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Endothelial dysfunction represents a potential pathomechanism of neurological post-COVID-19 syndrome (PCS). A recent study demonstrated reduced cerebrovascular reactivity (CVR) in patients with PCS. The aim of this pilot study was to prospectively assess CVR in patients with PCS using breath-hold functional MRI (bh-fMRI). Fourteen patients with neurological PCS and leading symptoms of fatigue/memory issues/concentration disorder (PCSfmc), 11 patients with PCS and leading symptoms of myopathy/neuropathy (PCSmn), and 17 healthy controls underwent bh-fMRI. Signal change and time to peak (TTP) were assessed globally and in seven regions of interest and compared between the subgroups using one-way ANCOVA adjusting for age, time since infection, Fazekas score, and sex. No significant differences were observed. In PCS patients, the global CVR exhibited a slight, non-significant tendency to be lower compared to healthy controls (PCSfmc: 0.78 ± 0.11%, PCSmn: 0.84 ± 0.10% and 0.87 ± 0.07%). There was a non-significant trend towards lower global TTP values in the PCS subgroups than in the control group (PCSfmc: 26.41 ± 1.39 s, PCSmn: 26.32 ± 1.36 s versus 29.52 ± 0.93 s). Endothelial dysfunction does not seem to be the sole pathomechanism of neurological symptoms in PCS. Further studies in larger cohorts are required. [ABSTRACT FROM AUTHOR]

Simple Summary: The identification of gliomas and the differentiation between different types is essential to evaluate patients' prognosis and guide optimal clinical management. The ideal multiparametric magnetic resonance imaging (MRI) protocol for the assessment of gliomas is a current topic of research. This study aimed to explore the performance of dynamic contrast-enhanced (DCE) MRI and diffusion kurtosis imaging (DKI) in differentiating molecular subtypes of adult-type diffuse gliomas. The results showed that a combined evaluation of DCE-MRI and DKI parameters reveals the best prediction of high-grade vs. low-grade gliomas, IDH1/2 wildtype vs. mutated gliomas, and astrocytomas/glioblastomas vs. oligodendrogliomas. The aim was to explore the performance of dynamic contrast-enhanced (DCE) MRI and diffusion kurtosis imaging (DKI) in differentiating the molecular subtypes of adult-type gliomas. A multicenter MRI study with standardized imaging protocols, including DCE-MRI and DKI data of 81 patients with WHO grade 2–4 gliomas, was performed at six centers. The DCE-MRI and DKI parameter values were quantitatively evaluated in ROIs in tumor tissue and contralateral normal-appearing white matter. Binary logistic regression analyses were performed to differentiate between high-grade (HGG) vs. low-grade gliomas (LGG), IDH1/2 wildtype vs. mutated gliomas, and high-grade astrocytic tumors vs. high-grade oligodendrogliomas. Receiver operating characteristic (ROC) curves were generated for each parameter and for the regression models to determine the area under the curve (AUC), sensitivity, and specificity. Significant differences between tumor groups were found in the DCE-MRI and DKI parameters. A combination of DCE-MRI and DKI parameters revealed the best prediction of HGG vs. LGG (AUC = 0.954 (0.900–1.000)), IDH1/2 wildtype vs. mutated gliomas (AUC = 0.802 (0.702–0.903)), and astrocytomas/glioblastomas vs. oligodendrogliomas (AUC = 0.806 (0.700–0.912)) with the lowest Akaike information criterion. The combination of DCE-MRI and DKI seems helpful in predicting glioma types according to the 2021 World Health Organization's (WHO) classification. [ABSTRACT FROM AUTHOR]

