98%
921
2 minutes
20
Introduction: Functional brain templates are often used in the analysis of clinical functional MRI (fMRI) studies. However, these templates are mostly built based on anatomy or fMRI of healthy subjects, which have not been fully vetted in clinical cohorts. Our aim was to evaluate language templates by comparing with primary language areas (PLAs) detected from presurgical fMRI of brain tumor patients.
Methods: Four language templates (A-D) based on anatomy, task-based fMRI, resting-state fMRI, and meta-analysis, respectively, were compared with PLAs detected by fMRI with word generation and sentence completion paradigms. For each template, the fraction of PLA activations enclosed by the template (positive inclusion fraction, [PIF]), the fraction of activations within the template but that did not belong to PLAs (false inclusion fraction, [FIF]), and their Dice similarity coefficient (DSC) with PLA activations were calculated.
Results: For anterior PLAs, Template A had the greatest PIF (median, 0.95), whereas Template D had both the lowest FIF (median, 0.074), and the highest DSC (median, 0.30), which were all significant compared to other templates. For posterior PLAs, Templates B and D had similar PIF (median, 0.91 and 0.90, respectively) and DSC (both medians, 0.059), which were all significantly higher than that of Template C. Templates B and C had significantly lower FIF (median, 0.061 and 0.054, respectively) compared to Template D.
Conclusion: This study demonstrated significant differences between language templates in their inclusiveness of and spatial agreement with the PLAs detected in the presurgical fMRI of the patient cohort. These findings may help guide the selection of language templates tailored to their applications in clinical fMRI studies.
Download full-text PDF |
Source |
---|---|
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11186848 | PMC |
http://dx.doi.org/10.1002/brb3.3497 | DOI Listing |
Cochrane Database Syst Rev
September 2025
Department of Family Medicine, University of Alberta, Edmonton, Alberta, Canada.
Background: Opioid use disorder (OUD) is commonly treated in specialized care settings with long-acting opioid agonists, also known as opioid agonist therapy, or OAT. Despite the rise in opioid use globally and evidence for a 50% reduction in mortality when OAT is employed, the proportion of people with OUD receiving OAT remains small. One initiative to improve the access and uptake of OAT could be to offer OAT in a primary care setting; primary care clinics are more numerous, might reduce the visibility and potential stigma of receiving treatment for OUD, and may facilitate the care of other medical conditions that are unrelated to OUD.
View Article and Find Full Text PDFMicrobiol Spectr
September 2025
Microbiome-X, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, China.
Novel antimicrobial agents are urgently needed to combat the antibiotic-resistance crisis, particularly in the face of multidrug-resistant (MDR) pathogens like carbapenem-resistant (CRAB) and methicillin-resistant (MRSA). In this study, we present an approach that combines generative large language model with sequence alignment to identify promising antimicrobial peptides. With this strategy, we rapidly identified five novel encrypted peptides based on a generated template, demonstrating significant antimicrobial activity against a broad spectrum of clinical MDR pathogens.
View Article and Find Full Text PDFJ Biomed Inform
August 2025
School of Electronic and Electrical Engineering, Shanghai University of Engineering Science, Shanghai 201620, China. Electronic address:
The monitoring and analysis of adverse drug reactions (ADRs ) are important for ensuring patient safety and improving treatment outcomes. Accurate identification of drug names, drug components, and ADR entities during named entity recognition (NER) processes is essential for ensuring drug safety and advancing the integration of drug information. Given that existing medical name entity recognition technologies rely on large amounts of manually annotated data for training, they are often less effective when applied to adverse drug reactions due to significant data variability and the high similarity between drug names.
View Article and Find Full Text PDFNurs Rep
July 2025
McAuley School of Nursing, University of Detroit Mercy, Detroit, MI 48221, USA.
The integration of AI chatbots in nursing education, particularly in simulation-based learning, is advancing rapidly. However, there is a lack of structured evaluation models, especially to assess AI-generated simulations. This article introduces the AI-Integrated Method for Simulation (AIMS) evaluation framework, a dual-phase evaluation framework adapted from the FAITA model, designed to evaluate both prompt design and chatbot performance in the context of nursing education.
View Article and Find Full Text PDFInterface Focus
August 2025
Institut Curie, Université PSL, 75005 Paris, France.
This tutorial provides stepwise instructions to install over 20 tools, written in multiple languages. Their integration in the software suite makes them accessible with a single popular language (), thereby enabling reproducible and sophisticated dynamical analyses of logical models of complex cellular networks. The tutorial specifically focuses on the analysis of a previously published model of the regulatory network controlling mammalian cell proliferation.
View Article and Find Full Text PDF