| Literature DB >> 30884283 |
Eleonora Catricalà1, Veronica Boschi2, Sofia Cuoco3, Francesco Galiano4, Marina Picillo3, Elena Gobbi5, Antonio Miozzo6, Cristiano Chesi1, Valentina Esposito7, Gabriella Santangelo8, Maria Teresa Pellecchia3, Virginia M Borsa9, Paolo Barone3, Peter Garrard10, Sandro Iannaccone11, Stefano F Cappa12.
Abstract
A progressive speech/language disorder, such as the non fluent/agrammatic variant of primary progressive aphasia and progressive apraxia of speech, can be due to neuropathologically verified Progressive Supranuclear Palsy (PSP). The prevalence of linguistic deficits and the linguistic profile in PSP patients who present primarily with a movement disorder is unknown. In the present study, we investigated speech and language performance in a sample of clinically diagnosed PSP patients using a comprehensive language battery, including, besides traditional language tests, a detailed analysis of connected speech (picture description task assessing 26 linguistic features). The aim was to identify the most affected linguistic levels in seventeen PSP with a movement disorder presentation, compared to 21 patients with Parkinson's disease and 27 healthy controls. Machine learning methods were used to detect the most relevant language tests and linguistic features characterizing the language profile of PSP patients. Our results indicate that even non-clinically aphasic PSP patients have subtle language deficits, in particular involving the lexical-semantic and discourse levels. Patients with the Richardson's syndrome showed a lower performance in the word comprehension task with respect to the other PSP phenotypes with predominant frontal presentation, parkinsonism and progressive gait freezing. The present findings support the usefulness of a detailed language assessment in all patients in the PSP spectrum.Entities:
Keywords: Connected speech; Language; Machine learning; Progressive supranuclear palsy; Richardson's syndrome
Year: 2019 PMID: 30884283 DOI: 10.1016/j.cortex.2019.02.013
Source DB: PubMed Journal: Cortex ISSN: 0010-9452 Impact factor: 4.027