BACKGROUND:Endovascular thrombectomy (EVT) dramatically improves clinical outcomes, but the final infarct volume (FIV) on magnetic resonance imaging only accounts for a minority of the treatment effect. An imaging biomarker that more strongly correlates with post-EVT functional outcome would be helpful for clinical prognosis and serve as a surrogate outcome measure in trials of EVT-adjuvant therapies. Here, we aimed to validate a novel magnetic resonance imaging-based metric, infarct density, which leverages post-EVT apparent diffusion coefficient as a marker of infarct severity. METHODS:A retrospective cohort was derived from a single-center prospective EVT registry. Consecutive patients treated with EVT for anterior circulation large vessel occlusion were included from 2018 to 2019 who achieved successful reperfusion (modified Thrombolysis in Cerebral Infarction ≥2b). Magnetic resonance imaging was performed 12 to 48 hours post-EVT and processed via RAPID to quantify FIV using the apparent diffusion coefficient <620 threshold. Lesion volume was also collected using an apparent diffusion coefficient <470 threshold, and infarct density was calculated as: (volume <470/volume <620)×100%. Good outcome was defined as ≤2 on the 90-day modified Rankin Scale. Multivariable logistic regression models quantified the association between clinical/imaging variables and outcome. Receiver operating characteristic analysis quantified model classification performance. RESULTS:Of 319 patients treated with EVT, 272 met inclusion criteria. The mean age was 69±13 years, 41% were female, and 62% achieved a good outcome. After adjusting for clinical and radiographic factors, FIV (adjusted odds ratio, 0.99 per 1 mL [95% CI, 0.98-1.00]; P=0.03) and infarct density (adjusted odds ratio, 0.95 per 1% [95% CI, 0.94-0.97]; P<0.001) were both independently inversely associated with good outcome. The final model incorporating both FIV and infarct density achieved excellent classification performance (area under the curve, 0.87 [95% CI, 0.83-0.91]). Removing infarct density from the model diminished its performance (area under the curve, 0.83 [95% CI, 0.78-0.88]; P=0.01). CONCLUSIONS:Apparent diffusion coefficient-based infarct density after EVT is independently associated with long-term outcome and provides greater prognostic information than FIV alone. Post-EVT infarct density may be useful in clinical care and as a surrogate outcome measure in trials of EVT-adjuvant therapies.

Background: Endovascular thrombectomy (EVT) dramatically improves clinical outcomes, but the final infarct volume (FIV) on MRI only accounts for a minority of the treatment effect. An imaging biomarker that more strongly correlates with post-EVT functional outcome would be helpful for clinical prognosis and serve as a surrogate outcome measure in trials of EVT-adjuvant therapies. Here, we aimed to validate a novel MRI-based metric, infarct density, which leverages post-EVT apparent diffusion coefficient (ADC) as a marker of infarct severity. Methods: A retrospective cohort was derived from a single-center prospective EVT registry. Consecutive anterior circulation EVT patients were included from 2018-2019 who achieved successful reperfusion (mTICI ≥2b). MRI was performed 12-48 hours post-EVT and processed via RAPID to quantify FIV using the ADC <620 threshold. Lesion volume was also collected using ADC <470 threshold, and infarct density was calculated as: (volume <470/volume <620)x100%. Good outcome was defined as ≤2 on the 90-day modified Rankin Scale. Multivariable logistic regression models quantified the association between clinical/imaging variables and outcome. ROC analysis quantified model classification performance. Results: Of 319 EVT patients, 272 met inclusion criteria. The mean age was 69 ±13 years, 41% were female, and 62% achieved a good outcome. After adjusting for clinical and radiographic factors, FIV (aOR 0.99 per 1mL; 95%CI: 0.98-1.00; p=0.03) and infarct density (aOR 0.95 per 1%; 95%CI: 0.94-0.97; p<0.001) were both independently inversely associated with good outcome. The final model incorporating both FIV and infarct density achieved excellent classification performance (AUC 0.87; 95%CI: 0.83-0.91). Removing infarct density from the model diminished its performance (AUC 0.83; 95%CI: 0.78-0.88; p=0.01). Conclusion: ADC-based infarct density after EVT is independently associated with long‐term outcome and provides greater prognostic information than FIV alone. Post-EVT infarct density may be useful in clinical care and as a surrogate outcome measure in trials of EVT-adjuvant therapies.

