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RISS 인기검색어
- 검색키워드
- 저자 : Regina EY Kim (검색결과 3 건)
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Lifestyle-dependent brain change: a longitudinal cohort MRI study
Kim, Regina EY.,Yun, Chang-Ho,Thomas, Robert J.,Oh, Jang-Hoon,Johnson, Hans J.,Kim, Soriul,Lee, Seungku,Seo, Hyung Suk,Shin, Chol Elsevier 2018 Neurobiology of aging Vol.69 No.-
<P><B>Abstract</B></P> <P>We investigated both independent and interconnected effects of 3 lifestyle factors on brain volume, measuring yearly changes using large-scale longitudinal magnetic resonance imaging, in middle-aged to older adults. We measured brain volumes in a cohort (<I>n</I> = 984, 49–79 years) from the Korean Genome and Epidemiology Study group, using baseline and follow-up estimates after 4 years. In our analysis, the accelerated brain atrophy in normal aging was observed across regions (e.g., brain tissue: -0.098 ± 0.01 mL/y, <I>p</I> < 0.001). An independent lifestyle-specific trend of brain atrophy across time was also evident in men, where smoking (<I>p</I> = 0.012) and physical activity (<I>p</I> = 0.014) showed the strongest association with the atrophy rate. Linear regression analysis of the interconnected effect revealed that brain atrophy is mitigated by intense physical activity in smoking males. Lifestyle factors did not show any significant effect on brain volume in women. These results provide important information regarding lifestyle factors that affect brain aging in mid-to-late adulthood. Our findings may aid in the identification of preventive measures against dementia.</P>
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Sunghwan Kim,Sheng-Min Wang,Dong Woo Kang,Yoo Hyun Um,Hyeonsik Yang,Hyunji Lee,Regina EY Kim,Donghyeon Kim,Chang Uk Lee,Hyun Kook Lim 대한신경정신의학회 2024 PSYCHIATRY INVESTIGATION Vol.21 No.1
Objective We aimed to create an efficient and valid predicting model which can estimate individuals’ brain age by quantifying their regional brain volumes.Methods A total of 2,560 structural brain magnetic resonance imaging (MRI) scans, along with demographic and clinical data, were obtained. Pretrained deep-learning models were employed to automatically segment the MRI data, which enabled fast calculation of regional brain volumes. Brain age gaps for each subject were estimated using volumetric values from predefined 12 regions of interest (ROIs): bilateral frontal, parietal, occipital, and temporal lobes, as well as bilateral hippocampus and lateral ventricles. A larger weight was given to the ROIs having a larger mean volumetric difference between the cognitively unimpaired (CU) and cognitively impaired group including mild cognitive impairment (MCI), and dementia groups. The brain age was predicted by adding or subtracting the brain age gap to the chronological age according to the presence or absence of the atrophy region.Results The study showed significant differences in brain age gaps among CU, MCI, and dementia groups. Furthermore, the brain age gaps exhibited significant correlations with education level and measures of cognitive function, including the clinical dementia rating sum-of-boxes and the Korean version of the Mini-Mental State Examination.Conclusion The brain age that we developed enabled fast and efficient brain age calculations, and it also reflected individual’s cognitive function and cognitive reserve. Thus, our study suggested that the brain age might be an important marker of brain health that can be used effectively in real clinical settings.
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Jiwon Kim,Sunghwan Kim,Yoo Hyun Um,Sheng-Min Wang,Regina EY Kim,Yeong Sim Choe,Jiyeon Lee,Donghyeon Kim,Hyun Kook Lim,Chang Uk Lee,Dong Woo Kang 대한정신약물학회 2024 CLINICAL PSYCHOPHARMACOLOGY AND NEUROSCIENCE Vol.22 No.1
Objective: Cognitive reserve has emerged as a concept to explain the variable expression of clinical symptoms in the pathology of Alzheimer’s disease (AD). The association between years of education, a proxy of cognitive reserve, and resting-state functional connectivity (rFC), a representative intermediate phenotype, has not been explored in the preclinical phase, considering risk factors for AD. We aimed to evaluate whether the relationship between years of education and rFC in cognitively preserved older adults differs depending on amyloid-beta deposition and APOE ε4 carrier status as effect modifiers. Methods: A total of 121 participants underwent functional magnetic resonance imaging, [18F] flutemetamol positron emission tomography-computed tomography, APOE genotyping, and a neuropsychological battery. Potential interactions between years of education and AD risk factors for rFC of AD-vulnerable neural networks were assessed with wholebrain voxel-wise analysis. Results: We found a significant education years-by-APOE ε4 carrier status interaction for the rFC from the seed region of the central executive (CEN) and dorsal attention networks. Moreover, there was a significant interaction of rFC between right superior occipital gyrus and the CEN seed region by APOE ε4 carrier status for memory performances and overall cognitive function. Conclusion: In preclinical APOE ε4 carriers, higher years of education were associated with higher rFC of the AD vulnerable network, but this contributed to lower cognitive function. These results contribute to a deeper understanding of the impact of cognitive reserve on sensitive functional intermediate phenotypic markers in the preclinical phase of AD.
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