Embodied cognition research on Parkinson's disease (PD) points to disruptions of frontostriatal language functions as sensitive targets for clinical assessment. However, no existing approach has been tested for crosslinguistic validity, let alone by combining naturalistic tasks with machine-learning tools. To address these issues, we conducted the first classifier-based examination of morphological processing (a core frontostriatal function) in spontaneous monologues from PD patients across three typologically different languages. The study comprised 330 participants, encompassing speakers of Spanish (61 patients, 57 matched controls), German (88 patients, 88 matched controls), and Czech (20 patients, 16 matched controls). All subjects described the activities they perform during a regular day, and their monologues were automatically coded via morphological tagging, a computerized method that labels each word with a part-of-speech tag (e.g., noun, verb) and specific morphological tags (e.g., person, gender, number, tense). The ensuing data were subjected to machine-learning analyses to assess whether differential morphological patterns could classify between patients and controls and reflect the former's degree of motor impairment. Results showed robust classification rates, with over 80% of patients being discriminated from controls in each language separately. Moreover, the most discriminative morphological features were associated with the patients' motor compromise (as indicated by Pearson r correlations between predicted and collected motor impairment scores that ranged from moderate to moderate-to-strong across languages). Taken together, our results suggest that morphological patterning, an embodied frontostriatal domain, may be distinctively affected in PD across languages and even under ecological testing conditions.

帕金森病 (PD) 的具身认知研究指出，额纹状体语言功能的破坏是临床评估的敏感目标。然而，没有任何现有方法经过跨语言有效性测试，更不用说将自然任务与机器学习工具相结合。为了解决这些问题，我们对来自 PD 患者的自发独白中的形态学处理（核心额纹状体功能）进行了第一次基于分类器的检查，其中包括三种类型不同的语言。该研究包括 330 名参与者，包括讲西班牙语的人（61 名患者，57 名匹配的对照组）、德语（88 名患者，88 名匹配的对照组）和捷克语（20 名患者，16 名匹配的对照组）。所有受试者都描述了他们在日常活动中进行的活动，他们的独白是通过词法标记自动编码的，这是一种计算机化的方法，用词性标签（例如，名词、动词）和特定的词法标签（例如，人、性别、数字、时态）标记每个单词。对随后的数据进行机器学习分析，以评估不同的形态模式是否可以在患者和对照组之间进行分类，并反映前者的运动障碍程度。结果显示了强大的分类率，超过 80% 的患者分别在每种语言中与对照组区分开来。此外，最具辨别力的形态特征与患者的运动障碍相关（如预测和收集的运动障碍评分之间的 Pearson r 相关性所表明的，这些评分范围从中度到中度到强烈的跨语言）。