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Coulter ME, Gillespie AK, Chu J, Denovellis EL, Nguyen TTK, Liu DF, Wadhwani K, Sharma B, Wang K, Deng X, Eden UT, Kemere C, Frank LM. Closed-loop modulation of remote hippocampal representations with neurofeedback. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.08.593085. [PMID: 38766135 PMCID: PMC11100667 DOI: 10.1101/2024.05.08.593085] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/22/2024]
Abstract
Humans can remember specific remote events without acting on them and influence which memories are retrieved based on internal goals. However, animal models typically present sensory cues to trigger memory retrieval and then assess retrieval based on action. Thus, it is difficult to determine whether measured neural activity patterns relate to the cue(s), the memory, or the behavior. We therefore asked whether retrieval-related neural activity could be generated in animals without cues or a behavioral report. We focused on hippocampal "place cells" which primarily represent the animal's current location (local representations) but can also represent locations away from the animal (remote representations). We developed a neurofeedback system to reward expression of remote representations and found that rats could learn to generate specific spatial representations that often jumped directly to the experimenter-defined target location. Thus, animals can deliberately engage remote representations, enabling direct study of retrieval-related activity in the brain.
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Affiliation(s)
- Michael E Coulter
- Kavli Institute and Department of Physiology UCSF
- Howard Hughes Medical Institute
| | - Anna K Gillespie
- Departments of Biological Structure and Lab Medicine and Pathology, University of Washington
| | - Joshua Chu
- Neuroengineering Initiative, Rice University
| | - Eric L Denovellis
- Kavli Institute and Department of Physiology UCSF
- Howard Hughes Medical Institute
| | | | - Daniel F Liu
- Kavli Institute and Department of Physiology UCSF
- Howard Hughes Medical Institute
| | - Katherine Wadhwani
- Kavli Institute and Department of Physiology UCSF
- Howard Hughes Medical Institute
| | - Baibhav Sharma
- Kavli Institute and Department of Physiology UCSF
- Howard Hughes Medical Institute
| | | | - Xinyi Deng
- Dept. of Statistics, Beijing University of Technology
| | - Uri T Eden
- Dept. of Mathematics and Statistics, Boston University
| | | | - Loren M Frank
- Kavli Institute and Department of Physiology UCSF
- Howard Hughes Medical Institute
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2
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Peng Z, Tong L, Shi W, Xu L, Huang X, Li Z, Yu X, Meng X, He X, Lv S, Yang G, Hao H, Jiang T, Miao X, Ye L. Multifunctional human visual pathway-replicated hardware based on 2D materials. Nat Commun 2024; 15:8650. [PMID: 39369011 PMCID: PMC11455896 DOI: 10.1038/s41467-024-52982-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2024] [Accepted: 09/26/2024] [Indexed: 10/07/2024] Open
Abstract
Artificial visual system empowered by 2D materials-based hardware simulates the functionalities of the human visual system, leading the forefront of artificial intelligence vision. However, retina-mimicked hardware that has not yet fully emulated the neural circuits of visual pathways is restricted from realizing more complex and special functions. In this work, we proposed a human visual pathway-replicated hardware that consists of crossbar arrays with split floating gate 2D tungsten diselenide (WSe2) unit devices that simulate the retina and visual cortex, and related connective peripheral circuits that replicate connectomics between the retina and visual cortex. This hardware experimentally displays advanced multi-functions of red-green color-blindness processing, low-power shape recognition, and self-driven motion tracking, promoting the development of machine vision, driverless technology, brain-computer interfaces, and intelligent robotics.
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Affiliation(s)
- Zhuiri Peng
- School of Integrated Circuits, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, China
| | - Lei Tong
- Department of Electronic Engineering, Materials Science and Technology Research Center, The Chinese University of Hong Kong, Hong Kong, China
| | - Wenhao Shi
- School of Integrated Circuits, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, China
| | - Langlang Xu
- School of Integrated Circuits, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, China
| | - Xinyu Huang
- School of Integrated Circuits, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, China
| | - Zheng Li
- School of Integrated Circuits, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, China
| | - Xiangxiang Yu
- School of Integrated Circuits, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, China
| | - Xiaohan Meng
- School of Integrated Circuits, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, China
| | - Xiao He
- School of Integrated Circuits, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, China
| | - Shengjie Lv
- School of Integrated Circuits, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, China
| | - Gaochen Yang
- School of Integrated Circuits, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, China
| | - Hao Hao
- College of Advanced Interdisciplinary Studies, National University of Defense Technology, Changsha, China
| | - Tian Jiang
- College of Advanced Interdisciplinary Studies, National University of Defense Technology, Changsha, China.
| | - Xiangshui Miao
- School of Integrated Circuits, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, China.
