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Horovitz DJ, Askins LA, Regnier GM, McQuail JA. Age-related synaptic signatures of brain and cognitive reserve in the rat hippocampus and parahippocampal regions. Neurobiol Aging 2025; 148:80-97. [PMID: 39954409 DOI: 10.1016/j.neurobiolaging.2025.01.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2024] [Revised: 01/30/2025] [Accepted: 01/31/2025] [Indexed: 02/17/2025]
Abstract
Age-related cognitive decline varies widely among individuals, with some showing resilience despite older age. This study examines synaptic markers of glutamatergic and GABAergic neurotransmission in the hippocampus and cortex of older rats with differing cognitive abilities, aiming to uncover mechanisms that contribute to cognitive resilience. We observed significant age-related reductions in vesicular glutamate transporter VGluT1, particularly in the stratum oriens (SO), radiatum (SR), and lacunosum-moleculare (SLM) of the dorsal CA3 and SLM of the dorsal CA1. Furthermore, loss of VGluT1 in the dorsal CA3-SLM correlated with severity of memory impairment. Higher levels of the vesicular GABA transporter (VGAT) were associated with better spatial learning in older rats, across several synaptic zones of the dorsal hippocampus, including the outer molecular layer of the dentate gyrus (DG), and the SO, SR, SLM, and pyramidal cell layers of both CA3 and CA1. This suggests that enhanced inhibitory neurotransmission specific to the dorsal aspect of the hippocampus may protect against age-related cognitive decline. While the dorsal hippocampus showed consistent age- and memory-related changes, markers in the ventral hippocampus remained largely intact. In the perirhinal cortex, VGluT1 declined with no changes in VGAT, while both markers remained unchanged in other cortical regions, including the lateral entorhinal, retrosplenial, and posterior parietal cortices. These findings highlight region-specific patterns of synaptic aging as potential markers of brain and cognitive reserve.
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Affiliation(s)
- David J Horovitz
- Department of Pharmacology, Physiology, and Neuroscience, University of South Carolina School of Medicine, Columbia, SC, USA.
| | - Laura A Askins
- Department of Pharmacology, Physiology, and Neuroscience, University of South Carolina School of Medicine, Columbia, SC, USA.
| | - Grace M Regnier
- Department of Pharmacology, Physiology, and Neuroscience, University of South Carolina School of Medicine, Columbia, SC, USA.
| | - Joseph A McQuail
- Department of Pharmacology, Physiology, and Neuroscience, University of South Carolina School of Medicine, Columbia, SC, USA.
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2
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Hasselmo ME, LaChance PA, Robinson JC, Malmberg SL, Patel M, Gross E, Everett DE, Sankaranarayanan S, Fang J. How does the response of egocentric boundary cells depend upon the coordinate system of environmental features? BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2025:2025.02.03.636060. [PMID: 39974880 PMCID: PMC11838506 DOI: 10.1101/2025.02.03.636060] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/21/2025]
Abstract
Neurons in the retrosplenial (RSC) (Alexander et al., 2020a; LaChance and Hasselmo, 2024) and postrhinal cortex (POR) respond to environmental boundaries and configurations in egocentric coordinates relative to an animals current position. Neurons in these structures and adjacent structures also respond to spatial dimensions of self- motion such as running velocity (Carstensen et al., 2021; Robinson et al., 2024). Data and modeling suggest that these responses could be essential for guiding behaviors such as barrier avoidance and goal finding (Erdem and Hasselmo, 2012; 2014). However, these findings still leave the unanswered question: What are the features and what are the coordinate systems of these features that drive these egocentric neural responses? Here we present models of the potential circuit mechanisms generating egocentric responses in RSC. These can be generated based on coding of internal representations of barriers in head-centered coordinates of distance and angle that are transformed based on current running velocity for trajectory planning and obstacle avoidance. This hypothesis is compared with an alternate potentially complementary hypothesis that neurons in the same regions might respond to retinotopic position of features at the top, bottom or edges of walls as a precursor to head-centered coordinates. Alternate hypotheses include the forward scanning of trajectories (ray tracing) to test for collision with barriers, or the comparison of optic flow on different sides of the animal. These hypotheses generate complementary modeling predictions about how changes in environmental parameters could alter the neural responses of egocentric boundary cells that are presented here.
