An Approach to Successful Development of Clinician-Scientists in Neurology: The NINDS R25 Experience.

OBJECTIVE: Training clinician-scientists is a primary objective of many academic neurology departments, as these individuals are uniquely positioned to perform insightful clinical or laboratory-based research informed both by clinical knowledge and their own experiences caring for patients. Despite its importance, training clinician-scientists has perhaps never been so challenging. The National Institute of Neurologic Disorders and Stroke (NINDS) R25 program was designed in an attempt to support these individuals, decrease the time needed to obtain National Institutes of Health K awards, and to help educate a cohort of trainees preparing for a career in academic neurology. We endeavored to describe the structure and features of the program while examining its outcomes. METHODS: R25 outcome data from 2009 to 2024 were reviewed. Statistical comparisons were made using 2-sided Mann-Whitney U testing. RESULTS: A total of 67% of adult neurologists who received an R25 had a successful application for a National Institutes of Health K award compared with 45% of adult neurologists who had not received R25 support (p < 0.0001). Among child neurologists, 73% who applied went on to receive K funding after R25 support, compared with 45% who had not been part of the R25 program (p < 0.001). The average time between completion of residency and obtaining a K award for R25 participants was decreased by 26 months among those with an MD/PhD degree, and 32 months for those with an MD degree compared with non-R25 individuals. INTERPRETATION: The R25 program has been successful in achieving its training goals, but stands as only one component of support for aspiring clinician-scientists. Investments and commitments made by academic neurology departments are key to supporting this success. ANN NEUROL 2024.

Deep learning for prediction of post-thrombectomy outcomes based on admission CT angiography in large vessel occlusion stroke.

Purpose: Computed Tomography Angiography (CTA) is the first line of imaging in the diagnosis of Large Vessel Occlusion (LVO) strokes. We trained and independently validated end-to-end automated deep learning pipelines to predict 3-month outcomes after anterior circulation LVO thrombectomy based on admission CTAs. Methods: We split a dataset of 591 patients into training/cross-validation (n = 496) and independent test set (n = 95). We trained separate models for outcome prediction based on admission "CTA" images alone, "CTA + Treatment" (including time to thrombectomy and reperfusion success information), and "CTA + Treatment + Clinical" (including admission age, sex, and NIH stroke scale). A binary (favorable) outcome was defined based on a 3-month modified Rankin Scale ≤ 2. The model was trained on our dataset based on the pre-trained ResNet-50 3D Convolutional Neural Network ("MedicalNet") and included CTA preprocessing steps. Results: We generated an ensemble model from the 5-fold cross-validation, and tested it in the independent test cohort, with receiver operating characteristic area under the curve (AUC, 95% confidence interval) of 70 (0.59-0.81) for "CTA," 0.79 (0.70-0.89) for "CTA + Treatment," and 0.86 (0.79-0.94) for "CTA + Treatment + Clinical" input models. A "Treatment + Clinical" logistic regression model achieved an AUC of 0.86 (0.79-0.93). Conclusion: Our results show the feasibility of an end-to-end automated model to predict outcomes from admission and post-thrombectomy reperfusion success. Such a model can facilitate prognostication in telehealth transfer and when a thorough neurological exam is not feasible due to language barrier or pre-existing morbidities.

Biological and Procedural Predictors of Outcome in the Stroke Preclinical Assessment Network (SPAN) Trial.

