Although rapid automatized naming (RAN) of letters, digits, and objects are popular tasks and have been used interchangeably to predict academic performance, it remains unknown if they tap into the same neural regions. Thus, the purpose of this study was to examine the neural overlap across different RAN tasks. Fifteen university students were assessed on RAN digits, letters, and objects using functional magnetic resonance imaging (fMRI). Results showed a common neural pattern that included regions related to motor planning (e.g., cerebellum), semantic access (middle temporal gyrus), articulation (supplementary motor association, motor/pre-motor, anterior cingulate cortex), and grapheme-phoneme mapping (ventral supramarginal gyrus). However, RAN digits and letters showed many unique regions of activation over and above RAN objects particularly in semantic and articulatory regions, including precuneus, bilateral supramarginal gyrus, nucleus accumbens and thalamus. The only region unique to RAN objects included bilateral fusiform, a region commonly implicated in object processing. Overall, our results provide the first neural evidence for a stronger relationship between RAN letters and digits than when either task is compared to RAN objects.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1016/j.bbr.2014.08.038 | DOI Listing |
Hand Surg Rehabil
January 2025
Service de Chiurgie Orthopédique CHU BREST Boulevard Tanguy Prigent 29200 Brest. Electronic address:
We report a case of CAF in the hand of a young adult woman. This patient's age exceeded the usual range for CAF. While surgical excision led to fifth digit stiffness and bowstringing, further diagnostic delays could have resulted in much worse outcomes due to tumor invasion of the tendons.
View Article and Find Full Text PDFHand Surg Rehabil
January 2025
Orthopaedic Surgery Department, Kaplan Medical Center, 1st Pasternak Road, Rehovot, 7661041, Israel; Hebrew University of Jerusalem, Faculty of Medicine, Campus Ein Carem, Jerusalem, 9112102, Israel.
NPJ Digit Med
January 2025
Graduate School of Public Health, St. Luke's International University, Tokyo, Japan.
Electronic health records (EHRs) secondary usage with large language models (LLMs) raise privacy challenges. National regulations like GDPR and HIPAA offer protection frameworks, but specific strategies are needed to mitigate risk in generative AI. Risks can be reduced by using strategies like privacy-preserving locally deployed LLMs, synthetic data generation, differential privacy, and deidentification.
View Article and Find Full Text PDFNPJ Digit Med
January 2025
Center for Advanced Studies in Bioscience Innovation Law (CeBIL), Faculty of Law, University of Copenhagen, Copenhagen, Denmark.
Can artificial intelligence improve clinical trial design? Despite their importance in medicine, over 40% of trials involve flawed protocols. We introduce and propose the development of application-specific language models (ASLMs) for clinical trial design across three phases: ASLM development by regulatory agencies, customization by Health Technology Assessment bodies, and deployment to stakeholders. This strategy could enhance trial efficiency, inclusivity, and safety, leading to more representative, cost-effective clinical trials.
View Article and Find Full Text PDFMicrosyst Nanoeng
January 2025
State Key Laboratory of Explosion Science and Safety Protection, Beijing Institute of Technology, Ministry of Education, 100081, Beijing, China.
Recently, the biologically inspired intelligent artificial visual neural system has aroused enormous interest. However, there are still significant obstacles in pursuing large-scale parallel and efficient visual memory and recognition. In this study, we demonstrate a 28 × 28 synaptic devices array for the artificial visual neuromorphic system, within the size of 0.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!