Centre for Medical Image Computing, Department of Computer Science, UCL, London, WC1E 6BT, UK.
Dementia Research Centre, UCL Queen Square Institute of Neurology, UCL, London, WC1N 3BG, UK.
Alzheimers Dement. 2020 Jul;16(7):965-973. doi: 10.1002/alz.12083. Epub 2020 Jun 2.
This work aims to characterize the sequence in which cognitive deficits appear in two dementia syndromes.
Event-based modeling estimated fine-grained sequences of cognitive decline in clinically-diagnosed posterior cortical atrophy (PCA) ( ) and typical Alzheimer's disease (tAD) ( ) at the UCL Dementia Research Centre. Our neuropsychological battery assessed memory, vision, arithmetic, and general cognition. We adapted the event-based model to handle highly non-Gaussian data such as cognitive test scores where ceiling/floor effects are common.
Experiments revealed differences and similarities in the fine-grained ordering of cognitive decline in PCA (vision first) and tAD (memory first). Simulation experiments reveal that our new model equals or exceeds performance of the classic event-based model, especially for highly non-Gaussian data.
Our model recovered realistic, phenotypical progression signatures that may be applied in dementia clinical trials for enrichment, and as a data-driven composite cognitive end-point.
本研究旨在描述两种痴呆症中认知障碍出现的顺序。
基于事件的建模估计了 UCL 痴呆症研究中心临床诊断的后部皮质萎缩(PCA)( )和典型阿尔茨海默病(tAD)( )患者认知衰退的精细序列。我们的神经心理学测试评估了记忆、视力、算术和一般认知能力。我们对基于事件的模型进行了改编,以处理认知测试分数等高度非正态数据,这些数据中常见天花板/地板效应。
实验揭示了 PCA(视力第一)和 tAD(记忆第一)中认知衰退的精细排序的差异和相似之处。模拟实验表明,我们的新模型等于或超过经典基于事件模型的性能,特别是对于高度非正态数据。
我们的模型恢复了现实的、表型的进展特征,可应用于痴呆症临床试验的富集,并作为数据驱动的综合认知终点。