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3
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Subramanian DL, Miller AMP, Smith DM. A comparison of hippocampal and retrosplenial cortical spatial and contextual firing patterns. Hippocampus 2024; 34:357-377. [PMID: 38770779 DOI: 10.1002/hipo.23610] [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: 10/16/2023] [Revised: 03/22/2024] [Accepted: 05/07/2024] [Indexed: 05/22/2024]
Abstract
The hippocampus (HPC) and retrosplenial cortex (RSC) are key components of the brain's memory and navigation systems. Lesions of either region produce profound deficits in spatial cognition and HPC neurons exhibit well-known spatial firing patterns (place fields). Recent studies have also identified an array of navigation-related firing patterns in the RSC. However, there has been little work comparing the response properties and information coding mechanisms of these two brain regions. In the present study, we examined the firing patterns of HPC and RSC neurons in two tasks which are commonly used to study spatial cognition in rodents, open field foraging with an environmental context manipulation and continuous T-maze alternation. We found striking similarities in the kinds of spatial and contextual information encoded by these two brain regions. Neurons in both regions carried information about the rat's current spatial location, trajectories and goal locations, and both regions reliably differentiated the contexts. However, we also found several key differences. For example, information about head direction was a prominent component of RSC representations but was only weakly encoded in the HPC. The two regions also used different coding schemes, even when they encoded the same kind of information. As expected, the HPC employed a sparse coding scheme characterized by compact, high contrast place fields, and information about spatial location was the dominant component of HPC representations. RSC firing patterns were more consistent with a distributed coding scheme. Instead of compact place fields, RSC neurons exhibited broad, but reliable, spatial and directional tuning, and they typically carried information about multiple navigational variables. The observed similarities highlight the closely related functions of the HPC and RSC, whereas the differences in information types and coding schemes suggest that these two regions likely make somewhat different contributions to spatial cognition.
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Affiliation(s)
| | - Adam M P Miller
- Department of Psychology, Cornell University, Ithaca, New York, USA
| | - David M Smith
- Department of Psychology, Cornell University, Ithaca, New York, USA
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4
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Kaltenecker D, Al-Maskari R, Negwer M, Hoeher L, Kofler F, Zhao S, Todorov M, Rong Z, Paetzold JC, Wiestler B, Piraud M, Rueckert D, Geppert J, Morigny P, Rohm M, Menze BH, Herzig S, Berriel Diaz M, Ertürk A. Virtual reality-empowered deep-learning analysis of brain cells. Nat Methods 2024; 21:1306-1315. [PMID: 38649742 PMCID: PMC11239522 DOI: 10.1038/s41592-024-02245-2] [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: 06/03/2022] [Accepted: 03/12/2024] [Indexed: 04/25/2024]
Abstract
Automated detection of specific cells in three-dimensional datasets such as whole-brain light-sheet image stacks is challenging. Here, we present DELiVR, a virtual reality-trained deep-learning pipeline for detecting c-Fos+ cells as markers for neuronal activity in cleared mouse brains. Virtual reality annotation substantially accelerated training data generation, enabling DELiVR to outperform state-of-the-art cell-segmenting approaches. Our pipeline is available in a user-friendly Docker container that runs with a standalone Fiji plugin. DELiVR features a comprehensive toolkit for data visualization and can be customized to other cell types of interest, as we did here for microglia somata, using Fiji for dataset-specific training. We applied DELiVR to investigate cancer-related brain activity, unveiling an activation pattern that distinguishes weight-stable cancer from cancers associated with weight loss. Overall, DELiVR is a robust deep-learning tool that does not require advanced coding skills to analyze whole-brain imaging data in health and disease.