BACKGROUND AND PURPOSE:Parenchymal hematomas (PHs) represent an important complication in ischemic stroke after endovascular thrombectomy (EVT), but the risk factors are incompletely understood. Neuroimaging data preintervention, such as infarct topography, may help elucidate predisposing factors and inform more nuanced patient care intra- and postprocedurally. METHODS:Large vessel occlusion patients with pre-EVT MRI were included from a single quaternary center. Diffusion-weighted imaging (DWI) lesions underwent manual segmentation and registration onto a standard brain space for topographical mapping. The presence of PH postintervention was determined. Associations between infarct topography, clinical characteristics, and PH were evaluated. RESULTS:A total of 165 patients (median age: 69; 56% female) were identified. Intravenous alteplase was administered to 52%, 70% achieved thrombolysis in cerebral infarction 2b-3 reperfusion, and 8% had PH postintervention. The preintervention DWI lesions were 48% (38%-60%) white matter, 23% (6%-47%) cortex, and 15% (4%-28%) basal ganglia. Basal ganglia infarct volume was independently associated with PH (adjusted odds ratio = 1.342, 95% confidence interval 1.002-1.797, p = 0.049), accounting for white matter and cortex infarct volume, among other key factors. Basal ganglia infarct volume was associated with susceptibility-weighted imaging vessel sign (betaadjusted = 0.233, p = 0.006) and the National Institutes of Health Stroke Scale (betaadjusted = 0.220, p = 0.012), controlling for other factors. CONCLUSIONS:Preintervention basal ganglia infarct volume may provide important insights into the risk of PH after intervention. Improved understanding of the biology of basal ganglia infarction and hemorrhagic transformation has implications for the management of patients undergoing EVT and may represent a future therapeutic target for neuroprotective strategies.

Once taken into consideration, sex differences in neurological diseases emerge in abundance: (i) Stroke severity is significantly higher in females than in males, (ii) Alzheimer's disease (AD) pathology is more pronounced in females, and (iii) conspicuous links with hormonal cycles led to female-specific diagnoses, such as catamenial migraines and epilepsy. While these differences receive increasing attention in isolation, they likely link to similar processes in the brain. Hence, this review aims to present an overview of the influences of sex chromosomes, hormones, and aging on male and female brains across health and disease, with a particular focus on AD and stroke. The focus here on advancements across several fields holds promise to fuel future research and to lead to an enriched understanding of the brain and more effective personalized neurologic care for all.

Introduction: As more patients with stroke, including those with large cores, are treated with endovascular thrombectomy (EVT), understanding the pathophysiology of hemorrhagic transformation (HT) is becoming increasingly important. Pre-EVT infarct topography may have implications for treatment decisions acutely (e.g. stenting) and for post EVT care (e.g. antithrombotics and blood pressure goals). We sought to identify associations between HT and brain regions involved in ischemic lesions. Methods: Consecutive patients with LVO treated with EVT who underwent pre-EVT MRI were identified from two tertiary referral centers (2011-2019). Acute ischemic lesions were extracted through a deep learning enabled pipeline from DWI and spatially normalized. Individual lesions were parcellated (atlas-defined 94 cortical regions, 14 subcortical nuclei, 20 white matter tracts) and reduced to ten essential lesion patterns using unsupervised dimensionality reduction techniques. HT, defined as ECASS PH1 or PH2, was modeled via Bayesian regression, taking the ten lesion patterns as inputs, and controlling for total lesion volume, age, sex, initial NIH Stroke Scale (NIHSS), thrombolysis treatment, good reperfusion (TICI2b-3), acute stenting, last known well-to-puncture time, diabetes mellitus, hypertension, coronary artery disease, smoking, atrial fibrillation, and site of enrollment. Results: A total of 567 (mean age 69 ±15 years; 45% female) patients had pre-EVT DWI without significant artifacts that could undergo lesion segmentation and registration. The median NIHSS was 16 (IQR 11-20) and mean total infarct volume was 22.5 ±36.7ml. Thrombolysis was administered in 51% and good reperfusion was achieved in 83%. HT occurred in 10%. Lesion locations predictive of HT ( Figure ) involved bilateral caudate, putamen, pallidum, and anterior thalamic radiation; and, right more than left thalamus, corticospinal tract, and inferior fronto-occipital fasciculus (area under the curve: 0.73). Conclusions: These data from a large, multicenter cohort with precise MRI-defined infarcts underscore the significance for HT of specific brain regions involved in ischemic lesions before EVT. An understanding of this pathophysiology can inform not only current clinical practice but also the development of future novel therapeutic strategies to prevent HT and reperfusion injury as more patients with large infarct cores are treated with EVT.

