Comparing sequential stimuli is crucial for guiding complex behaviors. To understand mechanisms underlying sequential decisions, we compared neuronal responses in the prefrontal cortex (PFC), the lateral intraparietal (LIP), and medial intraparietal (MIP) areas in monkeys trained to decide whether sequentially presented stimuli were from matching (M) or nonmatching (NM) categories. We found that PFC leads M/NM decisions, whereas LIP and MIP appear more involved in stimulus evaluation and motor planning, respectively. Compared to LIP, PFC showed greater nonlinear integration of currently visible and remembered stimuli, which correlated with the monkeys' M/NM decisions. Furthermore, multi-module recurrent networks trained on the same task exhibited key features of PFC and LIP encoding, including nonlinear integration in the PFC-like module, which was causally involved in the networks' decisions. Network analysis found that nonlinear units have stronger and more widespread connections with input, output, and within-area units, indicating putative circuit-level mechanisms for sequential decisions.
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http://dx.doi.org/10.7554/eLife.58782 | DOI Listing |
J Med Internet Res
January 2025
Division of Clinical Pathology, Department of Pathology, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan.
Background: Sepsis, a critical global health challenge, accounted for approximately 20% of worldwide deaths in 2017. Although the Sequential Organ Failure Assessment (SOFA) score standardizes the diagnosis of organ dysfunction, early sepsis detection remains challenging due to its insidious symptoms. Current diagnostic methods, including clinical assessments and laboratory tests, frequently lack the speed and specificity needed for timely intervention, particularly in vulnerable populations such as older adults, intensive care unit (ICU) patients, and those with compromised immune systems.
View Article and Find Full Text PDFBrain Sci
January 2025
Department of Biostructure, Wroclaw University of Health and Sport Sciences, 51-612 Wroclaw, Poland.
Objectives: This study analyzed the effects of parachute jump stress on the executive functions and attention of cadets. Executive functions, which includes processes such as attentional control and cognitive flexibility, are crucial for soldiers, especially in situations requiring rapid decision-making. Parachute jumping, as an intense stressor, mobilizes cognitive resources, which can lead to short-term improvements in executive functions.
View Article and Find Full Text PDFFront Pharmacol
January 2025
Institute of Pharmacology and the Gaston H. Glock Research Laboratories for Exploratory Drug Development, Centre of Physiology and Pharmacology, Medical University of Vienna, Vienna, Austria.
Objective: The expanding field of hematopoietic cell transplantation (HCT) for non-malignant diseases, including those amenable to gene therapy or gene editing, faces challenges due to limited donor availability and the toxicity associated with cell collection methods. Umbilical cord blood (CB) represents a readily accessible source of hematopoietic stem and progenitor cells (HSPCs); however, the cell dose obtainable from a single cord blood unit is frequently insufficient. This limitation can be addressed by enhancing the potency of HSPCs, specifically their capacity to reconstitute hematopoiesis.
View Article and Find Full Text PDFComput Biol Med
January 2025
Department of Biomedical Engineering, Islamic University, Kushtia, 7003, Bangladesh; Bio-Imaging Research Laboratory, Islamic University, Kushtia, 7003, Bangladesh. Electronic address:
Computed tomography (CT) scans play a key role in the diagnosis of stroke, a leading cause of morbidity and mortality worldwide. However, interpreting these scans is often challenging, necessitating automated solutions for timely and accurate diagnosis. This research proposed a novel hybrid model that integrates a Vision Transformer (ViT) and a Long Short Term Memory (LSTM) to accurately detect and classify stroke characteristics using CT images.
View Article and Find Full Text PDFComput Biol Med
January 2025
University of Rwanda, Rwanda. Electronic address:
Deep learning methods have significantly improved medical image analysis, particularly in detecting COVID-19 chest X-rays. Nonetheless, these methodologies frequently inhibit some drawbacks, such as limited interpretability, extensive computational resources, and the need for extensive datasets. To tackle these issues, we introduced two novel algorithms: the Dynamic Co-Occurrence Grey Level Matrix (DC-GLM) and the Contextual Adaptation Multiscale Gabor Network (CAMSGNeT).
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