- Hubei Yangtze Memory Laboratories, Wuhan, China.
| | - Lei Ye
- School of Integrated Circuits, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, China.
- Hubei Yangtze Memory Laboratories, Wuhan, China.
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3
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Ben-Ami Bartal I. The complex affective and cognitive capacities of rats. Science 2024; 385:1298-1305. [PMID: 39298607 DOI: 10.1126/science.adq6217] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2024] [Accepted: 08/19/2024] [Indexed: 09/22/2024]
Abstract
For several decades, although studies of rat physiology and behavior have abounded, research on rat emotions has been limited in scope to fear, anxiety, and pain. Converging evidence for the capacity of many species to share others' affective states has emerged, sparking interest in the empathic capacities of rats. Recent research has demonstrated that rats are a highly cooperative species and are motivated by others' distress to prosocial actions, such as opening a door or pulling a chain to release trapped conspecifics. Studies of rat affect, cognition, and neural function provide compelling evidence that rats have some capacity to represent others' needs, to instrumentally act to improve their well-being, and are thus capable of forms of targeted helping. Rats' complex abilities raise the importance of integrating new measures of rat well-being into scientific research.
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Affiliation(s)
- Inbal Ben-Ami Bartal
- School of School of Psychological Sciences, Tel-Aviv University, Tel Aviv, 6997801, Israel
- Sagol School of Neuroscience, Tel-Aviv University, Tel Aviv, 6997801, Israel
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Lebedeva A, Gerasimova S, Yashanova M, Naumov A, Ivanov A, Karchkov D, Martynova O, Malkov A, Levanova T, Pisarchik A. A Method for Assessing Working Memory in Rats Using Controlled Virtual Environment. Sovrem Tekhnologii Med 2024; 16:12-22. [PMID: 39650274 PMCID: PMC11618531 DOI: 10.17691/stm2024.16.3.02] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2024] [Indexed: 12/11/2024] Open
Abstract
The aim of the study is to develop an experimental method to effectively assess the working memory in rats. The method uses a state-of-the-art controlled virtual environment with a virtual maze. The setup includes a treadmill for rodents, a fixation system, a dome for displaying virtual environment, and a control unit. Materials and Methods Biological part of the investigation: In our study, young healthy Wistar rats aged 6-7 months were used. The initial stage involved habituating the experimental animals to the experimenter over a period of two weeks. The habituation process was conducted in several successive steps. First, the rats were acclimated to wearing a jacket, which is part of the apparatus that holds the animal in the experimental setup. Next, they were familiarized with the fixation system. Following this, the rats were introduced to the treadmill (a sphere), and finally, they were acclimated to the entire setup. Subsequently, the rats were gradually habituated to the virtual maze and the associated reward system through positive reinforcement. This approach helped minimize stress and facilitated their adaptation to the new conditions. The second stage involved exploring the virtual space and learning the features of the virtual maze, including walls, turns, and the end goal. During the learning phase, the animals received positive reinforcement in the form of sugared water from the automatic water dispenser for correctly performed tasks. To navigate the T-maze, the rats used visual cues such as wall color and figures on the wall. At this stage, the rats learned to use virtual space to achieve their goals. Once the rats showed evident progress in learning the virtual environment, we implemented a protocol to assess their working memory. This assessment was based on the time it took for the rats to find the maze arm that provided positive reinforcement.Engineering part of the investigation: The animal is positioned on a foam plastic sphere with a 30 cm radius, using a custom device that allows its head and paws to remain mobile. Bearing fix the sphere in place, enabling the rat to rotate freely around its vertical axis. The rat's forward and backward movements cause the sphere to rotate, simulating a treadmill. The sphere's movements are detected by two infrared sensors (adapted from optical LED mice with USB interfaces) and transmitted to a computer, which generates an image of the virtual environment - a maze with landmarks on its walls. The virtual environment, created using the Unity Real-Time 3D Development Platform, is projected onto a custom-designed dome display containing the sphere and the lab rat. The setup provided the rat with a 360° field of view. Conclusion In our study, we present a setup that includes a projector, a dome display, a sphere (treadmill), a virtual T-maze, motion capture sensors, systems for securing animals to the sphere, and positive reinforcement delivery systems. We have developed an optimal protocol for immersing laboratory animals into a virtual environment and evaluating their cognitive functions, particularly working memory. The application of virtual reality in biological experiments enables more precise control over study conditions and allows for the creation of highly accurate and realistic behavioral protocols to assess cognitive functions in animals. This approach enhances our understanding of the mechanisms underlying working memory and their relationship with behavioral processes in rats and other animals.