BACKGROUND: The SPAN trial (Stroke Preclinical Assessment Network) is the largest preclinical study testing acute stroke interventions in experimental focal cerebral ischemia using endovascular filament middle cerebral artery occlusion (MCAo). Besides testing interventions against controls, the prospective design captured numerous biological and procedural variables, highlighting the enormous heterogeneity introduced by the multicenter structure that might influence stroke outcomes. Here, we leveraged the unprecedented sample size achieved by the SPAN trial and the prospective design to identify the biological and procedural variables that affect experimental stroke outcomes in transient endovascular filament MCAo. METHODS: The study cohort included all mice enrolled and randomized in the SPAN trial (N=1789). Mice were subjected to 60-minute MCAo and followed for a month. Thirteen biological and procedural independent variables and 4 functional (weight loss and 4-point neuroscore on days 1 and 2, corner test on days 7 and 28, and mortality) and 3 tissue (day 2, magnetic resonance imaging infarct volumes and swelling; day 30, magnetic resonance imaging tissue loss) outcome variables were prospectively captured. Multivariable regression with stepwise elimination was used to identify the predictors and their effect sizes. RESULTS: Older age, active circadian stage at MCAo, and thinner and longer filament silicone tips predicted higher mortality. Older age, larger body weight, longer anesthesia duration, and longer filament tips predicted worse neuroscores, while high-fat diet and blood flow monitoring predicted milder neuroscores. Older age and a high-fat diet predicted worse corner test performance. While shorter filament tips predicted more ipsiversive turning, longer filament tips appeared to predict contraversive turning. Age, sex, and weight interacted when predicting the infarct volume. Older age was associated with smaller infarcts on day 2 magnetic resonance imaging, especially in animals with larger body weights; this association was most conspicuous in females. High-fat diet also predicted smaller infarcts. In contrast, the use of cerebral blood flow monitoring and more severe cerebral blood flow drop during MCAo, longer anesthesia, and longer filament tips all predicted larger infarcts. Bivariate analyses among the dependent variables highlighted a disconnect between tissue and functional outcomes. CONCLUSIONS: Our analyses identified variables affecting endovascular filament MCAo outcome, an experimental stroke model used worldwide. Multiple regression refuted some commonly reported predictors and revealed previously unrecognized associations. Given the multicenter prospective design that represents a sampling of real-world conditions, the degree of heterogeneity mimicking clinical trials, the large number of predictors adjusted for in the multivariable model, and the large sample size, we think this is the most definitive analysis of the predictors of preclinical stroke outcome to date. Future multicenter experimental stroke trials should standardize or at least ensure a balanced representation of the biological and procedural variables identified herein as potential confounders.

Automated detection of early signs of irreversible ischemic change on CTA source images in patients with large vessel occlusion.

PURPOSE: To create and validate an automated pipeline for detection of early signs of irreversible ischemic change from admission CTA in patients with large vessel occlusion (LVO) stroke. METHODS: We retrospectively included 368 patients for training and 143 for external validation. All patients had anterior circulation LVO stroke, endovascular therapy with successful reperfusion, and follow-up diffusion-weighted imaging (DWI). We devised a pipeline to automatically segment Alberta Stroke Program Early CT Score (ASPECTS) regions and extracted their relative Hounsfield unit (rHU) values. We determined the optimal rHU cut points for prediction of final infarction in each ASPECT region, performed 10-fold cross-validation in the training set, and measured the performance via external validation in patients from another institute. We compared the model with an expert neuroradiologist for prediction of final infarct volume and poor functional outcome. RESULTS: We achieved a mean area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity of 0.69±0.13, 0.69±0.09, 0.61±0.23, and 0.72±0.11 across all regions and folds in cross-validation. In the external validation cohort, we achieved a median [interquartile] AUC, accuracy, sensitivity, and specificity of 0.71 [0.68-0.72], 0.70 [0.68-0.73], 0.55 [0.50-0.63], and 0.74 [0.73-0.77], respectively. The rHU-based ASPECTS showed significant correlation with DWI-based ASPECTS (rS = 0.39, p<0.001) and final infarct volume (rS = -0.36, p<0.001). The AUC for predicting poor functional outcome was 0.66 (95%CI: 0.57-0.75). The predictive capabilities of rHU-based ASPECTS were not significantly different from the neuroradiologist's visual ASPECTS for either final infarct volume or functional outcome. CONCLUSIONS: Our study demonstrates the feasibility of an automated pipeline and predictive model based on relative HU attenuation of ASPECTS regions on baseline CTA and its non-inferior performance in predicting final infarction on post-stroke DWI compared to an expert human reader.

StrokeClassifier: ischemic stroke etiology classification by ensemble consensus modeling using electronic health records.