Background One third of all patients with acute ischemic strokes have a pre-existing disability. Patients with pre-existing disabilities have historically been excluded from landmark clinical trials of acute stroke interventions, leading to ongoing controversy about the risks and benefits of acute stroke interventions such as endovascular thrombectomy (EVT). To address this controversy, we compared long-term outcomes and end-of-life care in large vessel occlusion (LVO) patients with moderate-to-severe baseline disability treated with EVT versus medical management alone. Methods Patients who presented with an LVO to our comprehensive stroke center between January 2017 and December 2020 were retrospectively identified from a prospectively maintained database. Moderate-to-severe baseline disability was defined as a pre-stroke modified Rankin Scale (mRS) of 3-5. Delta mRS was defined as the difference between the 90-day and baseline mRS. Logistic and ordinal regressions were performed to evaluate the relationships between EVT and outcomes. An analysis of rates and reasons for transitions to comfort care was also performed, where applicable. Results A total of 175/1008 (17%) LVO patients with moderate-to-severe baseline disability were identified. The median age was 82 (IQR 70-89), and 59% were female. Thirty-two patients (18%) with moderate-to-severe baseline disability were treated with EVT. EVT was independently associated with improved delta mRS (B=-1.048; 95%CI=-1.777,-0.318; p=0.005) accounting for age and NIHSS. However, EVT did not reduce the odds of transitioning to comfort care (aOR=0.794; 95%CI=0.347,1.818; p=0.585) accounting for age and NIHSS. Seventy-six (43%) patients were transitioned to comfort care during their hospitalization. Of the 99 who were not transitioned to comfort care, 18 were treated with EVT, and EVT was independently associated with improved delta mRS (B=-2.794; 95%CI=-4.002,-1.586; p<0.0001). The median time from presentation to transition to comfort care was 2 days (IQR 1-7) in the non-EVT group, compared to 7 (IQR 4-11) in the EVT group (H(1)=5.46, p=0.019). The primary reasons for transitions to comfort care were poor perceived prognosis and medical complications. Conclusions Among patients with moderate-to-severe baseline disability, EVT is associated with less post-stroke accumulated disability without limiting transitions to comfort care. EVT is compatible with goal-concordant care and should not be routinely withheld because of baseline disability alone.

Objectives:To compare quantitative MRI markers of brain health in their ability to predict functional outcome after acute ischemic stroke (AIS). Methods:We included AIS survivors from the international MRI-GENIE study (multicenter; 2003-2011) with acute T2-FLAIR imaging. Automated pipelines estimated white matter hyperintensity volume (WMHv), brain volume, and intracranial volume (ICV). Assessed brain health markers included: brain parenchymal fraction (brain volume relative to ICV); radiomics derived brain age; brain reserve (normal appearing brain volume relative to ICV), and effective Reserve (eR, latent variable based on age, WMH load and brain volume). We added the markers to a clinical reference model, comparing model performances between separate multivariable regression models in their prediction of unfavorable outcome (90-day modified Rankin Scale score 3-5), using Bayesian Information Criterion (BIC). Results:We analyzed 2,223 patients (median age 67 years, 45% female, 24% unfavorable outcome). All models using brain health markers outperformed the clinical reference model (ΔBIC > 10). The eR model showed the lowest BIC value (BIC=2171.8), providing strong statistical evidence to outperform the brain age model (BIC=2179.5, ΔBIC > 6), and very strong (ΔBIC > 10) statistical evidence to outperform all other models. Discussion:Quantitative MRI markers of brain health, especially eR, enhance personalized outcome prognostication after AIS.

