of covariate control varies and can be strong, but it does not necessarily improve the precision of results. Instead, it systematically shifts results towards the inversed associations between imaging features and the covariate. This effect of covariate control can bias results and, as shown in another experiment, can even create effects out of nothing. The widespread use of covariate control in the statistical analysis of clinical brain imaging data – and, likely, other biological high-dimensional data as well – may not generally improve statistical results, but it may just change them. Therefore, covariate control constitutes a problematic degree of freedom in the analysis of brain imaging data and may often not be justified at all.### Competing Interest StatementThe authors have declared no competing interest.* BLDI : Bayesian Lesion-deficit inference

Journal Article The correlation of behavioural deficits post-stroke: a trivial issue? Get access Lorenzo Pini, Lorenzo Pini Padova Neuroscience Center, University of Padova, 35131 Padova, ItalyVeneto Institute of Molecular Medicine, VIMM, 35129 Padova, Italy https://orcid.org/0000-0002-9305-3376 Search for other works by this author on: Oxford Academic PubMed Google Scholar Antonio Luigi Bisogno, Antonio Luigi Bisogno Department of Neuroscience, University of Padova, 35131 Padova, Italy https://orcid.org/0000-0003-0587-6709 Search for other works by this author on: Oxford Academic PubMed Google Scholar Alessandro Salvalaggio, Alessandro Salvalaggio Padova Neuroscience Center, University of Padova, 35131 Padova, ItalyDepartment of Neuroscience, University of Padova, 35131 Padova, Italy https://orcid.org/0000-0002-1273-7566 Search for other works by this author on: Oxford Academic PubMed Google Scholar Gordon L Shulman, Gordon L Shulman Department of Neurology, Washington University in Saint Louis, St Louis, MO 63110, USADepartment of Radiology, Washington University in Saint Louis, St Louis, MO 63110, USA Search for other works by this author on: Oxford Academic PubMed Google Scholar Maurizio Corbetta Maurizio Corbetta Padova Neuroscience Center, University of Padova, 35131 Padova, ItalyVeneto Institute of Molecular Medicine, VIMM, 35129 Padova, ItalyDepartment of Neuroscience, University of Padova, 35131 Padova, Italy Correspondence to: Maurizio Corbetta Università degli Studi di Padova—Padova Neuroscience Center, Via Giuseppe Orus, 2 Padova 35122, Italy E-mail: maurizio.corbetta@unipd.it Search for other works by this author on: Oxford Academic PubMed Google Scholar Brain, Volume 146, Issue 10, October 2023, Pages e83–e85, https://doi.org/10.1093/brain/awad173 Published: 24 May 2023 Article history Received: 24 February 2023 Accepted: 06 May 2023 Published: 24 May 2023 Corrected and typeset: 20 June 2023

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.

Background and purposeLimited data is available on sex differences in young stroke patients describing discrepant findings. This study aims to investigate the sex differences in young stroke patients. MethodsProspective cohort study comparing risk factors, etiology, stroke localization, severity on admission, management and outcome in patients aged 16-55 years with acute ischemic stroke consecutively included in the Bernese stroke database between 01/2015 to 12/2018 with subgroup analyses for very young (16-35y) and young patients (36-55y). Results689 patients (39% female) were included. Stroke in women dominated in the very young (53.8%, p<0.001) and in men in the young (63.9%, p<0.001). As risk factors only sleep-disordered breathing was more predominant in men in the very young, whereas arterial hypertension, diabetes and atrial fibrillation did not differ in women and men older than 35y. The higher frequency of stroke in women in the very young may be explained by the sex specific risk factors such as pregnancy, puerperium, the use of oral contraceptives, and hormonal replacement therapy. Stroke severity at presentation, etiology, stroke localization, management, and outcome did not differ between women and men. ConclusionsThe main finding of this study is that sex specific risk factors in women may contribute to a large extent to the higher incidence of stroke in the very young in women. Important modifiable stroke risk factors, such as arterial hypertension, diabetes mellitus and atrial fibrillation did not differ in women and men, either in the young as well as in the very young. These findings have major implications for primary preventive strategies of stroke in young people.

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.