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Objectives: SARS-CoV-2 infection is associated with a decline in functional outcomes; many patients experience persistent symptoms, while the underlying pathophysiology remains unclear. This study investigated white matter (WM) integrity on brain MRI in hospitalized COVID-19 patients and its associations with clinical outcomes, including long COVID., Materials and Methods: We included hospitalized COVID-19 patients and controls from CORONavirus and Ischemic Stroke (CORONIS), an observational cohort study, who underwent MRI-DWI imaging at baseline shortly after discharge (<3 months after positive PCR) and 3 months after baseline scanning. We assessed WM integrity using diffusion tensor imaging (DTI) and neurite orientation dispersion and density imaging (NODDI) and performed comparisons between groups and within patients. Clinical assessment was conducted at 3 and 12 months with functional outcomes such as modified Rankin Scale (mRS), Post-COVID-19 Functional Status scale (PCFS), Visual Analogue Scale (VAS), and long COVID, cognitive assessment was conducted by the Modified Telephone Interview for Cognitive Status (TICS-M), and the Hospital Anxiety and Depression Scale (HADS) was used to assess mood disorder. Associations between WM integrity and clinical outcomes were evaluated using logistic regression and linear regression., Results: A total of 49 patients (mean age 59.5 years) showed higher overall peak width of skeletonized mean diffusivity (PSMD) ( p  = 0.030) and lower neurite density index (NDI) in several WM regions compared with 25 controls at the baseline ( p  < 0.05; FWE-corrected) but did not remain statistically significant after adjusting for WM hyperintensities. Orientation dispersion index (ODI) increased after 3-month follow-up in several WM regions within patients ( p  < 0.05), which remained significant after correction for changes in WMH volume. Patients exhibited worse clinical outcomes compared with controls. Low NDI at baseline was associated with worse performance on the Post-COVID-19 Functional Status scale after 12 months ( p  = 0.018)., Conclusion: After adjusting for WMH, hospitalized COVID-19 patients no longer exhibited lower WM integrity compared with controls. WM integrity was generally not associated with clinical assessments as measured shortly after discharge, suggesting that factors other than underlying WM integrity play a role in worse clinical outcomes or long COVID., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The author(s) declared that they were an editorial board member of Frontiers, at the time of submission. This had no impact on the peer review process and the final decision., (Copyright © 2024 van Lith, Li, van der Wijk, Wijers, Sluis, Wermer, de Leeuw, Meijer and Tuladhar.)

Cerebral amyloid angiopathy (CAA) is a type of cerebrovascular disorder characterised by the accumulation of amyloid within the leptomeninges and small/medium-sized cerebral blood vessels. Typically, cerebral haemorrhages are one of the first clinical manifestations of CAA, posing a considerable challenge to the timely diagnosis of CAA as the bleedings only occur during the later disease stages. Fluid biomarkers may change prior to imaging biomarkers, and therefore, they could be the future of CAA diagnosis. Additionally, they can be used as primary outcome markers in prospective clinical trials. Among fluid biomarkers, blood-based biomarkers offer a distinct advantage over cerebrospinal fluid biomarkers as they do not require a procedure as invasive as a lumbar puncture. This article aimed to provide an overview of the present clinical data concerning fluid biomarkers associated with CAA and point out the direction of future studies. Among all the biomarkers discussed, amyloid β, neurofilament light chain, matrix metalloproteinases, complement 3, uric acid, and lactadherin demonstrated the most promising evidence. However, the field of fluid biomarkers for CAA is an under-researched area, and in most cases, there are only one or two studies on each of the biomarkers mentioned in this review. Additionally, a small sample size is a common limitation of the discussed studies. Hence, it is hard to reach a solid conclusion on the clinical significance of each biomarker at different stages of the disease or in various subpopulations of CAA. In order to overcome this issue, larger longitudinal and multicentered studies are needed., Competing Interests: The authors declare that while some of their CAA research projects have been supported by commercial funding from Biogen Inc. and Alnylam Pharmaceuticals, this specific manuscript did not receive any funding and the funders of our research were not involved in the study design, collection, analysis, interpretation of data, the writing of this article, or the decision to submit it for publication. The author(s) declared that they were an editorial board member of Frontiers, at the time of submission. This had no impact on the peer review process and the final decision., (Copyright © 2024 Savar, Ma, Hone, Jahan, Markovic, Pedrini, Shemehsavar, Easwaran, Taddei, Gardener, Chhatwal, van Etten, van Osch, Clarke, Gnjec, van Buchem, Wermer, Hankey, Greenberg, Martins and Sohrabi.)

