Previous studies have suggested that recovery or compensation of language function after a lesion in the left hemisphere may depend on mechanisms in the right hemisphere. However, a direct relationship between performance and right hemisphere activity has not been established. Here, we show that patients with left frontal lesions and partially recovered aphasia learn, at a normal rate, a novel word retrieval task that requires the damaged cortex. Verbal learning is accompanied by specific response decrements in right frontal and right occipital cortex, strongly supporting the compensatory role of the right hemisphere. Furthermore, responses in left occipital cortex are abnormal and not modulated by practice. These findings indicate that frontal cortex is a source of top-down signals during learning.
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http://dx.doi.org/10.1016/s0896-6273(02)00936-4 | DOI Listing |
Sci Rep
January 2025
School of Economics and Management, Beijing jiaotong University, Shandong, 264401, China.
Text Graph Representation Learning through Graph Neural Networks (TG-GNN) is a powerful approach in natural language processing and information retrieval. However, it faces challenges in computational complexity and interpretability. In this work, we propose CoGraphNet, a novel graph-based model for text classification, addressing key issues.
View Article and Find Full Text PDFFront Behav Neurosci
December 2024
Basque Center on Cognition, Brain and Language (BCBL), Donostia-San Sebastián, Spain.
Psychol Res
December 2024
Dpto Metodología and ERI-Lectura, Universitat de València, Av. Blasco Ibáñez, 21; 46010, Valencia, Spain.
Brand names typically maintain a distinctive letter case (e.g., IKEA, Google).
View Article and Find Full Text PDFSci Rep
December 2024
Department of CSE, Adama Science and Technology University, Oromia, Ethiopia.
Afaan Oromo is a resource-scarce language with limited tools developed for its processing, posing significant challenges for natural language tasks. The tools designed for English do not work efficiently for Afaan Oromo due to the linguistic differences and lack of well-structured resources. To address this challenge, this work proposes a topic modeling framework for unstructured health-related documents in Afaan Oromo using latent dirichlet allocation (LDA) algorithms.
View Article and Find Full Text PDFEur J Phys Rehabil Med
December 2024
Laboratory of Neuropsychology, Department of Neurorehabilitation Sciences, IRCCS Istituto Auxologico Italiano, Milan, Italy.
Background: The defective spoken output of persons with aphasia has anomia as a main clinical manifestation. Improving anomia is therefore a main goal of any language treatment.
Aim: This study assessed the effectiveness of a novel, 2-week, rehabilitation protocol (PHOLEXSEM), focused on PHonological, SEmantic, and LExical deficits, aiming at improving lexical retrieval, and, generally, spoken output.
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