Stroke Location Is an Independent Predictor of Cognitive Outcome

White matter integrity and functional connectivity of the default mode network in acute stroke are associated with cognitive outcome three months post-stroke

BACKGROUND

Knowledge about factors that are associated with post-stroke cognitive outcome is important to identify patients with high risk for impairment. We therefore investigated the associations of white matter integrity and functional connectivity (FC) within the brain's default-mode network (DMN) in acute stroke patients with cognitive outcome three months post-stroke.

METHODS

Patients aged between 18 and 85 years with an acute symptomatic MRI-proven unilateral ischemic middle cerebral artery infarction, who had received reperfusion therapy, were invited to participate in this longitudinal study. All patients underwent brain MRI within 24-72 h after symptom onset, and participated in a neuropsychological assessment three months post-stroke. We performed hierarchical regression analyses to explore the incremental value of baseline white matter integrity and FC beyond demographic, clinical, and macrostructural information for cognitive outcome.

RESULTS

The study cohort comprised 34 patients (mean age: 64 ± 12 years, 35% female). The initial median National Institutes of Health Stroke Scale (NIHSS) score was 10, and significantly improved three months post-stroke to a median NIHSS = 1 (p < .001). Nonetheless, 50% of patients showed cognitive impairment three months post-stroke. FC of the non-lesioned anterior cingulate cortex of the affected hemisphere explained 15% of incremental variance for processing speed (p = .007), and fractional anisotropy of the non-lesioned cingulum of the affected hemisphere explained 13% of incremental variance for cognitive flexibility (p = .033).

CONCLUSIONS

White matter integrity and functional MRI markers of the DMN in acute stroke explain incremental variance for post-stroke cognitive outcome beyond demographic, clinical, and macrostructural information.

Longitudinal microstructural alterations surrounding subcortical ischemic stroke lesions detected by free-water imaging

In this study we explore the spatio-temporal trajectory and clinical relevance of microstructural white matter changes within and beyond subcortical stroke lesions detected by free-water imaging. Twenty-seven patients with subcortical infarct with mean age of 66.73 (SD 11.57) and median initial NIHSS score of 4 (IQR 3-7) received diffusion MRI 3-5 days, 1 month, 3 months, and 12 months after symptom-onset. Extracellular free-water and fractional anisotropy of the tissue (FAT) were averaged within stroke lesions and the surrounding tissue. Linear models showed increased free-water and decreased FAT in the white matter of patients with subcortical stroke (lesion [free-water/FAT, mean relative difference in %, ipsilesional vs. contralesional hemisphere at 3-5 days, 1 month, 3 months, and 12 months after symptom-onset]: +41/-34, +111/-37, +208/-26, +251/-18; perilesional tissue [range in %]: +[5-24]/-[0.2-7], +[2-20]/-[3-16], +[5-43]/-[2-16], +[10-110]/-[2-12]). Microstructural changes were most prominent within the lesion and gradually became less pronounced with increasing distance from the lesion. While free-water elevations continuously increased over time and peaked after 12 months, FAT decreases were most evident 1 month post-stroke, gradually returning to baseline values thereafter. Higher perilesional free-water and higher lesional FAT at baseline were correlated with greater reductions in lesion size (rho = -0.51, p = .03) in unadjusted analyses only, while there were no associations with clinical measures. In summary, we find a characteristic spatio-temporal pattern of extracellular and cellular alterations beyond subcortical stroke lesions, indicating a dynamic parenchymal response to ischemia characterized by vasogenic edema, cellular damage, and white matter atrophy.

Severity-Dependent Interhemispheric White Matter Connectivity Predicts Poststroke Neglect Recovery

Left-sided spatial neglect is a very common and challenging issue after right-hemispheric stroke, which strongly and negatively affects daily living behavior and recovery of stroke survivors. The mechanisms underlying recovery of spatial neglect remain controversial, particularly regarding the involvement of the intact, contralesional hemisphere, with potential contributions ranging from maladaptive to compensatory. In the present prospective, observational study, we assessed neglect severity in 54 right-hemispheric stroke patients (32 male; 22 female) at admission to and discharge from inpatient neurorehabilitation. We demonstrate that the interaction of initial neglect severity and spared white matter (dis)connectivity resulting from individual lesions (as assessed by diffusion tensor imaging, DTI) explains a significant portion of the variability of poststroke neglect recovery. In mildly impaired patients, spared structural connectivity within the lesioned hemisphere is sufficient to attain good recovery. Conversely, in patients with severe impairment, successful recovery critically depends on structural connectivity within the intact hemisphere and between hemispheres. These distinct patterns, mediated by their respective white matter connections, may help to reconcile the dichotomous perspectives regarding the role of the contralesional hemisphere as exclusively compensatory or not. Instead, they suggest a unified viewpoint wherein the contralesional hemisphere can - but must not necessarily - assume a compensatory role. This would depend on initial impairment severity and on the available, spared structural connectivity. In the future, our findings could serve as a prognostic biomarker for neglect recovery and guide patient-tailored therapeutic approaches.

Cerebral microstructural alterations in Post-COVID-condition are related to cognitive impairment, olfactory dysfunction and fatigue

After contracting COVID-19, a substantial number of individuals develop a Post-COVID-Condition, marked by neurologic symptoms such as cognitive deficits, olfactory dysfunction, and fatigue. Despite this, biomarkers and pathophysiological understandings of this condition remain limited. Employing magnetic resonance imaging, we conduct a comparative analysis of cerebral microstructure among patients with Post-COVID-Condition, healthy controls, and individuals that contracted COVID-19 without long-term symptoms. We reveal widespread alterations in cerebral microstructure, attributed to a shift in volume from neuronal compartments to free fluid, associated with the severity of the initial infection. Correlating these alterations with cognition, olfaction, and fatigue unveils distinct affected networks, which are in close anatomical-functional relationship with the respective symptoms.

Bayesian modelling disentangles language versus executive control disruption in stroke

Stroke is the leading cause of long-term disability worldwide. Incurred brain damage can disrupt cognition, often with persisting deficits in language and executive capacities. Yet, despite their clinical relevance, the commonalities and differences between language versus executive control impairments remain under-specified. To fill this gap, we tailored a Bayesian hierarchical modelling solution in a largest-of-its-kind cohort (1080 patients with stroke) to deconvolve language and executive control with respect to the stroke topology. Cognitive function was assessed with a rich neuropsychological test battery including global cognitive function (tested with the Mini-Mental State Exam), language (assessed with a picture naming task), executive speech function (tested with verbal fluency tasks), executive control functions (Trail Making Test and Digit Symbol Coding Task), visuospatial functioning (Rey Complex Figure), as well as verbal learning and memory function (Soul Verbal Learning). Bayesian modelling predicted interindividual differences in eight cognitive outcome scores three months after stroke based on specific tissue lesion topologies. A multivariate factor analysis extracted four distinct cognitive factors that distinguish left- and right-hemispheric contributions to ischaemic tissue lesions. These factors were labelled according to the neuropsychological tests that had the strongest factor loadings: One factor delineated language and general cognitive performance and was mainly associated with damage to left-hemispheric brain regions in the frontal and temporal cortex. A factor for executive control summarized mental flexibility, task switching and visual-constructional abilities. This factor was strongly related to right-hemispheric brain damage of posterior regions in the occipital cortex. The interplay of language and executive control was reflected in two distinct factors that were labelled as executive speech functions and verbal memory. Impairments on both factors were mainly linked to left-hemispheric lesions. These findings shed light onto the causal implications of hemispheric specialization for cognition; and make steps towards subgroup-specific treatment protocols after stroke.

Impact of the Alberta Stroke Program CT Score subregions on long-term functional outcomes in acute ischemic stroke: Results from two multicenter studies in China

Background and Objectives

The Alberta Stroke Program CT Score (ASPECTS) is a widely used rating system for assessing infarct extent and location. We aimed to investigate the prognostic value of ASPECTS subregions' involvement in the long-term functional outcomes of acute ischemic stroke (AIS).

Materials and Methods

Consecutive patients with AIS and anterior circulation large-vessel stenosis and occlusion between January 2019 and December 2020 were included. The ASPECTS score and subregion involvement for each patient was assessed using posttreatment magnetic resonance diffusion-weighted imaging. Univariate and multivariable regression analyses were conducted to identify subregions related to 3-month poor functional outcome (modified Rankin Scale scores, 3-6) in the reperfusion and medical therapy cohorts, respectively. In addition, prognostic efficiency between the region-based ASPECTS and ASPECTS score methods were compared using receiver operating characteristic curves and DeLong's test.

Results

A total of 365 patients (median age, 64 years; 70% men) were included, of whom 169 had poor outcomes. In the reperfusion therapy cohort, multivariable regression analyses revealed that the involvement of the left M4 cortical region in left-hemisphere stroke (adjusted odds ratio [aOR] 5.39, 95% confidence interval [CI] 1.53-19.02) and the involvement of the right M3 cortical region in right-hemisphere stroke (aOR 4.21, 95% CI 1.05-16.78) were independently associated with poor functional outcomes. In the medical therapy cohort, left-hemisphere stroke with left M5 cortical region (aOR 2.87, 95% CI 1.08-7.59) and caudate nucleus (aOR 3.14, 95% CI 1.00-9.85) involved and right-hemisphere stroke with right M3 cortical region (aOR 4.15, 95% CI 1.29-8.18) and internal capsule (aOR 3.94, 95% CI 1.22-12.78) affected were related to the increased risks of poststroke disability. In addition, region-based ASPECTS significantly improved the prognostic efficiency compared with the conventional ASPECTS score method.