Determining acute ischemic stroke (AIS) etiology is fundamental to secondary stroke prevention efforts but can be diagnostically challenging. We trained and validated an automated classification tool, StrokeClassifier, using electronic health record (EHR) text from 2039 non-cryptogenic AIS patients at 2 academic hospitals to predict the 4-level outcome of stroke etiology adjudicated by agreement of at least 2 board-certified vascular neurologists' review of the EHR. StrokeClassifier is an ensemble consensus meta-model of 9 machine learning classifiers applied to features extracted from discharge summary texts by natural language processing. StrokeClassifier was externally validated in 406 discharge summaries from the MIMIC-III dataset reviewed by a vascular neurologist to ascertain stroke etiology. Compared with vascular neurologists' diagnoses, StrokeClassifier achieved the mean cross-validated accuracy of 0.74 and weighted F1 of 0.74 for multi-class classification. In MIMIC-III, its accuracy and weighted F1 were 0.70 and 0.71, respectively. In binary classification, the two metrics ranged from 0.77 to 0.96. The top 5 features contributing to stroke etiology prediction were atrial fibrillation, age, middle cerebral artery occlusion, internal carotid artery occlusion, and frontal stroke location. We designed a certainty heuristic to grade the confidence of StrokeClassifier's diagnosis as non-cryptogenic by the degree of consensus among the 9 classifiers and applied it to 788 cryptogenic patients, reducing cryptogenic diagnoses from 25.2% to 7.2%. StrokeClassifier is a validated artificial intelligence tool that rivals the performance of vascular neurologists in classifying ischemic stroke etiology. With further training, StrokeClassifier may have downstream applications including its use as a clinical decision support system.

VEGF-C prophylaxis favors lymphatic drainage and modulates neuroinflammation in a stroke model.

Meningeal lymphatic vessels (MLVs) promote tissue clearance and immune surveillance in the central nervous system (CNS). Vascular endothelial growth factor-C (VEGF-C) regulates MLV development and maintenance and has therapeutic potential for treating neurological disorders. Herein, we investigated the effects of VEGF-C overexpression on brain fluid drainage and ischemic stroke outcomes in mice. Intracerebrospinal administration of an adeno-associated virus expressing mouse full-length VEGF-C (AAV-mVEGF-C) increased CSF drainage to the deep cervical lymph nodes (dCLNs) by enhancing lymphatic growth and upregulated neuroprotective signaling pathways identified by single nuclei RNA sequencing of brain cells. In a mouse model of ischemic stroke, AAV-mVEGF-C pretreatment reduced stroke injury and ameliorated motor performances in the subacute stage, associated with mitigated microglia-mediated inflammation and increased BDNF signaling in brain cells. Neuroprotective effects of VEGF-C were lost upon cauterization of the dCLN afferent lymphatics and not mimicked by acute post-stroke VEGF-C injection. We conclude that VEGF-C prophylaxis promotes multiple vascular, immune, and neural responses that culminate in a protection against neurological damage in acute ischemic stroke.

Radiomics-Based Prediction of Collateral Status from CT Angiography of Patients Following a Large Vessel Occlusion Stroke.

BACKGROUND: A major driver of individual variation in long-term outcomes following a large vessel occlusion (LVO) stroke is the degree of collateral arterial circulation. We aimed to develop and evaluate machine-learning models that quantify LVO collateral status using admission computed tomography angiography (CTA) radiomics. METHODS: We extracted 1116 radiomic features from the anterior circulation territories from admission CTAs of 600 patients experiencing an acute LVO stroke. We trained and validated multiple machine-learning models for the prediction of collateral status based on consensus from two neuroradiologists as ground truth. Models were first trained to predict (1) good vs. intermediate or poor, or (2) good vs. intermediate or poor collateral status. Then, model predictions were combined to determine a three-tier collateral score (good, intermediate, or poor). We used the receiver operating characteristics area under the curve (AUC) to evaluate prediction accuracy. RESULTS: We included 499 patients in training and 101 in an independent test cohort. The best-performing models achieved an averaged cross-validation AUC of 0.80 ± 0.05 for poor vs. intermediate/good collateral and 0.69 ± 0.05 for good vs. intermediate/poor, and AUC = 0.77 (0.67-0.87) and AUC = 0.78 (0.70-0.90) in the independent test cohort, respectively. The collateral scores predicted by the radiomics model were correlated with (rho = 0.45, p = 0.002) and were independent predictors of 3-month clinical outcome (p = 0.018) in the independent test cohort. CONCLUSIONS: Automated tools for the assessment of collateral status from admission CTA-such as the radiomics models described here-can generate clinically relevant and reproducible collateral scores to facilitate a timely treatment triage in patients experiencing an acute LVO stroke.

Association of Neighborhood-Level Socioeconomic Factors With Delay to Hospital Arrival in Patients With Acute Stroke.

