闫岳铭,魏静,王彬,杨艳丽,张朝峰.阿尔茨海默病患者认知衰退过程中皮质指标变化模式[J].中国医学影像技术,2022,38(6):917~922
阿尔茨海默病患者认知衰退过程中皮质指标变化模式
Changing patterns of cortical metrics during cognitive decline in Alzheimer's disease patients
投稿时间:2021-09-16  修订日期:2021-12-15
DOI:10.13929/j.issn.1003-3289.2022.06.028
中文关键词:  阿尔茨海默病  磁共振成像  形态计量法
英文关键词:Alzheimer disease  magnetic resonance imaging  morphometry
基金项目:山西省基础研究计划(202103021224070)、国家重点研发计划(2018AAA0102604)、山西省回国留学人员科研项目(2021-036、2021-039)。
作者单位E-mail
闫岳铭 太原理工大学生物医学工程学院, 山西 太原 030024  
魏静 太原理工大学信息与计算机学院, 山西 太原 030024  
王彬 太原理工大学信息与计算机学院, 山西 太原 030024  
杨艳丽 太原理工大学信息与计算机学院, 山西 太原 030024  
张朝峰 太原理工大学生物医学工程学院, 山西 太原 030024 zhangchaofeng@tyut.edu.cn 
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中文摘要:
      目的 基于表面形态学测量(SBM)技术评价阿尔茨海默病(AD)患者认知衰退过程中脑皮质指标的变化模式。方法 于阿尔茨海默病神经影像学倡议(ADNI)数据库中选取36例AD、32例轻度认知障碍(MCI)及28名认知正常受试者,记录其简易智能精神状态检查量表(MMSE)评分结果。基于脑结构T1WI获取皮质下表面形态学指标,包括高斯曲率(K)、内在曲率指数(ICI)、平均曲率(H)、折叠指数(FI)、平均皮质厚度(ACT)及皮质体积(CV),比较组间形态学指标差异,筛选差异显著的ROI;以年龄、性别、受教育年限、颅内体积及组别作为协变量,以MMSE评分作为预测变量,采用线性回归分析和二次回归分析判断并比较各指标在认知下降过程中的变化模式。结果 认知衰退过程中,皮质下表面K、ICI、H及FI曲率指标总体呈上升趋势,ACT、CV呈下降趋势,且部分ROI的指标存在非线性变化,而同一ROI的不同形态学指标变化模式不同。结论 随认知下降,AD患者曲率指标呈上升趋势,且变化模式与ACT、CV存在差异。
英文摘要:
      Objective To observe the changing patterns of different cortical metrics during the cognitive decline in Alzheimer's disease (AD) patients using surface based morphometric (SBM). Methods Totally 36 AD patients, 32 mild cognitive impairment (MCI) patients and 28 cognitively normal subjectswere selected from the Alzheimer's disease neuroimaging initiative (ADNI) database, and their mini-mental state examination (MMSE) scores were recorded. Based on structural T1WI, the cortical subsurface morphological metrics, including Gaussian curvature (K), intrinsic curvature index (ICI), mean curvature (H), folding index (FI), average cortical thickness (ACT) and cortical volume (CV) were obtained for comparing differences in morphological indicators and screening ROI being significantly different among 3 groups. Taken age, gender, education years, total intracranial volume and group as covariates and MMSE score as predictor, linear regression analysis and quadratic regression analysis were used to identify and compare changing patterns of different metrics in the process of cognitive decline. Results During the process of cognitive decline, the curvature metrics of K, ICI, H and FI on the cortical subsurface showed generally upward trends, while ACT and CV showed downward trends, but metrics of some ROI showed non-linear changes. Different morphology metrics of the same ROI might have different changing patterns. Conclusion The cortical subsurface curvature metrics showed upward trends with cognitive decline of AD patients, and the changing patterns might be different from that of ACT and CV.
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