Conclusion

The involvement of specific ASPECTS subregions depending on the affected hemisphere was associated with worse functional outcomes 3 months after stroke, and the critical subregion distribution varied by clinical management. Therefore, region-based ASPECTS could provide additional value in guiding individual decision making and neurological recovery in patients with AIS.

A Non-Coronary, Peripheral Arterial Atherosclerotic Disease (Carotid, Renal, Lower Limb) in Elderly Patients-A Review PART II-Pharmacological Approach for Management of Elderly Patients with Peripheral Atherosclerotic Lesions outside Coronary Territory

BACKGROUND

Aging is a key risk factor for atherosclerosis progression that is associated with increased incidence of ischemic events in supplied organs, including stroke, coronary events, limb ischemia, or renal failure. Cardiovascular disease is the leading cause of death and major disability in adults ≥ 75 years of age. Atherosclerotic occlusive disease affects everyday activity, quality of life, and it is associated with reduced life expectancy. As most multicenter randomized trials exclude elderly and very elderly patients, particularly those with severe comorbidities, physical or cognitive dysfunctions, frailty, or residence in a nursing home, there is insufficient data on the management of older patients presenting with atherosclerotic lesions outside coronary territory. This results in serious critical gaps in knowledge and a lack of guidance on the appropriate medical treatment. In addition, due to a variety of severe comorbidities in the elderly, the average daily number of pills taken by octogenarians exceeds nine. Polypharmacy frequently results in drug therapy problems related to interactions, drug toxicity, falls with injury, delirium, and non-adherence. Therefore, we have attempted to gather data on the medical treatment in patients with extra-cardiac atherosclerotic lesions indicating where there is some evidence of the management in elderly patients and where there are gaps in evidence-based medicine. Public PubMed databases were searched to review existing evidence on the effectiveness of lipid-lowering, antithrombotic, and new glucose-lowering medications in patients with extra-cardiac atherosclerotic occlusive disease.

A Non-Coronary, Peripheral Arterial Atherosclerotic Disease (Carotid, Renal, Lower Limb) in Elderly Patients-A Review: Part I-Epidemiology, Risk Factors, and Atherosclerosis-Related Diversities in Elderly Patients

Atherosclerosis is a generalized and progressive disease. Ageing is a key risk factor for atherosclerosis progression that is associated with the increased incidence of ischemic events in supplied organs, including stroke, coronary events, limb ischemia, or renal failure. Cardiovascular disease is the leading cause of death and major disability in adults ≥ 75 years of age. Atherosclerotic occlusive disease affects everyday activity and quality of life, and it is associated with reduced life expectancy. Although there is evidence on coronary artery disease management in the elderly, there is insufficient data on the management in older patients presented with atherosclerotic lesions outside the coronary territory. Despite this, trials and observational studies systematically exclude older patients, particularly those with severe comorbidities, physical or cognitive dysfunctions, frailty, or residence in a nursing home. This results in serious critical gaps in knowledge and a lack of guidance on the appropriate medical treatment and referral for endovascular or surgical interventions. Therefore, we attempted to gather data on the prevalence, risk factors, and management strategies in patients with extra-coronary atherosclerotic lesions.

Language Impairment in Vascular Dementia: A Clinical Review

Vascular cognitive impairment (VCI) encompasses a wide range of conditions, including cognitive impairment associated with stroke or vascular brain injury, mild vascular cognitive impairment, and vascular dementia (VD). Knowledge of language impairment associated with VD is far less extensive than that of Alzheimer's disease. Although not prevalent in VD, impairment in language skills has been reported. A better understanding of the neurolinguistic features associated with the different presentations of VD could facilitate medical diagnosis. In this article, we report data on language impairment in VD, with particular attention to their primary or secondary functional origin. To better appreciate this functional origin, we also outline the main characteristics of impairment in other cognitive functions. Key elements that should be considered in the speech-language assessment of individuals with possible or proven VD are also highlighted.

EEG microstates analysis after TMS in patients with subacute stroke during the resting state

To investigate whether intermittent theta burst stimulation over the cerebellum induces changes in resting-state electroencephalography microstates in patients with subacute stroke and its correlation with cognitive and emotional function. Twenty-four stroke patients and 17 healthy controls were included in this study. Patients and healthy controls were assessed at baseline, including resting-state electroencephalography and neuropsychological scales. Fifteen patients received lateral cerebellar intermittent theta burst stimulation as well as routine rehabilitation training (intermittent theta burst stimulation-RRT group), whereas 9 patients received only conventional rehabilitation training (routine rehabilitation training group). After 2 wk, baseline data were recorded again in both groups. Stroke patients exhibited reduced parameters in microstate D and increased parameters in microstate C compared with healthy controls. However, after the administration of intermittent theta burst stimulation over the lateral cerebellum, significant alterations were observed in the majority of metrics for both microstates D and C. Lateral cerebellar intermittent theta burst stimulation combined with conventional rehabilitation has a stronger tendency to improve emotional and cognitive function in patients with subacute stroke than conventional rehabilitation. The improvement of mood and cognitive function was significantly associated with microstates C and D. We identified electroencephalography microstate spatiotemporal dynamics associated with clinical improvement following a course of intermittent theta burst stimulation therapy.

[Stroke-Related Cognitive Dysfunction]

Eine kognitive Dysfunktion nach Schlaganfall besteht häufig und korreliert mit der Lokalisation und dem Ausmaß des Schlaganfalls sowie mit dem Zeitpunkt der Erhebung, die anhand standardisierter und etablierter Testverfahren erfolgen sollte. Eine kognitive Dysfunktion nach Schlaganfall ist im Kontext einer so genannten post-stroke dementia für das funktionelle Outcome relevant. Zudem ist das Bestehen einer kognitiven Dysfunktion mit einer erhöhten Wahrscheinlichkeit für ein Schlaganfallrezidiv assoziiert. Kognitive Defizite als mögliche Folge eines Schlaganfalls sollte daher auch abseits von Komplex- und Rehabilitationsbehandlungen Beachtung finden, zumal in Deutschland bis dato kein ambulantes Nachsorgekonzept nach stattgehabtem Schlaganfall etabliert wurde. Nicht nur zerebrovaskuläre Ereignisse selbst, sondern auch das Bestehen vaskulärer Risikofaktoren wie Herzinsuffizienz, Vorhofflimmern, Hypercholesterinämie und Niereninsuffizienz können zur Entwicklung einer kognitiven Funktionsstörung beitragen und eine kognitive Dysfunktion nach Schlaganfall verstärken. Die bestmögliche Therapie bekannter vaskulärer Risikofaktoren und eine gesunde Lebensweise sind im Kontext bis dato fehlender spezifischer medikamentöser Therapien einer kognitiven Dysfunktion nach Schlaganfall angezeigt. Eine gezielte Rehabilitation kann zur Erhaltung und Verbesserung kognitiver Funktionen bei kognitiver Dysfunktion nach Schlaganfall beitragen. Prospektive (randomisierte) Schlaganfallstudien sollten eine standardisierte Erfassung kognitiver Endpunkte einschließen und bestenfalls auf die Entwicklung präventiver Therapiestrategien für die kognitive Dysfunktion abzielen.

Bayesian modeling disentangles language versus executive control disruption in stroke

Stroke is the leading cause of long-term disability worldwide. Incurred brain damage disrupts cognition, often with persisting deficits in language and executive capacities. Despite their clinical relevance, the commonalities, and differences of language versus executive control impairments remain under-specified. We tailored a Bayesian hierarchical modeling solution in a largest-of-its-kind cohort (1080 stroke patients) to deconvolve language and executive control in the brain substrates of stroke insults. Four cognitive factors distinguished left- and right-hemispheric contributions to ischemic tissue lesion. One factor delineated language and general cognitive performance and was mainly associated with damage to left-hemispheric brain regions in the frontal and temporal cortex. A factor for executive control summarized control and visual-constructional abilities. This factor was strongly related to right-hemispheric brain damage of posterior regions in the occipital cortex. The interplay of language and executive control was reflected in two factors: executive speech functions and verbal memory. Impairments on both were mainly linked to left-hemispheric lesions. These findings shed light onto the causal implications of hemispheric specialization for cognition; and make steps towards subgroup-specific treatment protocols after stroke.

Association between post-stroke cognitive impairment and gut microbiota: A PRISMA-compliant systematic review and meta-analysis

BACKGROUND

Accumulating evidence has indicated a possible connection between post-stroke cognitive impairment (PSCI) and gut microbiota imbalance. To further investigate this association, the present work was designed to systematically assess the dissimilarity of gut microbiota between PSCI and healthy individuals or stroke patients.

METHODS

A meta-analysis and systematic review was conducted by searching various databases including PubMed, Web of Science, Embase, VIP, CNKI, and Wangfang for relevant studies. The pooled outcomes were used to estimate the combined dissimilarity of gut microbiota composition between PSCI and healthy individuals or patients with stroke.

RESULTS

Nine eligible studies were included in this meta-analysis. The results showed that there were no significant changes in observed richness indexes (Chao1 and ACE) and Shannon index. Notably, a significant decrease in Simpson index was observed in PSCI patients in comparison to the healthy individuals (-0.31, 95% CI: -0.62 to -0.01, P = 0.04). Moreover, the microbiota composition at the phylum level (increased abundance of Proteobacteria), family level (increased abundance of Bacteroidaceae, Lachnospiraceae, and Veillonellaceae; decreased abundance of Enterobacteriaceae), and genus level (increased abundance of Bacteroides, Clostridium XIVa, and Parabacteroides; decreased abundance of Prevotella and Ruminococcus) was found to be significantly different between PSCI and controls.