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Affiliation(s)
- Doris Kaltenecker
- Institute for Diabetes and Cancer (IDC), Helmholtz Munich, Neuherberg, Germany
- Joint Heidelberg-IDC Translational Diabetes Program, Heidelberg University Hospital, Heidelberg, Germany
- German Center for Diabetes Research (DZD), Neuherberg, Germany
- Institute for Stroke and Dementia Research, Klinikum der Universität München, Ludwig-Maximilians-Universität LMU, Munich, Germany
| | - Rami Al-Maskari
- Institute for Stroke and Dementia Research, Klinikum der Universität München, Ludwig-Maximilians-Universität LMU, Munich, Germany
- Institute for Tissue Engineering and Regenerative Medicine, Helmholtz Munich, Neuherberg, Germany
- Department of Computer Science, TUM Computation, Information and Technology, Technical University of Munich (TUM), Munich, Germany
- Center for Translational Cancer Research of the TUM (TranslaTUM), Munich, Germany
| | - Moritz Negwer
- Institute for Tissue Engineering and Regenerative Medicine, Helmholtz Munich, Neuherberg, Germany
| | - Luciano Hoeher
- Institute for Tissue Engineering and Regenerative Medicine, Helmholtz Munich, Neuherberg, Germany
| | - Florian Kofler
- Department of Computer Science, TUM Computation, Information and Technology, Technical University of Munich (TUM), Munich, Germany
- Center for Translational Cancer Research of the TUM (TranslaTUM), Munich, Germany
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
- Helmholtz AI, Helmholtz Munich, Neuherberg, Germany
| | - Shan Zhao
- Institute for Stroke and Dementia Research, Klinikum der Universität München, Ludwig-Maximilians-Universität LMU, Munich, Germany
- Institute for Tissue Engineering and Regenerative Medicine, Helmholtz Munich, Neuherberg, Germany
| | - Mihail Todorov
- Institute for Stroke and Dementia Research, Klinikum der Universität München, Ludwig-Maximilians-Universität LMU, Munich, Germany
- Institute for Tissue Engineering and Regenerative Medicine, Helmholtz Munich, Neuherberg, Germany
| | - Zhouyi Rong
- Institute for Stroke and Dementia Research, Klinikum der Universität München, Ludwig-Maximilians-Universität LMU, Munich, Germany
- Institute for Tissue Engineering and Regenerative Medicine, Helmholtz Munich, Neuherberg, Germany
| | - Johannes Christian Paetzold
- Institute for Tissue Engineering and Regenerative Medicine, Helmholtz Munich, Neuherberg, Germany
- Center for Translational Cancer Research of the TUM (TranslaTUM), Munich, Germany
- Department of Computing, Imperial College London, London, United Kingdom
| | - Benedikt Wiestler
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Marie Piraud
- Helmholtz AI, Helmholtz Munich, Neuherberg, Germany
| | - Daniel Rueckert
- Department of Computing, Imperial College London, London, United Kingdom
| | - Julia Geppert
- Institute for Diabetes and Cancer (IDC), Helmholtz Munich, Neuherberg, Germany
- Joint Heidelberg-IDC Translational Diabetes Program, Heidelberg University Hospital, Heidelberg, Germany
- German Center for Diabetes Research (DZD), Neuherberg, Germany
| | - Pauline Morigny
- Institute for Diabetes and Cancer (IDC), Helmholtz Munich, Neuherberg, Germany
- Joint Heidelberg-IDC Translational Diabetes Program, Heidelberg University Hospital, Heidelberg, Germany
- German Center for Diabetes Research (DZD), Neuherberg, Germany
| | - Maria Rohm
- Institute for Diabetes and Cancer (IDC), Helmholtz Munich, Neuherberg, Germany
- Joint Heidelberg-IDC Translational Diabetes Program, Heidelberg University Hospital, Heidelberg, Germany
- German Center for Diabetes Research (DZD), Neuherberg, Germany
| | - Bjoern H Menze
- Department of Computer Science, TUM Computation, Information and Technology, Technical University of Munich (TUM), Munich, Germany
- Department for Quantitative Biomedicine, University of Zurich, Zurich, Switzerland
| | - Stephan Herzig
- Institute for Diabetes and Cancer (IDC), Helmholtz Munich, Neuherberg, Germany
- Joint Heidelberg-IDC Translational Diabetes Program, Heidelberg University Hospital, Heidelberg, Germany
- German Center for Diabetes Research (DZD), Neuherberg, Germany
- Chair Molecular Metabolic Control, TU Munich, Munich, Germany
| | - Mauricio Berriel Diaz
- Institute for Diabetes and Cancer (IDC), Helmholtz Munich, Neuherberg, Germany.
- Joint Heidelberg-IDC Translational Diabetes Program, Heidelberg University Hospital, Heidelberg, Germany.
