Disrupted Place Cell Remapping and Impaired Grid Cells in a Knockin Model of Alzheimer's Disease.
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ABSTRACT:
SUBMITTER: Jun H
PROVIDER: S-EPMC7529950 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Alzheimer's Disease (AD) is characterized by progressive neurodegeneration and cognitive impairment. Synaptic dysfunction is an established early symptom, which correlates strongly with cognitive decline, and is hypothesised to mediate the diverse neuronal network abnormalities observed in AD. However, how synaptic dysfunction contributes to network pathology and cognitive impairment in AD remains elusive. Here, we present a grid-cell-to-place-cell transformation model of long-term CA1 place cell dynamics to interrogate the effect of synaptic loss on network function and environmental representation. Synapse loss modelled after experimental observations in the APP/PS1 mouse model was found to induce firing rate alterations and place cell abnormalities that have previously been observed in AD mouse models, including enlarged place fields and lower across-session stability of place fields. Our results support the hypothesis that synaptic dysfunction underlies cognitive deficits, and demonstrate how impaired environmental representation may arise in the early stages of AD. We further propose that dysfunction of excitatory and inhibitory inputs to CA1 pyramidal cells may cause distinct impairments in place cell function, namely reduced stability and place map resolution.
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Project description:Alzheimer's disease (AD) is often accompanied by changes in mood as well as increases in circulating cortisol levels, suggesting that regulation of the stress responsive hypothalamic-pituitary-adrenal (HPA) axis is disturbed. Here, we show that amyloid precursor protein (APP) is endogenously expressed in important limbic, hypothalamic, and midbrain nuclei that regulate hypothalamic-pituitary-adrenal axis activity. Furthermore, in a knockin mouse model of AD that expresses familial AD (FAD) mutations of both APP with humanized amyloid beta (hA?), and presenilin 1 (PS1), in their endogenous patterns (APP/hA?/PS1 animals), corticotropin releasing factor (CRF) levels are increased in key stress-related nuclei, resting corticosteroid levels are elevated, and animals display increased anxiety-related behavior. Endocrine and behavioral phenotypes can be normalized by loss of 1 copy of CRF receptor type-1 (Crfr1), consistent with a perturbation of central CRF signaling in APP/hA?/PS1 animals. However, reductions in anxiety and corticosteroid levels conferred by heterozygosity of CRF receptor type-1 do not improve a deficit in working memory observed in APP/hA?/PS1 mice, suggesting that perturbations of the CRF system are not the primary cause of decreased cognitive performance.
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Project description:In the present study, a new architecture for the generation of grid cells (GC) was implemented on a real robot. In order to test this model a simple place cell (PC) model merging visual PC activity and GC was developed. GC were first built from a simple "several to one" projection (similar to a modulo operation) performed on a neural field coding for path integration (PI). Robotics experiments raised several practical and theoretical issues. To limit the important angular drift of PI, head direction information was introduced in addition to the robot proprioceptive signal coming from the wheel rotation. Next, a simple associative learning between visual place cells and the neural field coding for the PI has been used to recalibrate the PI and to limit its drift. Finally, the parameters controlling the shape of the PC built from the GC have been studied. Increasing the number of GC obviously improves the shape of the resulting place field. Yet, other parameters such as the discretization factor of PI or the lateral interactions between GC can have an important impact on the place field quality and avoid the need of a very large number of GC. In conclusion, our results show our GC model based on the compression of PI is congruent with neurobiological studies made on rodent. GC firing patterns can be the result of a modulo transformation of PI information. We argue that such a transformation may be a general property of the connectivity from the cortex to the entorhinal cortex. Our model predicts that the effect of similar transformations on other kinds of sensory information (visual, tactile, auditory, etc…) in the entorhinal cortex should be observed. Consequently, a given EC cell should react to non-contiguous input configurations in non-spatial conditions according to the projection from its different inputs.
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Project description:Hippocampal formation contains several classes of neurons thought to be involved in navigational processes, in particular place cells and grid cells. Place cells have been associated with a topological strategy for navigation, while grid cells have been suggested to support metric vector navigation. Grid cell-based vector navigation can support novel shortcuts across unexplored territory by providing the direction toward the goal. However, this strategy is insufficient in natural environments cluttered with obstacles. Here, we show how navigation in complex environments can be supported by integrating a grid cell-based vector navigation mechanism with local obstacle avoidance mediated by border cells and place cells whose interconnections form an experience-dependent topological graph of the environment. When vector navigation and object avoidance fail (i.e., the agent gets stuck), place cell replay events set closer subgoals for vector navigation. We demonstrate that this combined navigation model can successfully traverse environments cluttered by obstacles and is particularly useful where the environment is underexplored. Finally, we show that the model enables the simulated agent to successfully navigate experimental maze environments from the animal literature on cognitive mapping. The proposed model is sufficiently flexible to support navigation in different environments, and may inform the design of experiments to relate different navigational abilities to place, grid, and border cell firing.
