From discourse to pathology: Automatic identification of Parkinson's disease patients via morphological measures across three languages. (November 2020)

• Record Type: Journal Article
• Authors: Eyigoz, Elif; Courson, Melody; Sedeño, Lucas; Rogg, Katharina; Orozco-Arroyave, Juan Rafael; Nöth, Elmar; Skodda, Sabine; Trujillo, Natalia; Rodríguez, Mabel; Rusz, Jan; Muñoz, Edinson; Cardona, Juan F.; Herrera, Eduar; Hesse, Eugenia; Ibáñez, Agustín; Cecchi, Guillermo; García, Adolfo M.
• Is Part Of: Cortex. Volume 132(2020)
• Page Start: 191
• Page End: 205
• Publication Date: 2020-11
• Subjects: Parkinson's disease -- Linguistic assessments -- Morphology -- Automated speech analysis -- Cross-linguistic validity; Neuropsychology -- Periodicals; Nervous system -- Periodicals; Neurology -- Periodicals; Psychophysiology -- Periodicals; Behavior -- Periodicals; Neurology -- Periodicals; 612.825
• DOI: 10.1016/j.cortex.2020.08.020
• Languages: English
• ISSNs: 0010-9452

Abstract

Abstract: 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.