CONCLUSION

This meta-analysis suggests a significant shift of observed species and microbiota composition in PSCI compared to healthy individuals or patients with stroke.

Subcortical infarcts on admission CTP predict poor outcome despite excellent reperfusion in delayed time windows

PURPOSE

The effect of pretreatment infarct location on clinical outcome after successful mechanical thrombectomy is not understood. Our aim was to evaluate the association between computed tomography perfusion (CTP)-based ischemic core location and clinical outcome following excellent reperfusion in late time windows.

METHODS

We retrospectively reviewed patients who underwent thrombectomy for acute anterior circulation large vessel occlusion in late time windows from October 2019 to June 2021 and enrolled 65 patients with visible ischemic core on admission CTP who had received excellent reperfusion (modified thrombolysis in cerebral infarction grade 2c/3). Poor outcome was defined as a modified Rankin scale score of 3-6 at 90 days. The ischemic core infarct territories were classified into the cortical and subcortical areas. Multivariate logistic regression and receiver operating characteristic (ROC) curve analyses were used in this study.

RESULTS

Of the 65 patients analyzed, 38 (58.5%) had a poor outcome. Multivariable logistic analysis showed that the subcortical infarcts (OR 11.75; 95% CI 1.79-77.32; P = 0.010) and their volume (OR 1.17; 95% CI 1.04-1.32; P = 0.011) were independently associated with poor outcome. The ROC curve indicated the capacity of the subcortical infarct involvement (areas under the curve (AUC) = 0.65; 95% CI, 0.53-0.77, P < 0.001) and subcortical infarct volume (AUC = 0.72; 95% CI, 0.60-0.83, P < 0.001) in predicting poor outcome accurately.

CONCLUSION

Subcortical infarcts and their volume on admission CTP are associated with poor outcome after excellent reperfusion in late time windows, rather than cortical infarcts.

Prevalence and predictors of post-stroke cognitive impairment among stroke survivors in Uganda

BACKGROUND

Little is known about the characteristics and determinants of post-stroke cognitive impairment in residents of low- and middle-income countries. The objective of this study was to determine the frequencies, patterns, and risk factors for cognitive impairment in a cross-sectional study of consecutive stroke patients cared for at Uganda's Mulago Hospital, located in sub-Saharan Africa.

METHODS

131 patients were enrolled a minimum of 3-months after hospital admission for stroke. A questionnaire, clinical examination findings, and laboratory test results were used to collect demographic information and data on vascular risk factors and clinical characteristics. Independent predictor variables associated with cognitive impairment were ascertained. Stroke impairments, disability, and handicap were assessed using the National Institute of Health Stroke Scale (NIHSS), Barthel Index (BI), and modified Rankin scale (mRS), respectively. The Montreal Cognitive Assessment (MoCA) was used to assess participants' cognitive function. Stepwise multiple logistic regression was used to identify variables independently associated with cognitive impairment.

RESULTS

The overall mean MoCA score was 11.7-points (range 0.0-28.0-points) for 128 patients with available data of whom 66.4% were categorized as cognitively impaired (MoCA < 19-points). Increasing age (OR 1.04, 95% CI 1.00-1.07; p = 0.026), low level of education (OR 3.23, 95% CI 1.25-8.33; p = 0.016), functional handicap (mRS 3-5; OR 1.84, 95% CI 1.28-2.63; p < 0.001) and high LDL cholesterol (OR 2.74, 95% CI 1.14-6.56; p = 0.024) were independently associated with cognitive impairment.

CONCLUSIONS

Our findings highlight the high burden and need for awareness of cognitive impairment in post stroke populations in the sub-Saharan region and serve to emphasize the importance of detailed cognitive assessment as part of routine clinical evaluation of patients who have had a stroke.

Microstructural Gray Matter Integrity Deteriorates After an Ischemic Stroke and Is Associated with Processing Speed

Microstructural changes after an ischemic stroke (IS) have mainly been described in white matter. Data evaluating microstructural changes in gray matter (GM) remain scarce. The aim of the present study was to evaluate the integrity of GM on longitudinal data using mean diffusivity (MD), and its influence on post-IS cognitive performances. A prospective study was conducted, including supra-tentorial IS patients without pre-stroke disability. A cognitive assessment was performed at baseline and 1 year, including a Montreal Cognitive Assessment, an Isaacs set test, and a Zazzo cancelation task (ZCT): completion time and number of errors. A 3-T brain MRI was performed at the same two time-points, including diffusion tensor imaging for the assessment of GM MD. GM volume was also computed, and changes in GM volume and GM MD were evaluated, followed by the assessment of the relationship between these structural changes and changes in cognitive performances. One hundred and four patients were included (age 68.5 ± 21.5, 38.5% female). While no GM volume loss was observed, GM MD increased between baseline and 1 year. The increase of GM MD in left fronto-temporal regions (dorsolateral prefrontal cortex, superior and medial temporal gyrus, p < 0.05, Threshold-Free Cluster Enhancement, 5000 permutations) was associated with an increase time to complete ZCT, regardless of demographic confounders, IS volume and location, GM, and white matter hyperintensity volume. GM integrity deterioration was thus associated with processing speed slowdown, and appears to be a biomarker of cognitive frailty. This broadens the knowledge of post-IS cognitive impairment mechanisms.

The elusive metric of lesion load

One of the widely used metrics in lesion-symptom mapping is lesion load that codes the amount of damage to a given brain region of interest. Lesion load aims to reduce the complex 3D lesion information into a feature that can reflect both site of damage, defined by the location of the region of interest, and size of damage within that region of interest. Basically, the process of estimation of lesion load converts a voxel-based lesion map into a region-based lesion map, with regions defined as atlas-based or data-driven spatial patterns. Here, after examining current definitions of lesion load, four methodological issues are discussed: (1) lesion load is agnostic to the location of damage within the region of interest, and it disregards damage outside the region of interest, (2) lesion load estimates are prone to errors introduced by the uncertainty in lesion delineation, spatial warping of the lesion/region, and binarization of the lesion/region, (3) lesion load calculation depends on brain parcellation selection, and (4) lesion load does not necessarily reflect a white matter disconnection. Overall, lesion load, when calculated in a robust way, can serve as a clinically-useful feature for explaining and predicting post-stroke outcome and recovery.

Interpretable deep learning for the prognosis of long-term functional outcome post-stroke using acute diffusion weighted imaging

Advances in deep learning can be applied to acute stroke imaging to build powerful and explainable prediction models that could supersede traditionally used biomarkers. We aimed to evaluate the performance and interpretability of a deep learning model based on convolutional neural networks (CNN) in predicting long-term functional outcome with diffusion-weighted imaging (DWI) acquired at day 1 post-stroke. Ischemic stroke patients (n = 322) were included from the ASTER and INSULINFARCT trials as well as the Pitié-Salpêtrière registry. We trained a CNN to predict long-term functional outcome assessed at 3 months with the modified Rankin Scale (dichotomized as good [mRS ≤ 2] vs. poor [mRS ≥ 3]) and compared its performance to two logistic regression models using lesion volume and ASPECTS. The CNN contained an attention mechanism, which allowed to visualize the areas of the brain that drove prediction. The deep learning model yielded a significantly higher area under the curve (0.83 95%CI [0.78-0.87]) than lesion volume (0.78 [0.73-0.83]) and ASPECTS (0.77 [0.71-0.83]) (p < 0.05). Setting all classifiers to the specificity as the deep learning model (i.e., 0.87 [0.82-0.92]), the CNN yielded a significantly higher sensitivity (0.67 [0.59-0.73]) than lesion volume (0.48 [0.40-0.56]) and ASPECTS (0.50 [0.41-0.58]) (p = 0.002). The attention mechanism revealed that the network learned to naturally attend to the lesion to predict outcome.

Location-weighted versus Volume-weighted Mismatch at MRI for Response to Mechanical Thrombectomy in Acute Stroke

Background A target mismatch profile can identify good clinical response to recanalization after acute ischemic stroke, but does not consider region specificities. Purpose To test whether location-weighted infarction core and mismatch, determined from diffusion and perfusion MRI performed in patients with acute stroke, could improve prediction of good clinical response to mechanical thrombectomy compared with a target mismatch profile. Materials and Methods In this secondary analysis, two prospectively collected independent stroke data sets (2012-2015 and 2017-2019) were analyzed. From the brain before stroke (BBS) study data (data set 1), an eloquent map was computed through voxel-wise associations between the infarction core (based on diffusion MRI on days 1-3 following stroke) and National Institutes of Health Stroke Scale (NIHSS) score. The French acute multimodal imaging to select patients for mechanical thrombectomy (FRAME) data (data set 2) consisted of large vessel occlusion-related acute ischemic stroke successfully recanalized. From acute MRI studies (performed on arrival, prior to thrombectomy) in data set 2, target mismatch and eloquent (vs noneloquent) infarction core and mismatch were computed from the intersection of diffusion- and perfusion-detected lesions with the coregistered eloquent map. Associations of these imaging metrics with early neurologic improvement were tested in multivariable regression models, and areas under the receiver operating characteristic curve (AUCs) were compared. Results Data sets 1 and 2 included 321 (median age, 69 years [IQR, 58-80 years]; 207 men) and 173 (median age, 74 years [IQR, 65-82 years]; 90 women) patients, respectively. Eloquent mismatch was positively and independently associated with good clinical response (odds ratio [OR], 1.14; 95% CI: 1.02, 1.27; P = .02) and eloquent infarction core was negatively associated with good response (OR, 0.85; 95% CI: 0.77, 0.95; P = .004), while noneloquent mismatch was not associated with good response (OR, 1.03; 95% CI: 0.98, 1.07; P = .20). Moreover, adding eloquent metrics improved the prediction accuracy (AUC, 0.73; 95% CI: 0.65, 0.81) compared with clinical variables alone (AUC, 0.65; 95% CI: 0.56, 0.73; P = .01) or a target mismatch profile (AUC, 0.67; 95% CI: 0.59, 0.76; P = .03). Conclusion Location-weighted infarction core and mismatch on diffusion and perfusion MRI scans improved the identification of patients with acute stroke who would benefit from mechanical thrombectomy compared with the volume-based target mismatch profile. Clinical trial registration no. NCT03045146 © RSNA, 2022 Online supplemental material is available for this article. See also the editorial by Nael in this issue.