Apraxia is a common symptom in Alzheimer's disease (AD) that reduces autonomy and quality of life. However, the neural basis underlying apraxia in AD, for example, reflected by functional connectivity (FC) alterations, remains unexplored. We investigated static and dynamic FC using resting-state functional imaging in 14 patients with biomarker-confirmed AD pathology and 14 matched healthy participants. FC was estimated as average (static) and short-term (dynamic) connectivity strengths between motor- and praxis-related functional networks. Recurring connectivity patterns were clustered into dynamic states to compute temporal connectivity measures. Connectivity measures were used for correlations with apraxic deficits. In AD patients, static connectivity between visual and inferior parietal networks correlated with apraxic imitation (r = 0.762, PFDR = 0.043) and arm/hand gesture deficits (r = 0.848, PFDR = 0.020), while dynamic connectivity between these networks correlated with apraxic imitation deficits (r = 0.851, PFDR = 0.020). Dynamic FC analysis revealed a segregated and integrated state. AD patients spent more time overall (fraction time, PFDR < 0.001) and remained longer without switching (dwell time, PFDR = 0.004) in the segregated state. Both fraction (ρ = -0.858, PFDR = 0.015) and dwell time (ρ = -0.914, PFDR = 0.003) correlated with apraxic imitation deficits. Connectivity strengths between visual and inferior parietal networks and fraction time in the segregated state predicted apraxic imitation deficits (adjusted R2 = 0.782, P < 0.001). We conclude that apraxia in AD patients is associated with altered FC in praxis-related networks, suggesting FC as a potential clinical indicator for predicting motor-cognitive deficits.

Introduction: Endovascular thrombectomy (EVT) dramatically improves clinical outcomes, but the reduction in final infarct volume only accounts for a minority of the treatment effect. There is a need for surrogate imaging biomarkers that more strongly associate with functional outcome, to refine prognostication and facilitate development of EVT-adjuvant neuroprotective therapies. Our group recently developed a straightforward ADC-based metric of post-EVT infarct density (i.e. a measure of infarct severity). We aimed to validate this novel metric in a multicenter study of EVT patients. Methods: A retrospective cohort included consecutive patients with anterior circulation LVO who underwent EVT at two stroke centers. MRI was performed 12–48 hours post-EVT. Good functional outcome was defined as a 90-day modified Rankin Scale score ≤2. MR imaging was processed via RAPID, and final infarct volume was based on the standard ADC <620 threshold. Lesion volume was also assessed using ADC <470, and infarct density was calculated as the percentage of final infarct volume with ADC <470 ( Figure 1 ). Multivariate logistic regression quantified the associations between clinical/imaging variables and functional outcome. Model performance was quantified by ROC analysis and compared to a model consisting solely of clinical variables and a model consisting of clinical variables and infarct volume. Results: Of 312 patients, 284 (92%) achieved successful recanalization (mTICI ≥2b), and 54% achieved a good outcome. The mean age was 69 years (+/- 14); 41% were female. The mean final infarct volume was 50mL (+/- 73). Infarct density was significantly lower in patients with a good outcome (8.3% vs 30.3%, p<0.0001). Table 1 reports the univariate and multivariate analyses. Infarct density was robustly associated with outcome after adjustment for other significant factors including infarct volume (aOR: 0.954 per 1% increase in infarct density). Figure 2 compares the AUC of the three prespecified models, and the best classification was achieved by including infarct density (AUC 0.87; 95%CI: 0.83-0.91). Conclusion: Infarct density after EVT is independently associated with long term clinical outcome and provides greater prognostic value than final infarct volume alone. Derived from routinely acquired MRI, infarct density is a potentially useful and easily calculable surrogate outcome measure that can be used in clinical trials of EVT-adjuvant therapies.

