How language flows when movements don't: An automated analysis of spontaneous discourse in Parkinson's disease
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...
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| Acceso en línea: | https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_0093934X_v162_n_p19_Garcia http://hdl.handle.net/20.500.12110/paper_0093934X_v162_n_p19_Garcia |
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paper:paper_0093934X_v162_n_p19_Garcia2023-06-08T15:09:02Z How language flows when movements don't: An automated analysis of spontaneous discourse in Parkinson's disease Sigman, Mariano Fernández Slezak, Diego Grammatical features Graph embedding Latent semantic analysis Parkinson's disease Part-of-speech tagging Semantic fields Spontaneous discourse Word repetition accuracy adult Article automation computer analysis controlled study disease severity female grammar human language ability language processing latent period major clinical study male medical parameters motor dysfunction neuropsychological test Parkinson disease semantics spontaneous discourse case control study middle aged motor performance movement (physiology) nerve cell network Parkinson disease pathophysiology physiology speech Case-Control Studies Female Humans Male Middle Aged Motor Skills Movement Nerve Net Parkinson Disease Semantics Speech 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. © 2016 Elsevier Inc. Fil:Sigman, M. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina. Fil:Fernández Slezak, D. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina. 2016 https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_0093934X_v162_n_p19_Garcia http://hdl.handle.net/20.500.12110/paper_0093934X_v162_n_p19_Garcia |
| institution |
Universidad de Buenos Aires |
| institution_str |
I-28 |
| repository_str |
R-134 |
| collection |
Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA) |
| topic |
Grammatical features Graph embedding Latent semantic analysis Parkinson's disease Part-of-speech tagging Semantic fields Spontaneous discourse Word repetition accuracy adult Article automation computer analysis controlled study disease severity female grammar human language ability language processing latent period major clinical study male medical parameters motor dysfunction neuropsychological test Parkinson disease semantics spontaneous discourse case control study middle aged motor performance movement (physiology) nerve cell network Parkinson disease pathophysiology physiology speech Case-Control Studies Female Humans Male Middle Aged Motor Skills Movement Nerve Net Parkinson Disease Semantics Speech |
| spellingShingle |
Grammatical features Graph embedding Latent semantic analysis Parkinson's disease Part-of-speech tagging Semantic fields Spontaneous discourse Word repetition accuracy adult Article automation computer analysis controlled study disease severity female grammar human language ability language processing latent period major clinical study male medical parameters motor dysfunction neuropsychological test Parkinson disease semantics spontaneous discourse case control study middle aged motor performance movement (physiology) nerve cell network Parkinson disease pathophysiology physiology speech Case-Control Studies Female Humans Male Middle Aged Motor Skills Movement Nerve Net Parkinson Disease Semantics Speech Sigman, Mariano Fernández Slezak, Diego How language flows when movements don't: An automated analysis of spontaneous discourse in Parkinson's disease |
| topic_facet |
Grammatical features Graph embedding Latent semantic analysis Parkinson's disease Part-of-speech tagging Semantic fields Spontaneous discourse Word repetition accuracy adult Article automation computer analysis controlled study disease severity female grammar human language ability language processing latent period major clinical study male medical parameters motor dysfunction neuropsychological test Parkinson disease semantics spontaneous discourse case control study middle aged motor performance movement (physiology) nerve cell network Parkinson disease pathophysiology physiology speech Case-Control Studies Female Humans Male Middle Aged Motor Skills Movement Nerve Net Parkinson Disease Semantics Speech |
| description |
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. © 2016 Elsevier Inc. |
| author |
Sigman, Mariano Fernández Slezak, Diego |
| author_facet |
Sigman, Mariano Fernández Slezak, Diego |
| author_sort |
Sigman, Mariano |
| title |
How language flows when movements don't: An automated analysis of spontaneous discourse in Parkinson's disease |
| title_short |
How language flows when movements don't: An automated analysis of spontaneous discourse in Parkinson's disease |
| title_full |
How language flows when movements don't: An automated analysis of spontaneous discourse in Parkinson's disease |
| title_fullStr |
How language flows when movements don't: An automated analysis of spontaneous discourse in Parkinson's disease |
| title_full_unstemmed |
How language flows when movements don't: An automated analysis of spontaneous discourse in Parkinson's disease |
| title_sort |
how language flows when movements don't: an automated analysis of spontaneous discourse in parkinson's disease |
| publishDate |
2016 |
| url |
https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_0093934X_v162_n_p19_Garcia http://hdl.handle.net/20.500.12110/paper_0093934X_v162_n_p19_Garcia |
| work_keys_str_mv |
AT sigmanmariano howlanguageflowswhenmovementsdontanautomatedanalysisofspontaneousdiscourseinparkinsonsdisease AT fernandezslezakdiego howlanguageflowswhenmovementsdontanautomatedanalysisofspontaneousdiscourseinparkinsonsdisease |
| _version_ |
1768543412594671616 |