A neural code for egocentric spatial maps in the human medial temporal lobe

Kunz, Lukas; Brandt, Armin; Reinacher, Peter C.; Staresina, Bernhard P.; Reifenstein, Eric T.; Weidemann, Christoph T.; Herweg, Nora A.; Patel, Anish H.; Tsitsiklis, Melina; Kempter, Richard; Kahana, Michael J.; Schulze‐Bonhage, Andreas; Jacobs, Joshua

doi:10.1016/j.neuron.2021.06.019

Cited by 59 publications

(60 citation statements)

References 106 publications

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“…Cells similar to ConSink cells have been reported in mouse open-field foraging 7 and in humans performing a multiple object-in-location virtual reality test 14 . In the present experiment, these cells represented only 13% of cells during a foraging task, fewer than half of the 31% seen during the navigation task, and, further, were not clustered around the concurrent navigation task goal.…”

Section: Discussionmentioning

confidence: 58%

“…When a goal is introduced, place cells become directional 9 and their fields move in the direction of the goal when it is moved 10 , 11 . Previous work has shown that cells in the hippocampal formation encode heading towards a goal and other locations in bats 12 , mice 7 , primates 13 and humans 14 . An important caveat in such studies is that the animal usually moves towards the goal whenever possible, precluding full assessment of the neuronal activity in non-goalward directions.…”

Section: Mainmentioning

confidence: 99%

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Hippocampal place cells have goal-oriented vector fields during navigation

Ormond

O’Keefe

2022

Nature

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The hippocampal cognitive map supports navigation towards, or away from, salient locations in familiar environments1. Although much is known about how the hippocampus encodes location in world-centred coordinates, how it supports flexible navigation is less well understood. We recorded CA1 place cells while rats navigated to a goal on the honeycomb maze2. The maze tests navigation via direct and indirect paths to the goal and allows the directionality of place cells to be assessed at each choice point. Place fields showed strong directional polarization characterized by vector fields that converged to sinks distributed throughout the environment. The distribution of these ‘convergence sinks’ (ConSinks) was centred near the goal location and the population vector field converged on the goal, providing a strong navigational signal. Changing the goal location led to movement of ConSinks and vector fields towards the new goal. The honeycomb maze allows independent assessment of spatial representation and spatial action in place cell activity and shows how the latter relates to the former. The results suggest that the hippocampus creates a vector-based model to support flexible navigation, allowing animals to select optimal paths to destinations from any location in the environment.

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Section: Discussionmentioning

confidence: 58%

Section: Mainmentioning

confidence: 99%

Hippocampal place cells have goal-oriented vector fields during navigation

Ormond

O’Keefe

2022

Nature

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“…On the other hand, the task-related activations observed in the aPFC and TC at both mid-and long latencies could be linked to the online processing of home direction during rotations. This interpretation is supported both by human studies showing that the aPFC is crucial to encoding spatial information about goals (Ekstrom et al, 2003;Ciaramelli, 2008;Spiers, 2008; see Poucet and Hok, 2017 for similar evidence in rodents) and by those reporting that the activities of the medial temporal lobe dynamically change according to the current Euclidean distance of the spatial goal during navigation (Ekstrom et al, 2003;Howard et al, 2014;Spiers and Barry, 2015;Kunz et al, 2021). Together, the sustained activation observed in the prefrontal and temporal regions while participants were passively rotated could have then contributed to the maintenance of home orientation in working memory and in the encoding of its angular distance for use in the upcoming goal-directed pointing movements.…”

Section: The Early Processing Of Rotation-related Cues Is Largely Ind...mentioning

