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In patients with acute stroke and a large infarct of unrestricted size, use of thrombectomy and medical care within 7 hours after symptom onset led to better functional outcomes and lower mortality than medical care alone.

ObjectivesThe relationship between stroke topography (ie, the regions damaged by the infarct) and functional outcome can aid clinicians in their decision-making at the acute and later stages. However, the side (left or right) of the stroke may also influence the identification of clinically relevant regions. We sought to determine which brain regions are associated with good functional outcome at 3 months in patients with left-sided and right-sided stroke treated by endovascular treatment using the diffusion-weighted imaging-Alberta Stroke Program Early CT Score (DWI-ASPECTS).MethodsPatients with ischaemic stroke (n = 405) were included from the ASTER trial and Pitié-Salpêtrière registry. Blinded readers rated ASPECTS on day 1 DWI. Stepwise logistic regression analyses were performed to identify the regions related to 3-month outcome in left (n = 190) and right (n = 215) sided strokes with the modified Rankin scale (0–2) as a binary independent variable and with the 10 regions-of-interest of the DWI-ASPECTS as independent variables.ResultsMedian National Institute of Health Stroke Scale (NIHSS) at baseline was 17 (IQR: 12–20), median age was 70 years (IQR: 58–80) and median day-one NIHSS 9 (IQR: 4–18). Not all brain regions have the same weight in predicting good outcome at 3 months; moreover, these regions depend on the affected hemisphere. In left-sided strokes, the multivariate analysis revealed that preservation of the caudate nucleus, the internal capsule and the cortical M5 region were independent predictors of good outcome. In right-sided strokes, the cortical M3 and M6 regions were found to be clinically relevant.ConclusionCortical non-motors areas related to outcome differed between left-sided and right-sided strokes. This difference might reflect the specialisation of the dominant and non-dominant hemispheres for language and attention, respectively. These results may influence decision-making at the acute and later stages.Trial registration number NCT02523261.

Clinical and subclinical (covert) stroke is a cause of cognitive loss and functional impairment. In the AVERROES trial, we performed serial brain magnetic resonance imaging (MRI) scans in a subgroup to explore the effect of apixaban, compared with aspirin, on clinical and covert brain infarction and on microbleeds in patients with atrial fibrillation. We performed brain MRI (T1, T2, fluid-attenuated inversion recovery, and T2* gradient echo sequences) in 1,180 at baseline and in 931 participants at follow-up. Mean interval from baseline to follow-up MRI scans was 1.0 year. The primary outcome was a composite of clinical ischemic stroke and covert embolic pattern infarction (defined as infarction >1.5 cm, cortical-based infarction, or new multiterritory infarction). Secondary outcomes included new MRI-detected brain infarcts and microbleeds and change in white matter hyperintensities. Baseline MRI scans revealed brain infarct(s) in 26.2% and microbleed(s) in 10.5%. The rate of the primary outcomes was 2.0% in the apixaban group and 3.3% in the aspirin group (hazard ratio [HR] 0.55; 0.27-1.14) from baseline to follow-up MRI scan (mean duration of follow-up: 1 year). In those who completed baseline and follow-up MRI scans, the rate of new infarction detected on MRI was 2.5% in the apixaban group and 2.2% in the aspirin group (HR 1.09; 0.47-2.52), but new infarcts were smaller in the apixaban group (P = .03). There was no difference in proportion with new microbleeds on follow-up MRI (HR 0.92; 0.53-1.60) between treatment groups. Apixaban treatment was associated with a nonsignificant trend toward reduction in the composite of clinical ischemic stroke and covert embolic-pattern infarction and did not increase the number of microbleeds in patients with atrial fibrillation compared with aspirin.

Stroke has a deleterious impact on quality of life. However, it is less well known if stroke lesions in different brain regions are associated with reduced quality of life (QoL). We therefore investigated this association by multivariate lesion-symptom mapping. We analyzed magnetic resonance imaging and clinical data from the WAKE-UP trial. European Quality of Life 5 Dimensions (EQ-5D) 3 level questionnaires were completed 90 days after stroke. Lesion symptom mapping was performed using a multivariate machine learning algorithm (support vector regression) based on stroke lesions 22-36 h after stroke. Brain regions with significant associations were explored in reference to white matter tracts. Of 503 randomized patients, 329 were included in the analysis (mean age 65.4 years, SD 11.5; median NIHSS = 6, IQR 4-9; median EQ-5D score 90 days after stroke 1, IQR 0-4, median lesion volume 3.3 ml, IQR 1.1-16.9 ml). After controlling for lesion volume, significant associations between lesions and EQ-5D score were detected for the right putamen, and internal capsules of both hemispheres. Multivariate lesion inference analysis revealed an association between injuries of the cortico-spinal tracts with worse self-reported quality of life 90 days after stroke in comparably small stroke lesions, extending previous reports of the association of striato-capsular lesions with worse functional outcome. Our findings are of value to identify patients at risk of impaired QoL after stroke.

