연령별 대뇌 피질 두께의 성별 차이에 대한 형태학적 분석
Morphological Analysis of Age-related Gender Differences in Cortical Thickness
0
조회
0
다운로드
0
인용
0
참고문헌
키워드
초록
There have been many studies from the genetic system to physical activity and emotional expression such that there are gender differences. The purpose of this study was to determine how the structural characteristics of cortical thickness differ between males and females. This study used data from the Human Connectome Project (HCP). To analyze age-specific sexual dimorphisms of cortical thickness, selected 8-80 year old subjects were divided into five detailed age range groups according to each criterion. A total of 1,700 individual brain MRI T1 data were registered in stereotaxic space for analysis and classified into white matter (WM), gray matter (GM), and cerebro- spinal fluid (CSF). For surface-based analysis, the WM/GM surface was reconstructed from a spherical polygon model with 40962 vertices per hemisphere, and each vertex was extended to the GM/CSF boundary. Cortical thickness was then measured between each vertex using the t-link method. In the statistical analysis, intracranial volume was used as a covariate to exclude the effect of the difference in brain size of each individual, and the result of using age as a covariate was added to confirm the age effect within each group. Gender differences in cortical thickness had sig- nificant results by group. This may be an index to explain diseases with sexual dimorphism in prevalence or become a basis for explaining the characteristics of each sex that appear in behavior, personality, and aging. Therefore, the results of our study could be a criterion for age classification in future studies and for understanding ‘normal’ sexual dimorphism.
Abstract
There have been many studies from the genetic system to physical activity and emotional expression such that there are gender differences. The purpose of this study was to determine how the structural characteristics of cortical thickness differ between males and females. This study used data from the Human Connectome Project (HCP). To analyze age-specific sexual dimorphisms of cortical thickness, selected 8-80 year old subjects were divided into five detailed age range groups according to each criterion. A total of 1,700 individual brain MRI T1 data were registered in stereotaxic space for analysis and classified into white matter (WM), gray matter (GM), and cerebro- spinal fluid (CSF). For surface-based analysis, the WM/GM surface was reconstructed from a spherical polygon model with 40962 vertices per hemisphere, and each vertex was extended to the GM/CSF boundary. Cortical thickness was then measured between each vertex using the t-link method. In the statistical analysis, intracranial volume was used as a covariate to exclude the effect of the difference in brain size of each individual, and the result of using age as a covariate was added to confirm the age effect within each group. Gender differences in cortical thickness had sig- nificant results by group. This may be an index to explain diseases with sexual dimorphism in prevalence or become a basis for explaining the characteristics of each sex that appear in behavior, personality, and aging. Therefore, the results of our study could be a criterion for age classification in future studies and for understanding ‘normal’ sexual dimorphism.
참고문헌 (15)
- Kumar M, "A Low-cost Ambu-bag Based Ventilator for Covid-19Pandemic", 2021 IEEE Biomedical Circuits and Systems Conference (BioCAS), Biomedical Circuits and Systems Conference (BioCAS) 2021 IEEE, pp.1~5 (2021)
- Ciotti M, "The COVID-19 pandemic", Critical Reviews in Clinical Laboratory Sciences, 57(6), pp.365~388 (2020)
- Hiremath A, "Integrated Clinical and CT Based Artificial Intelligence Nomogram for Predicting Severity and Need for Ventilator Support in COVID-19 Patients : A Multi-Site Study", IEEE Journal of Biomedical and Health Informatics, 25(11), pp.4110~4118 (2021)
- Gajdos O, "Failure mode and effects analysis application on a home mechanical ventilation", 2019 7th E-Health and Bioengineering Conference (2019)
- Motta D, "Collaborative and Structured Network for Maintenance of Mechanical Ventilators during the SARS-CoV-2 Pandemic", Healthcare, 9(6), pp.754~ (2021)
- Sampath T, "Medical Device Guidelines and Regulations Handbook", pp.31~56 (2022), Medical Device Guidelines and Regulations Handbook;
- "ISO 14971:2019: Medical devices, Application of risk managementto medical devices", 1~7 (2019)
- Hegde V, "Case study — Risk management for medical devices (based on ISO 14971)", 2011 Proceedings. Annual Reliability and Maintainability Symposium, Reliability and Maintainability Symposium (RAMS), 2011 Proceedings – Annual, pp.1~6 (2011)
- Duwe B, "Failure mode and effects analysis application to critical care medicine", Critical Care Clinics, 21(1), pp.21~30 (2005)
- Rusu I, "Failure mode and effects analysis of linac-based liver stereotactic body radiotherapy", Medical Physics, 47(3), pp.937~947 (2020)
- Rosen M, "Failure mode and effects analysis of the universal anaesthesia machine in two tertiary care hospitals in Sierra Leone", British journal of anaesthesia, 113(3), pp.410~415 (2014)
- 서기홍, "병원내 의료기기 교체의 우선순위 결정을 위한 단순평가법 개발", 의공학회지, 41(6), pp.256~263 (2020), 대한의용생체공학회
- Seo GH, "How to calculate the life cycle of high-risk medical devices for patient safety.", Frontiers in Public Health, 10 (2022)
- Frazão D, "The impact of human error on medical procedures", International Journal of Risk & Safety in Medicine, 33(3), pp.287~298 (2022)
- Sameera V, Bindra A, Rath G, "Human errors and their prevention in healthcare", Journal of Anaesthesiology Clinical Pharmacology, 37(3), pp.328~335 (2021)