Encoding an event that overlaps with a previous experience may involve reactivating an existing memory and integrating it with new information or suppressing the existing memory to promote formation of a distinct, new representation. We used fMRI during overlapping event encoding to track reactivation and suppression of individual, related memories. We further used a model of semantic knowledge based on Wikipedia to quantify both reactivation of semantic knowledge related to a previous event and formation of integrated memories containing semantic features of both events. Representational similarity analysis revealed that reactivation of semantic knowledge related to a prior event in posterior medial prefrontal cortex (pmPFC) supported memory integration during new learning. Moreover, anterior hippocampus (aHPC) formed integrated representations combining the semantic features of overlapping events. We further found evidence that aHPC integration may be modulated on a trial-by-trial basis by interactions between ventrolateral PFC and anterior mPFC, with suppression of item-specific memory representations in anterior mPFC inhibiting hippocampal integration. These results suggest that PFC-mediated control processes determine the availability of specific relevant memories during new learning, thus impacting hippocampal memory integration.
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10350843 | PMC |
| http://dx.doi.org/10.1093/cercor/bhad179 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Integrating attention mechanism and boundary detection for building segmentation from remote sensing images.
Front Neurorobot
January 2025
College of Artificial Intelligence, Taiyuan University of Technology, Jinzhong, Shanxi, China.
Accurate building segmentation has become critical in various fields such as urban management, urban planning, mapping, and navigation. With the increasing diversity in the number, size, and shape of buildings, convolutional neural networks have been used to segment and extract buildings from such images, resulting in increased efficiency and utilization of image features. We propose a building semantic segmentation method to improve the traditional Unet convolutional neural network by integrating attention mechanism and boundary detection.
View Article and Find Full Text PDFAdvancing presurgical non-invasive spread through air spaces prediction in clinical stage IA lung adenocarcinoma using artificial intelligence and CT signatures.
Front Surg
January 2025
Department of Thoracic Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Background: To accurately identify spread through air spaces (STAS) in clinical stage IA lung adenocarcinoma, our study developed a non-invasive and interpretable biomarker combining clinical and radiomics features using preoperative CT.
Methods: The study included a cohort of 1,325 lung adenocarcinoma patients from three centers, which was divided into four groups: a training cohort ( = 930), a testing cohort ( = 238), an external validation 1 cohort ( = 93), and 2 cohort ( = 64). We collected clinical characteristics and semantic features, and extracted radiomics features.
Optical information synthesis, which fuses LiDAR and optical cameras, has the potential for highly detailed 3D representations. However, due to the disparity of information density between point clouds and images, conventional matching methods based on points often lose significant information. To address this issue, we propose a regional matching method to bridge the differences in information density between point clouds and images.
View Article and Find Full Text PDFMultiple token rearrangement Transformer network with explicit superpixel constraint for segmentation of echocardiography.
Med Image Anal
January 2025
General Hospital of the Southern Theatre Command, PLA, Guangzhou, China; The First School of Clinical Medicine, Southern Medical University, Guangzhou, China. Electronic address:
Diagnostic cardiologists have considerable clinical demand for precise segmentation of echocardiography to diagnose cardiovascular disease. The paradox is that manual segmentation of echocardiography is a time-consuming and operator-dependent task. Computer-aided segmentation can reduce the workflow greatly.
View Article and Find Full Text PDFReview of Associations of Diabetes and Insulin Resistance With Brain Health in Three Harmonised Cohort Studies of Ageing and Dementia.
Diabetes Metab Res Rev
January 2025
Rush Alzheimer's Disease Centre, Rush University Medical Center, Chicago, Illinois, USA.
Diabetes increases the risk of dementia, and insulin resistance (IR) has emerged as a potential unifying feature. Here, we review published findings over the past 2 decades on the relation of diabetes and IR to brain health, including those related to cognition and neuropathology, in the Religious Orders Study, the Rush Memory and Aging Project, and the Minority Aging Research Study (ROS/MAP/MARS), three harmonised cohort studies of ageing and dementia at the Rush Alzheimer's Disease Center (RADC). A wide range of participant data, including information on medical conditions such as diabetes and neuropsychological tests, as well as other clinical and laboratory-based data collected annually.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!