Connectivity changes after brain lesions due to stroke are tightly linked to functional outcome. Recent analyses of fMRI time series indicate that dynamic functional network connectivity (dFNC), reflecting transient states of connectivity may capture network-level disruptions distant to the lesion site. Yet, the relevance of such dynamic connectivity patterns for motor recovery remains unclear. We, therefore, combined the analysis of static and dFNC and a repetitive transcranial magnetic stimulation (rTMS) lesion approach, to test whether dFNC provides region-specific insight into motor system reorganization after stroke. We focused on the contralesional primary motor cortex (M1) and anterior intraparietal sulcus (aIPS), two regions previously shown to modulate motor performance post-stroke in a time dependent manner. In 18 individuals in the chronic phase after stroke (with either persistent or recovered deficits) and 18 healthy participants, we analyzed static and dynamic resting-state connectivity. We then applied online rTMS intereference over contralesional aIPS and M1 during hand movement tasks to assess region-specific contributions to motor behavior. Consistent with previous studies, dFNC states were associated with persisting motor deficits, whereas static connectivity was not associated with motor outcome. dFNC but not static connectivity was associated with residual motor deficits and explained TMS-induced behavioral changes, when applying rTMS over contralesional M1. For contralesional aIPS, both static and dynamic connectivity were linked to TMS effects. This indicates that dFNC - more than static connectivity - contains information on the functional relevance of brain regions for motor outcome, specifically contralesional M1. Our results highlight the added value of temporal network analysis in understanding mechanisms of stroke recovery mechanisms.

Objectives: To determine the relationship between patient-reported outcome measures (PROMs) and volumetric biomarkers assessed on clinical imaging in acute ischemic stroke (AIS). Background: AIS is a leading cause of long-term disability. White matter hyperintensity (V_WMH), brain (V_Brain) and stroke lesion volume (V_Lesion) have been linked as potential determinants of functional outcomes after stroke. Recently, there has been surging attention to PROMs, which allow for direct, patient-centered health assessments of stroke victims. However, the link between neuroimaging biomarkers and PROMs is not well studied. Methods: Patients presenting to the Emergency Department at Massachusetts General Hospital between February 2017 and February 2020 with a confirmed AIS on MRI were eligible and underwent a follow-up telephone interview, including PROM-10 questionnaires. V_WMH and V_Brain were automatically determined from clinical MRI. V_Lesion was manually segmented. Regression analyses were performed to identify associations of brain volumetrics and clinical variables with PROM-10 subscores and global mental and physical summary scores. Results: Utilizing data from 150 patients (mean age: 64.7; 41.9% female), higher V_WMH was associated with worse global mental (β = -0.65), global physical (β = -0.60), social activities (OR = 0.59), physical health (OR = 0.68), fatigue (OR = 0.69) and social satisfaction (OR = 0.66) scores. V_Lesions were associated with poorer global mental (β = -0.79), social (OR = 0.55) and physical (OR = 0.66) activities, mental health (OR = 0.68) and emotional distress (OR = 0.68) scores. Higher V_Brain was linked to better global mental (β = 0.93), global physical (β = 0.79), physical activities (OR = 1.72) and mental health (OR = 1.54) scores. Conclusions: Imaging biomarkers were significantly associated with PROMs. The inclusion of these markers with PROMs in routine post-stroke assessment can enhance our understanding of recovery.

Coma is an unresponsive state of disordered consciousness characterized by impaired arousal and awareness. The epidemiology and pathophysiology of coma in ischemic stroke has been underexplored. We sought to characterize the incidence and clinical features of coma as a presentation of large vessel occlusion (LVO) stroke. Individuals who presented with LVO were retrospectively identified from July 2018 to December 2020. Coma was defined as an unresponsive state of impaired arousal and awareness, operationalized as a score of 3 on NIHSS item 1a. 28/637 (4.4

