Right hemispheric white matter hyperintensities improve the prediction of spatial neglect severity in acute stroke

Depression drives perceived quality of life following minor stroke

BACKGROUND

Stroke outcomes are typically assessed using objective scales focused on severity and functional ability that may overlook subtle cognitive changes and fail to account for patients' perceptions of recovery and quality of life. This study aimed to compare patient-reported outcomes (PROs) to objective recovery metrics in patients with minor stroke and identify factors associated with perceived recovery and quality of life.

METHODOLOGY

Data from 134 patients with minor stroke were prospectively collected at 1-, 6-, and 12-months post-infarct. Objective assessments measured stroke severity, functional outcomes, activities of daily living, and global cognitive function. PROs included assessments of function, depression, fatigue, symptomatic improvement, and quality of life. Regression models were used to evaluate the relationship between subjective PROs and physician-obtained measures.

RESULTS

Analyses revealed an important role for mental health factors in subjective measures of recovery, though cognitive dysfunction was not significantly associated with either subjective improvement or quality of life despite being commonly endorsed. Depression and fatigue were inversely associated with both satisfaction and quality of life, along with stroke severity and overall functional impairment during both short- and long-term recovery periods. The impact of depression on quality of life increased over time, while stroke severity and functional status were associated with perceived symptomatic improvement at all time points.

CONCLUSIONS

For patients with minor stroke, depression is negatively associated with perception of symptomatic recovery and quality of life, particularly at later time points. Addressing post-stroke depression may improve patient-reported outcomes, though further research is needed to determine its impact on broader measures of post-stroke morbidity.

Racial and ethnic differences in white matter hypointensities: The role of vascular risk factors

INTRODUCTION

White matter hypointensities (WMHs) are markers of cerebrovascular pathology associated with cognitive decline. Reports of racial and ethnic differences in WMHs have been inconsistent across studies. This study examined whether race and ethnicity influence WMH burden and whether vascular risk factors explain these differences.

METHODS

Data from the National Alzheimer's Coordinating Center included 7132 Whites, 892 Blacks, 283 Asians, and 661 Hispanics. Baseline and longitudinal WMHs were examined using linear regression and mixed-effects models across racial and ethnic groups, controlling for demographics and vascular risk factors.

RESULTS

Adjusting for vascular risk factors reduced WMH burden differences and eliminated differences in temporal regions in Black versus White older adults. For Hispanics, differences became significant after adjusting for vascular risk factors.

DISCUSSION

Although some racial and ethnic WMH disparities are influenced by vascular risk factors, others persist, highlighting the need for multidimensional approaches when targeting WMHs in diverse populations.

HIGHLIGHTS

Current research is inconsistent as to whether there are racial differences in white matter hypointensities (WMHs). Blacks exhibit higher WMH burden than Whites, mediated by vascular factors. In Hispanics, WMH differences emerged only after adjusting for vascular risk factors.

Predicting individual long-term prognosis of spatial neglect based on acute stroke patient data

One of the most pressing questions after a stroke is whether an individual patient will recover in the long term. Previous studies demonstrated that spatial neglect-a common cognitive deficit after right hemispheric stroke-is a strong predictor for poor performance on a wide range of everyday tasks and for resistance to rehabilitation. The possibility of predicting long-term prognosis of spatial neglect is therefore of great relevance. The aim of the present study was to test the prognostic value of different imaging and non-imaging features from right hemispheric stroke patients: individual demographics (age, sex), initial neglect severity and acute lesion information (size, location). Patients' behaviour was tested twice in the acute and the chronic phases of stroke and prediction models were built using machine learning-based algorithms with repeated nested cross-validation and feature selection. Model performances indicate that demographic information seemed less beneficial. The best variable combination comprised individual neglect severity in the acute phase of stroke, together with lesion location and size. The latter were based on individual lesion overlaps with a previously proposed chronic neglect region of interest that covers anterior parts of the superior and middle temporal gyri and the basal ganglia. These variables achieved a remarkably high level of accuracy by explaining 66% of the total variance of neglect patients, making them promising features in the prediction of individual outcome prognosis. An online tool is provided with which our algorithm can be used for individual outcome predictions (https://niivue.github.io/niivue-neglect/).

The challenge of long-term stroke outcome prediction and how statistical correlates do not imply predictive value

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 (R² = 0.41) than with disconnection data (R² = 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.