- German Center for Diabetes Research (DZD), Neuherberg, Germany.
| | - Ali Ertürk
- Institute for Stroke and Dementia Research, Klinikum der Universität München, Ludwig-Maximilians-Universität LMU, Munich, Germany.
- Institute for Tissue Engineering and Regenerative Medicine, Helmholtz Munich, Neuherberg, Germany.
- School of Medicine, Koç University, İstanbul, Turkey.
- Munich Cluster for Systems Neurology (SyNergy), Munich, Germany.
- Deep Piction, Munich, Germany.
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5
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Cheng N, Dong Q, Zhang Z, Wang L, Chen X, Wang C. Egocentric processing of items in spines, dendrites, and somas in the retrosplenial cortex. Neuron 2024; 112:646-660.e8. [PMID: 38101396 DOI: 10.1016/j.neuron.2023.11.018] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2023] [Revised: 08/31/2023] [Accepted: 11/15/2023] [Indexed: 12/17/2023]
Abstract
Egocentric representations of external items are essential for spatial navigation and memory. Here, we explored the neural mechanisms underlying egocentric processing in the retrosplenial cortex (RSC), a pivotal area for memory and navigation. Using one-photon and two-photon calcium imaging, we identified egocentric tuning for environment boundaries in dendrites, spines, and somas of RSC neurons (egocentric boundary cells) in the open-field task. Dendrites with egocentric tuning tended to have similarly tuned spines. We further identified egocentric neurons representing landmarks in a virtual navigation task or remembered cue location in a goal-oriented task, respectively. These neurons formed an independent population with egocentric boundary cells, suggesting that dedicated neurons with microscopic clustering of functional inputs shaped egocentric boundary processing in RSC and that RSC adopted a labeled line code with distinct classes of egocentric neurons responsible for representing different items in specific behavioral contexts, which could lead to efficient and flexible computation.
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Affiliation(s)
- Ning Cheng
- Shenzhen Key Laboratory of Precision Diagnosis and Treatment of Depression, Shenzhen-Hong Kong Institute of Brain Science, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China; CAS Key Laboratory of Brain Connectome and Manipulation, the Brain Cognition and Brain Disease Institute, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China; Guangdong Provincial Key Laboratory of Brain Connectome and Behavior, the Brain Cognition and Brain Disease Institute, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
| | - Qiqi Dong
- Shenzhen Key Laboratory of Precision Diagnosis and Treatment of Depression, Shenzhen-Hong Kong Institute of Brain Science, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China; CAS Key Laboratory of Brain Connectome and Manipulation, the Brain Cognition and Brain Disease Institute, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China; Guangdong Provincial Key Laboratory of Brain Connectome and Behavior, the Brain Cognition and Brain Disease Institute, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
| | - Zhen Zhang
- CAS Key Laboratory of Brain Connectome and Manipulation, the Brain Cognition and Brain Disease Institute, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China; Guangdong Provincial Key Laboratory of Brain Connectome and Behavior, the Brain Cognition and Brain Disease Institute, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
| | - Li Wang
- Brain Research Centre, Department of Biology, School of Life Sciences, Southern University of Science and Technology, Shenzhen 518055, China
| | - Xiaojing Chen
- Brain Research Centre, Department of Biology, School of Life Sciences, Southern University of Science and Technology, Shenzhen 518055, China.
| | - Cheng Wang
- Shenzhen Key Laboratory of Precision Diagnosis and Treatment of Depression, Shenzhen-Hong Kong Institute of Brain Science, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China; CAS Key Laboratory of Brain Connectome and Manipulation, the Brain Cognition and Brain Disease Institute, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China; Guangdong Provincial Key Laboratory of Brain Connectome and Behavior, the Brain Cognition and Brain Disease Institute, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China; CAS Centre for Excellence in Brain Science and Intelligent Technology, Shanghai, China.
