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The core use of human language is communicating complex ideas from one mind to another in everyday conversations. In conversations, comprehension and production processes are intertwined, as speakers soon become listeners, and listeners become speakers. Nonetheless, the neural systems underlying these faculties are typically studied in isolation using paradigms that cannot fully engage our capacity for interactive communication. Here, we used an fMRI hyperscanning paradigm to measure neural activity simultaneously in pairs of subjects engaged in real-time, interactive conversations. We used contextual word embeddings from a large language model to quantify the linguistic coupling between production and comprehension systems within and across individual brains. We found a highly overlapping network of regions involved in both production and comprehension spanning much of the cortical language network. Our findings reveal that shared representations for both processes extend beyond the language network into areas associated with social cognition. Together, these results suggest that the specialized neural systems for speech perception and production align on a common set of linguistic features encoded in a broad cortical network for language and communication.
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http://dx.doi.org/10.1101/2025.02.14.638276 | DOI Listing |
BMC Musculoskelet Disord
September 2025
Department of Clinical Sciences at Danderyds Hospital, Department of Orthopedic Surgery, Karolinska Institutet, Stockholm, 182 88, Sweden.
Background: This study evaluates the accuracy of an Artificial Intelligence (AI) system, specifically a convolutional neural network (CNN), in classifying elbow fractures using the detailed 2018 AO/OTA fracture classification system.
Methods: A retrospective analysis of 5,367 radiograph exams visualizing the elbow from adult patients (2002-2016) was conducted using a deep neural network. Radiographs were manually categorized according to the 2018 AO/OTA system by orthopedic surgeons.
Mol Psychiatry
September 2025
Department of Psychiatry and Behavioral Neuroscience, University of Chicago, Chicago, IL, USA.
Stimulant Use Disorder (StUD) is a pervasive and extremely dangerous form of addiction for which there are currently no approved medications. Discovering treatments will require a deep understanding of the neural mechanisms underlying the behavioral effects of stimulant drugs. A major target is the mesocorticolimbic system.
View Article and Find Full Text PDFArch Cardiovasc Dis
September 2025
Department of Orthopaedics, Shaoxing Keqiao Women & Children's Hospital, Shaoxing 312030, Zhejiang, China. Electronic address:
Background: Sacubitril/valsartan is a widely used cardiovascular agent characterized by its dual inhibition of the renin-angiotensin-aldosterone system and neprilysin. However, existing evidence on the safety of sacubitril/valsartan is primarily limited to clinical studies; this results in an inability to provide a timely update on associated adverse events.
Aim: To mine and systematically describe adverse events related to sacubitril/valsartan from September 2015 to June 2024 using the Food and Drug Administration Adverse Event Reporting System (FAERS) database.
Eur J Neurosci
September 2025
The Tampa Human Neurophysiology Lab, Department of Neurosurgery, Brain and Spine, Morsani College of Medicine, University of South Florida, Tampa, Florida, USA.
Sensory areas exhibit modular selectivity to stimuli, but they can also respond to features outside of their basic modality. Several studies have shown cross-modal plastic modifications between visual and auditory cortices; however, the exact mechanisms of these modifications are yet not completely known. To this aim, we investigated the effect of 12 min of visual versus sound adaptation (referring to forceful application of an optimal/nonoptimal stimulus to a neuron[s] under observation) on the infragranular and supragranular primary visual neurons (V1) of the cat (Felis catus).
View Article and Find Full Text PDFMed Eng Phys
October 2025
Biomedical Device Technology, Istanbul Aydın University, Istanbul, 34093, Istanbul, Turkey. Electronic address:
Deep learning approaches have improved disease diagnosis efficiency. However, AI-based decision systems lack sufficient transparency and interpretability. This study aims to enhance the explainability and training performance of deep learning models using explainable artificial intelligence (XAI) techniques for brain tumor detection.
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