Background: Perihematomal edema (PHE) after spontaneous intracerebral hemorrhage (sICH) is associated with clinical deterioration, but the etiology of PHE development is only partly understood., Aims: We aimed to investigate the association between systemic blood pressure (BP) variability (BPV) and formation of PHE., Methods: From a multicenter prospective observational study, we selected patients with sICH who underwent 3T brain MRI within 21 days after sICH, and had at least 5 BP measurements available in the first week after sICH. Primary outcome was the association between coefficient of variation (CV) of systolic BP (SBP) and edema extension distance (EED) using multivariable linear regression, adjusting for age, sex, ICH volume and timing of the MRI. In addition, we investigated the associations of mean SBP, mean arterial pressure (MAP), their CVs with EED and absolute and relative PHE volume., Results: We included 92 patients (mean age 64 years; 74% men; median ICH volume 16.8 mL (IQR 6.6-36.0), median PHE volume 22.5 mL (IQR 10.2-41.4). Median time between symptom onset and MRI was 6 days (IQR 4-11), median number of BP measurements was 25 (IQR 18-30). Log-transformed CV of SBP was not associated with EED (B = 0.050, 95%-CI -0.186 to 0.286, p = 0.673). Furthermore, we found no association between mean SBP, mean and CV of MAP and EED, nor between mean SBP, mean MAP or their CVs and absolute or relative PHE., Discussion: Our results do not support a contributing role for BPV on PHE, suggesting mechanisms other than hydrostatic pressure such as inflammatory processes, may play a more important role., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Sondag, Wolsink, Jolink, Voigt, van Walderveen, Wermer, Klijn and Schreuder.)

Objective: Blood-brain barrier (BBB) dysfunction is implicated in the pathophysiology of cerebral small vessel disease (cSVD)-related intracerebral hemorrhage (ICH). The formation of perihematomal edema (PHE) is presumed to reflect acute BBB permeability following ICH. We aimed to assess the association between cSVD burden and PHE formation in patients with spontaneous ICH., Methods: We selected patients with spontaneous ICH who underwent 3T MRI imaging within 21 days after symptom onset from a prospective observational multicenter cohort study. We rated markers of cSVD (white matter hyperintensities, enlarged perivascular spaces, lacunes and cerebral microbleeds) and calculated the composite score as a measure of the total cSVD burden. Perihematomal edema formation was measured using the edema extension distance (EED). We assessed the association between the cSVD burden and the EED using a multivariable linear regression model adjusting for age, (log-transformed) ICH volume, ICH location (lobar vs. non-lobar), and interval between symptom onset and MRI., Results: We included 85 patients (mean age 63.5 years, 75.3% male). Median interval between symptom onset and MRI imaging was 6 days (IQR 1-19). Median ICH volume was 17.0 mL (IQR 1.4-88.6), and mean EED was 0.54 cm (SD 0.17). We found no association between the total cSVD burden and EED (B = -0.003, 95% CI -0.003-0.03, p = 0.83), nor for any of the individual radiological cSVD markers., Conclusion: We found no association between the cSVD burden and PHE formation. This implies that mechanisms other than BBB dysfunction are involved in the pathophysiology of PHE., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Cliteur, Sondag, Wolsink, Rasing, Meijer, Jolink, Wermer, Klijn and Schreuder.)

The current study aimed to investigate whether diffusion-weighted imaging-positive (DWI+) lesions after acute intracerebral hemorrhage (ICH) are associated with underlying small vessel disease (SVD) or linked to the acute ICH. We included patients ≥18 years with spontaneous ICH confirmed on neuroimaging and performed 3T MRIs after a median of 11 days (interquartile range [IQR] 6-43). DWI+ lesions were assessed in relation to the hematoma (perihematomal vs. distant and ipsilateral vs. contralateral). Differences in clinical characteristics, ICH characteristics, and MRI markers of SVD between participants with or without DWI+ lesions were investigated using non-parametric tests. We observed 54 DWI+ lesions in 30 (22%) of the 138 patients (median age [IQR] 65 [55-73] years; 71% men, 59 lobar ICH) with available DWI images. We found DWI+ lesions ipsilateral (54%) and contralateral (46%) to the ICH, and 5 (9%) DWI+ lesions were located in the immediate perihematomal region. DWI+ lesion presence was associated with probable CAA diagnosis (38 vs. 15%, p = 0.01) and larger ICH volumes (37 [8-47] vs. 12 [6-24] ml, p = 0.01), but not with imaging features of SVD. Our findings suggest that DWI+ lesions after ICH are a feature of both the underlying SVD and ICH-related mechanisms., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Wiegertjes, Voigt, Jolink, Koemans, Schreuder, van Walderveen, Wermer, Meijer, Duering, de Leeuw and Klijn.)

