通过个体化认知损伤模式，实现阿尔茨海默病早期精准筛查
Identification of Patterns of Cognitive Impairment for Early Detection of Dementia
- 从2.4万例数据中挖掘出人群认知损伤的聚类模式
- 识别出与临床公认的轻度认知障碍亚型对应的损伤路径
- 可对无症状者预测潜在认知衰退方向，适合长期随访研究
早期发现痴呆对制定有效干预措施至关重要。尽管全面认知测试诊断最准确，但耗时长、繁琐，难以大规模应用，尤其在需要定期评估时。不同人群进展为不同类型的痴呆时，其认知损伤模式各异。本文提出一种新方法，通过从包含正常人和轻度认知障碍（MCI）的群体中学习个体特定的损伤模式，构建个性化随访测试。采用两步法：先用集成包装器特征选择，再进行聚类分析，识别出与临床公认的MCI亚型对应的认知损伤模式。这些模式可用于判断个体最可能的认知衰退路径，即使在无症状或看似正常者中亦然。研究基于来自NACC数据库的24,000名受试者的基础数据。
原文摘要 · Abstract (English)
Early detection of dementia is crucial to devise effective interventions. Comprehensive cognitive tests, while being the most accurate means of diagnosis, are long and tedious, thus limiting their applicability to a large population, especially when periodic assessments are needed. The problem is compounded by the fact that people have differing patterns of cognitive impairment as they progress to different forms of dementia. This paper presents a novel scheme by which individual-specific patterns of impairment can be identified and used to devise personalized tests for periodic follow-up. Patterns of cognitive impairment are initially learned from a population cluster of combined normals and MCIs, using a set of standardized cognitive tests. Impairment patterns in the population are identified using a 2-step procedure involving an ensemble wrapper feature selection followed by cluster identification and analysis. These patterns have been shown to correspond to clinically accepted variants of MCI, a prodrome of dementia. The learned clusters of patterns can subsequently be used to identify the most likely route of cognitive impairment, even for pre-symptomatic and apparently normal people. Baseline data of 24,000 subjects from the NACC database was used for the study.
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