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Affiliation(s)
- A.V. Lebedeva
- PhD, Associate Professor, Department of Neurotechnologies, Institute of Biology and Biomedicine; National Research Lobachevsky State University of Nizhny Novgorod, 23 Prospekt Gagarina, Nizhny Novgorod, 603022, Russia
| | - S.A. Gerasimova
- PhD, Researcher, Research Laboratory for Perspective Methods of Multidimensional Analysis, Institute of Information Technologies, Mathematics and Mechanics; National Research Lobachevsky State University of Nizhny Novgorod, 23 Prospekt Gagarina, Nizhny Novgorod, 603022, Russia
| | - M.I. Yashanova
- Assistant, Department of Biology; Privolzhsky Research Medical University, 10/1 Minin and Pozharsky Square, Nizhny Novgorod, 603005, Russia
| | - A.V. Naumov
- Research Assistant, Research Institute of Neurosciences; National Research Lobachevsky State University of Nizhny Novgorod, 23 Prospekt Gagarina, Nizhny Novgorod, 603022, Russia
| | - A.A. Ivanov
- Research Assistant, Research Institute of Neurosciences; National Research Lobachevsky State University of Nizhny Novgorod, 23 Prospekt Gagarina, Nizhny Novgorod, 603022, Russia
| | - D.A. Karchkov
- Senior Teacher, Department of Mathematical Support and Supercomputer Technologies, Institute of Information Technologies, Mathematics and Mechanics; National Research Lobachevsky State University of Nizhny Novgorod, 23 Prospekt Gagarina, Nizhny Novgorod, 603022, Russia
| | - O.V. Martynova
- PhD, Senior Teacher, Department of Electrodynamics, Faculty of Radiophysics; National Research Lobachevsky State University of Nizhny Novgorod, 23 Prospekt Gagarina, Nizhny Novgorod, 603022, Russia
| | - A.E. Malkov
- PhD, Senior Researcher, Research Institute of Neurosciences; National Research Lobachevsky State University of Nizhny Novgorod, 23 Prospekt Gagarina, Nizhny Novgorod, 603022, Russia
| | - T.A. Levanova
- PhD, Associate Professor, Department of System Dynamics and Control Theory, Institute of Information Technologies, Mathematics and Mechanics; National Research Lobachevsky State University of Nizhny Novgorod, 23 Prospekt Gagarina, Nizhny Novgorod, 603022, Russia
| | - A.N. Pisarchik
- PhD, Chair in Computational Systems Biology, Center for Biomedical Technology; Universidad Politécnica de Madrid, Madrid, 28223, Spain
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He J, Wei R, Ma X, Wu W, Pan X, Sun J, Tang J, Xu Z, Wang C, Pan C. Contactless User-Interactive Sensing Display for Human-Human and Human-Machine Interactions. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2024; 36:e2401931. [PMID: 38573797 DOI: 10.1002/adma.202401931] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/05/2024] [Revised: 03/18/2024] [Indexed: 04/06/2024]
Abstract
Creating a large-scale contactless user-interactive sensing display (CUISD) with optimal features is challenging but crucial for efficient human-human or human-machine interactions. This study reports a CUISD based on dynamic alternating current electroluminescence (ACEL) that responds to humidity. Subsecond humidity-induced luminescence is achieved by integrating a highly responsive hydrogel into the ACEL layer. The patterned silver nanofiber electrode and luminescence layer, produced through electrospinning and microfabrication, result in a stretchable, large-scale, high-resolution, multicolor, and dynamic CUISD. The CUISD is implemented for the real-time control of a remote-controlled car, wherein the luminescence signals induced by touchless finger movements are distinguished and encoded to deliver specific commands. Moreover, the distinctive recognition of breathing facilitates the CUISD to serve as a visual signal transmitter for information interaction, which is particularly beneficial for individuals with disabilities. The paradigm shift depicts in this work is expected to reshape the way authors interact with each other and devices, discovering niche applications in virtual/augmented reality and the metaverse.
