Endovascular therapy in basilar artery occlusion in Sweden 2016-2019-a nationwide, prospective registry study.

PURPOSE: We present the first nationwide study on endovascular therapy for basilar artery occlusion (BAO) from early hospital management to 3-month outcome. METHODS: Data were collected on all acute ischaemic stroke patients registered 2016-2019 in the two national quality registers for stroke care and endovascular therapy (EVT), receiving EVT for BAO and subclassified into proximal, middle and distal. RESULTS: In all, 251 patients were included: 69 proximal, 73 middle and 109 distal BAO. Patients with proximal BAO were younger (66, middle 71, distal 76, p < 0.0001), less often female (27.5%, middle 47.9%, distal 47.7%, p = 0.015), more often smokers (28.6%, middle 20.3%, distal 11.5%, p < 0.0001), and fewer had atrial fibrillation (13.2%, middle 24.7%, distal 48.6%, p < 0.0001). Level of consciousness and NIHSS score did not differ by BAO subtype and 52.2% were alert on admission. Time from groin puncture to revascularization was significantly longer in patients with proximal BAO (71, middle 46, distal 42 min, p < 0.0001), and angioplasty and/or stenting was more often performed in patients with proximal (43.4%) and middle (27.4%) than distal (6.4%) BAO (p < 0.0001). Cumulative 90-day mortality was 38.6% (proximal 50.7%, middle 32.9%, distal 34.9%, p = 0.02). Older and pre-stroke dependent patients had higher mortality, as did patients in whom angioplasty/stenting was performed. CONCLUSION: We confirm a serious outcome in BAO despite endovascular therapies, and demonstrate important differences relating to occlusion location in baseline characteristics, procedural time, therapeutic measures and outcome. Further in-depth analyses of factors affecting outcome in BAO are warranted.

Fetal posterior cerebral artery configurations in an ischemic stroke versus an unselected hospital population.

BACKGROUND: Few MRA-based studies have systematically evaluated the prevalence and laterality of a fetal configuration of the posterior cerebral artery (FTP) in ischemic stroke populations versus other populations. This common variant is important in the setting of acute stroke and secondary prevention decisions. OBJECTIVE: To determine the prevalence and laterality of FTP configurations in MRI-DWI verified acute ischemic stroke patients investigated with MRA, and compare the findings with an unselected hospital population investigated with computed tomography angiography (CTA). We also evaluated the association of FTP with posterior cerebral artery (PCA) territory infarctions. METHODS: We reviewed the MRAs of 1407 ischemic stroke patients with acute lesions on MRI-DWI sequences and 546 consecutive CTAs of patients investigated on any indication in a tertiary hospital. The MRA and CTA assessments were made by neuroradiologists blinded to original reports on stroke location and vessel anatomy. RESULTS: The prevalence of any FTP was similar in ischemic stroke patients (31%) and unselected patients (32%). Unilateral FTP was significantly more frequent on the right than on the left side in both groups (15% right vs. 8% left). The presence of FTP ipsilateral to stroke side was not associated with involvement of the PCA territory versus no FTP on the stroke side. CONCLUSIONS: FTP is present in approximately 30% of ischemic stroke patients and unselected hospital populations and was detected significantly more frequently on the right versus left side in both groups. PCA territory infarction was not associated with the presence of ipsilateral FTP.

Diffusion-Weighted Imaging, MR Angiography, and Baseline Data in a Systematic Multicenter Analysis of 3,301 MRI Scans of Ischemic Stroke Patients-Neuroradiological Review Within the MRI-GENIE Study.

Background: Magnetic resonance imaging (MRI) serves as a cornerstone in defining stroke phenotype and etiological subtype through examination of ischemic stroke lesion appearance and is therefore an essential tool in linking genetic traits and stroke. Building on baseline MRI examinations from the centralized and structured radiological assessments of ischemic stroke patients in the Stroke Genetics Network, the results of the MRI-Genetics Interface Exploration (MRI-GENIE) study are described in this work. Methods: The MRI-GENIE study included patients with symptoms caused by ischemic stroke (N = 3,301) from 12 international centers. We established and used a structured reporting protocol for all assessments. Two neuroradiologists, using a blinded evaluation protocol, independently reviewed the baseline diffusion-weighted images (DWIs) and magnetic resonance angiography images to determine acute lesion and vascular occlusion characteristics. Results: In this systematic multicenter radiological analysis of clinical MRI from 3,301 acute ischemic stroke patients according to a structured prespecified protocol, we identified that anterior circulation infarcts were most prevalent (67.4%), that infarcts in the middle cerebral artery (MCA) territory were the most common, and that the majority of large artery occlusions 0 to 48 h from ictus were in the MCA territory. Multiple acute lesions in one or several vascular territories were common (11%). Of 2,238 patients with unilateral DWI lesions, 52.6% had left-sided infarct lateralization (P = 0.013 for χ2 test). Conclusions: This large-scale analysis of a multicenter MRI-based cohort of AIS patients presents a unique imaging framework facilitating the relationship between imaging and genetics for advancing the knowledge of genetic traits linked to ischemic stroke.