Prevalence and Predictors of Post-stroke Cognitive Impairment among Stroke Survivors in Uganda

Background Little is known about the characteristics and determinants of post-stroke cognitive impairments in low- and middle-income countries. The objective of this study was to determine the frequencies, patterns, and risk factors for cognitive impairment in a cross-sectional study of consecutive stroke patients cared for at Uganda's Mulago Hospital, located in sub-Saharan Africa. Methods From August 2019 to July 2020, patients were enrolled a minimum of 3-months post-stroke hospital admission. We collected data on their demographics, vascular risk factors and clinical factors using a questionnaire, clinical examination findings, and test results. Independent predictor variables associated with cognitive impairment were ascertained. Stroke impairments, disability, and handicap were assessed using the National Institute of Health Stroke Scale (NIHSS), Barthel Index (BI), and modified Rankin scale (mRS), respectively. The Montreal Cognitive Assessment (MoCA) was used to assess participants' cognitive function. Stepwise multiple logistic regression was used to identify variables independently associated with cognitive impairment. Results The overall mean MoCA score was 11.7-points (range 0.0-28.0-points) for 128 patients with available data of whom 66.4% were categorized as cognitively impaired (MoCA < 19-points). Increasing age (OR 1.04, 95% CI 1.00-1.07; p = 0.026), low level of education (OR 3.23, 95% CI 1.25-8.33; p = 0.016), functional handicap (mRS 3-5; OR 1.84, 95% CI 1.28-2.63; p < 0.001) and high LDL cholesterol (OR 2.74, 95% CI 1.14-6.56; p = 0.024) were independently associated with cognitive impairment. Discussion Further longitudinal, prospective studies are required to confirm these findings and identify strategies for reducing the risk of post-stroke cognitive impairment in this population.

Cortical structural changes after subcortical stroke: Patterns and correlates

Subcortical ischemic stroke can lead to persistent structural changes in the cerebral cortex. The evolution of cortical structural changes after subcortical stroke is largely unknown, as are their relations with motor recovery, lesion location, and early impairment of specific subsets of fibers in the corticospinal tract (CST). In this observational study, cortical structural changes were compared between 181 chronic patients with subcortical stroke involving the motor pathway and 113 healthy controls. The impacts of acute lesion location and early impairments of specific CSTs on cortical structural changes were investigated in the patients by combining voxel-based correlation analysis with an association study that compared CST damage and cortical structural changes. Longitudinal patterns of cortical structural change were explored in a group of 81 patients with subcortical stroke using a linear mixed-effects model. In the cross-sectional analyses, patients with partial recovery showed more significant reductions in cortical thickness, surface area, or gray matter volume in the sensorimotor cortex, cingulate gyrus, and gyrus rectus than did patients with complete recovery; however, patients with complete recovery demonstrated more significant increases in the cortical structural measures in frontal, temporal, and occipital regions than did patients with partial recovery. Voxel-based correlation analysis in these patients showed that acute stroke lesions involving the CST fibers originating from the primary motor cortex were associated with cortical thickness reductions in the ipsilesional motor cortex in the chronic stage. Acute stroke lesions in the putamen were correlated with increased surface area in the temporal pole in the chronic stage. The early impairment of the CST fibers originating from the primary sensory area was associated with increased cortical thickness in the occipital cortex. In the longitudinal analyses, patients with partial recovery showed gradually reduced cortical thickness, surface area, and gray matter volume in brain regions with significant structural damage in the chronic stage. Patients with complete recovery demonstrated gradually increasing cortical thickness, surface area, and gray-matter volume in the frontal, temporal, and occipital regions. The directions of slow structural changes in the frontal, occipital, and cingulate cortices were completely different between patients with partial and complete recovery. Complex cortical structural changes and their dynamic evolution patterns were different, even contrasting, in patients with partial and complete recovery, and were associated with lesion location and with impairment of specific CST fiber subsets.

Cognitive Impairment in Strategic Infarct Dementia: A Report of Three Cases

Strokes involving specific areas regulating cognition and behavioral functions constitute strategic infarct vascular dementia. We present three patients with acute behavioral changes and cognitive impairment following a strategic infarct. Case 1 is of a 59-year-old male, a known patient of diabetes mellitus under treatment, who presented with acute onset of memory deficit along with difficulty in recognizing faces, and left hemispatial neglect. Case 2 is of a 62-year-old male, a smoker, who presented with acute onset of behavioral abnormalities, gait apraxia, and decreased word output. Case 3 is of a 64-year-old female, a known patient of type 2 diabetes mellitus and cerebrovascular accident with left hemiparesis, who presented with psychomotor withdrawal, depression, and cautious gait. One of the most prevalent forms of dementia in adults is vascular dementia, often caused by multiple small strokes, termed multi-infarct dementia. Strategic infarct dementia, on the other hand, is usually caused by a small, single cerebral infarct. The strategic brain regions specifically involved in post-stroke cognitive impairment requires detailed clinical examination along with radiological imaging for accurate localization. Thus the cognitive impact of ischemic strokes can be understood and predicted by clinicians with the help of maps of strategic brain regions associated with global and domain-specific cognitive functions.

How Do We Connect Brain Areas with Cognitive Functions? The Past, the Present and the Future

One of the central goals of cognitive neuroscience is to understand how structure relates to function. Over the past century, clinical studies on patients with lesions have provided key insights into the relationship between brain areas and behavior. Since the early efforts for characterization of cognitive functions focused on localization, we provide an account of cognitive function in terms of localization. Next, using body perception as an example, we summarize the contemporary techniques. Finally, we outline the trajectory of current progress into the future and discuss the implications for clinical and basic neuroscience.

Automatic Segmentation and Quantitative Assessment of Stroke Lesions on MR Images

Lesion studies are crucial in establishing brain-behavior relationships, and accurately segmenting the lesion represents the first step in achieving this. Manual lesion segmentation is the gold standard for chronic strokes. However, it is labor-intensive, subject to bias, and limits sample size. Therefore, our objective is to develop an automatic segmentation algorithm for chronic stroke lesions on T1-weighted MR images. Methods: To train our model, we utilized an open-source dataset: ATLAS v2.0 (Anatomical Tracings of Lesions After Stroke). We partitioned the dataset of 655 T1 images with manual segmentation labels into five subsets and performed a 5-fold cross-validation to avoid overfitting of the model. We used a deep neural network (DNN) architecture for model training. Results: To evaluate the model performance, we used three metrics that pertain to diverse aspects of volumetric segmentation, including shape, location, and size. The Dice similarity coefficient (DSC) compares the spatial overlap between manual and machine segmentation. The average DSC was 0.65 (0.61−0.67; 95% bootstrapped CI). Average symmetric surface distance (ASSD) measures contour distances between the two segmentations. ASSD between manual and automatic segmentation was 12 mm. Finally, we compared the total lesion volumes and the Pearson correlation coefficient (ρ) between the manual and automatically segmented lesion volumes, which was 0.97 (p-value < 0.001). Conclusions: We present the first automated segmentation model trained on a large multicentric dataset. This model will enable automated on-demand processing of MRI scans and quantitative chronic stroke lesion assessment.

Exploring Cognitive Impairment in Patients With Bilateral Capsular Genu Lesions

OBJECTIVE

The authors investigated for presence of cognitive impairment after occurrence of bilateral lesions of the genu of the internal capsule (GIC). Clinical and neuropsychological features of unilateral GIC lesions have previously been studied, but the cognitive profile of bilateral lesions of the GIC has not been fully explored.

METHODS

An investigation was conducted of neurocognitive deficits and computerized tomography MRI findings among 4,200 stroke patients with bilateral GIC involvement who were admitted to the hospital between January 2010 and October 2018.

RESULTS

Eight patients with bilateral lesions of the capsular genu were identified and their data analyzed. Overall, behavioral and cognitive dysfunction were characterized by impairment of frontal, memory, and executive functions. Attention and abstraction were present among all eight patients (100%); apathy, abulia, and executive dysfunctions, among seven (87.5%); global mental dysfunction and planning deficits, among six (75.0%); short-term verbal memory deficits and language dysfunctions, among five (62.5%); long-term verbal memory deficits, among four (50.0%); and spatial memory deficits, reading, writing, counting dysfunctions, and anarthria, among two (25.0%). Four of the patients (50.0%) without a history of cognitive disorder showed severe mental deterioration compatible with the clinical picture of dementia. A clinical picture of dementia was still present in these patients 6 months after stroke.

CONCLUSIONS

Bilateral lesions of the capsular genu appearing either simultaneously or at different times were significantly associated with executive dysfunctions.

