Browsing by Author "Choe, Yeong Sim"

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    Clinical outcomes of increased focal amyloid uptake in individuals with subthreshold global amyloid levels
    (Frontiers Media, 2023-03-02) Kim, Jaeho; Choe, Yeong Sim; Park, Yuhyun; Kim, Yeshin; Kim, Jun Pyo; Jang, Hyemin; Kim, Hee Jin; Na, Duk L.; Cho, Soo-Jin; Moon, Seung Hwan; Seo, Sang Won; Radiology and Imaging Sciences, School of Medicine
    Background: Although the standardized uptake value ratio (SUVR) method is objective and simple, cut-off optimization using global SUVR values may not reflect focal increased uptake in the cerebrum. The present study investigated clinical and neuroimaging characteristics according to focally increased β-amyloid (Aβ) uptake and global Aβ status. Methods: We recruited 968 participants with cognitive continuum. All participants underwent neuropsychological tests and 498 18F-florbetaben (FBB) amyloid positron emission tomography (PET) and 470 18F-flutemetamol (FMM) PET. Each PET scan was assessed in 10 regions (left and right frontal, lateral temporal, parietal, cingulate, and striatum) with focal-quantitative SUVR-based cutoff values for each region by using an iterative outlier approach. Results: A total of 62 (6.4%) subjects showed increased focal Aβ uptake with subthreshold global Aβ status [global (-) and focal (+) Aβ group, G(-)F(+) group]. The G(-)F(+) group showed worse performance in memory impairment (p < 0.001), global cognition (p = 0.009), greater hippocampal atrophy (p = 0.045), compared to those in the G(-)F(-). Participants with widespread Aβ involvement in the whole region [G(+)] showed worse neuropsychological (p < 0.001) and neuroimaging features (p < 0.001) than those with focal Aβ involvement G(-)F(+). Conclusion: Our findings suggest that individuals show distinctive clinical outcomes according to focally increased Aβ uptake and global Aβ status. Thus, researchers and clinicians should pay more attention to focal increased Aβ uptake in addition to global Aβ status.
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    Emerging role of vascular burden in AT(N) classification in individuals with Alzheimer's and concomitant cerebrovascular burdens
    (BMJ, 2023-12-14) Chun, Min Young; Jang, Hyemin; Kim, Soo-Jong; Park, Yu Hyun; Yun, Jihwan; Lockhart, Samuel N.; Weiner, Michael; De Carli, Charles; Moon, Seung Hwan; Choi, Jae Yong; Nam, Kyung Rok; Byun, Byung-Hyun; Lim, Sang-Moo; Kim, Jun Pyo; Choe, Yeong Sim; Kim, Young Ju; Na, Duk L.; Kim, Hee Jin; Seo, Sang Won; Radiology and Imaging Sciences, School of Medicine
    Objectives: Alzheimer's disease (AD) is characterised by amyloid-beta accumulation (A), tau aggregation (T) and neurodegeneration (N). Vascular (V) burden has been found concomitantly with AD pathology and has synergistic effects on cognitive decline with AD biomarkers. We determined whether cognitive trajectories of AT(N) categories differed according to vascular (V) burden. Methods: We prospectively recruited 205 participants and classified them into groups based on the AT(N) system using neuroimaging markers. Abnormal V markers were identified based on the presence of severe white matter hyperintensities. Results: In A+ category, compared with the frequency of Alzheimer's pathological change category (A+T-), the frequency of AD category (A+T+) was significantly lower in V+ group (31.8%) than in V- group (64.4%) (p=0.004). Each AT(N) biomarker was predictive of cognitive decline in the V+ group as well as in the V- group (p<0.001). Additionally, the V+ group showed more severe cognitive trajectories than the V- group in the non-Alzheimer's pathological changes (A-T+, A-N+; p=0.002) and Alzheimer's pathological changes (p<0.001) categories. Conclusion: The distribution and longitudinal outcomes of AT(N) system differed according to vascular burdens, suggesting the importance of incorporating a V biomarker into the AT(N) system.
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    Predicting conversion of brain β-amyloid positivity in amyloid-negative individuals
    (BMC, 2022-09-12) Park, Chae Jung; Seo, Younghoon; Choe, Yeong Sim; Jang, Hyemin; Lee, Hyejoo; Kim, Jun Pyo; Alzheimer’s Disease Neuroimaging Initiative; Radiology and Imaging Sciences, School of Medicine
    Background: Cortical deposition of β-amyloid (Aβ) plaque is one of the main hallmarks of Alzheimer's disease (AD). While Aβ positivity has been the main concern so far, predicting whether Aβ (-) individuals will convert to Aβ (+) has become crucial in clinical and research aspects. In this study, we aimed to develop a classifier that predicts the conversion from Aβ (-) to Aβ (+) using artificial intelligence. Methods: Data were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort regarding patients who were initially Aβ (-). We developed an artificial neural network-based classifier with baseline age, gender, APOE ε4 genotype, and global and regional standardized uptake value ratios (SUVRs) from positron emission tomography. Ten times repeated 10-fold cross-validation was performed for model measurement, and the feature importance was assessed. To validate the prediction model, we recruited subjects at the Samsung Medical Center (SMC). Results: A total of 229 participants (53 converters) from the ADNI dataset and a total of 40 subjects (10 converters) from the SMC dataset were included. The average area under the receiver operating characteristic values of three developed models are as follows: Model 1 (age, gender, APOE ε4) of 0.674, Model 2 (age, gender, APOE ε4, global SUVR) of 0.814, and Model 3 (age, gender, APOE ε4, global and regional SUVR) of 0.841. External validation result showed an AUROC of 0.900. Conclusion: We developed prediction models regarding Aβ positivity conversion. With the growing recognition of the need for earlier intervention in AD, the results of this study are expected to contribute to the screening of early treatment candidates.