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As we learn, dynamic memory processes build structured knowledge across our experiences. Such knowledge enables the formation of internal models of the world that we use to plan, make decisions, and act. Recent theorizing posits that mnemonic mechanisms of differentiation and integration - which at one level may seem to be at odds - both contribute to the emergence of structured knowledge. We tested this possibility using fMRI as human participants learned to navigate within local and global virtual environments over the course of 3 days. Pattern similarity analyses on entorhinal cortical and hippocampal patterns revealed evidence that differentiation and integration work concurrently to build local and global environmental representations, and that variability in integration relates to differences in navigation efficiency. These results offer new insights into the neural machinery and the underlying mechanisms that translate experiences into structured knowledge that allows us to navigate to achieve goals.
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9928422 | PMC |
http://dx.doi.org/10.7554/eLife.80281 | DOI Listing |
JMIR Ment Health
September 2025
Department of Psychology, University of California, Los Angeles, 1285 Franz Hall, Box 951563, Los Angeles, CA, 90095, United States, 1 3107941262.
Background: Youth mental health issues have been recognized as a pressing crisis in the United States in recent years. Effective, evidence-based mental health research and interventions require access to integrated datasets that consolidate diverse and fragmented data sources. However, researchers face challenges due to the lack of centralized, publicly available datasets, limiting the potential for comprehensive analysis and data-driven decision-making.
View Article and Find Full Text PDFJMIR Hum Factors
September 2025
College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, China.
Background: The rapid advancement of next-generation sequencing has significantly expanded the landscape of precision medicine. However, health care professionals face increasing challenges in keeping pace with the growing body of oncological knowledge and integrating it effectively into clinical workflows. Precision oncology decision support (PODS) tools aim to assist clinicians in navigating this complexity, yet their current functionalities only partially address clinical needs.
View Article and Find Full Text PDFPLoS One
September 2025
Department of Internal Medicine, School of Medical Sciences, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana.
In resource-limited settings in Africa, which harbour the greatest burden of Sickle Cell Disease (SCD) globally, poor care outcomes are driven in part, by a lack of trained healthcare providers (HCP) and an absence of context-specific treatment guidelines appropriate to the level of healthcare facility. The study aimed to evaluate the impact of a structured training program on HCP's knowledge of SCD in Ghana. This was prospective cross-sectional study involving HCPs from 46 health facilities from 4 out of 16 regions in Ghana.
View Article and Find Full Text PDFPLoS Biol
September 2025
Center for Neural Science, Department of Biology and Department of Psychology, New York University, New York, New York, United States of America.
Investigating social and independent behavior structure in early life is critical for understanding development and brain maturation in social mammals. However, this investigation necessitates monitoring animals over weeks to months often with subsecond time resolution creating challenges for both lab studies focused on brief observation periods and field studies in which animal tracking can be imprecise. Here we used machine vision and two-week long continuous behavior recordings of families of gerbils, a highly social rodent, in large, undisturbed home environments to quantify the behavioral development of individual pups.
View Article and Find Full Text PDFIEEE Trans Comput Biol Bioinform
September 2025
Artificial intelligence (AI) based anticancer drug recommendation systems have emerged as powerful tools for precision dosing. Although existing methods have advanced in terms of predictive accuracy, they encounter three significant obstacles, including the "black-box" problem resulting in unexplainable reasoning, the computational difficulty for graphbased structures, and the combinatorial explosion during multistep reasoning. To tackle these issues, we introduce a novel Macro-Micro agent Drug sensitivity inference (MarMirDrug).
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