Structural disconnectome mapping of cognitive function in poststroke patients

BACKGROUND AND PURPOSE

Sequalae following stroke represents a significant challenge in current rehabilitation. The location and size of focal lesions are only moderately predictive of the diverse cognitive outcome after stroke. One explanation building on recent work on brain networks proposes that the cognitive consequences of focal lesions are caused by damages to anatomically distributed brain networks supporting cognition rather than specific lesion locations.

METHODS

To investigate the association between poststroke structural disconnectivity and cognitive performance, we estimated individual level whole-brain disconnectivity probability maps based on lesion maps from 102 stroke patients using normative data from healthy controls. Cognitive performance was assessed in the whole sample using Montreal Cognitive Assessment, and a more comprehensive computerized test protocol was performed on a subset (n = 82).

RESULTS

Multivariate analysis using Partial Least Squares on the disconnectome maps revealed that higher disconnectivity in right insular and frontal operculum, superior temporal gyrus and putamen was associated with poorer MoCA performance, indicating that lesions in regions connected with these brain regions are more likely to cause cognitive impairment. Furthermore, our results indicated that disconnectivity within these clusters was associated with poorer performance across multiple cognitive domains.

CONCLUSIONS

These findings demonstrate that the extent and distribution of structural disconnectivity following stroke are sensitive to cognitive deficits and may provide important clinical information predicting poststroke cognitive sequalae.

Automatic Segmentation in Acute Ischemic Stroke: Prognostic Significance of Topological Stroke Volumes on Stroke Outcome

BACKGROUND

Stroke infarct volume predicts patient disability and has utility for clinical trial outcomes. Accurate infarct volume measurement requires manual segmentation of stroke boundaries in diffusion-weighted magnetic resonance imaging scans which is time-consuming and subject to variability. Automatic infarct segmentation should be robust to rotation and reflection; however, prior work has not encoded this property into deep learning architecture. Here, we use rotation-reflection equivariance and train a deep learning model to segment stroke volumes in a large cohort of well-characterized patients with acute ischemic stroke in different vascular territories.

METHODS

In this retrospective study, patients were selected from a stroke registry at Houston Methodist Hospital. Eight hundred seventy-five patients with acute ischemic stroke in any brain area who had magnetic resonance imaging with diffusion-weighted imaging were included for analysis and split 80/20 for training/testing. Infarct volumes were manually segmented by consensus of 3 independent clinical experts and cross-referenced against radiology reports. A rotation-reflection equivariant model was developed based on U-Net and grouped convolutions. Segmentation performance was evaluated using Dice score, precision, and recall. Ninety-day modified Rankin Scale outcome prediction was also evaluated using clinical variables and segmented stroke volumes in different brain regions.

RESULTS

Segmentation model Dice scores are 0.88 (95% CI, 0.87-0.89; training) and 0.85 (0.82-0.88; testing). The modified Rankin Scale outcome prediction AUC using stroke volume in 30 refined brain regions based upon modified Rankin Scale-relevance areas adjusted for clinical variables was 0.80 (0.76-0.83) with an accuracy of 0.75 (0.72-0.78).

CONCLUSIONS

We trained a deep learning model with encoded rotation-reflection equivariance to segment acute ischemic stroke lesions in diffusion- weighted imaging using a large data set from the Houston Methodist stroke center. The model achieved competitive performance in 175 well-balanced hold-out testing cases that include strokes from different vascular territories. Furthermore, the location specific stroke volume segmentations from the deep learning model combined with clinical factors demonstrated high AUC and accuracy for 90-day modified Rankin Scale in an outcome prediction model.

Neural Substrates of Poststroke Depression: Current Opinions and Methodology Trends

Poststroke depression (PSD), affecting about one-third of stroke survivors, exerts significant impact on patients’ functional outcome and mortality. Great efforts have been made since the 1970s to unravel the neuroanatomical substrate and the brain-behavior mechanism of PSD. Thanks to advances in neuroimaging and computational neuroscience in the past two decades, new techniques for uncovering the neural basis of symptoms or behavioral deficits caused by focal brain damage have been emerging. From the time of lesion analysis to the era of brain networks, our knowledge and understanding of the neural substrates for PSD are increasing. Pooled evidence from traditional lesion analysis, univariate or multivariate lesion-symptom mapping, regional structural and functional analyses, direct or indirect connectome analysis, and neuromodulation clinical trials for PSD, to some extent, echoes the frontal-limbic theory of depression. The neural substrates of PSD may be used for risk stratification and personalized therapeutic target identification in the future. In this review, we provide an update on the recent advances about the neural basis of PSD with the clinical implications and trends of methodology as the main features of interest.

The Severity of Sensorimotor Tracts Degeneration May Predict Motor Performance in Chronic Stroke Patients, While Brain Structural Network Dysfunction May Not

Although the relationship between corticospinal tract (CST) fiber degeneration and motor outcome after stroke has been established, the relationship of sensorimotor cortical areas with CST fibers has not been clarified. Also limited research has been conducted on how abnormalities in brain structural networks are related to motor recovery. To address these gaps in knowledge, we conducted a diffusion tensor imaging (DTI) study with 12 chronic stroke patients (CSPs) and 12 age-matched healthy controls (HCs). We compared fractional anisotropy (FA) and mean diffusivity (MD) in 60 CST segments using the probabilistic sensorimotor area tract template (SMATT). Least Absolute Shrinkage and Selection Operator (LASSO) regressions were used to select independent predictors of Fugl-Meyer upper extremity (FM-UE) scores among FA and MD values of SMATT regions. The Graph Theoretical Network Analysis Toolbox was used to assess the structural network of each subject's brain. Global and nodal metrics were calculated, compared between the groups, and correlated with FM-UE scores. Mann–Whitney U-tests revealed reduced FA values in CSPs, compared to HCs, in many ipsilesional SMATT regions and in two contralesional regions. Mean FA value of the left (L.) primary motor cortex (M1)/supplementary motor area (SMA) region was predictive of FM-UE score (P = 0.004). Mean MD values for the L. M1/ventral premotor cortex (PMv) region (P = 0.001) and L. PMv/SMA region (P = 0.001) were found to be significant predictors of FM-UE scores. Network efficiency was the only global metric found to be reduced in CSPs (P = 0.006 vs. HCs). Nodal efficiency of the L. hippocampus, L. parahippocampal gyrus, L. fusiform gyrus (P = 0.001), and nodal local efficiency of the L. supramarginal gyrus (P < 0.001) were reduced in CSPs relative to HCs. No graph metric was associated with FM-UE scores. In conclusion, the integrity of CSTs connected to M1, SMA, and PMv were shown to be independent predictors of motor performance in CSPs, while stroke-induced topological changes in the brain's structural connectome may not be. A sensorimotor cortex-specific tract template can refine CST degeneration data and the relationship of CST degeneration with motor performance.

Stroke population–specific neuroanatomical CT-MRI brain atlas

Purpose

Development of a freely available stroke population–specific anatomical CT/MRI atlas with a reliable normalisation pipeline for clinical CT.

Methods

By reviewing CT scans in suspected stroke patients and filtering the AIBL MRI database, respectively, we collected 50 normal-for-age CT and MRI scans to build a standard-resolution CT template and a high-resolution MRI template. The latter was manually segmented into anatomical brain regions. We then developed and validated a MRI to CT registration pipeline to align the MRI atlas onto the CT template. Finally, we developed a CT-to-CT-normalisation pipeline and tested its reliability by calculating Dice coefficient (Dice) and Average Hausdorff Distance (AHD) for predefined areas in 100 CT scans from ischaemic stroke patients.

Results

The resulting CT/MRI templates were age and sex matched to a general stroke population (median age 71.9 years (62.1–80.2), 60% male). Specifically, this accounts for relevant structural changes related to aging, which may affect registration. Applying the validated MRI to CT alignment (Dice > 0.78, Average Hausdorff Distance < 0.59 mm) resulted in our final CT-MRI atlas. The atlas has 52 manually segmented regions and covers the whole brain. The alignment of four cortical and subcortical brain regions with our CT-normalisation pipeline was reliable for small/medium/large infarct lesions (Dice coefficient > 0.5).

Conclusion

The newly created CT-MRI brain atlas has the potential to standardise stroke lesion segmentation. Together with the automated normalisation pipeline, it allows analysis of existing and new datasets to improve prediction tools for stroke patients (free download at https://forms.office.com/r/v4t3sWfbKs).

Supplementary Information

The online version contains supplementary material available at 10.1007/s00234-021-02875-9.

Deep learning-based detection and segmentation of diffusion abnormalities in acute ischemic stroke

Background

Accessible tools to efficiently detect and segment diffusion abnormalities in acute strokes are highly anticipated by the clinical and research communities.

Methods

We developed a tool with deep learning networks trained and tested on a large dataset of 2,348 clinical diffusion weighted MRIs of patients with acute and sub-acute ischemic strokes, and further tested for generalization on 280 MRIs of an external dataset (STIR).

Results

Our proposed model outperforms generic networks and DeepMedic, particularly in small lesions, with lower false positive rate, balanced precision and sensitivity, and robustness to data perturbs (e.g., artefacts, low resolution, technical heterogeneity). The agreement with human delineation rivals the inter-evaluator agreement; the automated lesion quantification of volume and contrast has virtually total agreement with human quantification.

Conclusion

Our tool is fast, public, accessible to non-experts, with minimal computational requirements, to detect and segment lesions via a single command line. Therefore, it fulfills the conditions to perform large scale, reliable and reproducible clinical and translational research.