Apraxia is a common symptom in individuals with Alzheimer’s disease (AD). However, the neural mechanisms underlying apraxic deficits in AD remain elusive. Therefore, the current study focuses on the association between altered functional connectivity and apraxia in individuals with AD examining the hypothesis that apraxic deficits in AD result from dysfunction in praxis-related networks.To this aim, we examined the association between changes in static and dynamic functional connectivity (FC) of resting-state networks and apraxia in AD. Resting-state functional MRI data was acquired in 13 patients with tau-and amyloid-positive AD, who underwent an extensive neuropsychological, motor and apraxia assessment, and 13 matched healthy control participants. The static and dynamic functional connectivity of resting state networks revealed by independent component analysis (ICA) were assessed and connectivity measures were correlated with apraxia scores.Across all participants, we identified two distinct dynamic FC states using a sliding window approach. Patients with AD exhibited prolonged dwell times in the first state characterized by weaker connectivity and spent overall more time in this state, i.e., showed an increased fraction time in the first state. Apraxic deficits, especially deficits in imitating gestures, correlated significantly with the fraction time of both states as well as with the dwell time of the weakly connected dynamic state.Data suggest that apraxic imitation deficits in AD are associated with dysfunction of praxis networks characterized by altered dynamic FC.### Competing Interest StatementThe authors have declared no competing interest.### Funding StatementThe study was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) - Project-ID 431549029 - SFB 1451.### Author DeclarationsI confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.YesThe details of the IRB/oversight body that provided approval or exemption for the research described are given below:The local ethics committee of the Medical Faculty of the University of Cologne gave ethical approval for this work.I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals.YesI understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).YesI have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable.Yes

AbstractObjectiveTo systematically evaluate which lesion‐based imaging features and methods allow for the best statistical prediction of poststroke deficits across independent datasets.MethodsWe utilized imaging and clinical data from three independent datasets of patients experiencing acute stroke (N1 = 109, N2 = 638, N3 = 794) to statistically predict acute stroke severity (NIHSS) based on lesion volume, lesion location, and structural and functional disconnection with the lesion location using normative connectomes.ResultsWe found that prediction models trained on small single‐center datasets could perform well using within‐dataset cross‐validation, but results did not generalize to independent datasets (median R2N1 = 0.2%). Performance across independent datasets improved using large single‐center training data (R2N2 = 15.8%) and improved further using multicenter training data (R2N3 = 24.4%). These results were consistent across lesion attributes and prediction models. Including either structural or functional disconnection in the models outperformed prediction based on volume or location alone (P < 0.001, FDR‐corrected).InterpretationWe conclude that (1) prediction performance in independent datasets of patients with acute stroke cannot be inferred from cross‐validated results within a dataset, as performance results obtained via these two methods differed consistently, (2) prediction performance can be improved by training on large and, importantly, multicenter datasets, and (3) structural and functional disconnection allow for improved prediction of acute stroke severity.

ABSTRACT Objectives To determine the relationship between patient-reported outcome measures (PROMs) and volumetric imaging markers in acute ischemic stroke (AIS). Patients and Methods Patients presenting at Massachusetts General Hospital between February 14, 2017 and February 5, 2020 with a confirmed AIS by MRI were eligible and underwent a telephone interview including PROM-10 questionnaires 3-15 months after stroke. White matter hyperintensity (V WMH ) and brain volumes (V Brain ) were automatically determined using admission clinical MRI. Stroke lesions were manually segmented and volumes calculated (V Lesion ). Multivariable and ordinal regression analyses were performed to identify associations between global and PROM-10 subscores with brain volumetrics and clinical variables. Results Utilizing data from 167 patients (mean age: 64.7; 41.9% female), higher V WMH was associated with worse global physical (β=-0.6), global mental (β=-0.65), physical health (OR=0.68), social satisfaction (OR=0.66), fatigue (OR=0.69) and social activities (OR=0.59) scores. Higher V Lesion was associated with poorer global mental (β=-0.79), mental health (OR=0.68), physical (OR=0.66) and social activities (OR=0.55), and emotional distress (OR=0.68) scores. Higher V Brain was linked to better global mental (β=0.93), global physical (β=0.79), mental health (OR=1.54) and physical activities (OR=1.72) scores. Conclusions Neuroimaging biomarkers were significantly associated with PROMs, where higher V WMH and V Lesion led to worse outcome, while higher V Brain was protective. The inclusion of neuroimaging analyses and PROMs in routine assessment provides enhanced understanding of post-stroke outcomes.