Cognitive Determinants of Dysarthria in Parkinson's Disease: An Automated Machine Learning Approach
Garcia AM, Arias-Vergara T, C. Vasquez-Correa J, Nöth E, Schuster M, Welch AE, Bocanegra Y, Baena A, Orozco-Arroyave JR (2021)
Publication Type: Journal article
Publication year: 2021
Journal
DOI: 10.1002/mds.28751
Abstract
Background Dysarthric symptoms in Parkinson's disease (PD) vary greatly across cohorts. Abundant research suggests that such heterogeneity could reflect subject-level and task-related cognitive factors. However, the interplay of these variables during motor speech remains underexplored, let alone by administering validated materials to carefully matched samples with varying cognitive profiles and combining automated tools with machine learning methods. Objective We aimed to identify which speech dimensions best identify patients with PD in cognitively heterogeneous, cognitively preserved, and cognitively impaired groups through tasks with low (reading) and high (retelling) processing demands. Methods We used support vector machines to analyze prosodic, articulatory, and phonemic identifiability features. Patient groups were compared with healthy control subjects and against each other in both tasks, using each measure separately and in combination. Results Relative to control subjects, patients in cognitively heterogeneous and cognitively preserved groups were best discriminated by combined dysarthric signs during reading (accuracy = 84% and 80.2%). Conversely, patients with cognitive impairment were maximally discriminated from control subjects when considering phonemic identifiability during retelling (accuracy = 86.9%). This same pattern maximally distinguished between cognitively spared and impaired patients (accuracy = 72.1%). Also, cognitive (executive) symptom severity was predicted by prosody in cognitively preserved patients and by phonemic identifiability in cognitively heterogeneous and impaired groups. No measure predicted overall motor dysfunction in any group. Conclusions Predominant dysarthric symptoms appear to be best captured through undemanding tasks in cognitively heterogeneous and preserved cohorts and through cognitively loaded tasks in patients with cognitive impairment. Further applications of this framework could enhance dysarthria assessments in PD. (c) 2021 International Parkinson and Movement Disorder Society
Authors with CRIS profile
Involved external institutions
Universidad de Antioquía (UDEA)
Colombia (CO)
Ludwig-Maximilians-Universität (LMU)
Germany (DE)
University of California San Francisco (UCSF)
United States (USA) (US)
Universidad de San Andrés
Argentina (AR)
Colombia (CO)
Ludwig-Maximilians-Universität (LMU)
Germany (DE)
University of California San Francisco (UCSF)
United States (USA) (US)
Universidad de San Andrés
Argentina (AR)
How to cite
APA:
Garcia, A.M., Arias-Vergara, T., C. Vasquez-Correa, J., Nöth, E., Schuster, M., Welch, A.E.,... Orozco-Arroyave, J.R. (2021). Cognitive Determinants of Dysarthria in Parkinson's Disease: An Automated Machine Learning Approach. Movement Disorders. https://dx.doi.org/10.1002/mds.28751
MLA:
Garcia, Adolfo M., et al. "Cognitive Determinants of Dysarthria in Parkinson's Disease: An Automated Machine Learning Approach." Movement Disorders (2021).
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