Relevance of Brain Regions' Eloquence Assessment in Patients With a Large Ischemic Core Treated With Mechanical Thrombectomy

OBJECTIVE

Individualized patient selection for mechanical thrombectomy (MT) in patients with acute ischemic stroke (AIS) and large ischemic core (LIC) at baseline is an unmet need. We tested the hypothesis that assessing the functional relevance of both infarcted and hypoperfused brain tissue would improve the selection framework of patients with LIC for MT.

METHODS

We performed a multicenter, retrospective study of adults with LIC (ischemic core volume >70 mL on MRI diffusion-weighted imaging) with MRI perfusion treated with MT or best medical management (BMM). Primary outcome was 3-month modified Rankin Scale (mRS), favorable if 0-3. Global and regional eloquence-based core perfusion mismatch ratios were derived. The predictive accuracy for clinical outcome of eloquent regions involvement was compared in multivariable and bootstrap random forest models.

RESULTS

A total of 138 patients with baseline LIC were included (MT n = 96 or BMM n = 42; mean age ± SD, 72.4 ± 14.4 years; 34.1% female; mRS 0-3: 45.1%). Mean core and critically hypoperfused volume were 100.4 mL ± 36.3 mL and 157.6 ± 56.2 mL, respectively, and did not differ between groups. Models considering the functional relevance of the infarct location showed a better accuracy for the prediction of mRS 0-3 with a c statistic of 0.76 and 0.83 for logistic regression model and bootstrap random forest testing sets, respectively. In these models, the interaction between treatment effect of MT and the mismatch was significant (p = 0.04). In comparison, in the logistic regression model disregarding functional eloquence, the c statistic was 0.67 and the interaction between MT and the mismatch was insignificant.

CONCLUSIONS

Considering functional eloquence of hypoperfused tissue in patients with a large infarct core at baseline allows for a more precise estimation of treatment expected benefit.

CLASSIFICATION OF EVIDENCE

This study provides Class II evidence that, in patients with AIS and LIC, considering the functional eloquence of the infarct location improves prediction of disability status at 3 months.

Cortical Thickness of Brain Areas Beyond Stroke Lesions and Sensory-Motor Recovery: A Systematic Review

Background: The clinical outcome of patients suffering from stroke is dependent on multiple factors. The features of the lesion itself play an important role but clinical recovery is remarkably influenced by the plasticity mechanisms triggered by the stroke and occurring at a distance from the lesion. The latter translate into functional and structural changes of which cortical thickness might be easy to quantify one of the main players. However, studies on the changes of cortical thickness in brain areas beyond stroke lesion and their relationship to sensory-motor recovery are sparse. Objectives: To evaluate the effects of cerebral stroke on cortical thickness (CT) beyond the stroke lesion and its association with sensory-motor recovery. Materials and Methods: Five electronic databases (PubMed, Embase, Web of Science, Scopus and the Cochrane Library) were searched. Methodological quality of the included studies was assessed with the Newcastle-Ottawa Scale for non-randomized controlled trials and the Risk of Bias Cochrane tool for randomized controlled trials. Results: The search strategy retrieved 821 records, 12 studies were included and risk of bias assessed. In most of the included studies, cortical thinning was seen at the ipsilesional motor area (M1). Cortical thinning can occur beyond the stroke lesion, typically in regions anatomically connected because of anterograde degeneration. Nonetheless, studies also reported cortical thickening of regions of the unaffected hemisphere, likely related to compensatory plasticity. Some studies revealed a significant correlation between changes in cortical thickness of M1 or somatosensory (S1) cortical areas and motor function recovery. Discussion and Conclusions: Following a stroke, changes in cortical thickness occur both in regions directly connected to the stroke lesion and in contralateral hemisphere areas as well as in the cerebellum. The underlying mechanisms leading to these changes in cortical thickness are still to be fully understood and further research in the field is needed. Systematic Review Registration: https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42020200539; PROSPERO 2020, identifier: CRD42020200539.

Post-Stroke Cognitive Impairment: Pathophysiological Insights into Brain Disconnectome from Advanced Neuroimaging Analysis Techniques

The neurological symptoms of stroke have traditionally provided the foundation for functional mapping of the brain. However, there are many unresolved aspects in our understanding of cerebral activity, especially regarding high-level cognitive functions. This review provides a comprehensive look at the pathophysiology of post-stroke cognitive impairment in light of recent findings from advanced imaging techniques. Combining network neuroscience and clinical neurology, our research focuses on how changes in brain networks correlate with post-stroke cognitive prognosis. More specifically, we first discuss the general consequences of stroke lesions due to damage of canonical resting-state large-scale networks or changes in the composition of the entire brain. We also review emerging methods, such as lesion-network mapping and gradient analysis, used to study the aforementioned events caused by stroke lesions. Lastly, we examine other patient vulnerabilities, such as superimposed amyloid pathology and blood-brain barrier leakage, which potentially lead to different outcomes for the brain network compositions even in the presence of similar stroke lesions. This knowledge will allow a better understanding of the pathophysiology of post-stroke cognitive impairment and provide a theoretical basis for the development of new treatments, such as neuromodulation.

European Stroke Organisation and European Academy of Neurology joint guidelines on post-stroke cognitive impairment

BACKGROUND AND PURPOSE

The optimal management of post-stroke cognitive impairment (PSCI) remains controversial. These joint European Stroke Organisation (ESO) and European Academy of Neurology (EAN) guidelines provide evidence-based recommendations to assist clinicians in decision making regarding prevention, diagnosis, treatment and prognosis.

METHODS

Guidelines were developed according to the Grading of Recommendations, Assessment, Development and Evaluation (GRADE) methodology. The working group identified relevant clinical questions, performed systematic reviews, assessed the quality of the available evidence, and made specific recommendations. Expert consensus statements were provided where insufficient evidence was available to provide recommendations.

RESULTS

There was limited randomized controlled trial (RCT) evidence regarding single or multicomponent interventions to prevent post-stroke cognitive decline. Lifestyle interventions and treating vascular risk factors have many health benefits, but a cognitive effect is not proven. We found no evidence regarding routine cognitive screening following stroke, but recognize the importance of targeted cognitive assessment. We describe the accuracy of various cognitive screening tests, but found no clearly superior approach to testing. There was insufficient evidence to make a recommendation for use of cholinesterase inhibitors, memantine nootropics or cognitive rehabilitation. There was limited evidence on the use of prediction tools for post-stroke cognition. The association between PSCI and acute structural brain imaging features was unclear, although the presence of substantial white matter hyperintensities of presumed vascular origin on brain magnetic resonance imaging may help predict cognitive outcomes.

CONCLUSIONS

These guidelines highlight fundamental areas where robust evidence is lacking. Further definitive RCTs are needed, and we suggest priority areas for future research.

Value of infarct location in the prediction of functional outcome in patients with an anterior large vessel occlusion: results from the HERMES study

Purpose

Follow-up infarct volume (FIV) is moderately associated with functional outcome. We hypothesized that accounting for infarct location would strengthen the association of FIV with functional outcome.

Methods

We included 252 patients from the HERMES collaboration with follow-up diffusion weighted imaging. Patients received endovascular treatment combined with best medical management (n = 52%) versus best medical management alone (n = 48%). FIV was quantified in low, moderate and high modified Rankin Scale (mRS)-relevant regions. We used binary logistic regression to study the relation between the total, high, moderate or low mRS-relevant FIVs and favorable outcome (mRS < 2) after 90 days. The strength of association was evaluated using the c-statistic.

Results

Small lesions only occupied high mRS-relevant brain regions. Lesions additionally occupied lower mRS-relevant brain regions if FIV expanded. Higher FIV was associated with a higher risk of unfavorable outcome, as were volumes of tissue with low, moderate and high mRS relevance. In multivariable modeling, only the volume of high mRS-relevant infarct was significantly associated with favorable outcome. The c-statistic was highest (0.76) for the models that included high mRS-relevant FIV or the combination of high, moderate and low mRS-relevant FIV but was not significantly different from the model that included only total FIV (0.75).

Conclusion

This study confirms the association of FIV and unfavorable functional outcome but showed no strengthened association if lesion location was taken into account.

European Stroke Organisation and European Academy of Neurology joint guidelines on post-stroke cognitive impairment

The optimal management of post-stroke cognitive impairment remains controversial. These joint European Stroke Organisation (ESO) and European Academy of Neurology (EAN) guidelines provide evidence-based recommendations to assist clinicians in decision making around prevention, diagnosis, treatment and prognosis. These guidelines were developed according to ESO standard operating procedure and the Grading of Recommendations, Assessment, Development and Evaluation (GRADE) methodology. The working group identified relevant clinical questions, performed systematic reviews and, where possible, meta-analyses of the literature, assessed the quality of the available evidence and made specific recommendations. Expert consensus statements were provided where insufficient evidence was available to provide recommendations based on the GRADE approach. There was limited randomised controlled trial evidence regarding single or multicomponent interventions to prevent post-stroke cognitive decline. Interventions to improve lifestyle and treat vascular risk factors may have many health benefits but a beneficial effect on cognition is not proven. We found no evidence around routine cognitive screening following stroke but recognise the importance of targeted cognitive assessment. We described the accuracy of various cognitive screening tests but found no clearly superior approach to testing. There was insufficient evidence to make a recommendation for use of cholinesterase inhibitors, memantine nootropics or cognitive rehabilitation. There was limited evidence on the use of prediction tools for post-stroke cognitive syndromes (cognitive impairment, dementia and delirium). The association between post-stroke cognitive impairment and most acute structural brain imaging features was unclear, although the presence of substantial white matter hyperintensities of presumed vascular origin on acute MRI brain may help predict cognitive outcomes. These guidelines have highlighted fundamental areas where robust evidence is lacking. Further, definitive randomised controlled trials are needed, and we suggest priority areas for future research.