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Affiliation(s)
- Jiaqi He
- Beijing Institute of Nanoenergy and Nanosystems, Chinese Academy of Sciences, Beijing, 101400, China
- Institute of Atomic Manufacturing, Beihang University, Beijing, 100191, China
- School of Nanoscience and Engineering, University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Ruilai Wei
- Beijing Institute of Nanoenergy and Nanosystems, Chinese Academy of Sciences, Beijing, 101400, China
| | - Xiaole Ma
- Beijing Institute of Nanoenergy and Nanosystems, Chinese Academy of Sciences, Beijing, 101400, China
| | - Wenqiang Wu
- Beijing Institute of Nanoenergy and Nanosystems, Chinese Academy of Sciences, Beijing, 101400, China
| | - Xiaojun Pan
- Beijing Institute of Nanoenergy and Nanosystems, Chinese Academy of Sciences, Beijing, 101400, China
| | - Junlu Sun
- Beijing Institute of Nanoenergy and Nanosystems, Chinese Academy of Sciences, Beijing, 101400, China
| | - Jiaqi Tang
- Beijing Institute of Nanoenergy and Nanosystems, Chinese Academy of Sciences, Beijing, 101400, China
- School of Nanoscience and Engineering, University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Zhangsheng Xu
- Beijing Institute of Nanoenergy and Nanosystems, Chinese Academy of Sciences, Beijing, 101400, China
- School of Nanoscience and Engineering, University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Chunfeng Wang
- Guangdong Research Center for Interfacial Engineering of Functional Materials, College of Materials Science and Engineering, Shenzhen University, Shenzhen, 518060, China
| | - Caofeng Pan
- Beijing Institute of Nanoenergy and Nanosystems, Chinese Academy of Sciences, Beijing, 101400, China
- Institute of Atomic Manufacturing, Beihang University, Beijing, 100191, China
- School of Nanoscience and Engineering, University of Chinese Academy of Sciences, Beijing, 100049, China
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6
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Zeng YF, Yang KX, Cui Y, Zhu XN, Li R, Zhang H, Wu DC, Stevens RC, Hu J, Zhou N. Conjunctive encoding of exploratory intentions and spatial information in the hippocampus. Nat Commun 2024; 15:3221. [PMID: 38622129 PMCID: PMC11018604 DOI: 10.1038/s41467-024-47570-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2023] [Accepted: 04/02/2024] [Indexed: 04/17/2024] Open
Abstract
The hippocampus creates a cognitive map of the external environment by encoding spatial and self-motion-related information. However, it is unclear whether hippocampal neurons could also incorporate internal cognitive states reflecting an animal's exploratory intention, which is not driven by rewards or unexpected sensory stimuli. In this study, a subgroup of CA1 neurons was found to encode both spatial information and animals' investigatory intentions in male mice. These neurons became active before the initiation of exploration behaviors at specific locations and were nearly silent when the same fields were traversed without exploration. Interestingly, this neuronal activity could not be explained by object features, rewards, or mismatches in environmental cues. Inhibition of the lateral entorhinal cortex decreased the activity of these cells during exploration. Our findings demonstrate that hippocampal neurons may bridge external and internal signals, indicating a potential connection between spatial representation and intentional states in the construction of internal navigation systems.
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Affiliation(s)
- Yi-Fan Zeng
- iHuman Institute, ShanghaiTech University, Shanghai, 201210, China
- School of Life Science and Technology, ShanghaiTech University, Shanghai, 201210, China
| | - Ke-Xin Yang
- School of Life Science and Technology, ShanghaiTech University, Shanghai, 201210, China
| | - Yilong Cui
- iHuman Institute, ShanghaiTech University, Shanghai, 201210, China
- School of Life Science and Technology, ShanghaiTech University, Shanghai, 201210, China
| | - Xiao-Na Zhu
- School of Life Science and Technology, ShanghaiTech University, Shanghai, 201210, China
| | - Rui Li
- iHuman Institute, ShanghaiTech University, Shanghai, 201210, China
- School of Life Science and Technology, ShanghaiTech University, Shanghai, 201210, China
| | - Hanqing Zhang
- iHuman Institute, ShanghaiTech University, Shanghai, 201210, China
| | - Dong Chuan Wu
- Neuroscience and Brain Disease Center, Graduate Institute of Biomedical Sciences, China Medical University, Taichung City, 404333, Taiwan
- Translational Medicine Research Center, China Medical University Hospital, Taichung City, 404333, Taiwan
| | - Raymond C Stevens
- iHuman Institute, ShanghaiTech University, Shanghai, 201210, China
- School of Life Science and Technology, ShanghaiTech University, Shanghai, 201210, China
| | - Ji Hu
- iHuman Institute, ShanghaiTech University, Shanghai, 201210, China
- School of Life Science and Technology, ShanghaiTech University, Shanghai, 201210, China
| | - Ning Zhou
- iHuman Institute, ShanghaiTech University, Shanghai, 201210, China.