Detailed phenotyping of posterior vs. anterior circulation ischemic stroke: a multi-center MRI study.

OBJECTIVE: Posterior circulation ischemic stroke (PCiS) constitutes 20-30% of ischemic stroke cases. Detailed information about differences between PCiS and anterior circulation ischemic stroke (ACiS) remains scarce. Such information might guide clinical decision making and prevention strategies. We studied risk factors and ischemic stroke subtypes in PCiS vs. ACiS and lesion location on magnetic resonance imaging (MRI) in PCiS. METHODS: Out of 3,301 MRIs from 12 sites in the National Institute of Neurological Disorders and Stroke (NINDS) Stroke Genetics Network (SiGN), we included 2,381 cases with acute DWI lesions. The definition of ACiS or PCiS was based on lesion location. We compared the groups using Chi-squared and logistic regression. RESULTS: PCiS occurred in 718 (30%) patients and ACiS in 1663 (70%). Diabetes and male sex were more common in PCiS vs. ACiS (diabetes 27% vs. 23%, p < 0.05; male sex 68% vs. 58%, p < 0.001). Both were independently associated with PCiS (diabetes, OR = 1.29; 95% CI 1.04-1.61; male sex, OR = 1.46; 95% CI 1.21-1.78). ACiS more commonly had large artery atherosclerosis (25% vs. 20%, p < 0.01) and cardioembolic mechanisms (17% vs. 11%, p < 0.001) compared to PCiS. Small artery occlusion was more common in PCiS vs. ACiS (20% vs. 14%, p < 0.001). Small artery occlusion accounted for 47% of solitary brainstem infarctions. CONCLUSION: Ischemic stroke subtypes differ between the two phenotypes. Diabetes and male sex have a stronger association with PCiS than ACiS. Definitive MRI-based PCiS diagnosis aids etiological investigation and contributes additional insights into specific risk factors and mechanisms of injury in PCiS.

Big Data Approaches to Phenotyping Acute Ischemic Stroke Using Automated Lesion Segmentation of Multi-Center Magnetic Resonance Imaging Data.

Background and Purpose- We evaluated deep learning algorithms' segmentation of acute ischemic lesions on heterogeneous multi-center clinical diffusion-weighted magnetic resonance imaging (MRI) data sets and explored the potential role of this tool for phenotyping acute ischemic stroke. Methods- Ischemic stroke data sets from the MRI-GENIE (MRI-Genetics Interface Exploration) repository consisting of 12 international genetic research centers were retrospectively analyzed using an automated deep learning segmentation algorithm consisting of an ensemble of 3-dimensional convolutional neural networks. Three ensembles were trained using data from the following: (1) 267 patients from an independent single-center cohort, (2) 267 patients from MRI-GENIE, and (3) mixture of (1) and (2). The algorithms' performances were compared against manual outlines from a separate 383 patient subset from MRI-GENIE. Univariable and multivariable logistic regression with respect to demographics, stroke subtypes, and vascular risk factors were performed to identify phenotypes associated with large acute diffusion-weighted MRI volumes and greater stroke severity in 2770 MRI-GENIE patients. Stroke topography was investigated. Results- The ensemble consisting of a mixture of MRI-GENIE and single-center convolutional neural networks performed best. Subset analysis comparing automated and manual lesion volumes in 383 patients found excellent correlation (ρ=0.92; P<0.0001). Median (interquartile range) diffusion-weighted MRI lesion volumes from 2770 patients were 3.7 cm3 (0.9-16.6 cm3). Patients with small artery occlusion stroke subtype had smaller lesion volumes ( P<0.0001) and different topography compared with other stroke subtypes. Conclusions- Automated accurate clinical diffusion-weighted MRI lesion segmentation using deep learning algorithms trained with multi-center and diverse data is feasible. Both lesion volume and topography can provide insight into stroke subtypes with sufficient sample size from big heterogeneous multi-center clinical imaging phenotype data sets.