Strategic infarct locations for post-stroke cognitive impairment: a pooled analysis of individual patient data from 12 acute ischaemic stroke cohorts

BACKGROUND

Post-stroke cognitive impairment (PSCI) occurs in approximately half of people in the first year after stroke. Infarct location is a potential determinant of PSCI, but a comprehensive map of strategic infarct locations predictive of PSCI is unavailable. We aimed to identify infarct locations most strongly predictive of PSCI after acute ischaemic stroke and use this information to develop a prediction model.

METHODS

In this large-scale multicohort lesion-symptom mapping study, we pooled and harmonised individual patient data from 12 cohorts through the Meta-analyses on Strategic Lesion Locations for Vascular Cognitive Impairment using Lesion-Symptom Mapping (Meta VCI Map) consortium. The identified cohorts (as of Jan 1, 2019) comprised patients with acute symptomatic infarcts on CT or MRI (with available infarct segmentations) and a cognitive assessment up to 15 months after acute ischaemic stroke onset. PSCI was defined as performance lower than the fifth percentile of local normative data, on at least one cognitive domain on a multidomain neuropsychological assessment or on the Montreal Cognitive Assessment. Voxel-based lesion-symptom mapping (VLSM) was used to calculate voxel-wise odds ratios (ORs) for PSCI that were mapped onto a three-dimensional brain template to visualise PSCI risk per location. For the prediction model of PSCI risk, a location impact score on a 5-point scale was derived from the VLSM results on the basis of the mean voxel-wise coefficient (ln[OR]) within each patient's infarct. We did combined internal-external validation by leave-one-cohort-out cross-validation for all 12 cohorts using logistic regression. Predictive performance of a univariable model with only the location impact score was compared with a multivariable model with addition of other clinical PSCI predictors (age, sex, education, time interval between stroke onset and cognitive assessment, history of stroke, and total infarct volume). Testing of visual ratings was done by three clinicians, and accuracy, inter-rater reliability, and intra-rater reliability were assessed with Cohen's weighted kappa.

FINDINGS

In our sample of 2950 patients (mean age 66·8 years [SD 11·6]; 1157 [39·2%] women), 1286 (43·6%) had PSCI. We achieved high lesion coverage of the brain in our analyses (86·9%). Infarcts in the left frontotemporal lobes, left thalamus, and right parietal lobe were strongly associated with PSCI (after false discovery rate correction, q<0·01; voxel-wise ORs >20). On cross-validation, the location impact score showed good correspondence, based on visual assessment of goodness of fit, between predicted and observed risk of PSCI across cohorts after adjusting for cohort-specific PSCI occurrence. Cross-validations showed that the location impact score by itself had similar performance to the combined model with other PSCI predictors, while allowing for easy visual assessment. Therefore the univariable model with only the location impact score was selected as the final model. Correspondence between visual ratings and actual location impact score (Cohen's weighted kappa: range 0·88-0·92), inter-rater agreement (0·85-0·87), and intra-rater agreement (for a single rater, 0·95) were all high.

INTERPRETATION

To the best of our knowledge, this study provides the first comprehensive map of strategic infarct locations associated with risk of PSCI. A location impact score was derived from this map that robustly predicted PSCI across cohorts. Furthermore, we developed a quick and reliable visual rating scale that might in the future be applied by clinicians to identify individual patients at risk of PSCI.

FUNDING

The Netherlands Organisation for Health Research and Development.

Generative lesion pattern decomposition of cognitive impairment after stroke

Cognitive impairment is a frequent and disabling sequela of stroke. There is however incomplete understanding of how lesion topographies in the left and right cerebral hemisphere brain interact to cause distinct cognitive deficits. We integrated machine learning and Bayesian hierarchical modelling to enable a hemisphere-aware analysis of 1080 acute ischaemic stroke patients with deep profiling ∼3 months after stroke. We show the relevance of the left hemisphere in the prediction of language and memory assessments and relevance of the right hemisphere in the prediction of visuospatial functioning. Global cognitive impairments were equally well predicted by lesion topographies from both sides. Damage to the hippocampal and occipital regions on the left was particularly informative about lost naming and memory functions, while damage to these regions on the right was linked to lost visuospatial functioning. Global cognitive impairment was predominantly linked to lesioned tissue in the supramarginal and angular gyrus, the post-central gyrus as well as the lateral occipital and opercular cortices of the left hemisphere. Hence, our analysis strategy uncovered that lesion patterns with unique hemispheric distributions are characteristic of how cognitive capacity is lost due to ischaemic brain tissue damage.

EASY score (Eloquent, Age and baseline SYmptoms score) for outcome prediction in patients with acute ischemic stroke

OBJECTIVE

A pragmatic tool for the early and reliable prediction of recovery in patients with acute ischemic stroke is needed. We aimed to test the addition of brain eloquent areas involvement in variables predicting poor outcome, using a simple scoring system.

METHODS

Retrospective study of patients with anterior circulation acute ischemic stroke treated with best medical treatment and/or endovascular reperfusion. Primary outcome measure was 3-months poor outcome (mRs 3-6). We developed a prognostic model based on clinical data and a quantitative scoring system of the main eloquent brain areas involved on early follow-up CT, and analyzed its accuracy to predict poor outcome comparatively to three other prognostic models. The final model was used to develop a score for outcome prediction based on the multivariable analysis.

RESULTS

A total of 197 patients were included (poor outcome = 62; mean age 67 ± 15.1 years; 44% females). Independent predictors of poor outcome were increasing age (p < 0.001), baseline NIHSS (p = 0.03), and the involvement of two brain areas: posterior limb of internal capsule (p < 0.001) and postero-superior corona radiata (p < 0.001). This model showed to be the most accurate to predict poor outcome (Balance Accuracy = 77.74%; C-Statistic = 0.891). The derived risk score attributing points for each of these variables (EASY score) showed similar performances (Balance Accuracy = 82.11%; C-Statistic = 0.90).

CONCLUSION

The EASY score is an easy-to-apply and accurate tool to predict the 3-months functional outcome after ischemic stroke, relying on simple clinical features and the assessment of two key eloquent brain areas on early follow-up CT.

Predictors of dementia after first ischemic stroke

Various mechanisms contribute to dementia after first ischemic stroke as lesions on strategic areas of cognition and stroke premorbidity.

Objectives

Assessing clinical and neuroimaging predictors of dementia after first ischemic stroke and its relation to stroke location, subtypes and severity.

Methods

Eighty first ischemic stroke patients were included. Forty patients with dementia after first stroke and forty patients without dementia according to DSM-IV diagnostic criteria of vascular dementia. All patients were subjected to general and neurological assessment, National Institute Health Stroke Scale (NIHSS) for stroke severity, Montreal Cognitive Assessment (MoCA) scale for cognition assessment, MRI brain and Trial of Org 10172 in acute stroke treatment (TOAST) classification for stroke subtypes.

Results

Left hemispheric ischemic stroke, strategic infarctions, diabetes mellitus and stroke of anterior circulation were found to be independent risk factors for dementia after first ischemic stroke (OR=3.09, 95%CI 1.67-10.3, OR=2.33, 95%CI 1.87-8.77, OR=1.88, 95%CI 1.44-4.55, OR=1.86, 95%CI 1.45-6.54, respectively). Hypertension, dyslipidemia, smoking, ischemic heart disease, high NIHSS score and large vessel infarction were significantly higher among post stroke dementia patients. However, on binary logistic regression, they did not reach to be independent risk factors.

Conclusion

Stroke location (left stroke, strategic infarction, anterior circulation stroke) and diabetes mellitus could be predictors of dementia after first ischemic stroke, but stroke severity, stroke subtypes, hypertension, dyslipidemia, smoking and ischemic heart could not.

Clinically accessible neuroimaging predictors of post-stroke neurocognitive disorder: a prospective observational study

Background

Neurocognitive disorder (NCD) is common in stroke survivors. We aimed to identify clinically accessible imaging markers of stroke and chronic pathology that are associated with early post-stroke NCD.

Methods

We included 231 stroke survivors from the “Norwegian Cognitive Impairment after Stroke (Nor-COAST)” study who underwent a standardized cognitive assessment 3 months after the stroke. Any NCD (mild cognitive impairment and dementia) and major NCD (dementia) were diagnosed according to “Diagnostic and Statistical Manual of Mental Disorders (DSM-5)” criteria. Clinically accessible imaging findings were analyzed on study-specific brain MRIs in the early phase after stroke. Stroke lesion volumes were semi automatically quantified and strategic stroke locations were determined by an atlas based coregistration. White matter hyperintensities (WMH) and medial temporal lobe atrophy (MTA) were visually scored. Logistic regression was used to identify neuroimaging findings associated with major NCD and any NCD.

Results

Mean age was 71.8 years (SD 11.1), 101 (43.7%) were females, mean time from stroke to imaging was 8 (SD 16) days. At 3 months 63 (27.3%) had mild NCD and 65 (28.1%) had major NCD. Any NCD was significantly associated with WMH pathology (odds ratio (OR) = 2.73 [1.56 to 4.77], p = 0.001), MTA pathology (OR = 1.95 [1.12 to 3.41], p = 0.019), and left hemispheric stroke (OR = 1.8 [1.05 to 3.09], p = 0.032). Major NCD was significantly associated with WMH pathology (OR = 2.54 [1.33 to 4.84], p = 0.005) and stroke lesion volume (OR (per ml) =1.04 [1.01 to 1.06], p = 0.001).

