From discourse to pathology: Automatic identification of Parkinson's disease patients via morphological measures across three languages

Eyigoz E, Courson M, Sedeño L, Rogg K, Orozco-Arroyave JR, Nöth E, Skodda S, Trujillo N, Rodríguez M, Rusz J, Muñoz E, Cardona JF, Herrera E, Hesse E, Ibáñez A, Cecchi G, García AM (2020)

Publication Type: Journal article

Publication year: 2020

Journal

Cortex Elsevier

Book Volume: 132

Pages Range: 191-205

DOI: 10.1016/j.cortex.2020.08.020

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.

Authors with CRIS profile

Elmar Nöth Professur für Informatik (Mustererkennung)

Involved external institutions

University of Valle / Universidad del Valle (Univalle) CO Colombia (CO) Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET) / National Scientific and Technical Research Council AR Argentina (AR) Julius-Maximilians-Universität Würzburg DE Germany (DE) Universidad de Antioquía (UDEA) CO Colombia (CO) Czech Technical University in Prague / České vysoké učení technické v Praze CZ Czech Republic (CZ) National Institute of Mental Health Information Resource Center US United States (USA) (US) IBM Thomas J. Watson Research Center US United States (USA) (US) Institut Universitaire De Gériatrie De Montréal (IUGM) CA Canada (CA) Universidad Icesi CO Colombia (CO) Universidad de Santiago de Chile (USACH) / University of Santiago, Chile CL Chile (CL) Universitätsklinikum Knappschaftskrankenhaus Bochum DE Germany (DE)

How to cite

APA:

Eyigoz, E., Courson, M., Sedeño, L., Rogg, K., Orozco-Arroyave, J.R., Nöth, E.,... García, A.M. (2020). From discourse to pathology: Automatic identification of Parkinson's disease patients via morphological measures across three languages. Cortex, 132, 191-205. https://dx.doi.org/10.1016/j.cortex.2020.08.020

MLA:

Eyigoz, Elif, et al. "From discourse to pathology: Automatic identification of Parkinson's disease patients via morphological measures across three languages." Cortex 132 (2020): 191-205.

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