Objectives Magnetic resonance imaging has high sensitivity in detecting early brainstem infarction (EBI). However, MRI is not practical for all patients who present with possible stroke and would lead to delayed treatment. The detection rate of EBI on non-contrast computed tomography (NCCT) is currently very low. Thus, we aimed to develop and validate the radiomics feature-based machine learning models to detect EBI (RMEBIs) on NCCT. Methods In this retrospective observational study, 355 participants from a multicentre multimodal database established by Huashan Hospital were randomly divided into two data sets: a training cohort (70%) and an internal validation cohort (30%). Fifty-seven participants from the Second Affiliated Hospital of Xuzhou Medical University were included as the external validation cohort. Brainstems were segmented by a radiologist committee on NCCT and 1781 radiomics features were automatically computed. After selecting the relevant features, 7 machine learning models were assessed in the training cohort to predict early brainstem infarction. Accuracy, sensitivity, specificity, positive predictive value, negative predictive value, F1-score, and the area under the receiver operating characteristic curve (AUC) were used to evaluate the performance of the prediction models. Results The multilayer perceptron (MLP) RMEBI showed the best performance (AUC: 0.99 [95% CI: 0.96–1.00]) in the internal validation cohort. The AUC value in external validation cohort was 0.91 (95% CI: 0.82–0.98). Conclusions RMEBIs have the potential in routine clinical practice to enable accurate computer-assisted diagnoses of early brainstem infarction in patients with NCCT, which may have important clinical value in reducing therapeutic decision-making time. Key Points • RMEBIs have the potential to enable accurate diagnoses of early brainstem infarction in patients with NCCT. • RMEBIs are suitable for various multidetector CT scanners. • The patient treatment decision-making time is shortened.

Background Prevalence, radiological characteristics, and risk factors for peritumoral infarctions after glioma surgery are not much studied. In this study, we assessed shape, volume, and prevalence of peritumoral infarctions and investigated possible associated factors. Methods In a prospective single-center cohort study, we included all adult patients operated for diffuse gliomas from January 2007 to December 2018. Postoperative infarctions were segmented using early postoperative MRI images, and volume, shape, and location of postoperative infarctions were assessed. Heatmaps of the distribution of tumors and infarctions were created. Results MRIs from 238 (44%) of 539 operations showed restricted diffusion in relation to the operation cavity, interpreted as postoperative infarctions. Of these, 86 (36%) were rim-shaped, 103 (43%) were sector-shaped, 40 (17%) were a combination of rim- and sector-shaped, and six (3%) were remote infarctions. Median infarction volume was 1.7 cm 3 (IQR 0.7–4.3, range 0.1–67.1). Infarctions were more common if the tumor was in the temporal lobe, and the map shows more infarctions in the periventricular watershed areas. Sector-shaped infarctions were more often seen in patients with known cerebrovascular disease (47.6% vs. 25.5%, p = 0.024). There was a positive correlation between infarction volume and tumor volume (r = 0.267, p < 0.001) and infarction volume and perioperative bleeding (r = 0.176, p = 0.014). Moreover, there was a significant positive association between age and larger infarction volumes (r = 0.193, p = 0.003). Infarction rates and infarction volumes varied across individual surgeons, p = 0.037 (range 32–72%) and p = 0.026. Conclusions In the present study, peritumoral infarctions occurred in 44% after diffuse glioma operations. Infarctions were more common in patients operated for tumors in the temporal lobe but were not more common following recurrent surgeries. Sector-shaped infarctions were more common in patients with known cerebrovascular disease. Increasing age, larger tumors, and more perioperative bleeding were factors associated with infarction volumes. The risk of infarctions and infarction volumes may also be surgeon-dependent.

This project assessed performance of natural language processing (NLP) and machine learning (ML) algorithms for classification of brain MRI radiology reports into acute ischemic stroke (AIS) and non-AIS phenotypes. All brain MRI reports from a single academic institution over a two year period were randomly divided into 2 groups for ML: training (70%) and testing (30%). Using \"quanteda\" NLP package, all text data were parsed into tokens to create the data frequency matrix. Ten-fold cross-validation was applied for bias correction of the training set. Labeling for AIS was performed manually, identifying clinical notes. We applied binary logistic regression, naïve Bayesian classification, single decision tree, and support vector machine for the binary classifiers, and we assessed performance of the algorithms by F1-measure. We also assessed how n-grams or term frequency-inverse document frequency weighting affected the performance of the algorithms. Of all 3,204 brain MRI documents, 432 (14.3%) were labeled as AIS. AIS documents were longer in character length than those of non-AIS (median [interquartile range]; 551 [377-681] vs. 309 [164-396]). Of all ML algorithms, single decision tree had the highest F1-measure (93.2) and accuracy (98.0%). Adding bigrams to the ML model improved F1-mesaure of naïve Bayesian classification, but not in others, and term frequency-inverse document frequency weighting to data frequency matrix did not show any additional performance improvements. Supervised ML based NLP algorithms are useful for automatic classification of brain MRI reports for identification of AIS patients. Single decision tree was the best classifier to identify brain MRI reports with AIS.

