In this study the relationship between language skill and frontotemporal volumes was compared in 69 medically treated subjects with epilepsy and 34 healthy children, aged 6.1-16.6 years. Also, whether patients with linguistic deficits had abnormal volumes and atypical associations between volumes and language skills in these brain regions was determined. The children underwent language testing and MRI scans at 1.5 T. Brain tissue was segmented and frontotemporal volumes were computed. Higher mean language scores were significantly associated with larger inferior frontal gyrus, temporal lobe, and posterior superior temporal gyrus gray matter volumes in the epilepsy group and in the children with epilepsy with average language scores. Increased total brain and dorsolateral prefrontal gray and white matter volumes, however, were associated with higher language scores in the healthy controls. Within the epilepsy group, linguistic deficits were related to smaller anterior superior temporal gyrus gray matter volumes and there was a negative association between language scores and dorsolateral prefrontal gray matter volumes. These findings demonstrate abnormal development of language-related brain regions, and imply differential reorganization of brain regions subserving language in children with epilepsy with normal linguistic skills and in those with impaired language.
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http://dx.doi.org/10.1016/j.yebeh.2010.01.009 | DOI Listing |
Int J Lang Commun Disord
January 2025
Division of Communication Sciences and Disorders, University of Cape Town, Rondebosch, South Africa.
Background: There is a global need for synthetic speech development in multiple languages and dialects, as many children who cannot communicate using their natural voice struggle to find synthetic voices on high-technology devices that match their age, social and linguistic background.
Aims: To document multiple stakeholders' perspectives surrounding the quality, acceptability and utility of newly created synthetic speech in three under-resourced South African languages, namely South African English, Afrikaans and isiXhosa.
Methods & Procedures: A mixed methods research design was selected.
Turk Kardiyol Dern Ars
January 2025
Department of Cardiology, Dr Siyami Ersek Thoracic and Cardiovascular Surgery Training Hospital, İstanbul, Türkiye.
Objective: Coronary artery disease (CAD) is the leading cause of morbidity and mortality globally. The growing interest in natural language processing chatbots (NLPCs) has driven their inevitable widespread adoption in healthcare. The purpose of this study was to evaluate the accuracy and reproducibility of responses provided by NLPCs, such as ChatGPT, Gemini, and Bing, to frequently asked questions about CAD.
View Article and Find Full Text PDFSensors (Basel)
December 2024
College of Automotive Engineering, Jilin University, Changchun 130025, China.
The cockpit is evolving from passive, reactive interaction toward proactive, cognitive interaction, making precise predictions of driver intent a key factor in enhancing proactive interaction experiences. This paper introduces Cockpit-Llama, a novel language model specifically designed for predicting driver behavior intent. Cockpit-Llama predicts driver intent based on the relationship between current driver actions, historical interactions, and the states of the driver and cockpit environment, thereby supporting further proactive interaction decisions.
View Article and Find Full Text PDFNutrients
December 2024
Department of Preventive Medicine and Public Health, School of Medicine, University of Valencia, 46010 Valencia, Spain.
Background And Objectives: Depression often results in premature aging, which increases the risk of other chronic diseases, but very few studies have analyzed the association between epigenetic biomarkers of aging and depressive symptoms. Similarly, limited research has examined the joint effects of adherence to the Mediterranean diet (MedDiet) and chronotype on depressive symptoms, accounting for sex differences. Therefore, these are the objectives of our investigation in a Mediterranean population at high cardiovascular risk.
View Article and Find Full Text PDFNeurosurg Rev
January 2025
Department of Neurosurgery, University Hospital Leipzig, Leipzig, Saxony, Germany.
To assess the predictive accuracy of advanced AI language models and established clinical scales in prognosticating outcomes for patients with aneurysmal subarachnoid hemorrhage (aSAH). This retrospective cohort study included 82 patients suffering from aSAH. We evaluated the predictive efficacy of AtlasGPT and ChatGPT 4.
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