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首页> 外文期刊>NeuroImage: Clinical >Visualizing modules of coordinated structural brain atrophy during the course of conversion to Alzheimer's disease by applying methodology from gene co-expression analysis
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Visualizing modules of coordinated structural brain atrophy during the course of conversion to Alzheimer's disease by applying methodology from gene co-expression analysis

机译:通过应用基因共表达分析方法,可视化转化为阿尔茨海默氏病期间协调性结构性脑萎缩的模块

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ObjectiveWe aimed to identify modularized structural atrophy of brain regions with a high degree of connectivity and its longitudinal changes associated with the progression of Alzheimer's disease (AD) using weighted gene co-expression network analysis (WGCNA), which is an unsupervised hierarchical clustering method originally used in genetic analysis.MethodsWe included participants with late mild cognitive impairment (MCI) at baseline from the Japanese Alzheimer's Disease Neuroimaging Initiative (J-ADNI) study. We imputed normalized andZ-transformed structural volume or cortical thickness data of 164 parcellated brain regions/structures based on the calculations of theFreeSurfersoftware. We applied the WGCNA to extract modules with highly interconnected structural atrophic patterns and examined the correlation between the identified modules and clinical AD progression.ResultsWe included 204 participants from the baseline dataset, and performed a follow-up with 100 in the 36-month dataset of MCI cohort participants from the J-ADNI. In the univariate correlation or variable importance analysis, baseline atrophy in temporal lobe regions/structures significantly predicted clinical AD progression. In the WGCNA consensus analysis, co-atrophy modules associated with MCI conversion were first distributed in the temporal lobe and subsequently extended to adjacent parietal cortical regions in the following 36?months.ConclusionsWe identified coordinated modules of brain atrophy and demonstrated their longitudinal extension along with the clinical course of AD progression using WGCNA, which showed a good correspondence with previous pathological studies of the tau propagation theory. Our results suggest the potential applicability of this methodology, originating from genetic analyses, for the surrogate visualization of the underlying pathological progression in neurodegenerative diseases not limited to AD.
机译:目的我们旨在使用加权基因共表达网络分析(WGCNA)来识别具有高度连通性的大脑区域的模块化结构性萎缩及其与阿尔茨海默氏病(AD)的进展相关的纵向变化,这是最初的无监督分层聚类方法方法我们纳入了来自日本阿尔茨海默氏病神经影像学倡议(J-ADNI)研究的基线晚期轻度认知障碍(MCI)的参与者。我们基于FreeSurfer软件的计算结果,估算了164个散开的大脑区域/结构的标准化和Z转换的结构体积或皮质厚度数据。我们使用WGCNA提取具有高度相互联系的结构性萎缩模式的模块,并检查已识别模块与临床AD进展之间的相关性。结果我们纳入了基线数据集中的204名参与者,并在36个月的数据集中进行了100次随访来自J-ADNI的MCI队列参与者。在单变量相关性或重要性重要性分析中,颞叶区域/结构的基线萎缩显着预测了临床AD进展。在WGCNA共识分析中,与MCI转换相关的共萎缩模块首先分布于颞叶,然后在接下来的36个月内扩展至相邻的顶叶皮层区域。结论我们确定了脑萎缩的协调模块,并证明了其纵向延伸以及使用WGCNA进行AD进展的临床过程,与tau传播理论的先前病理学研究显示出很好的对应性。我们的结果表明,这种方法的潜在适用性来自遗传分析,可用于替代性可视化神经退行性疾病(不仅限于AD)的潜在病理进展。

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