Purpose: L-dopa has been shown to improve outcome of moderate-intensity language training after stroke in acute aphasia. Given the critical role of training intensity we probed the effect of l-dopa in combination with high-intensity language training in chronic post-stroke aphasia.
Methods: In this prospective, randomized, placebo-controlled, double-blind study, aphasia patients (>1 year post stroke) were administered 100/25 mg of l-dopa/carbidopa or placebo daily prior to four hours of language training for two weeks. Conditions were crossed-over after a wash-out period of 4 weeks.
Results: An a-priori planned interim analysis (n = 10) showed that naming performance and verbal communication improved significantly and persistently for at least 6 months in every patient, but l-dopa had no incremental effect to intensive training.
Conclusion: High-intensity language training in chronic aphasia may take learning to a ceiling that precludes additive benefits from l-dopa. Effects of l-dopa on post-stroke recovery during less intense treatment in chronic aphasia remain to be evaluated.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.3233/RNN-140435 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Swallowing, speech and voice impairments in head and neck cancer patients treated at a multidisciplinary integrated patient unit.
Int J Lang Commun Disord
December 2024
Hearing, Speech & Language Center, Sheba Medical Center, Tel Hashomer, Israel.
Background: Head and neck cancer (HNC) is amongst the 10 most common cancers worldwide and has a major effect on patients' quality of life. Given the complexity of this unique group of patients, a multidisciplinary team approach is preferable. Amongst the debilitating sequels of HNC and/or its treatment, swallowing, speech and voice impairments are prevalent and require the involvement of speech-language pathologists (SLPs).
View Article and Find Full Text PDFInformation extraction from green channel textual records on expressways using hybrid deep learning.
Sci Rep
December 2024
Xi'an Shiyou University School of Electronic Engineering, Xi'an, 710065, China.
The expressway green channel is an essential transportation policy for moving fresh agricultural products in China. In order to extract knowledge from various records, this study presents a cutting-edge approach to extract information from textual records of failure cases in the vertical field of expressway green channel. We proposed a hybrid approach based on BIO labeling, pre-trained model, deep learning and CRF to build a named entity recognition (NER) model with the optimal prediction performance.
View Article and Find Full Text PDFEvent co-occurrences for prompt-based generative event argument extraction.
Sci Rep
December 2024
School of Computer Science and Technology (School of Cyberspace Security), Xinjiang University, Urumqi, 830046, China.
Recent works have introduced prompt learning for Event Argument Extraction (EAE) since prompt-based approaches transform downstream tasks into a more consistent format with the training task of Pre-trained Language Model (PLM). This helps bridge the gap between downstream tasks and model training. However, these previous works overlooked the complex number of events and their relationships within sentences.
View Article and Find Full Text PDFComprehension of acoustically degraded emotional prosody in Alzheimer's disease and primary progressive aphasia.
Sci Rep
December 2024
Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, University College London, 1st Floor, 8-11 Queen Square, London, WC1N 3AR, UK.
Previous research suggests that emotional prosody perception is impaired in neurodegenerative diseases like Alzheimer's disease (AD) and primary progressive aphasia (PPA). However, no previous research has investigated emotional prosody perception in these diseases under non-ideal listening conditions. We recruited 18 patients with AD, and 31 with PPA (nine logopenic (lvPPA); 11 nonfluent/agrammatic (nfvPPA) and 11 semantic (svPPA)), together with 24 healthy age-matched individuals.
View Article and Find Full Text PDFThe aluminum standard: using generative Artificial Intelligence tools to synthesize and annotate non-structured patient data.
BMC Med Inform Decis Mak
December 2024
Klinikum Stuttgart, Stuttgart Cancer Center - Tumorzentrum Eva Mayr-Stihl DE, Kriegsbergstraße 60, Stuttgart, D-70174, Germany.
Background: Medical narratives are fundamental to the correct identification of a patient's health condition. This is not only because it describes the patient's situation. It also contains relevant information about the patient's context and health state evolution.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!