BackgroundThe association between the location and the mechanism of a stroke lesion remains unclear. A diffusion‐weighted imaging study may help resolve this lack of clarity.Methods and ResultsWe studied a consecutive series of 2702 acute ischemic stroke patients whose stroke lesions were confirmed by diffusion‐weighted imaging and who underwent a thorough etiological investigation. The vascular territory in which an ischemic lesion was situated was identified using standard anatomic maps of the dominant arterial territories. Stroke subtype was based on the Trial of ORG 10172 in Acute Stroke Treatment, or TOAST, classification. Large‐artery atherosclerosis (37.3%) was the most common stroke subtype, and middle cerebral artery (49.6%) was the most frequently involved territory. Large‐artery atherosclerosis was the most common subtype for anterior cerebral, middle cerebral, vertebral, and anterior and posterior inferior cerebellar artery territory infarctions. Small vessel occlusion was the leading subtype in basilar and posterior cerebral artery territories. Cardioembolism was the leading cause in superior cerebellar artery territory. Compared with carotid territory stroke, vertebrobasilar territory stroke was more likely to be caused by small vessel occlusion (21.4% versus 30.1%, P<0.001) and less likely to be caused by cardioembolism (23.2% versus 13.8%, P<0.001). Multiple‐vascular‐territory infarction was frequently caused by cardioembolism (44.2%) in carotid territory and by large‐artery atherosclerosis (52.1%) in vertebrobasilar territory.ConclusionsInformation on vascular territory of a stroke lesion may be helpful in timely investigation and accurate diagnosis of stroke etiology.

To assess the glymphatic activity in patients with idiopathic normal pressure hydrocephalus (NPH) using the "Diffusion Tensor Image-Analysis aLong the Perivascular Space (DTI-ALPS)" method, and determine the feasibility of non-invasive MRI for the evaluation of the glymphatic function. Methods: Between April 2017 and March 2019, 16 patients diagnosed with NPH and 16 age-and sex-matched controls were included. On 3T DTI-MRI, the diffusivities along x-, y-, and z-axes were measured, and the ALPS-indexa ratio that accentuated water diffusion along the perivascular spacewas calculated by two independent readers. The inter-observer agreement was tested using the interclass correlation coefficient. The differences in the diffusivities and the ALPS-index between the NPH and control groups were compared using the Mann-Whitney test. The values were also compared according to the treatment response to the cerebrospinal fluid drainage and correlated with the callosal angle using a correlation coefficient. Results: The inter-observer agreements were excellent for the diffusivities and the ALPS-index. The diffusivity along the x-axis in the projection fibers area and the ALPS-index were significantly lower in patients with NPH (median, 0.556/1.181) than in the controls (0.610/1.540), respectively (P = 0.032/< 0.0001). The ALPS-index was significantly lower in the NPH group who did not show treatment response than those who showed symptomatic relief (0.987/1.329; P < 0.0001). The ALPS-index showed a significant positive correlation with the callosal angle (r = 0.82, P = 0.0001). Conclusions: The DTI-ALPS method can be a useful imaging tool for identifying glymphatic dysfunction and for individually quantifying glymphatic activity in patients with NPH.

This study used resting state functional magnetic resonance imaging (rsfMRI) to investigate whole brain networks in patients with persistent postural perceptual dizziness (PPPD). We compared rsfMRI data from 38 patients with PPPD and 38 healthy controls using whole brain and region of interest analyses. We examined correlations among connectivity and clinical variables and tested the ability of a machine learning algorithm to classify subjects using rsfMRI results. Patients with PPPD showed: (a) increased connectivity of subcallosal cortex with left superior lateral occipital cortex and left middle frontal gyrus, (b) decreased connectivity of left hippocampus with bilateral central opercular cortices, left posterior opercular cortex, right insular cortex and cerebellum, and (c) decreased connectivity between right nucleus accumbens and anterior left temporal fusiform cortex. After controlling for anxiety and depression as covariates, patients with PPPD still showed decreased connectivity between left hippocampus and right inferior frontal gyrus, bilateral temporal lobes, bilateral insular cortices, bilateral central opercular cortex, left parietal opercular cortex, bilateral occipital lobes and cerebellum (bilateral lobules VI and V, and left I-IV). Dizziness handicap, anxiety, and depression correlated with connectivity in clinically meaningful brain regions. The machine learning algorithm correctly classified patients and controls with a sensitivity of 78.4%, specificity of 76.9%, and area under the curve = 0.88 using 11 connectivity parameters. Patients with PPPD showed reduced connectivity among the areas involved in multisensory vestibular processing and spatial cognition, but increased connectivity in networks linking visual and emotional processing. Connectivity patterns may become an imaging biomarker of PPPD.

The deep learning algorithm could diagnose maxillary sinusitis on Waters' view radiograph with superior AUC and comparable sensitivity and specificity to those of radiologists.