BACKGROUND AND OBJECTIVES: Delivery of acute ischemic stroke (AIS) therapies is contingent on the duration from last known well (LKW) to emergency department arrival time (EDAT). One reason for treatment ineligibility is delay in presentation to the hospital. We evaluate patient and neighborhood characteristics associated with time from LKW to EDAT. METHODS: This was a retrospective observational study of patients presenting to the Yale New Haven Hospital in the AIS code pathway from 2010 to 2020. Patients presenting within 4.5 hours from LKW who were recorded in the institutional Get With the Guidelines Stroke registry were classified as early while those presenting beyond 4.5 hours were designated as late. Temporal trends in late presentation were explored by univariate logistic regression. Using variables significant in univariate analysis at p < 0.05, we developed a mixed-effect logistic regression model to estimate the probability of late presentation as a function of patient-level and neighborhood (ZIP)-level characteristics (area deprivation index [ADI] derived from the Health Resources and Services Administration), adjusted for calendar year and geographic distance from the centroid of the ZIP code to the hospital. RESULTS: A total of 2,643 patients with AIS from 2010 to 2020 were included (63.4% presented late and 36.6% presented early). The frequency of late presentation increased significantly from 68% in 2010 to 71% in 2020 (p = 0.002) and only among non-White patients. Patients presenting late were more likely to be non-White (37.1% vs 26.9%, p < 0.0001), arrive by means other than emergency medical services (EMS) (32.7% vs 16.1%, p < 0.0001), have an NIHSS <6 (68.7% vs 55.2%, p < 0.0001), and present from a neighborhood with a higher ADI category (p = 0.0001) that was nearer to the hospital (median 5.8 vs 7.7 miles, p = 0.0032). In the mixed model, the ADI by units of 10 (odds ratio [OR] 1.022, 95% confidence interval [CI] 1.020-1.024), non-White race (OR 1.083, 95% CI 1.039-1.127), arrival by means other than EMS (OR 1.193, 95% CI 1.145-1.124), and an NIHSS <6 (OR 1.085, 95% CI 1.041-1.129) were associated with late presentation. DISCUSSION: In addition to patient-level factors, socioeconomic deprivation of neighborhood of residence contributes to delays in hospital presentation for AIS. These findings may provide opportunities for targeted interventions to improve presentation times in at-risk communities.

Clinical Trial Protocol for BEACH: A Phase 2a Study of MW189 in Patients with Acute Nontraumatic Intracerebral Hemorrhage.

Patients with acute spontaneous intracerebral hemorrhage (ICH) develop secondary neuroinflammation and cerebral edema that can further damage the brain and lead to increased risk of neurologic complications. Preclinical studies in animal models of acute brain injury have shown that a novel small-molecule drug candidate, MW01-6-189WH (MW189), decreases neuroinflammation and cerebral edema and improves functional outcomes. MW189 was also safe and well tolerated in phase 1 studies in healthy adults. The proof-of-concept phase 2a Biomarker and Edema Attenuation in IntraCerebral Hemorrhage (BEACH) clinical trial is a first-in-patient, multicenter, randomized, double-blind, placebo-controlled trial. It is designed to determine the safety and tolerability of MW189 in patients with acute ICH, identify trends in potential mitigation of neuroinflammation and cerebral edema, and assess effects on functional outcomes. A total of 120 participants with nontraumatic ICH will be randomly assigned 1:1 to receive intravenous MW189 (0.25 mg/kg) or placebo (saline) within 24 h of symptom onset and every 12 h for up to 5 days or until hospital discharge. The 120-participant sample size (60 per group) will allow testing of the null hypothesis of noninferiority with a tolerance limit of 12% and assuming a "worst-case" safety assumption of 10% rate of death in each arm with 10% significance and 80% power. The primary outcome is all-cause mortality at 7 days post randomization between treatment arms. Secondary end points include all-cause mortality at 30 days, perihematomal edema volume after symptom onset, adverse events, vital signs, pharmacokinetics of MW189, and inflammatory cytokine concentrations in plasma (and cerebrospinal fluid if available). Other exploratory end points are functional outcomes collected on days 30, 90, and 180. BEACH will provide important information about the utility of targeting neuroinflammation in ICH and will inform the design of future larger trials of acute central nervous system injury.

Characterization of CSF inflammatory markers after hemorrhagic stroke and their relationship to disease severity.

