How language flows when movements don’t: An automated analysis of spontaneous discourse in Parkinson’s disease

Brain and Language

García, A. M., Carrillo, F., Orozco-Arroyave, J. R., Trujillo, N., Vargas Bonilla, J. F., Fittipaldi, S., Adolfi, F., Nöth, E., Sigman, M., Fernández Slezak, D., Ibáñez, A. & Cecchi, G. A. (2016). How language flows when movements don’t: An automated analysis of spontaneous discourse in Parkinson’s disease. Brain and Language 162, 19-28. Online: http://bit.ly/2aXVTTX

Mediante sofisticados métodos computarizados, demostramos que el discurso espontáneo de pacientes con enfermedad de Parkinson, en comparación con sujetos sanos, se caracteriza por un menor desarrollo de redes conceptuales vinculadas con el movimiento y un mayor uso de construcciones digresivas. Estos atributos permitieron clasificar si cada texto individual pertenecía a un paciente o a un control con 75% de precisión. Además, al analizarse las repeticiones de palabras en pacientes, se logró clasificar su nivel de deterioro motriz con una exactitud del 76%. En suma, los textos naturales de los pacientes podrían guardar claves específicas de su patología.

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How language flows when movements don’t: An automated analysis of spontaneous discourse in Parkinson’s disease

Brain and Language

García, A. M., Carrillo, F., Orozco-Arroyave, J. R., Trujillo, N., Vargas Bonilla, J. F., Fittipaldi, S., Adolfi, F., Nöth, E., Sigman, M., Fernández Slezak, D., Ibáñez, A. & Cecchi, G. A. (2016). How language flows when movements don’t: An automated analysis of spontaneous discourse in Parkinson’s disease. Brain and Language 162, 19-28. Online: http://bit.ly/2aXVTTX

Abstract 
To assess the impact of Parkinson’s disease (PD) on spontaneous discourse, we conducted computerized analyses of brief monologues produced by 51 patients and 50 controls. We explored differences in semantic fields (via latent semantic analysis), grammatical choices (using part-of-speech tagging), and word-level repetitions (with graph embedding tools). Although overall output was quantitatively similar between groups, patients relied less heavily on action-related concepts and used more subordinate structures. Also, a classification tool operating on grammatical patterns identified monologues as pertaining to patients or controls with 75% accuracy. Finally, while the incidence of dysfluent word repetitions was similar between groups, it allowed inferring the patients’ level of motor impairment with 77% accuracy.
Our results highlight the relevance of studying naturalistic discourse features to tap the integrity of neural (and, particularly, motor) networks, beyond the possibilities of standard token-level instruments.
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