RESUMO
Around 80% of persons with Parkinson's disease (PD) present symptoms of dysphagia. Although cognitive impairment may contribute to dysphagia, few studies have investigated the association between the PD neuropsychological profile and objective measures of swallowing dysfunction. Since the swallowing function comprises involuntary but also voluntary actions, we hypothesize that specific measures of attention and executive functions can be underlined in PD-related dysphagia. Therefore, the aim of this study was to extensively investigate the correlation and the relationship between attentive and executive functions and safety/efficiency of pharyngeal phase of swallowing in people with PD. All participants received a fiberoptic endoscopic evaluation of swallowing and were evaluated using the Penetration Aspiration Scale (PAS); the Yale Pharyngeal Residue Severity Rating Scale (IT-YPRSRS), and the Functional Oral Intake Scale (FOIS-IT). Participants also underwent a neuropsychological assessment covering global cognitive status, attention, and frontal executive functions. Correlations and associations between neuropsychological measures and swallowing components were calculated. Twenty-one participants with PD (mean age 69.38 ± 6.58 years, mean disease duration 8.38 ± 5.31 years; mean MDS-UPDRS III 43.95 ± 24.18) completed all evaluations. The most significant correlations were found between attentive functions (i.e., Stroop Time), and executive functions (i.e., Raven's Progressive Matrices, Digit Backward and Semantic Fluency), and FOIS-IT, PAS, and IT-YPRSRS sinuses and valleculae. These associations were not influenced by disease duration. These results suggest that a dysfunction to attentional processes and/or to executive functions can contribute to penetration and the presence of pharyngeal residue in participants with middle-stage PD.
RESUMO
While extensive research has documented the cognitive changes associated with Parkinson's disease (PD), a relatively small portion of the empirical literature investigated the language abilities of individuals with PD. Recently, artificial intelligence applied to linguistic data has shown promising results in predicting the clinical diagnosis of neurodegenerative disorders, but a deeper investigation of the current literature available on PD is lacking. This systematic review investigates the nature of language disorders in PD by assessing the contribution of machine learning (ML) to the classification of patients with PD. A total of 10 studies published between 2016 and 2023 were included in this review. Tasks used to elicit language were mainly structured or unstructured narrative discourse. Transcriptions were mostly analyzed using Natural Language Processing (NLP) techniques. The classification accuracy (%) ranged from 43 to 94, sensitivity (%) ranged from 8 to 95, specificity (%) ranged from 3 to 100, AUC (%) ranged from 32 to 97. The most frequent optimal linguistic measures were lexico-semantic (40%), followed by NLP-extracted features (26%) and morphological consistency features (20%). Artificial intelligence applied to linguistic markers provides valuable insights into PD. However, analyzing measures derived from narrative discourse can be time-consuming, and utilizing ML requires specialized expertise. Moving forward, it is important to focus on facilitating the integration of both narrative discourse analysis and artificial intelligence into clinical practice.