Background: The inflammatory response within the central nervous system is a key driver of secondary brain injury after hemorrhagic stroke, both in patients with intracerebral hemorrhage (ICH) and aneurysmal subarachnoid hemorrhage (aSAH). In this study, we aimed to characterize inflammatory molecules in the blood and cerebrospinal fluid (CSF) of patients within 72 hours of hemorrhage to understand how such molecules vary across disease types and disease severity. Methods: Biological samples were collected from patients admitted to a single-center Neurosciences Intensive Care Unit with a diagnosis of ICH or aSAH between 2014 and 2022. Control CSF samples were collected from patients undergoing CSF diversion for normal pressure hydrocephalus. A panel of immune molecules in the plasma and CSF samples was analyzed using Cytometric Bead Array assays. Clinical variables, including demographics, disease severity, and intensive care unit length of stay were collected. Results: Plasma and/or CSF samples were collected from 260 patients (188 ICH patients, 54 aSAH patients, 18 controls). C-C motif chemokine ligand-2 (CCL2), interleukin-6 (IL-6), granulocyte-colony stimulating factor (G-CSF), interleukin-8 (IL-8), and vascular endothelial growth factor (VEGF), were detectable in the CSF within the first 3 days after hemorrhage, and all were elevated compared to plasma. Compared with controls, CCL2, IL-6, IL-8, G-CSF, and VEGF were elevated in the CSF of both ICH and aSAH patients (p<0.01 for all comparisons). VEGF was increased in ICH patients compared to aSAH patients (p<0.01). CCL2, G-CSF, and VEGF in the CSF were associated with more severe disease in aSAH patients only. Conclusions: Within 3 days of hemorrhagic stroke, proinflammatory molecules can be detected in the CSF at higher concentrations than in the plasma. Early concentrations of some pro-inflammatory molecules may be associated with markers of disease severity.

StrokeClassifier: Ischemic Stroke Etiology Classification by Ensemble Consensus Modeling Using Electronic Health Records.

Determining the etiology of an acute ischemic stroke (AIS) is fundamental to secondary stroke prevention efforts but can be diagnostically challenging. We trained and validated an automated classification machine intelligence tool, StrokeClassifier, using electronic health record (EHR) text data from 2,039 non-cryptogenic AIS patients at 2 academic hospitals to predict the 4-level outcome of stroke etiology determined by agreement of at least 2 board-certified vascular neurologists' review of the stroke hospitalization EHR. StrokeClassifier is an ensemble consensus meta-model of 9 machine learning classifiers applied to features extracted from discharge summary texts by natural language processing. StrokeClassifier was externally validated in 406 discharge summaries from the MIMIC-III dataset reviewed by a vascular neurologist to ascertain stroke etiology. Compared with stroke etiologies adjudicated by vascular neurologists, StrokeClassifier achieved the mean cross-validated accuracy of 0.74 (±0.01) and weighted F1 of 0.74 (±0.01). In the MIMIC-III cohort, the accuracy and weighted F1 of StrokeClassifier were 0.70 and 0.71, respectively. SHapley Additive exPlanation analysis elucidated that the top 5 features contributing to stroke etiology prediction were atrial fibrillation, age, middle cerebral artery occlusion, internal carotid artery occlusion, and frontal stroke location. We then designed a certainty heuristic to deem a StrokeClassifier diagnosis as confidently non-cryptogenic by the degree of consensus among the 9 classifiers, and applied it to 788 cryptogenic patients. This reduced the percentage of the cryptogenic strokes from 25.2% to 7.2% of all ischemic strokes. StrokeClassifier is a validated artificial intelligence tool that rivals the performance of vascular neurologists in classifying ischemic stroke etiology for individual patients. With further training, StrokeClassifier may have downstream applications including its use as a clinical decision support system.

Trained immunity induced by high-salt diet impedes stroke recovery.

A high-salt diet (HSD) elicits sustained sterile inflammation and worsens tissue injury. However, how this occurs after stroke, a leading cause of morbidity and mortality, remains unknown. Here, we report that HSD impairs long-term brain recovery after intracerebral hemorrhage, a severe form of stroke, despite salt withdrawal prior to the injury. Mechanistically, HSD induces innate immune priming and training in hematopoietic stem and progenitor cells (HSPCs) by downregulation of NR4a family and mitochondrial oxidative phosphorylation. This training compromises alternative activation of monocyte-derived macrophages (MDMs) without altering the initial inflammatory responses of the stroke brain. Healthy mice transplanted with bone marrow from HSD-fed mice retain signatures of reduced MDM reparative functions, further confirming a persistent form of innate immune memory that originates in the bone marrow. Loss of NR4a1 in macrophages recapitulates HSD-induced negative impacts on stroke outcomes while gain of NR4a1 enables stroke recovery in HSD animals. Together, we provide the first evidence that links HSD-induced innate immune memory to the acquisition of persistent dysregulated inflammatory responses and unveils NR4a1 as a potential therapeutic target.