Background: The relationship between heart rate variability (HRV) changes potentially indicating autonomic dysregulation following spontaneous intracerebral hemorrhage (ICH) and functional outcome has not yet been fully elucidated. This study investigated the effects of HRV during the initial 96 h after admission on 90-day functional outcome in ICH patients. Methods: We included patients with spontaneous ICH in a prospective cohort single-center study. Continuous HR data were retrieved from the Intellispace Critical Care and Anesthesia information system (Philips Healthcare) and analyzed within the following time intervals: 0–2, 0–8, 0–12, 0–24, 0–48, 0–72, and 8–16, 16–24, 24–48, 48–72, 72–96 h after admission. HRV was determined from all available HR values by calculating the successive variability (SV), standard deviation (SD), and coefficient of variation (CV). Low HRV was set as SD ≤ 11.4 ms, and high HRV as SD > 11.4 ms. The clinical severity of ICH was assessed using the National Institutes of Health Stroke Scale (NIHSS) and functional outcome using the modified Rankin Scale (mRS). Good functional outcome was defined as mRS 0–2. Results: The cohort included 261 ICH patients (mean age ± SD 69.6 ± 16.5 years, 48.7% female, median NIHSS 6 (2, 12), median ICH score 1 (0, 2), of whom 106 (40.6%) had good functional outcome. All patients had the lowest HRV at admission, which increased during the first two days. Comparing ICH patients with low HRV (n = 141) and high HRV (n = 118), those with good outcome showed significantly lower HRV during the first three days (0–72 h: HRV SD good outcome 10.6 ± 3.5 ms vs. poor outcome 12.0 ± 4.0 ms; p = 0.004). Logistic regression revealed that advanced age, high premorbid mRS, and high NIHSS at admission were significant predictors of poor functional outcome, while reduced SD of HRV showed a non-significant trend towards good functional outcome (0–72 h: OR 0.898; CI 0.800–1.008; p = 0.067). Conclusions: Our results indicate autonomic dysfunction with sympathetic hyperactivity after spontaneous ICH, as reflected by the evidence of the lower HRV in the first days. Initially increased sympathetic tone appears to have a protective effect, as suggested by the comparatively lower HRV in patients with good functional outcome at the first days. [ABSTRACT FROM AUTHOR]

Before revascularization, moyamoya patients require hemodynamic evaluation. In this study, we evaluated the scoring system Prior Infarcts, Reactivity and Angiography in Moyamoya Disease (PIRAMID). We also devised a new scoring system, MRI-Based Assessment of Risk for Stroke in Moyamoya Angiopathy (MARS-MMA), and compared the scoring systems with respect to the capability to predict impaired [15O]water PET cerebral perfusion reserve capacity (CPR). We evaluated 69 MRI, 69 DSA and 38 [15O]water PET data sets. The PIRAMID system was validated by ROC curve analysis with neurological symptomatology as a dependent variable. The components of the MARS-MMA system and their weightings were determined by binary logistic regression analysis. The comparison of PIRAMID and MARS-MMA was performed by ROC curve analysis. The PIRAMID score correlated well with the symptomatology (AUC = 0.784). The MARS-MMA system, including impaired breath-hold-fMRI, the presence of the Ivy sign and arterial wall contrast enhancement, correlated slightly better with CPR impairment than the PIRAMID system (AUC = 0.859 vs. 0.827, Akaike information criterion 140 vs. 146). For simplified clinical use, we determined three MARS-MMA grades without loss of diagnostic performance (AUC = 0.855). The entirely MRI-based MARS-MMA scoring system might be a promising tool to predict the risk of stroke. [ABSTRACT FROM AUTHOR]

