Topographical Association Between Left Ventricular Strain and Brain Lesions in Patients With Acute Ischemic Stroke and Normal Cardiac Function.

Background Although it is well known that the disordered brain provokes cardiac autonomic dysfunction, the detailed location of brain lesions related to cardiac function warrants further investigation. We aimed to elucidate the brain lesions topographically associated with left ventricular (LV) systolic function measured by myocardial strain in patients with acute ischemic stroke without preexisting primary cardiac dysfunction by using support vector regression lesion-symptom mapping. Methods and Results Subjects were those with LV ejection fraction of 50% or more among patients with acute ischemic stroke registered in the Samsung Medical Center stroke registry between 2016 and 2017. To evaluate LV systolic performance and contractility, we measured LV ejection fraction and LV global and regional longitudinal strain using 2-dimensional speckle-tracking echocardiography. The association between stroke lesion location and cardiac strain was assessed using support vector regression lesion-symptom mapping. Of a total of 776 patients, 286 subjects (mean age of 67.0 years, 65.4% men) were finally enrolled in this study. The mean global longitudinal strain was -17.0±3.4%, and the mean LV ejection fraction was 64.7±5.7%. The support vector regression lesion-symptom mapping analysis revealed that the right insula and peri-insular regions and left parietal cortex were associated with impaired LV global longitudinal strain in patients with acute ischemic stroke. In addition, impaired regional longitudinal strain showed topographical associations with these regions. Conclusions This study suggests that brain lesions in the right insula and peri-insular regions and left parietal cortex are topographically associated with impaired LV strain in patients with acute ischemic stroke without preexisting cardiac dysfunction.

Automated in-depth cerebral arterial labelling using cerebrovascular vasculature reframing and deep neural networks.

Identifying the cerebral arterial branches is essential for undertaking a computational approach to cerebrovascular imaging. However, the complexity and inter-individual differences involved in this process have not been thoroughly studied. We used machine learning to examine the anatomical profile of the cerebral arterial tree. The method is less sensitive to inter-subject and cohort-wise anatomical variations and exhibits robust performance with an unprecedented in-depth vessel range. We applied machine learning algorithms to disease-free healthy control subjects (n = 42), patients with stroke with intracranial atherosclerosis (ICAS) (n = 46), and patients with stroke mixed with the existing controls (n = 69). We trained and tested 70% and 30% of each study cohort, respectively, incorporating spatial coordinates and geometric vessel feature vectors. Cerebral arterial images were analyzed based on the 'segmentation-stacking' method using magnetic resonance angiography. We precisely classified the cerebral arteries across the exhaustive scope of vessel components using advanced geometric characterization, redefinition of vessel unit conception, and post-processing algorithms. We verified that the neural network ensemble, with multiple joint models as the combined predictor, classified all vessel component types independent of inter-subject variations in cerebral arterial anatomy. The validity of the categorization performance of the model was tested, considering the control, ICAS, and control-blended stroke cohorts, using the area under the receiver operating characteristic (ROC) curve and precision-recall curve. The classification accuracy rarely fell outside each image's 90-99% scope, independent of cohort-dependent cerebrovascular structural variations. The classification ensemble was calibrated with high overall area rates under the ROC curve of 0.99-1.00 [0.97-1.00] in the test set across various study cohorts. Identifying an all-inclusive range of vessel components across controls, ICAS, and stroke patients, the accuracy rates of the prediction were: internal carotid arteries, 91-100%; middle cerebral arteries, 82-98%; anterior cerebral arteries, 88-100%; posterior cerebral arteries, 87-100%; and collections of superior, anterior inferior, and posterior inferior cerebellar arteries, 90-99% in the chunk-level classification. Using a voting algorithm on the queued classified vessel factors and anatomically post-processing the automatically classified results intensified quantitative prediction performance. We employed stochastic clustering and deep neural network ensembles. Ma-chine intelligence-assisted prediction of vessel structure allowed us to personalize quantitative predictions of various types of cerebral arterial structures, contributing to precise and efficient decisions regarding the cerebrovascular disease.