Polygenic Risk of Epilepsy and Post-Stroke Epilepsy.

Background and Aims: Epilepsy is highly heritable, with numerous known genetic risk loci. However, the genetic predisposition's role in post-acute brain injury epilepsy remains understudied. This study assesses whether a higher genetic predisposition to epilepsy raises post-stroke or Transient Ischemic Attack (TIA) survivor's risk of Post-Stroke Epilepsy (PSE). Methods: We conducted a three-stage genetic analysis. First, we identified independent epilepsy-associated ( p <5x10 -8 ) genetic variants from public data. Second, we estimated PSE-specific variant weights in stroke/TIA survivors from the UK Biobank. Third, we tested for an association between a polygenic risk score (PRS) and PSE risk in stroke/TIA survivors from the All of Us Research Program. Primary analysis included all ancestries, while a secondary analysis was restricted to European ancestry only. A sensitivity analysis excluded TIA survivors. Association testing was conducted via multivariable logistic regression, adjusting for age, sex, and genetic ancestry. Results: Among 19,708 UK Biobank participants with stroke/TIA, 805 (4.1%) developed PSE. Likewise, among 12,251 All of Us participants with stroke/TIA, 394 (3.2%) developed PSE. After establishing PSE-specific weights for 39 epilepsy-linked genetic variants in the UK Biobank, the resultant PRS was associated with elevated odds of PSE development in All of Us (OR:1.16[1.02-1.32]). A similar result was obtained when restricting to participants of European ancestry (OR:1.23[1.02-1.49]) and when excluding participants with a TIA history (OR:1.18[1.02-1.38]). Conclusions: Our findings suggest that akin to other forms of epilepsy, genetic predisposition plays an essential role in PSE. Because the PSE data were sparse, our results should be interpreted cautiously.

A multi-laboratory preclinical trial in rodents to assess treatment candidates for acute ischemic stroke.

Human diseases may be modeled in animals to allow preclinical assessment of putative new clinical interventions. Recent, highly publicized failures of large clinical trials called into question the rigor, design, and value of preclinical assessment. We established the Stroke Preclinical Assessment Network (SPAN) to design and implement a randomized, controlled, blinded, multi-laboratory trial for the rigorous assessment of candidate stroke treatments combined with intravascular thrombectomy. Efficacy and futility boundaries in a multi-arm multi-stage statistical design aimed to exclude from further study highly effective or futile interventions after each of four sequential stages. Six independent research laboratories performed a standard focal cerebral ischemic insult in five animal models that included equal numbers of males and females: young mice, young rats, aging mice, mice with diet-induced obesity, and spontaneously hypertensive rats. The laboratories adhered to a common protocol and efficiently enrolled 2615 animals with full data completion and comprehensive animal tracking. SPAN successfully implemented treatment masking, randomization, prerandomization inclusion and exclusion criteria, and blinded assessment of outcomes. The SPAN design and infrastructure provide an effective approach that could be used in similar preclinical, multi-laboratory studies in other disease areas and should help improve reproducibility in translational science.

Radiomic markers of intracerebral hemorrhage expansion on non-contrast CT: independent validation and comparison with visual markers.

Objective: To devise and validate radiomic signatures of impending hematoma expansion (HE) based on admission non-contrast head computed tomography (CT) of patients with intracerebral hemorrhage (ICH). Methods: Utilizing a large multicentric clinical trial dataset of hypertensive patients with spontaneous supratentorial ICH, we developed signatures predictive of HE in a discovery cohort (n = 449) and confirmed their performance in an independent validation cohort (n = 448). In addition to n = 1,130 radiomic features, n = 6 clinical variables associated with HE, n = 8 previously defined visual markers of HE, the BAT score, and combinations thereof served as candidate variable sets for signatures. The area under the receiver operating characteristic curve (AUC) quantified signatures' performance. Results: A signature combining select radiomic features and clinical variables attained the highest AUC (95% confidence interval) of 0.67 (0.61-0.72) and 0.64 (0.59-0.70) in the discovery and independent validation cohort, respectively, significantly outperforming the clinical (pdiscovery = 0.02, pvalidation = 0.01) and visual signature (pdiscovery = 0.03, pvalidation = 0.01) as well as the BAT score (pdiscovery < 0.001, pvalidation < 0.001). Adding visual markers to radiomic features failed to improve prediction performance. All signatures were significantly (p < 0.001) correlated with functional outcome at 3-months, underlining their prognostic relevance. Conclusion: Radiomic features of ICH on admission non-contrast head CT can predict impending HE with stable generalizability; and combining radiomic with clinical predictors yielded the highest predictive value. By enabling selective anti-expansion treatment of patients at elevated risk of HE in future clinical trials, the proposed markers may increase therapeutic efficacy, and ultimately improve outcomes.