Structural Disconnections Caused by White Matter Hyperintensities in Post-Stroke Spatial Neglect

White matter hyperintensities (WMH), a common feature of cerebral small vessel disease, affect a wide range of cognitive dysfunctions, including spatial neglect. The latter is a disorder of spatial attention and exploration typically after right hemisphere brain damage. To explore the impact of WMH on neglect-related structural disconnections, the present study investigated the indirectly quantified structural disconnectome induced by either stroke lesion alone, WMH alone, or their combination. Furthermore, we compared different measures of structural disconnection-voxel-wise, pairwise, tract-wise, and parcel-wise-to identify neural correlates and predict acute neglect severity. We observed that WMH-derived disconnections alone were not associated with neglect behavior. However, when combined with disconnections derived from individual stroke lesions, pre-stroke WMH contributed to post-stroke neglect severity by affecting right frontal and subcortical substrates, like the middle frontal gyrus, basal ganglia, thalamus, and the fronto-pontine tract. Predictive modeling demonstrated that voxel-wise disconnection data outperformed other measures of structural disconnection, explaining 42% of the total variance; interestingly, the best model used predictors of stroke-based disconnections only. We conclude that prestroke alterations in the white matter microstructure due to WMH contribute to poststroke deficits in spatial attention, likely by impairing the integrity of human attention networks.

Virtual reality gameplay classification illustrates the multidimensionality of visuospatial neglect

Brain injuries can significantly impact mental processes and lead to hidden disabilities not easily detectable. Traditional methods for assessing these impacts are imprecise, leading to unreliable prevalence estimates and treatments with uncertain effectiveness. Immersive virtual reality has shown promise for assessment, but its use as a standalone tool is rare. Our research focused on developing and validating a standalone immersive virtual reality classification system for unilateral spatial neglect, a condition common following brain injury characterized by inattention to one side of space. Our study involved 51 brain injury inpatients and 30 controls, all engaging with 'The Attention Atlas', an immersive virtual reality game for testing visual search skills. Our classification system aimed to identify patients with neglect, 'minor atypicality' (indicative of inattention not consistent enough to be labelled as neglect) or non-neglect. This categorization was based on a simple mathematical definition, utilizing gameplay to describe spatial orientation (to the left or right side) and attentional challenge (indicative of search inefficiency). These metrics were benchmarked against a normative model to detect atypical visual search, which refers to gameplay beyond the usual bounds. The combination of neglected side, orientation and challenge factors was used to categorize neglect. We discovered a strong correlation between atypical visual search patterns and neglect risk factors, such as middle cerebral artery stroke, parietal injuries and existing neglect diagnoses (Poisson regression incidence rate ratio = 7.18, 95% confidence interval = 4.41-11.90). In our study, immersive virtual reality-identified neglect in one-fourth of the patients (n = 13, 25.5%), minor atypicality in 17.6% (n = 9) and non-neglect in the majority, 56.9% (n = 29). This contrasts with standard assessments, which detected neglect in 17.6% (n = 9) of cases and had no intermediate category. Our analysis determined six categories of neglect, the most common being left hemispace neglect with above-median orientation and challenge scores. Traditional assessments were not significantly more accurate (accuracy = 84.3%, P = 0.06) than a blanket assumption of non-neglect. Traditional assessments were also relatively insensitive in detecting immersive virtual reality-identified neglect (53.8%), particularly in less severe cases and those involving right-side inattention. Our findings underline the effectiveness of immersive virtual reality in revealing various dimensions of neglect, surpassing traditional methods in sensitivity and detail and operating independently from them. To integrate immersive virtual reality into real-world clinical settings, collaboration with healthcare professionals, patients and other stakeholders is crucial to ensure practical applicability and accessibility.

A promising frontier: targeting NETs for stroke treatment breakthroughs

Stroke is a prevalent global acute cerebrovascular condition, with ischaemic stroke being the most frequently occurring type. After a stroke, neutrophils accumulate in the brain and subsequently generate and release neutrophil extracellular traps (NETs). The accumulation of NETs exacerbates the impairment of the blood‒brain barrier (BBB), hampers neovascularization, induces notable neurological deficits, worsens the prognosis of stroke patients, and can facilitate the occurrence of t-PA-induced cerebral haemorrhage subsequent to ischaemic stroke. Alternative approaches to pharmacological thrombolysis or endovascular thrombectomy are being explored, and targeting NETs is a promising treatment that warrants further investigation.

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.

