Yitong Bian

Area of research

Epidemiology · Pulmonary and Respiratory Medicine

Research interest

Research topics from publications: Association of white matter hyperintensities with migraine features and prognosis; Small vessel disease burden predicts functional outcomes in patients with acute ischemic stroke using machine learning; Arterial Spin Labeling‐Based MRI Estimation of Penumbral Tissue in Acute Ischemic Stroke; FLAIR vessel hyperintensities predict functional outcomes in patients with acute ischemic stroke treated with medical therapy; MRI Assessment of Brain Frailty and Clinical Outcome in Patients With Acute Posterior Perforating Artery Infarction; Impact of the Alberta Stroke Program CT Score subregions on long-term functional outcomes in acute ischemic stroke: Results from two multicenter studies in China. Representative work: BACKGROUND: White matter hyperintensities (WMHs) are frequently detected in migraine patients. However, their significance and correlation to migraine disease burden remain unclear. This study aims to examine the correlation of WMHs with migraine features and explore the relationship between WMHs and migraine prognosis. METHODS: A total of 69 migraineurs underwent MRI scans to evaluate WMHs. Migraine features were compared between patients with and without WMHs. After an average follow-up period of 3 years, these patients were divided into two groups, according to the reduction of headache frequency: improved and non-improved groups. The percentage and degree of WMHs were compared between th AIMS: Our purpose is to assess the role of cerebral small vessel disease (SVD) in prediction models in patients with different subtypes of acute ischemic stroke (AIS). METHODS: We enrolled 398 small-vessel occlusion (SVO) and 175 large artery atherosclerosis (LAA) AIS patients. Functional outcomes were assessed using the modified Rankin Scale (mRS) at 90 days. MRI was performed to assess white matter hyperintensity (WMH), perivascular space (PVS), lacune, and cerebral microbleed (CMB). Logistic regression (LR) and machine learning (ML) were used to develop predictive models to assess the influences of SVD on the prognosis. RESULTS: In the feature evaluation of SVO-AIS for different outcomes,