Common Pathways of Epileptogenesis in Patients With Epilepsy Post-Brain Injury: Findings From a Systematic Review and Meta-analysis.

BACKGROUND AND OBJECTIVES: Epilepsy may result from various brain injuries, including stroke (ischemic and hemorrhagic), traumatic brain injury, and infections. Identifying shared common biological pathways and biomarkers of the epileptogenic process initiated by the different injuries may lead to novel targets for preventing the development of epilepsy. We systematically reviewed biofluid biomarkers to test their association with the risk of post-brain injury epilepsy. METHODS: We searched articles until January 25, 2022, in MEDLINE, Embase, PsycInfo, Web of Science, and Cochrane. The primary outcome was the difference in mean biomarker levels in patients with and without post-brain injury epilepsy. We used the modified quality score on prognostic studies for risk of bias assessment. We calculated each biomarker's pooled standardized mean difference (SMD) and 95% CI. Molecular interaction network and enrichment analyses were conducted in Cytoscape (PROSPERO CRD42021297110). RESULTS: We included 22 studies with 1,499 cases with post-brain injury epilepsy and 7,929 controls without post-brain injury epilepsy. Forty-five biomarkers in the blood or CSF were investigated with samples collected at disparate time points. Of 22 studies, 21 had a moderate-to-high risk of bias. Most of the biomarkers (28/45) were investigated in single studies; only 9 provided validation data, and studies used variable definitions for early-onset and late-onset seizures. A meta-analysis was possible for 19 biomarkers. Blood glucose levels in 4 studies were significantly higher in patients with poststroke epilepsy (PSE) than those without PSE (SMD 0.44; CI 0.19-0.69). From individual studies, 15 biomarkers in the blood and 7 in the CSF were significantly associated with post-brain injury epilepsy. Enrichment analysis identified that the significant biomarkers (interleukin [IL]-6, IL-1ß]) were predominantly inflammation related. DISCUSSION: We cannot yet recommend using the reported biomarkers for designing antiepileptogenesis trials or use in the clinical setting because of methodological heterogeneity, bias in the included studies, and insufficient validation studies. Although our analyses indicate the plausible role of inflammation in epileptogenesis, this is likely not the only mechanism. For example, an individual's genetic susceptibilities might contribute to his/her risk of epileptogenesis after brain injury. Rigorously designed biomarker studies with methods acceptable to the regulatory bodies should be conducted.

VEGF-C promotes brain-derived fluid drainage, confers neuroprotection, and improves stroke outcomes.

Meningeal lymphatic vessels promote tissue clearance and immune surveillance in the central nervous system (CNS). Vascular endothelium growth factor-C (VEGF-C) is essential for meningeal lymphatic development and maintenance and has therapeutic potential for treating neurological disorders, including ischemic stroke. We have investigated the effects of VEGF-C overexpression on brain fluid drainage, single cell transcriptome in the brain, and stroke outcomes in adult mice. Intra-cerebrospinal fluid administration of an adeno-associated virus expressing VEGF-C (AAV-VEGF-C) increases the CNS lymphatic network. Post-contrast T1 mapping of the head and neck showed that deep cervical lymph node size and drainage of CNS-derived fluids were increased. Single nuclei RNA sequencing revealed a neuro-supportive role of VEGF-C via upregulation of calcium and brain-derived neurotrophic factor (BDNF) signaling pathways in brain cells. In a mouse model of ischemic stroke, AAV-VEGF-C pretreatment reduced stroke injury and ameliorated motor performances in the subacute stage. AAV-VEGF-C thus promotes CNS-derived fluid and solute drainage, confers neuroprotection, and reduces ischemic stroke damage. Short abstract: Intrathecal delivery of VEGF-C increases the lymphatic drainage of brain-derived fluids confers neuroprotection, and improves neurological outcomes after ischemic stroke.

Targeted drug delivery to the brain endothelium dominates over passive delivery via vascular leak in experimental intracerebral hemorrhage.

