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Relative rate of change in cognitive score network dynamics via Bayesian hierarchical models reveal spatial patterns of neurodegeneration.
Statistics in Medicine ( IF 2 ) Pub Date : 2020-05-17 , DOI: 10.1002/sim.8568
Marcela I Cespedes 1 , James M McGree 2 , Christopher C Drovandi 2 , Kerrie Mengersen 2 , Jurgen Fripp 1 , James D Doecke 1
Affiliation  

The degeneration of the human brain is a complex process, which often affects certain brain regions due to healthy aging or disease. This degeneration can be evaluated on regions of interest (ROI) in the brain through probabilistic networks and morphological estimates. Current approaches for finding such networks are limited to analyses at discrete neuropsychological stages, which cannot appropriately account for connectivity dynamics over the onset of cognitive deterioration, and morphological changes are seldom unified with connectivity networks, despite known dependencies. To overcome these limitations, a probabilistic wombling model is proposed to simultaneously estimate ROI cortical thickness and covariance networks contingent on rates of change in cognitive decline. This proposed model was applied to analyze longitudinal data from healthy control (HC) and Alzheimer's disease (AD) groups and found connection differences pertaining to regions, which play a crucial role in lasting cognitive impairment, such as the entorhinal area and temporal regions. Moreover, HC cortical thickness estimates were significantly higher than those in the AD group across all ROIs. The analyses presented in this work will help practitioners jointly analyze brain tissue atrophy at the ROI‐level conditional on neuropsychological networks, which could potentially allow for more targeted therapeutic interventions.

中文翻译:

通过贝叶斯分级模型的认知评分网络动力学的相对变化率揭示了神经变性的空间模式。

人脑的退化是一个复杂的过程,由于健康的衰老或疾病,它通常会影响某些大脑区域。可以通过概率网络和形态估计在大脑的感兴趣区域(ROI)上评估这种退化。当前找到这种网络的方法仅限于在离散的神经心理学阶段进行的分析,这些阶段无法适当地考虑认知退化开始时的连通性动态,尽管存在已知的依赖性,但形态变化很少与连通性网络统一。为了克服这些限制,提出了一种概率摆动模型,以同时估计ROI皮质厚度和协方差网络,具体取决于认知能力下降的变化率。该提议的模型用于分析来自健康对照组(HC)和阿尔茨海默氏病(AD)组的纵向数据,发现与区域相关的连接差异,这些区域在持久性认知障碍(例如内嗅区和颞区)中起着至关重要的作用。此外,在所有ROI中,HC皮质厚度估计值均显着高于AD组。这项工作中提出的分析将帮助从业者以神经心理学网络为条件,在ROI级别上共同分析脑组织萎缩,这有可能允许更有针对性的治疗性干预。此外,在所有ROI中,HC皮质厚度估计值均显着高于AD组。这项工作中提出的分析将帮助从业者以神经心理学网络为条件,在ROI级别上共同分析脑组织萎缩,这有可能允许更有针对性的治疗性干预。此外,在所有ROI中,HC皮质厚度估计值均显着高于AD组。这项工作中提出的分析将帮助从业者以神经心理学网络为条件,在ROI级别上共同分析脑组织萎缩,这有可能允许更有针对性的治疗性干预。
更新日期:2020-05-17
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