Background: Accurate prediction of clinical outcome is of utmost importance for choices regarding the endovascular treatment (EVT) of acute stroke. Recent studies on the prediction modeling for stroke focused mostly on clinical characteristics and radiological scores available at baseline. Radiological images are composed of millions of voxels, and a lot of information can be lost when representing this information by a single value. Therefore, in this study we aimed at developing prediction models that take into account the whole imaging data combined with clinical data available at baseline., Methods: We included 3,279 patients from the MR CLEAN Registry; a prospective, observational, multicenter registry of patients with ischemic stroke treated with EVT. We developed two approaches to combine the imaging data with the clinical data. The first approach was based on radiomics features, extracted from 70 atlas regions combined with the clinical data to train machine learning models. For the second approach, we trained 3D deep learning models using the whole images and the clinical data. Models trained with the clinical data only were compared with models trained with the combination of clinical and image data. Finally, we explored feature importance plots for the best models and identified many known variables and image features/brain regions that were relevant in the model decision process., Results: From 3,279 patients included, 1,241 (37%) patients had a good functional outcome [modified Rankin Scale (mRS) ≤ 2] and 1,954 (60%) patients had good reperfusion [modified Thrombolysis in Cerebral Infarction (eTICI) ≥ 2b]. There was no significant improvement by combining the image data to the clinical data for mRS prediction [mean area under the receiver operating characteristic (ROC) curve (AUC) of 0.81 vs. 0.80] above using the clinical data only, regardless of the approach used. Regarding predicting reperfusion, there was a significant improvement when image and clinical features were combined (mean AUC of 0.54 vs. 0.61), with the highest AUC obtained by the deep learning approach., Conclusions: The combination of radiomics and deep learning image features with clinical data significantly improved the prediction of good reperfusion. The visualization of prediction feature importance showed both known and novel clinical and imaging features with predictive values., Competing Interests: Erasmus MC received funds from AL. Amsterdam UMC received funds from Stryker for consultations by CM and YR. MUMC received funds from Stryker and Codman for consultations by WZ. CM reports grants from the TWIN Foundation, the CVON/Dutch Heart Foundation, and the European Commission. HM is cofounder and shareholder of Nico.lab. YR and CM own stock in Nico.lab. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Ramos, Os, Hilbert, Olabarriaga, Lugt, Roos, Zwam, Walderveen, Ernst, Zwinderman, Strijkers, Majoie, Wermer and Marquering.)

Background: Women are more affected by stroke than men. This might, in part, be explained by sex differences in stroke pathophysiology. The hemostasis system is influenced by sex hormones and associated with female risk factors for stroke, such as migraine. Aim: To systematically review possible sex differences in hemostatic related factors in patients with ischemic stroke in general, and the influence of migraine on these factors in women with ischemic stroke. Results: We included 24 studies with data on sex differences of hemostatic factors in 7247 patients with ischemic stroke (mean age 57-72 years, 27-57% women) and 25 hemostatic related factors. Levels of several factors were higher in women compared with men; FVII:C (116% ± 30% vs. 104% ± 30%), FXI (0.14 UI/mL higher in women), PAI-1 (125.35 ± 49.37 vs. 96.67 ± 38.90 ng/mL), D-dimer (1.25 ± 0.31 vs. 0.95 ± 0.24 μg/mL), and aPS (18.7% vs. 12.0% positive). In contrast, protein-S (86.2% ± 23.0% vs. 104.7% ± 19.8% antigen) and P-selectin (48.9 ± 14.4 vs. 79.1 ± 66.7 pg/mL) were higher in men. Most factors were investigated in single studies, at different time points after stroke, and in different stroke subtypes. Only one small study reported data on migraine and hemostatic factors in women with ischemic stroke. No differences in fibrinogen, D-dimer, t-PA, and PAI-1 levels were found between women with and without migraine. Conclusion: Our systematic review suggests that sex differences exist in the activation of the hemostatic system in ischemic stroke. Women seem to lean more toward increased levels of procoagulant factors whereas men exhibit increased levels of coagulation inhibitors. To obtain better insight in sex-related differences in hemostatic factors, additional studies are needed to confirm these findings with special attention for different stroke phases, stroke subtypes, and not in the least women specific risk factors, such as migraine., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 van der Weerd, van Os, Ali, Schoones, van den Maagdenberg, Kruyt, Siegerink and Wermer.)

