Background and Purpose Acute infarct volume, often proposed as a biomarker for evaluating novel interventions for acute ischemic stroke (AIS), correlates only moderately with traditional clinical endpoints such as the modified Rankin Scale (mRS). We hypothesized that the topography of acute stroke lesions on diffusion-weighted MRI (DWI) may provide further information with regard to presenting stroke severity and long-term functional outcomes. Methods Data from a prospective stroke repository were limited to AIS subjects with MRI completed within 48 hours from last known well, admission NIH Stroke Scale (NIHSS), and 3-to-6 months mRS scores. Using voxel-based lesion symptom mapping techniques including age, sex and DWI lesion volume as covariates, statistical maps were calculated to determine the significance of lesion location for clinical outcome and admission stroke severity. Results 490 subjects were analyzed. Acute stroke lesions in the left hemisphere were associated with more severe NIHSS at admission and poor mRS at 3 to 6 months. Specifically, injury to white matter (corona radiata, internal and external capsules, superior longitudinal fasciculus, and uncinate fasciculus), post-central gyrus, putamen, and operculum were implicated in poor mRS. More severe NIHSS involved these regions as well as the amygdala, caudate, pallidum, inferior frontal gyrus, insula, and pre-central gyrus. Conclusions Acute lesion topography provides important insights into anatomical correlates of admission stroke severity and post-stroke outcomes. Future models that account for infarct location in addition to DWI volume may improve stroke outcome prediction and identify patients likely to benefit from aggressive acute intervention and personalized rehabilitation strategies.

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Background and Purpose-We evaluated deep learning algorithms' segmentation of acute ischemic lesions on heterogeneous multi-center clinical diffusion-weighted (DWI) datasets and explored the potential role of this tool for phenotyping acute ischemic stroke. Methods-Ischemic stroke data sets from the MRI-GENetics Interface Exploration (MRI-GENIE) repository consisting of 12 international genetic research centers were retrospectively analyzed using an automated deep learning segmentation algorithm consisting of an ensemble of 3D convolutional neural networks (CNNs). Three ensembles were trained using data from: (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 DWI 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 CNNs performed best. Subset analysis comparing automated and manual lesion volumes in 383 patients found excellent correlation (ρ=0.92, p<0.0001). Median [IQR] DWI lesion volumes from 2770 patients were 3.7 [0.9-16.6] cm 3. Patients with small artery occlusion (SAO) stroke subtype had smaller lesion volumes (p<0.0001) and different topography compared to other stroke subtypes. Conclusions-Automated accurate clinical DWI 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 etiology with sufficient sample size from "big" heterogeneous multi-center clinical imaging phenotype datasets. Wu et al.

Background / Objective:

Individualized stroke treatment decisions can be improved by accurate identification of the extent of salvageable tissue. Magnetic resonance imaging (MRI)-based approaches, including measurement of a 'perfusion-diffusion mismatch' and calculation of infarction probability, allow assessment of tissue-at-risk; however, the ability to explicitly depict potentially salvageable tissue remains uncertain. In this study, five predictive algorithms (generalized linear model (GLM), generalized additive model, support vector machine, adaptive boosting, and random forest) were tested in their potency to depict acute cerebral ischemic tissue that can recover after reperfusion. Acute T 2 -, diffusion-, and perfusion-weighted MRI, and follow-up T 2 maps were collected from rats subjected to right-sided middle cerebral artery occlusion without subsequent reperfusion, for training of algorithms (Group I), and with spontaneous (Group II) or thrombolysis-induced reperfusion (Group III), to determine infarction probability-based viability thresholds and prediction accuracies. The infarction probability difference between irreversible-i.e., infarcted after reperfusionand salvageable tissue injury-i.e., noninfarcted after reperfusion-was largest for GLM (20±7%) with highest accuracy of riskbased identification of acutely ischemic tissue that could recover on subsequent reperfusion (Dice's similarity index ¼ 0.79±0.14). Our study shows that assessment of the heterogeneity of infarction probability with MRI-based algorithms enables estimation of the extent of potentially salvageable tissue after acute ischemic stroke.

We sought to investigate the relationship between blood-brain barrier (BBB) permeability and microstructural white matter integrity, and their potential impact on long-term functional outcomes in patients with acute ischemic stroke (AIS). We studied 184 AIS subjects with perfusion-weighted MRI (PWI) performed <9 h from last known well time. White matter hyperintensity (WMH), acute infarct, and PWI-derived mean transit time lesion volumes were calculated. Mean BBB leakage rates (K2 coefficient) and mean diffusivity values were measured in contralesional normal-appearing white matter (NAWM). Plasma matrix metalloproteinase-2 (MMP-2) levels were studied at baseline and 48 h. Admission stroke severity was evaluated using the NIH Stroke Scale (NIHSS). Modified Rankin Scale (mRS) was obtained at 90-days post-stroke. We found that higher mean K2 and diffusivity values correlated with age, elevated baseline MMP-2 levels, greater NIHSS and worse 90-day mRS (all p < 0.05). In multivariable analysis, WMH volume was associated with mean K2 ( p = 0.0007) and diffusivity ( p = 0.006) values in contralesional NAWM. In summary, WMH severity measured on brain MRI of AIS patients is associated with metrics of increased BBB permeability and abnormal white matter microstructural integrity. In future studies, these MRI markers of diffuse cerebral microvascular dysfunction may improve prediction of cerebral tissue infarction and functional post-stroke outcomes.

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.

Restoration of function after stroke may be associated with structural remodeling of neuronal connections outside the infarcted area. However, the spatiotemporal profile of poststroke alterations in neuroanatomical connectivity in relation to functional recovery is still largely unknown. We performed in vivo magnetic resonance imaging (MRI)-based neuronal tract tracing with manganese in combination with immunohistochemical detection of the neuronal tracer wheatgerm agglutinin horseradish peroxidase (WGA-HRP), to assess changes in intra-and interhemispheric sensorimotor network connections from 2 to 10 weeks after unilateral stroke in rats. In addition, functional recovery was measured by repetitive behavioral testing. Four days after tracer injection in perilesional sensorimotor cortex, manganese enhancement and WGA-HRP staining were decreased in subcortical areas of the ipsilateral sensorimotor network at 2 weeks after stroke, which was restored at later time points. At 4 to 10 weeks after stroke, we detected significantly increased manganese enhancement in the contralateral hemisphere. Behaviorally, sensorimotor functions were initially disturbed but subsequently recovered and plateaued 17 days after stroke. This study shows that manganese-enhanced MRI can provide unique in vivo information on the spatiotemporal pattern of neuroanatomical plasticity after stroke. Our data suggest that the plateau stage of functional recovery is associated with restoration of ipsilateral sensorimotor pathways and enhanced interhemispheric connectivity.