认知完整APOE-ε4携带者早期言语运动变化的运动学相关性:基于色字干扰任务的初步研究

Mehrdad Dadgostar, Lindsay C Hanford, Jordan R Green, Brian D Richburg, Averi Taylor Cannon, Nelson Barnett, David H Salat, Steven E Arnold, Marziye Eshghi
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引用次数: 0

摘要

阿尔茨海默病(AD)是最普遍的痴呆症形式,也是一个重大的公共卫生挑战。在无法治愈的情况下,准确和创新的早期诊断方法对于积极主动的生活和医疗保健规划至关重要。语音测量在识别轻度认知障碍(MCI)和AD患者方面显示出了很大的潜力,这促使人们开始研究语音运动特征是否可以在认知能力下降之前检测到风险升高。本研究考察了在色词干扰任务中测量的语音运动特征是否可以区分认知正常的APOE-ε4携带者(E4+)和非携带者(E4-)。唇部运动特性在干扰前、干扰中和干扰后被提取出来。采用描述性统计和独立t检验来检验唇部运动持续时间、平均速度和范围的群体水平趋势。虽然组间差异没有达到统计学意义,但一些特征显示出中等效应大小,提示潜在的神经运动差异。基于2度多项式核的支持向量机模型利用干扰前唇运动持续时间、干扰期间平均唇速、干扰前后唇运动幅度变化3个特征对APOE-ε4状态进行分类,准确率达到87.5%。这些特征反映了两组在基线运动规划、对认知运动干扰的易感性和发音适应性方面的细微差异。该模型的高精度(88.90%)、灵敏度(88.90%)和特异性(85.70%)强调了言语运动学在早期风险识别方面的潜在临床应用。这些发现支持使用无创、低负担的语音分析作为认知完整个体AD风险的有前途的数字生物标志物,并强调了其在不同人群中可扩展、远程筛查和纵向监测的潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Kinematic Correlates of Early Speech Motor Changes in Cognitively Intact APOE-ε4 Carriers: A Preliminary Study Using a Color-Word Interference Task.

Alzheimer's disease (AD) is the most prevalent form of dementia and a major public health challenge. In the absence of a cure, accurate and innovative early diagnostic methods are essential for proactive life and healthcare planning. Speech metrics have shown promising potential for identifying individuals with mild cognitive impairment (MCI) and AD, prompting investigation into whether speech motor features can detect elevated risk even prior to cognitive decline. This study examined whether speech kinematic features measured during a color-word interference task could distinguish cognitively normal APOE-ε4 carriers (E4+) from non-carriers (E4-). Lip movement properties were extracted across pre-, during-, and post-interference sentence segments. Descriptive statistics and independent t-tests were conducted to examine group-level trends in lip movement duration, average speed, and range. Although no group differences reached statistical significance, several features showed moderate effect sizes, suggesting potential neuromotor differences. A support vector machine model with a degree-2 polynomial kernel achieved 87.5% accuracy in classifying APOE-ε4 status using three features: lip movement duration prior to interference, average lip speed during interference, and the change in lip movement range from pre- to during-interference segments. These features reflect subtle differences between the two groups in baseline motor planning, susceptibility to cognitive-motor interference, and articulatory adaptability. The model's high precision (88.90%), sensitivity (88.90%), and specificity (85.70%) underscore the potential clinical utility of speech kinematics for early risk identification. These findings support the use of non-invasive, low-burden speech analysis as a promising digital biomarker for AD risk in cognitively intact individuals and highlight its potential for scalable, remote screening and longitudinal monitoring in diverse populations.

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