Intracerebral hemorrhage (ICH) is one of the most common causes of fatal stroke, yet has no specific drug therapies. Many attempts at passive intravenous (IV) delivery in ICH have failed to deliver drugs to the salvageable area around the hemorrhage. The passive delivery method assumes vascular leak through the ruptured blood-brain barrier will allow drug accumulation in the brain. Here we tested this assumption using intrastriatal injection of collagenase, a well-established experimental model of ICH. Fitting with hematoma expansion in clinical ICH, we showed that collagenase-induced blood leak drops significantly by 4 h after ICH onset and is gone by 24 h. We observed passive-leak brain accumulation also declines rapidly over ∼4 h for 3 model IV therapeutics (non-targeted IgG; a protein therapeutic; PEGylated nanoparticles). We compared these passive leak results with targeted brain delivery by IV monoclonal antibodies (mAbs) that actively bind vascular endothelium (anti-VCAM, anti-PECAM, anti-ICAM). Even at early time points after ICH induction, where there is high vascular leak, brain accumulation via passive leak is dwarfed by brain accumulation of endothelial-targeted agents: At 4 h after injury, anti-PECAM mAbs accumulate at 8-fold higher levels in the brain vs. non-immune IgG; anti-VCAM nanoparticles (NPs) deliver a protein therapeutic (superoxide dismutase, SOD) at 4.5-fold higher levels than the carrier-free therapeutic at 24 h after injury. These data suggest that relying on passive vascular leak provides inefficient delivery of therapeutics even at early time points after ICH, and that a better strategy might be targeted delivery to the brain endothelium, which serves as the gateway for the immune attack on the peri-hemorrhage inflamed brain region.

Role of Inflammatory Processes in Hemorrhagic Stroke.

Hemorrhagic stroke is the deadliest form of stroke and includes the subtypes of intracerebral hemorrhage and subarachnoid hemorrhage. A common cause of hemorrhagic stroke in older individuals is cerebral amyloid angiopathy. Intracerebral hemorrhage and subarachnoid hemorrhage both lead to the rapid collection of blood in the central nervous system and generate inflammatory immune responses that involve both brain resident and infiltrating immune cells. These responses are complex and can contribute to both tissue recovery and tissue injury. Despite the interconnectedness of these major subtypes of hemorrhagic stroke, few reviews have discussed them collectively. The present review provides an update on inflammatory processes that occur in response to intracerebral hemorrhage and subarachnoid hemorrhage, and the role of inflammation in the pathophysiology of cerebral amyloid angiopathy-related hemorrhage. The goal is to highlight inflammatory processes that underlie disease pathology and recovery. We aim to discuss recent advances in our understanding of these conditions and identify gaps in knowledge with the potential to develop effective therapeutic strategies.

Embracing Heterogeneity in The Multicenter Stroke Preclinical Assessment Network (SPAN) Trial.

The Stroke Preclinical Assessment Network (SPAN) is a multicenter preclinical trial platform using rodent models of transient focal cerebral ischemia to address translational failure in experimental stroke. In addition to centralized randomization and blinding and large samples, SPAN aimed to introduce heterogeneity to simulate the heterogeneity embodied in clinical trials for robust conclusions. Here, we report the heterogeneity introduced by allowing the 6 SPAN laboratories to vary most of the biological and experimental model variables and the impact of this heterogeneity on middle cerebral artery occlusion (MCAo) performance. We included the modified intention-to-treat population of the control mouse cohort of the first SPAN trial (n=421) and examined the biological and procedural independent variables and their covariance. We then determined their impact on the dependent variables cerebral blood flow drop during MCAo, time to achieve MCAo, and total anesthesia duration using multivariable analyses. We found heterogeneity in biological and procedural independent variables introduced mainly by the site. Consequently, all dependent variables also showed heterogeneity among the sites. Multivariable analyses with the site as a random effect variable revealed filament choice as an independent predictor of cerebral blood flow drop after MCAo. Comorbidity, sex, use of laser Doppler flow to monitor cerebral blood flow, days after trial onset, and maintaining anesthesia throughout the MCAo emerged as independent predictors of time to MCAo. Total anesthesia duration was predicted by most independent variables. We present with high granularity the heterogeneity introduced by the biological and model selections by the testing sites in the first trial of cerebroprotection in rodent transient filament MCAo by SPAN. Rather than trying to homogenize all variables across all sites, we embraced the heterogeneity to better approximate clinical trials. Awareness of the heterogeneity, its sources, and how it impacts the study performance may further improve the study design and statistical modeling for future multicenter preclinical trials.