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6
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Lande AS, Garvert AC, Ebbesen NC, Jordbræk SV, Vervaeke K. Representations of tactile object location in the retrosplenial cortex. Curr Biol 2023; 33:4599-4610.e7. [PMID: 37774708 DOI: 10.1016/j.cub.2023.09.019] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2022] [Revised: 07/23/2023] [Accepted: 09/06/2023] [Indexed: 10/01/2023]
Abstract
How animals use tactile sensation to detect important objects and remember their location in a world-based coordinate system is unclear. Here, we hypothesized that the retrosplenial cortex (RSC), a key network for contextual memory and spatial navigation, represents the location of objects based on tactile sensation. We studied mice palpating objects with their whiskers while navigating in a tactile virtual reality in darkness. Using two-photon Ca2+ imaging, we discovered that a population of neurons in the agranular RSC signal the location of objects. Responses to objects do not simply reflect the sensory stimulus. Instead, they are highly position, task, and context dependent and often predict the upcoming object before it is within reach. In addition, a large fraction of neurons encoding object location maintain a memory trace of the object's location. These data show that the RSC encodes the location and arrangement of tactile objects in a spatial reference frame.
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Affiliation(s)
- Andreas Sigstad Lande
- Institute of Basic Medical Sciences, Section of Physiology, University of Oslo, Sognsvannsveien 9, 0372 Oslo, Norway
| | - Anna Christina Garvert
- Institute of Basic Medical Sciences, Section of Physiology, University of Oslo, Sognsvannsveien 9, 0372 Oslo, Norway
| | - Nora Cecilie Ebbesen
- Institute of Basic Medical Sciences, Section of Physiology, University of Oslo, Sognsvannsveien 9, 0372 Oslo, Norway
| | - Sondre Valentin Jordbræk
- Institute of Basic Medical Sciences, Section of Physiology, University of Oslo, Sognsvannsveien 9, 0372 Oslo, Norway
| | - Koen Vervaeke
- Institute of Basic Medical Sciences, Section of Physiology, University of Oslo, Sognsvannsveien 9, 0372 Oslo, Norway.
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7
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Alexander AS, Robinson JC, Stern CE, Hasselmo ME. Gated transformations from egocentric to allocentric reference frames involving retrosplenial cortex, entorhinal cortex, and hippocampus. Hippocampus 2023; 33:465-487. [PMID: 36861201 PMCID: PMC10403145 DOI: 10.1002/hipo.23513] [Citation(s) in RCA: 26] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2022] [Revised: 01/22/2023] [Accepted: 01/25/2023] [Indexed: 03/03/2023]
Abstract
This paper reviews the recent experimental finding that neurons in behaving rodents show egocentric coding of the environment in a number of structures associated with the hippocampus. Many animals generating behavior on the basis of sensory input must deal with the transformation of coordinates from the egocentric position of sensory input relative to the animal, into an allocentric framework concerning the position of multiple goals and objects relative to each other in the environment. Neurons in retrosplenial cortex show egocentric coding of the position of boundaries in relation to an animal. These neuronal responses are discussed in relation to existing models of the transformation from egocentric to allocentric coordinates using gain fields and a new model proposing transformations of phase coding that differ from current models. The same type of transformations could allow hierarchical representations of complex scenes. The responses in rodents are also discussed in comparison to work on coordinate transformations in humans and non-human primates.
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Affiliation(s)
- Andrew S Alexander
- Center for Systems Neuroscience, Boston University, Boston, Massachusetts, USA
| | - Jennifer C Robinson
- Center for Systems Neuroscience, Boston University, Boston, Massachusetts, USA
| | - Chantal E Stern
- Center for Systems Neuroscience, Boston University, Boston, Massachusetts, USA
| | - Michael E Hasselmo
- Center for Systems Neuroscience, Boston University, Boston, Massachusetts, USA
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Alexander AS, Place R, Starrett MJ, Chrastil ER, Nitz DA. Rethinking retrosplenial cortex: Perspectives and predictions. Neuron 2023; 111:150-175. [PMID: 36460006 PMCID: PMC11709228 DOI: 10.1016/j.neuron.2022.11.006] [Citation(s) in RCA: 44] [Impact Index Per Article: 22.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2022] [Revised: 08/09/2022] [Accepted: 11/06/2022] [Indexed: 12/03/2022]
Abstract
The last decade has produced exciting new ideas about retrosplenial cortex (RSC) and its role in integrating diverse inputs. Here, we review the diversity in forms of spatial and directional tuning of RSC activity, temporal organization of RSC activity, and features of RSC interconnectivity with other brain structures. We find that RSC anatomy and dynamics are more consistent with roles in multiple sensorimotor and cognitive processes than with any isolated function. However, two more generalized categories of function may best characterize roles for RSC in complex cognitive processes: (1) shifting and relating perspectives for spatial cognition and (2) prediction and error correction for current sensory states with internal representations of the environment. Both functions likely take advantage of RSC's capacity to encode conjunctions among sensory, motor, and spatial mapping information streams. Together, these functions provide the scaffold for intelligent actions, such as navigation, perspective taking, interaction with others, and error detection.
