Sangwon Seo
Sungkyunkwan University · 医学
研究室紹介
Professor Sangwon Seo's research lab specializes in neuroimaging and biomarker discovery for neurodegenerative and cerebrovascular diseases, with a focus on understanding the pathophysiological mechanisms underlying subcortical vascular dementia, cerebral small vessel disease, and Alzheimer’s disease. The lab employs advanced neuroimaging techniques—such as MRI, PET, and cortical thickness analysis—to investigate microbleeds, small vessel disease markers, and brain age estimation as indicators of cognitive decline. A key research direction involves differentiating neurodegenerative subtypes, particularly in the context of mild cognitive impairment and frontotemporal dementia, using automated classification models. The lab also explores the interplay between cerebral amyloid angiopathy and small vessel disease in shaping cognitive outcomes.
Research Overview
Research Output Trend
Figures are computed from collected data and may differ slightly.
Selected Papers
15BACKGROUND AND PURPOSE: Despite many studies investigating the association between the ischemic changes and cognitive impairment in subcortical vascular dementia (SVaD), few studies correlated cognitive impairment with microbleeds (MBs) frequently seen in SVaD. METHODS: Participants consisted of 86 patients with SVaD who fulfilled the criteria proposed by Erkinjuntti et al. RESULTS: MBs occurred in 73 of 86 (84.9%) patients with SVaD. MBs were most commonly distributed in the cortex, and the cor
OBJECTIVE: Cerebral microbleeds (CMBs) are a neuroimaging marker of small vessel disease (SVD) with relevance for understanding disease mechanisms in cerebrovascular disease, cognitive impairment, and normal aging. It is hypothesized that lobar CMBs are due to cerebral amyloid angiopathy (CAA) and deep CMBs are due to subcortical ischemic SVD. We tested this hypothesis using structural magnetic resonance imaging (MRI) markers of subcortical SVD and in vivo imaging of amyloid in patients with cog
Brain age estimation from anatomical features has been attracting more attention in recent years. This interest in brain age estimation is motivated by the importance of biological age prediction in health informatics, with an application to early prediction of neurocognitive disorders. It is well-known that normal brain aging follows a specific pattern, which enables researchers and practitioners to predict the age of a human's brain from its degeneration. In this paper, we model brain age pred
In the present study, our automated classifier successfully classified FTD clinical subtypes with good to excellent accuracy. Our classifier may help clinicians diagnose FTD subtypes with subtle cortical atrophy and facilitate appropriate specific interventions.
BACKGROUND AND PURPOSE: Most studies on mild cognitive impairment (MCI) have been focused on amnestic MCI (aMCI) that is the preclinical stage of Alzheimer's disease (AD). In contrast, only a few studies have involved patients in the preclinical stages of subcortical vascular dementia (subcortical vascular MCI, svMCI). We tried to compare the overall glucose metabolism in patients with svMCI with that of patients with aMCI. METHODS: We compared the regional metabolic patterns shown on 18 F-FDG (
ClnicalTrials.gov NCT01596205.
BACKGROUND AND PURPOSE: The progression pattern of brain structural changes in patients with isolated cerebrovascular disease (CVD) remains unclear. To investigate the role of isolated CVD in cognitive impairment patients, patterns of cortical thinning and hippocampal atrophy in pure subcortical vascular mild cognitive impairment (svMCI) and pure subcortical vascular dementia (SVaD) patients were characterized. METHODS: Forty-five patients with svMCI and 46 patients with SVaD who were negative o
BACKGROUND: We investigated the independent effects of Alzheimer's disease (AD) and cerebrovascular disease (CVD) pathologies on brain structural changes and cognition. METHODS: Amyloid burden (Pittsburgh compound B [PiB] retention ratio), CVD markers (volume of white matter hyperintensities [WMH] and number of lacunae), and structural changes (cortical thickness and hippocampal shape) were measured in 251 cognitively impaired patients. Path analyses were utilized to assess the effects of these
Imaging-pathological correlation studies show that <i>in vivo</i> amyloid-β (Aβ) positron emission tomography (PET) strongly predicts the presence of significant Aβ pathology at autopsy. We sought to determine whether regional PiB-PET uptake would improve sensitivity for amyloid detection in comparison with global measures (experiment 1), and to estimate the relative contributions of different Aβ aggregates to <i>in vivo</i> PET signal (experiment 2). In experiment 1, 54 subjects with [<sup>11</
To develop a new method for measuring Alzheimer's disease (AD)-specific similarity of cortical atrophy patterns at the individual-level, we employed an individual-level machine learning algorithm. A total of 869 cognitively normal (CN) individuals and 473 patients with probable AD dementia who underwent high-resolution 3T brain MRI were included. We propose a machine learning-based method for measuring the similarity of an individual subject's cortical atrophy pattern with that of a representati