Simple Summary: Interest in applying artificial intelligence to medical imaging to enhance image quality has grown in both clinical practice and research. Nonetheless, these artificial intelligence models require further validation for use in diagnosing and monitoring disease progression to effectively compare them with traditional techniques. This study aimed to assess the effectiveness of a deep learning-optimized glioma protocol for MRI scans in patients with glioblastoma. The study protocol focuses on improving image quality and shortening scan time, which might be particularly beneficial for critically ill or uncooperative patients. The results showed that deep learning-reconstructed sequences can save scan time and simultaneously increase subjective image quality but with less subjective image noise. In addition, tumor volume measurements and neuro-oncologic response assessments were not affected negatively by deep learning. This study demonstrated the feasibility of the deep learning protocol for MRI of glioblastomas. A fully diagnostic MRI glioma protocol is key to monitoring therapy assessment but is time-consuming and especially challenging in critically ill and uncooperative patients. Artificial intelligence demonstrated promise in reducing scan time and improving image quality simultaneously. The purpose of this study was to investigate the diagnostic performance, the impact on acquisition acceleration, and the image quality of a deep learning optimized glioma protocol of the brain. Thirty-three patients with histologically confirmed glioblastoma underwent standardized brain tumor imaging according to the glioma consensus recommendations on a 3-Tesla MRI scanner. Conventional and deep learning-reconstructed (DLR) fluid-attenuated inversion recovery, and T2- and T1-weighted contrast-enhanced Turbo spin echo images with an improved in-plane resolution, i.e., super-resolution, were acquired. Two experienced neuroradiologists independently evaluated the image datasets for subjective image quality, diagnostic confidence, tumor conspicuity, noise levels, artifacts, and sharpness. In addition, the tumor volume was measured in the image datasets according to Response Assessment in Neuro-Oncology (RANO) 2.0, as well as compared between both imaging techniques, and various clinical–pathological parameters were determined. The average time saving of DLR sequences was 30% per MRI sequence. Simultaneously, DLR sequences showed superior overall image quality (all p < 0.001), improved tumor conspicuity and image sharpness (all p < 0.001, respectively), and less image noise (all p < 0.001), while maintaining diagnostic confidence (all p > 0.05), compared to conventional images. Regarding RANO 2.0, the volume of non-enhancing non-target lesions (p = 0.963), enhancing target lesions (p = 0.993), and enhancing non-target lesions (p = 0.951) did not differ between reconstruction types. The feasibility of the deep learning-optimized glioma protocol was demonstrated with a 30% reduction in acquisition time on average and an increased in-plane resolution. The evaluated DLR sequences improved subjective image quality and maintained diagnostic accuracy in tumor detection and tumor classification according to RANO 2.0. [ABSTRACT FROM AUTHOR]

A 61-year-old patient was diagnosed with a left-sided falx meningioma. Histopathological analysis following extirpation showed a meningothelial meningioma ZNS WHO grade 1 with sparse mitoses. Over the course of 12 years, the patient received irradiation (54.0 Gy), peptide radio-receptor therapy (177Lu-DOMITATE) and targeted therapy (mTOR inhibitor). Follow-up imaging revealed an increased size of the residual tumor. Due to increased liver function parameters, imaging of the liver was performed, showing widespread space-occupying lesions with atypical appearance. Biopsy revealed metastasis of the meningioma, now with 2.7 mitoses/mm2, necrosis and homozygous CDKN2A/B deletion, corresponding to an anaplastic CNS meningioma WHO grade 3. A second small meningioma on the left petroclival side has been consistent in size over 12 years. Metastatic meningiomas pose a pertinent clinical challenge due to poor prognosis. The lung, bone, liver and cervical lymph nodes are the most common sites of extracranial metastasis. According to the World Health Organization criteria, the most important predictive factor for recurrence and metastasis is the tumor grade. [ABSTRACT FROM AUTHOR]

Current standard care for acute cerebral venous sinus thrombosis (CVST) includes either intravenous heparin or subcutaneous low-molecular-weight heparin, but patients with refractory CVST, despite adequate anticoagulation therapy, may benefit from mechanical thrombectomy (MT). A retrospective study of patients with CVST, who underwent MT between 2011 and 2019, was performed looking at procedure success rate and clinical outcomes. Two raters evaluated the cerebral venous system of every patient before and after the intervention using the following scoring system: (0) No obvious thrombosis; (1) thrombosis without impaired blood flow; (2) thrombosis with impaired blood flow; (3) and thrombosis with complete vascular occlusion. The success of MT was measured using a score quotient (Q = A/B), dividing the sum of the patient's scores after the intervention (A) by the sum of scores before the intervention (B). Overall, 21 MTs were performed on 20 patients with refractory or severe CVST. Clinical improvement was seen in 61.9% during hospital stay and in 80% at 6-month follow-up, with complete recovery in 70% of patients. Patients with favorable outcomes had significantly lower recanalization quotients (p = 0.008). Our study provides evidence supporting that MT may be a safe and effective treatment with favorable clinical outcomes for selected patients with CVST. [ABSTRACT FROM AUTHOR]