The volume and the distribution of premorbid white matter hyperintensities: Impact on post-stroke aphasia

White matter hyperintensities (WMH) are a radiological manifestation of progressive white matter integrity loss. The total volume and distribution of WMH within the corpus callosum have been associated with pathological cognitive ageing processes but have not been considered in relation to post-stroke aphasia outcomes. We investigated the contribution of both the total volume of WMH, and the extent of WMH lesion load in the corpus callosum to the recovery of language after first-ever stroke. Behavioural and neuroimaging data from individuals (N = 37) with a left-hemisphere stroke were included at the early subacute stage of recovery. Spoken language comprehension and production abilities were assessed using word and sentence-level tasks. Neuroimaging data was used to derive stroke lesion variables (volume and lesion load to language critical regions) and WMH variables (WMH volume and lesion load to three callosal segments). WMH volume did not predict variance in language measures, when considered together with stroke lesion and demographic variables. However, WMH lesion load in the forceps minor segment of the corpus callosum explained variance in early subacute comprehension abilities (t = -2.59, p = .01) together with corrected stroke lesion volume and socio-demographic variables. Premorbid WMH lesions in the forceps minor were negatively associated with early subacute language comprehension after aphasic stroke. This negative impact of callosal WMH on language is consistent with converging evidence from pathological ageing suggesting that callosal WMH disrupt the neural networks supporting a range of cognitive functions.

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.

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.

Machine learning in the prediction of post-stroke cognitive impairment: a systematic review and meta-analysis

OBJECTIVE

Cognitive impairment is a detrimental complication of stroke that compromises the quality of life of the patients and poses a huge burden on society. Due to the lack of effective early prediction tools in clinical practice, many researchers have introduced machine learning (ML) into the prediction of post-stroke cognitive impairment (PSCI). However, the mathematical models for ML are diverse, and their accuracy remains highly contentious. Therefore, this study aimed to examine the efficiency of ML in the prediction of PSCI.

METHODS

Relevant articles were retrieved from Cochrane, Embase, PubMed, and Web of Science from the inception of each database to 5 December 2022. Study quality was evaluated by PROBAST, and c-index, sensitivity, specificity, and overall accuracy of the prediction models were meta-analyzed.

RESULTS

A total of 21 articles involving 7,822 stroke patients (2,876 with PSCI) were included. The main modeling variables comprised age, gender, education level, stroke history, stroke severity, lesion volume, lesion site, stroke subtype, white matter hyperintensity (WMH), and vascular risk factors. The prediction models used were prediction nomograms constructed based on logistic regression. The pooled c-index, sensitivity, and specificity were 0.82 (95% CI 0.77-0.87), 0.77 (95% CI 0.72-0.80), and 0.80 (95% CI 0.71-0.86) in the training set, and 0.82 (95% CI 0.77-0.87), 0.82 (95% CI 0.70-0.90), and 0.80 (95% CI 0.68-0.82) in the validation set, respectively.

CONCLUSION

ML is a potential tool for predicting PSCI and may be used to develop simple clinical scoring scales for subsequent clinical use.

SYSTEMATIC REVIEW REGISTRATION

https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=383476.

Association between Citrullinated Histone H3 and White Matter Lesions Burden in Patients with Ischemic Stroke

INTRODUCTION

Neutrophil extracellular traps play a role in the pathophysiology of stroke and are associated with severity and mortality. We aimed to investigate whether the citrullinated histone H3 (CitH3), a biomarker for neutrophil extracellular traps formation, is associated with the white matter lesion (WML) burden in ischemic stroke patients.

METHODS

Between September 2021 and April 2022, 322 patients were enrolled in this prospective observational cohort study. Serum CitH3 levels were measured after admission using an enzyme-linked immunosorbent assay. WMLs severity was graded according to the Fazekas scale and conceptually defined as mild (total Fazekas score 0-2) and severe (total Fazekas score 3-6). We used multivariable regression models to determine the relationship between CitH3 concentrations and the severity of WMLs burden.

RESULTS

One-hundred and forty-eight (46.0%) patients were diagnosed with severe WMLs burden after admission. Increased CitH3 levels (first quartile vs. fourth quartile of H3Cit, odds ratio, 3.311, 95% confidence interval, 1.336-8.027; p = 0.011) were independently associated with a greater WML burden in the fully adjusted multivariable model. Similar results were found when the H3Cit was analyzed as a continuous variable. Furthermore, the multiple-adjusted spline regression model showed a linear association between H3Cit levels and severe WMLs (P = 0.001 for linearity).

CONCLUSIONS

In the present study, increased CitH3 levels were positively associated with extensive WMLs in ischemic stroke patients, indicating a role of neutrophil extracellular traps formation in the pathogenesis of WMLs.