情景性下降是阿尔茨海默病的早期效应，在自发言语中显现，并且可以由人类和机器可靠地测量。
*引用: He, R., Yuan, X., & Hinzen, W. (2023). Episodic Thinking in Alzheimer’s Disease Through the Lens of Language: Linguistic Analysis and Transformer-Based Classification. American journal of speech-language pathology, 1–9. Advance online publication. https://doi.org/10.1044/2023_AJSLP-23-00066
Abstract
Purpose: Episodic memory decline is a hallmark of Alzheimer’s disease (AD) and linked to deficits in episodic thinking directed to the future. We addressed the question whether a deficit in episodic thinking can be picked up directly from connected speech and its detection can be automatized.
Method: We linguistically classified 2,809 utterances (including embedded clauses in the utterances) from picture descriptions from 70 healthy older controls, 82 people with mild probable AD (pAD), and 46 people with moderate pAD for whether they were episodic, nonepisodic, or “other” (e.g., off-task). Generalized linear regression models were used to investigate how ratios of these categories change in AD, controlling for age, gender, and education. Finally, we applied deep learning technique to explore the feasibility of automating the episodicity analysis.
Results: Decline in episodicity significantly distinguished controls from both mild pAD and moderate pAD. Correlation analysis suggested this decline not to be an effect of age, gender, and education but of cognitive ability. The decline was not compensated by an increase of nonepisodic utterances but mainly of off-task expressions. A transformer-based classifier to explore the possibility of automatizing the classification of episodicity achieved a macro F1 score of 0.913 in the ternary classification.
Conclusion: These results show that a loss of episodicity is an early effect in AD that is manifested in spontaneous speech and can be reliably measured by both humans and machines.
以下为机器翻译摘要：
目的： 情景记忆衰退是阿尔茨海默病 (AD) 的一个标志，与针对未来的情景思维缺陷有关。 我们研究的问题是，情景思维的缺陷是否可以直接从连贯的语音中捕捉到，而且其检测是否可以自动化。
方法： 我们对来自 70 名健康老年对照者、82 名轻度疑似 AD (pAD) 患者和 46 名中度 pAD 患者的图片描述中的 2,809 条话语（包括话语中嵌入的子句）进行语言分类，以确定它们是否为情景性、 非情景的，或“其他”（例如，脱离任务）。 广义线性回归模型用于研究 AD 中这些类别的比率如何变化，并控制年龄、性别和教育程度。 最后，我们应用深度学习技术来探索自动化情景性话语分析的可行性。
结果： 发作性下降将对照组与轻度 pAD 和中度 pAD 显着区分开来。 相关分析表明，这种下降不是年龄、性别和教育程度的影响，而是认知能力的影响。 这种下降并没有因为非情景性话语的增加而得到补偿，而主要是非任务性表达的增加。 基于 Transformer 的分类器探索了情景性分类自动化的可能性，在三元分类中取得了 0.913 的宏观 F1 分数。
结论： 这些结果表明，情景性下降是阿尔茨海默病的早期效应，在自发言语中显现，并且可以由人类和机器可靠地测量。