Data Sheet 1_CharMark: character-level Markov modeling for interpretable linguistic biomarkers of cognitive decline.pdf

Dementia, one of the most prevalent neurodegenerative diseases, affects millions worldwide. Understanding linguistic markers of dementia is crucial for elucidating how cognitive decline manifests in speech patterns. Current non-invasive assessments like the Montreal Cognitive Assessment (MoCA) and Saint Louis University Mental Status (SLUMS) tests rely on manual interpretation and often lack detailed linguistic insight. This paper introduces a first-of-its-kind interpretable artificial intelligence (IAI) framework, CharMark, which leverages first-order Markov Chain models to characterize language production at the character level. By computing steady-state probabilities of character transitions in speech transcripts from individuals with dementia and healthy controls, we uncover distinctive character-usage patterns. The space character “ ”, representing pauses, (treated here as the space token between words rather than acoustic pauses), and letters such as “n” and “i” showed statistically significant differences between groups. Principal Component Analysis (PCA) revealed natural clustering aligned with cognitive status, while Kolmogorov-Smirnov tests confirmed distributional shifts. A Lasso Logistic Regression model further demonstrated that these character-level features possess strong discriminative potential. Our primary contribution is the identification and characterization of candidate linguistic biomarkers of cognitive decline; features that are both interpretable and easily computable. These findings highlight the potential of character-level modeling as a lightweight, scalable strategy for early-stage dementia screening, particularly in settings where more complex or audio-dependent models may be impractical.

痴呆症是全球范围内高发的神经退行性疾病之一，影响数百万人群。明确痴呆症的语言标记物，对于阐释认知衰退如何在言语模式中体现至关重要。当前的非侵入性评估手段，如蒙特利尔认知评估量表（Montreal Cognitive Assessment, MoCA）与圣路易斯大学精神状态量表（Saint Louis University Mental Status, SLUMS），均依赖人工解读，且往往无法提供细致的语言学洞察。本研究提出了一种首创的可解释人工智能（interpretable artificial intelligence, IAI）框架CharMark，该框架利用一阶马尔可夫链模型对字符层面的语言产出进行特征刻画。通过计算痴呆症患者与健康对照者的言语转录文本中字符转换的稳态概率，本研究揭示了具有区分性的字符使用模式：代表停顿的空格字符（本研究中将其视为词间的空间Token，而非声学停顿）以及字母“n”与“i”等，在两组间呈现出具有统计学意义的差异。主成分分析（Principal Component Analysis, PCA）结果显示，样本可依据认知状态自然聚类；柯尔莫哥洛夫-斯米尔诺夫检验则证实了两组间的分布偏移。套索逻辑回归（Lasso Logistic Regression）模型进一步证明，这些字符级特征具备极强的区分潜力。本研究的核心贡献在于，识别并刻画了认知衰退相关的候选语言生物标志物——这类特征兼具可解释性与易计算性。上述研究结果凸显了字符级建模作为一种轻量、可扩展的早期痴呆症筛查策略的潜力，尤其适用于难以部署复杂或依赖音频的模型的场景。