Background: An increased risk of stroke in patients with migraine has been primarily found for women. The sex-dependent mechanisms underlying the migraine-stroke association, however, remain unknown. This study aims to explore these sex differences to improve our understanding of pathophysiological mechanisms behind the migraine-stroke association. Methods: We included 2,492 patients with ischemic stroke from the prospective multicenter Dutch Parelsnoer Institute Initiative study, 425 (17%) of whom had a history of migraine. Cardiovascular risk profile, stroke cause (TOAST classification), and outcome [modified Rankin scale (mRS) at 3 months] were compared with both sexes between patients with and without migraine. Results: A history of migraine was not associated with sex differences in the prevalence of conventional cardiovascular risk factors. Women with migraine had an increased risk of stroke at young age (onset < 50 years) compared with women without migraine (RR: 1.7; 95% CI: 1.3-2.3). Men with migraine tended to have more often stroke in the TOAST category other determined etiology (RR: 1.7; 95% CI: 1.0-2.7) in comparison with men without migraine, whereas this increase was not found in women with migraine. Stroke outcome was similar for women with or without migraine (mRS ≥ 3 RR 1.1; 95% CI 0.7-1.5), whereas men seemed to have a higher risk of poor outcome compared with their counterparts without migraine (mRS ≥ 3 RR: 1.5; 95% CI: 1.0-2.1). Conclusion: Our results indicate possible sex differences in the pathophysiology underlying the migraine-stroke association, which are unrelated to conventional cardiovascular risk factors. Further research in larger cohorts is needed to validate these findings., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Linstra, van Os, Ruigrok, Nederkoorn, van Dijk, Kappelle, Koudstaal, Visser, Ferrari, MaassenVanDenBrink, Terwindt and Wermer.)

Background: Although endovascular treatment (EVT) has greatly improved outcomes in acute ischemic stroke, still one third of patients die or remain severely disabled after stroke. If we could select patients with poor clinical outcome despite EVT, we could prevent futile treatment, avoid treatment complications, and further improve stroke care. We aimed to determine the accuracy of poor functional outcome prediction, defined as 90-day modified Rankin Scale (mRS) score ≥5, despite EVT treatment. Methods: We included 1,526 patients from the MR CLEAN Registry, a prospective, observational, multicenter registry of ischemic stroke patients treated with EVT. We developed machine learning prediction models using all variables available at baseline before treatment. We optimized the models for both maximizing the area under the curve (AUC), reducing the number of false positives. Results: From 1,526 patients included, 480 (31%) of patients showed poor outcome. The highest AUC was 0.81 for random forest. The highest area under the precision recall curve was 0.69 for the support vector machine. The highest achieved specificity was 95% with a sensitivity of 34% for neural networks, indicating that all models contained false positives in their predictions. From 921 mRS 0-4 patients, 27-61 (3-6%) were incorrectly classified as poor outcome. From 480 poor outcome patients in the registry, 99-163 (21-34%) were correctly identified by the models. Conclusions: All prediction models showed a high AUC. The best-performing models correctly identified 34% of the poor outcome patients at a cost of misclassifying 4% of non-poor outcome patients. Further studies are necessary to determine whether these accuracies are reproducible before implementation in clinical practice., (Copyright © 2020 Ramos, Kappelhof, van Os, Chalos, Van Kranendonk, Kruyt, Roos, van der Lugt, van Zwam, van der Schaaf, Zwinderman, Strijkers, van Walderveen, Wermer, Olabarriaga, Majoie and Marquering.)

Background: Endovascular treatment (EVT) is effective for stroke patients with a large vessel occlusion (LVO) of the anterior circulation. To further improve personalized stroke care, it is essential to accurately predict outcome after EVT. Machine learning might outperform classical prediction methods as it is capable of addressing complex interactions and non-linear relations between variables. Methods: We included patients from the Multicenter Randomized Clinical Trial of Endovascular Treatment for Acute Ischemic Stroke in the Netherlands (MR CLEAN) Registry, an observational cohort of LVO patients treated with EVT. We applied the following machine learning algorithms: Random Forests, Support Vector Machine, Neural Network, and Super Learner and compared their predictive value with classic logistic regression models using various variable selection methodologies. Outcome variables were good reperfusion (post-mTICI ≥ 2b) and functional independence (modified Rankin Scale ≤2) at 3 months using (1) only baseline variables and (2) baseline and treatment variables. Area under the ROC-curves (AUC) and difference of mean AUC between the models were assessed. Results: We included 1,383 EVT patients, with good reperfusion in 531 (38%) and functional independence in 525 (38%) patients. Machine learning and logistic regression models all performed poorly in predicting good reperfusion (range mean AUC: 0.53-0.57), and moderately in predicting 3-months functional independence (range mean AUC: 0.77-0.79) using only baseline variables. All models performed well in predicting 3-months functional independence using both baseline and treatment variables (range mean AUC: 0.88-0.91) with a negligible difference of mean AUC (0.01; 95%CI: 0.00-0.01) between best performing machine learning algorithm (Random Forests) and best performing logistic regression model (based on prior knowledge). Conclusion: In patients with LVO machine learning algorithms did not outperform logistic regression models in predicting reperfusion and 3-months functional independence after endovascular treatment. For all models at time of admission radiological outcome was more difficult to predict than clinical outcome.