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Project description:The subjective sense of space may result in part from the combined activity of place cells in the hippocampus and grid cells in posterior cortical regions such as the entorhinal cortex and pre- and parasubiculum. In horizontal planar environments, place cells provide focal positional information, whereas grid cells supply odometric (distance measuring) information. How these cells operate in three dimensions is unknown, even though the real world is three-dimensional. We investigated this issue in rats exploring two different kinds of apparatus: a climbing wall (the 'pegboard') and a helix. Place and grid cell firing fields had normal horizontal characteristics but were elongated vertically, with grid fields forming stripes. It seems that grid cell odometry (and by implication path integration) is impaired or absent in the vertical domain, at least when the rat itself remains horizontal. These findings suggest that the mammalian encoding of three-dimensional space is anisotropic.
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Project description:Neurons in the hippocampus and adjacent brain areas show a large diversity in their tuning to location and head direction, and the underlying circuit mechanisms are not yet resolved. In particular, it is unclear why certain cell types are selective to one spatial variable, but invariant to another. For example, place cells are typically invariant to head direction. We propose that all observed spatial tuning patterns - in both their selectivity and their invariance - arise from the same mechanism: Excitatory and inhibitory synaptic plasticity driven by the spatial tuning statistics of synaptic inputs. Using simulations and a mathematical analysis, we show that combined excitatory and inhibitory plasticity can lead to localized, grid-like or invariant activity. Combinations of different input statistics along different spatial dimensions reproduce all major spatial tuning patterns observed in rodents. Our proposed model is robust to changes in parameters, develops patterns on behavioral timescales and makes distinctive experimental predictions.
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Project description:While grid cells in the medial entorhinal cortex (MEC) of rodents have multiple, regularly arranged firing fields, place cells in the cornu ammonis (CA) regions of the hippocampus mostly have single spatial firing fields. Since there are extensive projections from MEC to the CA regions, many models have suggested that a feedforward network can transform grid cell firing into robust place cell firing. However, these models generate place fields that are consistently too small compared to those recorded in experiments. Here, we argue that it is implausible that grid cell activity alone can be transformed into place cells with robust place fields of realistic size in a feedforward network. We propose two solutions to this problem. Firstly, weakly spatially modulated cells, which are abundant throughout EC, provide input to downstream place cells along with grid cells. This simple model reproduces many place cell characteristics as well as results from lesion studies. Secondly, the recurrent connections between place cells in the CA3 network generate robust and realistic place fields. Both mechanisms could work in parallel in the hippocampal formation and this redundancy might account for the robustness of place cell responses to a range of disruptions of the hippocampal circuitry.
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Project description:Experience-induced expression of immediate-early gene Arc (also known as Arg3.1) is known to be important for consolidation of memory. Using in vivo longitudinal multiphoton imaging, we found orchestrated activity-dependent expression of Arc in the mouse extrastriate visual cortex in response to a structured visual stimulation. In wild-type mice, the amplitude of the Arc response in individual neurons strongly predicted the probability of reactivation by a subsequent presentation of the same stimulus. In a mouse model of Alzheimer's disease, this association was markedly disrupted in the cortex, specifically near senile plaques. Neurons in the vicinity of plaques were less likely to respond, but, paradoxically, there were stronger responses in those few neurons around plaques that did respond. To the extent that the orchestrated pattern of Arc expression reflects nervous system responses to and physiological consolidation of behavioral experience, the disruption in Arc patterns reveals plaque-associated interference with neural network integration.
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Project description:The spatial scale of grid cells may be provided by self-generated motion information or by external sensory information from environmental cues. To determine whether grid cell activity reflects distance traveled or elapsed time independent of external information, we recorded grid cells as animals ran in place on a treadmill. Grid cell activity was only weakly influenced by location, but most grid cells and other neurons recorded from the same electrodes strongly signaled a combination of distance and time, with some signaling only distance or time. Grid cells were more sharply tuned to time and distance than non-grid cells. Many grid cells exhibited multiple firing fields during treadmill running, parallel to the periodic firing fields observed in open fields, suggesting a common mode of information processing. These observations indicate that, in the absence of external dynamic cues, grid cells integrate self-generated distance and time information to encode a representation of experience.
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Project description:Navigation is one of the most fundamental skills of animals. During spatial navigation, grid cells in the medial entorhinal cortex process speed and direction of the animal to map the environment. Hippocampal place cells, in turn, encode place using sensory signals and reduce the accumulated error of grid cells for path integration. Although both cell types are part of the path integration system, the dynamic relationship between place and grid cells and the error reduction mechanism is yet to be understood. We implemented a realistic model of grid cells based on a continuous attractor model. The grid cell model was coupled to a place cell model to address their dynamic relationship during a simulated animal's exploration of a square arena. The grid cell model processed the animal's velocity and place field information from place cells. Place cells incorporated salient visual features and proximity information with input from grid cells to define their place fields. Grid cells had similar spatial phases but a diversity of spacings and orientations. To determine the role of place cells in error reduction for path integration, the animal's position estimates were decoded from grid cell activities with and without the place field input. We found that the accumulated error was reduced as place fields emerged during the exploration. Place fields closer to the animal's current location contributed more to the error reduction than remote place fields. Place cells' fields encoding space could function as spatial anchoring signals for precise path integration by grid cells.
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