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7
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Negrón-Oyarzo I, Dib T, Chacana-Véliz L, López-Quilodrán N, Urrutia-Piñones J. Large-scale coupling of prefrontal activity patterns as a mechanism for cognitive control in health and disease: evidence from rodent models. Front Neural Circuits 2024; 18:1286111. [PMID: 38638163 PMCID: PMC11024307 DOI: 10.3389/fncir.2024.1286111] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2023] [Accepted: 03/11/2024] [Indexed: 04/20/2024] Open
Abstract
Cognitive control of behavior is crucial for well-being, as allows subject to adapt to changing environments in a goal-directed way. Changes in cognitive control of behavior is observed during cognitive decline in elderly and in pathological mental conditions. Therefore, the recovery of cognitive control may provide a reliable preventive and therapeutic strategy. However, its neural basis is not completely understood. Cognitive control is supported by the prefrontal cortex, structure that integrates relevant information for the appropriate organization of behavior. At neurophysiological level, it is suggested that cognitive control is supported by local and large-scale synchronization of oscillatory activity patterns and neural spiking activity between the prefrontal cortex and distributed neural networks. In this review, we focus mainly on rodent models approaching the neuronal origin of these prefrontal patterns, and the cognitive and behavioral relevance of its coordination with distributed brain systems. We also examine the relationship between cognitive control and neural activity patterns in the prefrontal cortex, and its role in normal cognitive decline and pathological mental conditions. Finally, based on these body of evidence, we propose a common mechanism that may underlie the impaired cognitive control of behavior.
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Affiliation(s)
- Ignacio Negrón-Oyarzo
- Instituto de Fisiología, Facultad de Ciencias, Universidad de Valparaíso, Valparaíso, Chile
| | - Tatiana Dib
- Instituto de Fisiología, Facultad de Ciencias, Universidad de Valparaíso, Valparaíso, Chile
| | - Lorena Chacana-Véliz
- Instituto de Fisiología, Facultad de Ciencias, Universidad de Valparaíso, Valparaíso, Chile
- Programa de Doctorado en Ciencias Mención en Neurociencia, Facultad de Ciencias, Universidad de Valparaíso, Valparaíso, Chile
| | - Nélida López-Quilodrán
- Instituto de Fisiología, Facultad de Ciencias, Universidad de Valparaíso, Valparaíso, Chile
- Programa de Doctorado en Ciencias Mención en Neurociencia, Facultad de Ciencias, Universidad de Valparaíso, Valparaíso, Chile
| | - Jocelyn Urrutia-Piñones
- Instituto de Fisiología, Facultad de Ciencias, Universidad de Valparaíso, Valparaíso, Chile
- Programa de Doctorado en Ciencias Mención en Neurociencia, Facultad de Ciencias, Universidad de Valparaíso, Valparaíso, Chile
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8
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Vorhees CV, Williams MT. Tests for learning and memory in rodent regulatory studies. Curr Res Toxicol 2024; 6:100151. [PMID: 38304257 PMCID: PMC10832385 DOI: 10.1016/j.crtox.2024.100151] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2023] [Revised: 01/11/2024] [Accepted: 01/16/2024] [Indexed: 02/03/2024] Open
Abstract
For decades, regulatory guidelines for safety assessment in rodents for drugs, chemicals, pesticides, and food additives with developmental neurotoxic potential have recommended a single test of learning and memory (L&M). In recent years some agencies have requested two such tests. Given the importance of higher cognitive function to health, and the fact that different types of L&M are mediated by different brain regions assessing higher functions represents a step forward in providing better evidence-based protection against adverse brain effects. Given the myriad of tests available for assessing L&M in rodents this leads to the question of which tests best fit regulatory guidelines. To address this question, we begin by describing the central role of two types of L&M essential to all mammalian species and the regions/networks that mediate them. We suggest that the tests recommended possess characteristics that make them well suited to the needs in regulatory safety studies. By brain region, these are (1) the hippocampus and entorhinal cortex for spatial navigation, which assesses explicit L&M for reference and episodic memory and (2) the striatum and related structures for egocentric navigation, which assesses implicit or procedural memory and path integration. Of the tests available, we suggest that in this context, the evidence supports the use of water mazes, specifically, the Morris water maze (MWM) for spatial L&M and the Cincinnati water maze (CWM) for egocentric/procedural L&M. We review the evidentiary basis for these tests, describe their use, and explain procedures that optimize their sensitivity.
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Affiliation(s)
- Charles V. Vorhees
- Corresponding author at: Div. of Neurology, Cincinnati Children’s Hospital Medical Center, 3333 Burnet Ave., Cincinnati, OH 45229, USA.
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