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Affiliation(s)
- Andrew S Alexander
- Department of Psychological and Brain Sciences, Boston University, Boston, MA 02215, USA
| | - Ryan Place
- Department of Cognitive Science, University of California, San Diego, La Jolla, CA 92093, USA
| | - Michael J Starrett
- Department of Neurobiology & Behavior, University of California, Irvine, Irvine, CA 92697, USA
| | - Elizabeth R Chrastil
- Department of Neurobiology & Behavior, University of California, Irvine, Irvine, CA 92697, USA; Department of Cognitive Sciences, University of California, Irvine, Irvine, CA 92697, USA.
| | - Douglas A Nitz
- Department of Cognitive Science, University of California, San Diego, La Jolla, CA 92093, USA.
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9
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Kopsick JD, Hartzell K, Lazaro H, Nambiar P, Hasselmo ME, Dannenberg H. Temporal dynamics of cholinergic activity in the septo-hippocampal system. Front Neural Circuits 2022; 16:957441. [PMID: 36092276 PMCID: PMC9452968 DOI: 10.3389/fncir.2022.957441] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2022] [Accepted: 07/18/2022] [Indexed: 11/13/2022] Open
Abstract
Cholinergic projection neurons in the medial septum and diagonal band of Broca are the major source of cholinergic modulation of hippocampal circuit functions that support neural coding of location and running speed. Changes in cholinergic modulation are known to correlate with changes in brain states, cognitive functions, and behavior. However, whether cholinergic modulation can change fast enough to serve as a potential speed signal in hippocampal and parahippocampal cortices and whether the temporal dynamics in such a signal depend on the presence of visual cues remain unknown. In this study, we use a fiber-photometric approach to quantify the temporal dynamics of cholinergic activity in freely moving mice as a function of the animal's movement speed and visual cues. We show that the population activity of cholinergic neurons in the medial septum and diagonal band of Broca changes fast enough to be aligned well with changes in the animal's running speed and is strongly and linearly correlated to the logarithm of the animal's running speed. Intriguingly, the cholinergic modulation remains strongly and linearly correlated to the speed of the animal's neck movements during periods of stationary activity. Furthermore, we show that cholinergic modulation is unaltered during darkness. Lastly, we identify rearing, a stereotypic behavior where the mouse stands on its hindlimbs to scan the environment from an elevated perspective, is associated with higher cholinergic activity than expected from neck movements on the horizontal plane alone. Taken together, these data show that temporal dynamics in the cholinergic modulation of hippocampal circuits are fast enough to provide a potential running speed signal in real-time. Moreover, the data show that cholinergic modulation is primarily a function of the logarithm of the animal's movement speed, both during locomotion and during stationary activity, with no significant interaction with visual inputs. These data advance our understanding of temporal dynamics in cholinergic modulation of hippocampal circuits and their functions in the context of neural coding of location and running speed.
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Affiliation(s)
- Jeffrey D. Kopsick
- Department of Bioengineering, George Mason University, Fairfax, VA, United States
- Interdisciplinary Program for Neuroscience, George Mason University, Fairfax, VA, United States
| | - Kyle Hartzell
- Department of Bioengineering, George Mason University, Fairfax, VA, United States
| | - Hallie Lazaro
- Center for Systems Neuroscience, Boston University, Boston, MA, United States
- Department of Psychological and Brain Sciences, Boston University, Boston, MA, United States
| | - Pranav Nambiar
- Center for Systems Neuroscience, Boston University, Boston, MA, United States
- Department of Psychological and Brain Sciences, Boston University, Boston, MA, United States
| | - Michael E. Hasselmo
- Center for Systems Neuroscience, Boston University, Boston, MA, United States
- Department of Psychological and Brain Sciences, Boston University, Boston, MA, United States
| | - Holger Dannenberg
- Department of Bioengineering, George Mason University, Fairfax, VA, United States
- Interdisciplinary Program for Neuroscience, George Mason University, Fairfax, VA, United States
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