Conclusion

WMH pathology, MTA pathology and left hemispheric stroke were associated with the development of any NCD. Stroke lesion volume and WMH pathology were associated with the development of major NCD 3 months after stroke. These imaging findings may be used in the routine clinical setting to identify patients at risk for early post-stroke NCD.

Trial registration

ClinicalTrials.gov, NCT02650531, Registered 8 January 2016 – Retrospectively registered.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12883-021-02117-8.

Prognostic rules for predicting cognitive syndromes following stroke: A systematic review

Purpose

Stroke survivors are at high risk of developing cognitive syndromes, such as delirium and dementia. Accurate prediction of future cognitive outcomes may aid timely diagnosis, intervention planning, and stratification in clinical trials. We aimed to identify, describe and appraise existing multivariable prognostic rules for prediction of post-stroke cognitive status.

Method

We systematically searched four electronic databases from inception to November 2019 for publications describing a method to estimate individual probability of developing a cognitive syndrome following stroke. We extracted data from selected studies using a pre-specified proforma and applied the Prediction model Risk Of Bias Assessment Tool (PROBAST) for critical appraisal.

Findings

Of 17,390 titles, we included 10 studies (3143 participants), presenting the development of 11 prognostic rules – 7 for post-stroke cognitive impairment and 4 for delirium. Most commonly incorporated predictors were: demographics, imaging findings, stroke type and symptom severity. Among studies assessing predictive discrimination, the area under the receiver operating characteristic (AUROC) in apparent validation ranged from 0.80 to 0.91. The overall risk of bias for each study was high. Only one prognostic rule had been externally validated.

Discussion/conclusion: Research into the prognosis of cognitive outcomes following stroke is an expanding field, still at its early stages. Recommending use of specific prognostic rules is limited by the high risk of bias in all identified studies, and lack of supporting evidence from external validation. To ensure the quality of future research, investigators should adhere to current, endorsed best practice guidelines for conduct of prediction model studies.

Prediction of Long-term Cognitive Function After Minor Stroke Using Functional Connectivity

OBJECTIVE

To determine whether functional MRI connectivity can predict long-term cognitive function 36 months after minor stroke.

METHODS

Seventy-two participants with first-ever stroke were included at baseline and followed up for 36 months. A ridge regression machine learning algorithm was developed and used to predict cognitive scores 36 months poststroke on the basis of the functional networks measured using MRI at 6 months (referred to here as the poststroke cognitive impairment [PSCI] network). The prediction accuracy was evaluated in 4 domains (memory, attention/executive, language, and visuospatial functions) and compared with clinical data and other functional networks. The models' statistical significance was probed with permutation tests. The potential involvement of cortical atrophy was assessed 6 months poststroke. A second, independent dataset (n = 40) was used to validate the results and assess their generalizability.

RESULTS

Based on the PSCI network, a machine learning model was able to predict memory, attention, visuospatial functions, and language functions 36 months poststroke (r 2: 0.67, 0.73, 0.55, and 0.48, respectively). The PSCI-based model was at least as accurate as models based on other functional networks or clinical data. Specific patterns were demonstrated for the 4 cognitive domains, with involvement of the left superior frontal cortex for memory, attention, and visuospatial functions. The cortical thickness 6 months poststroke was not correlated with cognitive function 36 months poststroke. The independent validation dataset gave similar results.

CONCLUSIONS

A machine learning model based on the PSCI network can predict long-term cognitive outcome after stroke.

Exploring the predictive value of lesion topology on motor function outcomes in a porcine ischemic stroke model

Harnessing the maximum diagnostic potential of magnetic resonance imaging (MRI) by including stroke lesion location in relation to specific structures that are associated with particular functions will likely increase the potential to predict functional deficit type, severity, and recovery in stroke patients. This exploratory study aims to identify key structures lesioned by a middle cerebral artery occlusion (MCAO) that impact stroke recovery and to strengthen the predictive capacity of neuroimaging techniques that characterize stroke outcomes in a translational porcine model. Clinically relevant MRI measures showed significant lesion volumes, midline shifts, and decreased white matter integrity post-MCAO. Using a pig brain atlas, damaged brain structures included the insular cortex, somatosensory cortices, temporal gyri, claustrum, and visual cortices, among others. MCAO resulted in severely impaired spatiotemporal gait parameters, decreased voluntary movement in open field testing, and higher modified Rankin Scale scores at acute timepoints. Pearson correlation analyses at acute timepoints between standard MRI metrics (e.g., lesion volume) and functional outcomes displayed moderate R values to functional gait outcomes. Moreover, Pearson correlation analyses showed higher R values between functional gait deficits and increased lesioning of structures associated with motor function, such as the putamen, globus pallidus, and primary somatosensory cortex. This correlation analysis approach helped identify neuroanatomical structures predictive of stroke outcomes and may lead to the translation of this topological analysis approach from preclinical stroke assessment to a clinical biomarker.

Effectiveness of very low profile thrombectomy device in primary distal medium vessel occlusion, as rescue therapy after incomplete proximal recanalization or following iatrogenic thromboembolic events

Background

Recent progress with smaller retrievers has expanded the ability to reach distal brain arteries. We herein report recanalization, bleeding complications and short-term clinical outcomes with the smallest currently known low profile thrombectomy device in patients with primary or secondary distal medium vessel occlusion (DMVO).

Methods

We performed a retrospective analysis of 115 patients receiving mechanical thrombectomy (MT) in DMVO using the extended Thrombolysis in Cerebral Infarction (eTICI), European Cooperative Acute Stroke Study (ECASS) II classification, The National Institutes of Health Stroke Scale (NIHSS) and modified Rankin Scale (mRS) scores at admission and discharge to evaluate outcomes. Patients were stratified into three groups: (1) primary isolated distal occlusion (n=34), (2) secondary distal occlusion after MT of a proximal vessel occlusion (n=71), or (3) during endovascular treatment of aneurysms or arteriovenous malformations (AVMs) (n=10).

Results

Successful distal recanalization, defined as an eTICI score of 2b67, 2c and 3, was achieved in 74.7% (86/115) of patients. More specifically, it was 70.5% (24/34), 73.2% (52/71), and 100% (10/10) of primary DMVO, secondary DMVO after proximal MT, and rescue MT during aneurysm or AVM embolization, respectively. Symptomatic intraparenchymal bleeding occurred in 6.9% (eight patients). In-hospital mortality occurred in 18.1% (19/105) of patients with stroke. The most common cause of death was large infarct, old age, and therapy limitation.

Conclusion

Direct or rescue MT of DMVO using a very low profile thrombectomy device is associated with a high rate of successful recanalization and a reasonable rate of symptomatic hemorrhagic complication, despite a risk of 18.1% hospital mortality in elderly patients. Further trials are needed to confirm our results and assess long-term clinical outcomes.

Cognitive Impairment in Patients with Stroke

Despite substantial advances in stroke care, vascular cognitive impairment remains a prominent source of disability. Unlike sensorimotor impairments, cognition often continues to decline after stroke. An aging population will increase the prevalence of vascular cognitive impairment, with stroke playing an important role. Ten percent of patients presenting with stroke have pre-stroke dementia; an additional 10% will develop incident dementia with a first stroke, and 30% with a recurrent stroke. While stroke increases the risk of cognitive impairment, the presence of cognitive impairment also impacts acute stroke treatment and increases risk of poor outcome by nearly twofold. There is substantial overlap in the clinical and pathological aspects of vascular and degenerative dementias in many patients. How they relate to one another is controversial. The treatment of vascular cognitive impairment remains supportive, focusing on treating vascular risk factors. Cognitive rehabilitation after stroke is an area of active research, and existing pharmacologic treatments have limited benefit. Heightened awareness of cognitive impairment in the setting of stroke is imperative for prognostication and management, impetus for research and, ultimately, the discovery of efficacious treatments.

Cerebral Small Vessel Disease MRI Features Do Not Improve the Prediction of Stroke Outcome

OBJECTIVE

To determine whether the total small vessel disease (SVD) score adds information to the prediction of stroke outcome compared to validated predictors, we tested different predictive models of outcome in patients with stroke.

METHODS

White matter hyperintensity, lacunes, perivascular spaces, microbleeds, and atrophy were quantified in 2 prospective datasets of 428 and 197 patients with first-ever stroke, using MRI collected 24 to 72 hours after stroke onset. Functional, cognitive, and psychological status were assessed at the 3- to 6-month follow-up. The predictive accuracy (in terms of calibration and discrimination) of age, baseline NIH Stroke Scale score (NIHSS), and infarct volume was quantified (model 1) on dataset 1, the total SVD score was added (model 2), and the improvement in predictive accuracy was evaluated. These 2 models were also developed in dataset 2 for replication. Finally, in model 3, the MRI features of cerebral SVD were included rather than the total SVD score.

RESULTS

Model 1 showed excellent performance for discriminating poor vs good functional outcomes (area under the curve [AUC] 0.915), and fair performance for identifying cognitively impaired and depressed patients (AUCs 0.750 and 0.688, respectively). A higher SVD score was associated with a poorer outcome (odds ratio 1.30 [1.07-1.58], p = 0.0090 at best for functional outcome). However, adding the total SVD score (model 2) or individual MRI features (model 3) did not improve the prediction over model 1. Results for dataset 2 were similar.

CONCLUSIONS

Cerebral SVD was independently associated with functional, cognitive, and psychological outcomes, but had no clinically relevant added value to predict the individual outcomes of patients when compared to the usual predictors, such as age and baseline NIHSS.