Metric String Action Gauge Pdf 439
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Metric String Action Gauge Pdf 439
Here we examined whether automated discourse-level analysis of action semantics can (i) identify PD patients in a cognitively heterogeneous cohort and (ii) differentiate between PD-MCI and PD-nMCI individuals. Early-stage patients and HCs read and immediately retold two matched, validated stories: an action text (AT, rich in movement descriptions) and a non-action text (nAT, focused on non-motoric events)4,7,8,9,21,22. For each text, we extracted semantic features via latent semantic analysis (LSA) and implemented a Proximity-to-Reference-Semantic-Field (P-RSF) metric, capturing the weight of action and non-action concepts across retold texts. We then used inferential statistical models (ANCOVAs, controlling for cognitive symptom severity) and support vector machine (SVM) classifiers to assess whether patients and HCs could be discriminated via semantic information. Moreover, we performed additional analyses based on corpus-derived word embeddings as a benchmark to gauge the robustness of our metric. Finally, exploratory correlations were performed between P-RSF scores and an index of motor symptom severity. The pipeline is depicted in Fig. 1.
We developed an automated framework to capture semantic markers of PD and its cognitive phenotypes through AT and nAT retelling. The weight of action and non-action concepts in each retold story was quantified with our P-RSF metric, compared between groups through ANCOVAs, and used to classify between patients and HCs via machine learning. P-RSF scores from AT (but not nAT) retelling robustly discriminated between PD patients and HCs. Subgroup analyses replicated this pattern in PD-nMCI patients but not in PD-MCI patients, who exhibited reduced P-RSF scores for both AT and nAT retellings. Also, though not systematic, discrimination between PD-nMCI and PD-MCI was better when derived from AT than nAT retellings. Moreover, our approach outperformed classifiers based on corpus-derived word embeddings. Finally, no significant associations emerged between P-RSF and UPDRS-III scores. These findings have translational implications, as discussed next.
It is worth stressing that present results were obtained with naturalistic tasks and automated methods. Action-semantic deficits are well-established in the PD literature1, but they are typically captured through burdensome tasks that are rarely, if ever, found in real life. For example, participants have been asked to decide whether successive letter strings constitute real words12, name or associate decontextualized pictures16, or press buttons with particular hand positions after sentence listening33. Such settings may prove tiring, frustrating, and cognitively taxing, compromising data quality, task completion, and ecological validity. Moreover, performance in several relevant tasks, such as fluency34 and picture naming16, is established by examiners, who must single-handedly decide whether each response meets correctness criteria. Ensuing scores may thus be prone to inter-rater variability, potentially undermining reliability. Automated analysis of free speech overcomes these issues, offering a patient-friendly, ecologically valid, and objective framework to collect clinically usable data. In particular, our approach, rooted in a strategic task and a theory-driven metric, combines the sensitivity of action-semantic assessments for PD with the clinical potential of automated discourse analysis. Further work in this direction could hone the translational relevance of linguistic assessments in the quest for early markers of PD.