confidence: 84%

On the Dynamics of Spatial Updating

et al. 2022

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Most of our knowledge on the human neural bases of spatial updating comes from functional magnetic resonance imaging (fMRI) studies in which recumbent participants moved in virtual environments. As a result, little is known about the dynamic of spatial updating during real body motion. Here, we exploited the high temporal resolution of electroencephalography (EEG) to investigate the dynamics of cortical activation in a spatial updating task where participants had to remember their initial orientation while they were passively rotated about their vertical axis in the dark. After the rotations, the participants pointed toward their initial orientation. We contrasted the EEG signals with those recorded in a control condition in which participants had no cognitive task to perform during body rotations. We found that the amplitude of the P1N1 complex of the rotation-evoked potential (RotEPs) (recorded over the vertex) was significantly greater in the Updating task. The analyses of the cortical current in the source space revealed that the main significant task-related cortical activities started during the N1P2 interval (136–303 ms after rotation onset). They were essentially localized in the temporal and frontal (supplementary motor complex, dorsolateral prefrontal cortex, anterior prefrontal cortex) regions. During this time-window, the right superior posterior parietal cortex (PPC) also showed significant task-related activities. The increased activation of the PPC became bilateral over the P2N2 component (303–470 ms after rotation onset). In this late interval, the cuneus and precuneus started to show significant task-related activities. Together, the present results are consistent with the general scheme that the first task-related cortical activities during spatial updating are related to the encoding of spatial goals and to the storing of spatial information in working memory. These activities would precede those involved in higher order processes also relevant for updating body orientation during rotations linked to the egocentric and visual representations of the environment.

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“…Functional understanding of neural ensembles requires the ability to reliably measure the activity of single neurons, as well as to discriminate the activity of many neighboring neurons, across extended intervals of time (Buzsáki, 2004;Csicsvari, et al, 2003). In vivo electrophysiological techniques with extracellular electrodes that measure action potentials ('spikes') and subthreshold oscillations have been used to record activity from the brain of several mammalian species and have generated a tremendous amount of information (Cacucci et al, 2008;O'Keefe and Dostrovsky, 1971;O'Keefe and Nadel, 1976;Kunz, et al, 2021). Moreover, continuous technological advances have ensured the lasting relevance of this technique (Steinmetz, et al, 2021;Wu, et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

Framework for acquisition and automated sorting of neural spikes from multielectrode recordings in freely-moving mice

Strohl

Gallagher

Gómez

et al. 2021

Preprint

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BackgroundExtracellular recording represents a crucial electrophysiological technique in neuroscience for studying the activity of single neurons and neuronal populations. The electrodes capture voltage traces that, with the help of analytical tools, reveal action potentials (‘spikes’) as well as local field potentials. The process of spike sorting is used for the extraction of action potentials generated by individual neurons. Until recently, spike sorting was performed with manual techniques, which are laborious and unreliable due to inherent operator bias. As neuroscientists add multiple electrodes to their probes, the high-density devices are able to record hundreds to thousands of neurons simultaneously, making the manual spike sorting process increasingly difficult. The advent of automated spike sorting software has offered a compelling solution to this issue. The purpose of this study is to provide a simple-to-execute framework for using MountainSort, an automated spike sorting pipeline, in conjunction with MATLAB and the acquisition system (Cheetah, Neuralynx). We validate this automated framework with neural recordings from the hippocampus and prelimbic cortex. MethodsMultielectrode recordings of freely-moving mice are obtained from the CA1 region of the hippocampus as they navigate a linear track. Multielectrode recordings are also acquired from the prelimbic cortex, a region of the medial prefrontal cortex, while the mice are tested in a T maze. All animals are implanted with custom-designed, 3D-printed microdrives that carry 16 electrodes, which are bundled in a 4-tetrode geometry. ResultsWe provide an overview of a framework for analyzing single-unit data in which we have concatenated the acquisition system (Cheetah, Neuralynx) with analytical software (MATLAB) and an automated spike sorting pipeline (MountainSort). We give precise instructions on how to implement the different steps of the framework, as well as explanations of our design logic. We validate this framework by comparing manually-sorted spikes against automatically-sorted spikes, using neural recordings of the hippocampus and prelimbic cortex in freely-moving mice. ConclusionsAutomated spike sorting is a necessity for medium and large-scale extracellular neural recordings. Here, we have smoothly integrated MountainSort-based spike sorting into a framework for acquisition and analysis of multielectrode brain recordings in mice.

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A neural code for egocentric spatial maps in the human medial temporal lobe

Cited by 59 publications

References 106 publications

Hippocampal place cells have goal-oriented vector fields during navigation

Hippocampal place cells have goal-oriented vector fields during navigation

On the Dynamics of Spatial Updating

Framework for acquisition and automated sorting of neural spikes from multielectrode recordings in freely-moving mice

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