Background: Neuronal plasticity within the basolateral amygdala (BLA) is fundamental for fear learning. Metaplasticity, the regulation of plasticity states, has emerged as a key mechanism mediating the subsequent impact of emotional and stressful experiences. After mRNA knockdown of synaptic plasticity-related TrkB, we examined the impact of chronically altered activity in the rat BLA (induced metaplasticity) on anxiety-like behavior, fear memory-related behaviors, and neural plasticity in brain regions modulated by the BLA. These effects were investigated under both basal conditions and following exposure to acute trauma (UWT).
Results: Under basal conditions, TrkB knockdown increased anxiety-like behavior and impaired extinction learning. TrkBKD also reduced LTP in the vSub-mPFC pathway but not in the dentate gyrus. Compared with those of control animals, acute trauma exposure led to increased anxiety-like behavior and impaired extinction learning in both the trauma-exposed group (CTR-UWT) and the trauma-exposed group on the background of TrkB knockdown (TrkBKD-UWT). However, the deficit in extinction learning was more pronounced in the TrkBKD-UWT group than in the CTR-UWT group. Accordingly, TrkBKD-UWT, but not CTR-UWT, resulted in impaired LTP in the vSub- mPFC pathway. Since LTP in this pathway is independent of BLA involvement, this result suggests that lasting intra-BLA-induced metaplasticity may also lead to transregional metaplasticity within the mPFC, as suggested previously.
Conclusions: Taken together, these findings reveal the dissociative involvement of BLA function, on the one hand, in anxiety, which is affected by the knockdown of TrkB, and, on the other hand, in extinction learning, which is more significantly affected by the combination of intra-BLA-induced metaplasticity and exposure to emotional trauma.
Download full-text PDF |
Source |
---|---|
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11874401 | PMC |
http://dx.doi.org/10.1186/s12993-025-00267-0 | DOI Listing |
Camb Prism Extinct
September 2024
School of English, School of Earth and Environment, University of Leeds, Leeds, UK.
The Scottish Small Isles - comprising Muck, Rùm, Eigg, Canna, and by extension, Coll - are geologically complex, with intersecting rock samples from the Archean (Lewisian Gneiss basements formed approximately 3 billion years ago), Proterozoic (Torridonian sandstone formed approximately 1 billion years ago), Mesozoic (sedimentation deposited approximately 200 million years ago) and Palaeocene (basalt formed approximately 55.8 million years ago as part of the Palaeocene-Eocene Thermal Maximum event). This practice research article - drawing on palaeontology, kinaesthetic learning and creative writing - takes the Small Isles as a case study for what geologist Marcia Bjornerud defines as a discernible "timeness" that humans should seek to attain: "an acute consciousness of how the world is made by-indeed, made of-time" (2020, , 5).
View Article and Find Full Text PDFJ Chem Inf Model
March 2025
Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai 200237, China.
With the rapid advancements in the field of fluorescent dyes, accurate prediction of optical properties and efficient retrieval of dye-related data are essential for effective dye design. However, there is a lack of tools for comprehensive data integration and convenient data retrieval. Moreover, existing prediction models mainly focus on a single property of fluorescent dyes and fail to account for the diverse fluorophores and solutions in a systematic manner.
View Article and Find Full Text PDFNanomicro Lett
March 2025
State Key Laboratory of Tribology in Advanced Equipment, Tsinghua University, Beijing, 100084, People's Republic of China.
Photonic computing has emerged as a promising technology for the ever-increasing computational demands of machine learning and artificial intelligence. Due to the advantages in computing speed, integrated photonic chips have attracted wide research attention on performing convolutional neural network algorithm. Programmable photonic chips are vital for achieving practical applications of photonic computing.
View Article and Find Full Text PDFFront Vet Sci
February 2025
Canine Performance Sciences, College of Veterinary Medicine, Auburn University, Auburn, AL, United States.
Training detection dogs to alert to an odor requires precision in the timing and delivery of stimulus presentations in order to condition a strong association between odor and reward and to train a desired alert behavior that communicates the presence and location of the odor source. Marker training, in which a signal that predicts a reward is used to deliver immediate feedback for a correct response and bridge the delay between the desired behavior and reward, is a popular technique in the animal training industry. However, the application of marker training to detection dog training has not been examined, and empirical evidence of the purported benefits of marker training in general is lacking.
View Article and Find Full Text PDFLearning when to initiate or withhold actions is essential for survival and requires integration of past experiences with new information to adapt to changing environments. While stable prelimbic cortex (PL) ensembles have been identified during reward learning, it remains unclear how they adapt when contingencies shift. Does the same ensemble adjust its activity to support behavioral suppression upon reward omission, or is a distinct ensemble recruited for this new learning? We used single-cell calcium imaging to longitudinally track PL neurons in rats across operant food reward Training, Extinction and Reinstatement, trained rat-specific decoders to predict trial-wise behavior, and implemented an deletion approach to characterize ensemble contributions to behavior.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!