Background Aphasic disorders are observed in patients with both vascular and neurodegenerative pathology. Although spontaneous speech in the various forms of aphasia has some features that are identifiable on a purely linguistic level, diagnosing the type of aphasia critically relies on the support of clinical and neuroimaging data. Objective To identify some core characteristics of different types of fluent aphasias (i.e., disorders of speech production due to lesions in the posterior regions of the left perisylvian areas not associated with articulatory deficits or apraxia of speech) in spontaneous speech using T-LAB computer-assisted qualitative analyses. This is a mixed-method software that allows exploring narratives by highlighting their key features using linguistic, statistical and graphical tools. Methods We collected samples of spontaneous speech (narratives) from 34 fluent aphasic Italian speakers (i.e.,11 post-stroke aphasic patients, 17 with the logopenic variant of Primary Progressive Aphasia and 6 with the semantic variant) during the description of the Cookie Theft Picture of the Boston Diagnostic Aphasia Examination. Thirty-four healthy control subjects were asked to complete the same task. Analyses of the entire corpus (all of the narratives), specific metadata introduction and tagging were performed by two raters and any conflicts were resolved by a third rater. Results T-LAB analysis revealed statistically significant differences between both aphasic patients and healthy controls and between vascular and degenerative patients. Although the main distinction emerged between post-stroke and neurodegenerative aphasias, important differences also emerged between the individuals with the logopenic variant and the semantic variant. Discussion These findings underline the potential usefulness of a computer-assisted analysis of speech production to identify the core linguistic characteristics of different aphasic disorders, independently of any clinical support.

Molgora, S., Corbetta, D., Di Tella, S., Raynaud, S., Silveri, M. C., Linguistic characteristics of different types of aphasia: A computer-assisted qualitative analysis using T-LAB, <<JOURNAL OF NEUROLINGUISTICS>>, 2022; 2022 (62): 2171-2181. [doi:10.1016/j.jneuroling.2021.101056] [http://hdl.handle.net/10807/216564]

Linguistic characteristics of different types of aphasia: A computer-assisted qualitative analysis using T-LAB

Molgora, Sara;Di Tella, Sonia;Raynaud, Savina;Silveri, Maria Caterina
2022

Abstract

Background Aphasic disorders are observed in patients with both vascular and neurodegenerative pathology. Although spontaneous speech in the various forms of aphasia has some features that are identifiable on a purely linguistic level, diagnosing the type of aphasia critically relies on the support of clinical and neuroimaging data. Objective To identify some core characteristics of different types of fluent aphasias (i.e., disorders of speech production due to lesions in the posterior regions of the left perisylvian areas not associated with articulatory deficits or apraxia of speech) in spontaneous speech using T-LAB computer-assisted qualitative analyses. This is a mixed-method software that allows exploring narratives by highlighting their key features using linguistic, statistical and graphical tools. Methods We collected samples of spontaneous speech (narratives) from 34 fluent aphasic Italian speakers (i.e.,11 post-stroke aphasic patients, 17 with the logopenic variant of Primary Progressive Aphasia and 6 with the semantic variant) during the description of the Cookie Theft Picture of the Boston Diagnostic Aphasia Examination. Thirty-four healthy control subjects were asked to complete the same task. Analyses of the entire corpus (all of the narratives), specific metadata introduction and tagging were performed by two raters and any conflicts were resolved by a third rater. Results T-LAB analysis revealed statistically significant differences between both aphasic patients and healthy controls and between vascular and degenerative patients. Although the main distinction emerged between post-stroke and neurodegenerative aphasias, important differences also emerged between the individuals with the logopenic variant and the semantic variant. Discussion These findings underline the potential usefulness of a computer-assisted analysis of speech production to identify the core linguistic characteristics of different aphasic disorders, independently of any clinical support.
Inglese
Molgora, S., Corbetta, D., Di Tella, S., Raynaud, S., Silveri, M. C., Linguistic characteristics of different types of aphasia: A computer-assisted qualitative analysis using T-LAB, <<JOURNAL OF NEUROLINGUISTICS>>, 2022; 2022 (62): 2171-2181. [doi:10.1016/j.jneuroling.2021.101056] [http://hdl.handle.net/10807/216564]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10807/216564
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