Background In structural Magnetic Resonance Imaging (MRI) of patients with a recent small subcortical infarct (RSSI) and small vessel disease (SVD) imaging markers coexist. However, their spatial distribution and prevalence with respect to the hemisphere of the RSSI remain unknown. Materials and Methods From brain MRI in 187 patients with an acute lacunar ischemic stroke clinical syndrome and a relevant diffusion weighted imaging (DWI)‐positive lesion, we semiautomatically extracted the RSSI, microbleeds, lacunes, old cortical infarcts, and white matter hyperintensities (WMH) using optimized thresholding in the relevant sequences, and rated the load of perivascular spaces. We registered all images to an age‐relevant brain template and calculated the probability distribution of all SVD markers mentioned for patients who had the RSSI in each hemisphere separately. We used the Wilcoxon and chi‐squared tests to compare the volumes and frequencies of occurrence, respectively, of the SVD markers between hemispheres throughout the sample. Results Fifty‐two percent patients (n = 97) had the RSSI in the left hemisphere, 42% (n = 78) in the right, 2.7% (n = 5) in both, and 3.7% (n = 7) in the cerebellum or brainstem. There was no significant difference in RSSI frequency between left and right hemispheres (p = .10) in the sample. The median volume of the RSSI (expressed as a percentage of the total intracranial volume) was 0.05% (IQR = 0.06). There was no difference in median percent volume of the right RSSIs versus left (p = .16). Neither was there a significant interhemispheric difference in the volume of any of the SVD markers regardless of the location of the RSSI and they were equally distributed in both hemispheres. Conclusion Assessment of SVD imaging markers in the contralateral hemisphere could be used as a proxy for the SVD load in the whole brain to avoid contamination by the RSSI of the measurements, especially of WMH. We report the spatial distribution and prevalence of small vessel disease imaging markers with respect to the location of the recent subcortical infarct on 187 patients who had an acute ischemic stroke clinical syndrome and a relevant DWI‐positive lesion. The MRI scan was obtained in the interval between stroke onset and 4 weeks after. There was no difference in median percent volume of the right RSSIs versus left (p = .16). Neither was there a significant interhemispheric difference in the volume of any of the markers regardless of the location of the infarct and they were equally distributed in both hemispheres.

The feeling of body-ownership can be experimentally manipulated using the rubber hand illusion (RHI) paradigm. Participants experience a sense of ownership over an artificial hand when their hidden real hand and the visible artificial hand are synchronously stroked. Using lesion masks and behavioral data from a previous study on RHI failure in acute stroke patients, we here employed lesion network-symptom-mapping (LNSM) based on normative functional connectome data to identify lesion-dependent network connectivity related to the experience of self-attribution of an artificial hand in the RHI paradigm. We found that failure to experience the RHI was associated with higher normative lesion-dependent network connectivity to the right temporoparietal junction (rTPJ), right anterior Insula (raI) and right inferior frontal gyrus (rIFG). Since these areas were spared by the infarction in most patients with RHI failure (89% for rTPJ and 94% for raI/rIFG), the analysis suggests that remote dysfunction in rTPJ, raI, and rIFG accounted for RHI failure. These results highlight the potential role of rTPJ, raI, and rIFG in bodily self-consciousness. LNSM is a powerful tool capable of delineating the architecture of functional networks underlying complex cognitive function. •Normative functional connectome data can be utilized to indirectly map networks affected by focal brain lesions.•Using this method, connections to several cortical regions were associated with failure to induce the rubber hand illusion.•These regions included the right temporoparietal junction, inferior frontal gyrus and anterior insula.•This highlights the potential role of these regions in bodily self-consciousness.

Background and purposeTo determine the frequency and distribution pattern of acute DWI lesions outside the hippocampus in patients clinically presenting with Transient Global Amnesia (TGA).MethodsConsecutive patients clinically presenting with TGA between January 2010 and January 2017 admitted to our hospital were retrospectively evaluated. All patients fulfilled diagnostic criteria of TGA. We analyzed imaging and clinical data of all patients undergoing MRI with high-resolution diffusion-weighted imaging within 72 h from symptom onset.ResultsA total of 126 cases were included into the study. Fifty-three percent (n = 71/126) presented with one or more acute lesions in hippocampal CA1-area. Additional acute DWI lesions in other cortical regions were found in 11% (n = 14/126). All patients with DWI lesions outside the hippocampus presented with neurological symptoms typical for TGA (without additional symptoms.)ConclusionsIn a relevant proportion of clinical TGA patients, MRI reveals acute ischemic cerebral lesions. Therefore, cerebral MRI should be performed in patients with TGA to identify a possible cardiac involvement and to detect stroke chameleons.