White matter hyperintensity quantification in large-scale clinical acute ischemic stroke cohorts - The MRI-GENIE study.

White matter hyperintensity (WMH) burden is a critically important cerebrovascular phenotype linked to prediction of diagnosis and prognosis of diseases, such as acute ischemic stroke (AIS). However, current approaches to its quantification on clinical MRI often rely on time intensive manual delineation of the disease on T2 fluid attenuated inverse recovery (FLAIR), which hinders high-throughput analyses such as genetic discovery. In this work, we present a fully automated pipeline for quantification of WMH in clinical large-scale studies of AIS. The pipeline incorporates automated brain extraction, intensity normalization and WMH segmentation using spatial priors. We first propose a brain extraction algorithm based on a fully convolutional deep learning architecture, specifically designed for clinical FLAIR images. We demonstrate that our method for brain extraction outperforms two commonly used and publicly available methods on clinical quality images in a set of 144 subject scans across 12 acquisition centers, based on dice coefficient (median 0.95; inter-quartile range 0.94-0.95; p < 0.01) and Pearson correlation of total brain volume (r = 0.90). Subsequently, we apply it to the large-scale clinical multi-site MRI-GENIE study (N = 2783) and identify a decrease in total brain volume of -2.4 cc/year. Additionally, we show that the resulting total brain volumes can successfully be used for quality control of image preprocessing. Finally, we obtain WMH volumes by building on an existing automatic WMH segmentation algorithm that delineates and distinguishes between different cerebrovascular pathologies. The learning method mimics expert knowledge of the spatial distribution of the WMH burden using a convolutional auto-encoder. This enables successful computation of WMH volumes of 2533 clinical AIS patients. We utilize these results to demonstrate the increase of WMH burden with age (0.950 cc/year) and show that single site estimates can be biased by the number of subjects recruited.

Design and rationale for examining neuroimaging genetics in ischemic stroke: The MRI-GENIE study.

OBJECTIVE: To describe the design and rationale for the genetic analysis of acute and chronic cerebrovascular neuroimaging phenotypes detected on clinical MRI in patients with acute ischemic stroke (AIS) within the scope of the MRI-GENetics Interface Exploration (MRI-GENIE) study. METHODS: MRI-GENIE capitalizes on the existing infrastructure of the Stroke Genetics Network (SiGN). In total, 12 international SiGN sites contributed MRIs of 3,301 patients with AIS. Detailed clinical phenotyping with the web-based Causative Classification of Stroke (CCS) system and genome-wide genotyping data were available for all participants. Neuroimaging analyses include the manual and automated assessments of established MRI markers. A high-throughput MRI analysis pipeline for the automated assessment of cerebrovascular lesions on clinical scans will be developed in a subset of scans for both acute and chronic lesions, validated against gold standard, and applied to all available scans. The extracted neuroimaging phenotypes will improve characterization of acute and chronic cerebrovascular lesions in ischemic stroke, including CCS subtypes, and their effect on functional outcomes after stroke. Moreover, genetic testing will uncover variants associated with acute and chronic MRI manifestations of cerebrovascular disease. CONCLUSIONS: The MRI-GENIE study aims to develop, validate, and distribute the MRI analysis platform for scans acquired as part of clinical care for patients with AIS, which will lead to (1) novel genetic discoveries in ischemic stroke, (2) strategies for personalized stroke risk assessment, and (3) personalized stroke outcome assessment.

Congo red and protein aggregation in neurodegenerative diseases.

Congo red is a commonly used histological dye for amyloid detection. The specificity of this staining results from Congo red's affinity for binding to fibril proteins enriched in beta-sheet conformation. Unexpectedly, recent investigations indicate that the dye also possesses the capacity to interfere with processes of protein misfolding and aggregation, stabilizing native protein monomers or partially folded intermediates, while reducing concentration of more toxic protein oligomers. Inhibitory effects of Congo red upon amyloid toxicity may also range from blockade of channel formation and interference with glycosaminoglycans binding or immune functions, to the modulation of gene expression. Particularly, Congo red exhibits ameliorative effect in models of neurodegenerative disorders, such as Alzheimer's, Parkinson's, Huntington's and prion diseases. Another interesting application of Congo red analogues is the development of imaging probes. Based on their small molecular size and penetrability through blood-brain barrier, Congo red congeners can be used for both antemortem and in vivo visualization and quantification of brain amyloids. Therefore, understanding mechanisms involved in dye-amyloidal fibril binding and inhibition of aggregation will provide instructive guides for the design of future compounds, potentially useful for monitoring and treating neurodegenerative diseases.