Safety learning generates associative links between neutral stimuli and the absence of threat, promoting the inhibition of fear and security-seeking behaviors. Precisely how safety learning is mediated at the level of underlying brain systems, particularly in humans, remains unclear. Here, we integrated a novel Pavlovian conditioned inhibition task with ultra-high field (7 Tesla) fMRI to examine the neural basis of safety learning in 49 healthy participants. In our task, participants were conditioned to two safety signals: a conditioned inhibitor that predicted threat omission when paired with a known threat signal (A+/AX-), and a standard safety signal that generally predicted threat omission (BC-). Both safety signals evoked equivalent autonomic and subjective learning responses but diverged strongly in terms of underlying brain activation ( whole-brain corrected). The conditioned inhibitor was characterized by more prominent activation of the dorsal striatum, anterior insular, and dorsolateral PFC compared with the standard safety signal, whereas the latter evoked greater activation of the ventromedial PFC, posterior cingulate, and hippocampus, among other regions. Further analyses of the conditioned inhibitor indicated that its initial learning was characterized by consistent engagement of dorsal striatal, midbrain, thalamic, premotor, and prefrontal subregions. These findings suggest that safety learning via conditioned inhibition involves a distributed cortico-striatal circuitry, separable from broader cortical regions involved with processing standard safety signals (e.g., CS). This cortico-striatal system could represent a novel neural substrate of safety learning, underlying the initial generation of "stimulus-safety" associations, distinct from wider cortical correlates of safety processing, which facilitate the behavioral outcomes of learning. Identifying safety is critical for maintaining adaptive levels of anxiety, but the neural mechanisms of human safety learning remain unclear. Using 7 Tesla fMRI, we compared learning-related brain activity for a conditioned inhibitor, which actively predicted threat omission, and a standard safety signal (CS), which was passively unpaired with threat. The inhibitor engaged an extended circuitry primarily featuring the dorsal striatum, along with thalamic, midbrain, and premotor/PFC regions. The CS exclusively involved cortical safety-related regions observed in basic safety conditioning, such as the vmPFC. These findings extend current models to include learning-specific mechanisms for encoding stimulus-safety associations, which might be distinguished from expression-related cortical mechanisms. These insights may suggest novel avenues for targeting dysfunctional safety learning in psychopathology.
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http://dx.doi.org/10.1523/JNEUROSCI.2181-21.2022 | DOI Listing |
PLoS Comput Biol
January 2025
Department of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, China.
Transfer learning aims to integrate useful information from multi-source datasets to improve the learning performance of target data. This can be effectively applied in genomics when we learn the gene associations in a target tissue, and data from other tissues can be integrated. However, heavy-tail distribution and outliers are common in genomics data, which poses challenges to the effectiveness of current transfer learning approaches.
View Article and Find Full Text PDFAdv Skin Wound Care
January 2025
At the Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hempstead, New York, United States, Adrian Chen, BS, Aleksandra Qilleri, BS, and Timothy Foster, BS, are Medical Students. Amit S. Rao, MD, is Project Manager, Department of Surgery, Wound Care Division, Northwell Wound Healing Center and Hyperbarics, Northwell Health, Hempstead. Sandeep Gopalakrishnan, PhD, MAPWCA, is Associate Professor and Director, Wound Healing and Tissue Repair Analytics Laboratory, School of Nursing, College of Health Professions, University of Wisconsin-Milwaukee. Jeffrey Niezgoda, MD, MAPWCA, is Founder and President Emeritus, AZH Wound Care and Hyperbaric Oxygen Therapy Center, Milwaukee, and President and Chief Medical Officer, WebCME, Greendale, Wisconsin. Alisha Oropallo, MD, is Professor of Surgery, Donald and Barbara Zucker School of Medicine and The Feinstein Institutes for Medical Research, Manhasset New York; Director, Comprehensive Wound Healing Center, Northwell Health; and Program Director, Wound and Burn Fellowship program, Northwell Health.
Generative artificial intelligence (AI) models are a new technological development with vast research use cases among medical subspecialties. These powerful large language models offer a wide range of possibilities in wound care, from personalized patient support to optimized treatment plans and improved scientific writing. They can also assist in efficiently navigating the literature and selecting and summarizing articles, enabling researchers to focus on impactful studies relevant to wound care management and enhancing response quality through prompt-learning iterations.
View Article and Find Full Text PDFJ Alzheimers Dis
January 2025
Department of Neurology and the Franke Barrow Global Neuroscience Education Center, Barrow Neurological Institute, Phoenix, AZ, USA.
Background: The aim of this study was to examine the potential added value of including neuropsychiatric symptoms (NPS) in machine learning (ML) models, along with demographic features and Alzheimer's disease (AD) biomarkers, to predict decline or non-decline in global and domain-specific cognitive scores among community-dwelling older adults.
Objective: To evaluate the impact of adding NPS to AD biomarkers on ML model accuracy in predicting cognitive decline among older adults.
Methods: The study was conducted in the setting of the Mayo Clinic Study of Aging, including participants aged ≥ 50 years with information on demographics (i.
Hawaii J Health Soc Welf
January 2025
Office of Medical Education, John A. Burns School of Medicine, University of Hawai'i, Honolulu, HI (SFTF).
The transition to virtual learning formats during the COVID-19 pandemic necessitated substantial curricular adjustments to the University of Hawai'i John A. Burns School of Medicine. This study compares student satisfaction and academic performance between the pre-pandemic (up through March 25, 2020) and pandemic (after March 25, 2020) periods.
View Article and Find Full Text PDFClin Pharmacol Ther
January 2025
Drug Clinical Trial Center, Peking University Third Hospital, Beijing, China.
OATP1B, P-gp, BCRP, and CYP3A are the most contributing drug-metabolizing enzymes or transporters (DMETs) for commonly prescribed medication. Their activities may change in end-stage renal disease (ESRD) patients with large inter-individual variabilities (IIVs), leading to altered substrate drug exposure and ultimately elevated safety risk. However, the changing extent and indictive influencing factors are not quantified so far.
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