Grid cells are a type of neuron with a key role in how the brain represents or processes spatial information. Two of the co-authors of the current pre-print – Edvard Moser and May-Britt Moser – were awarded the Nobel Prize in 2014 for their work on grid cells. The cells are found in the medial entorhinal cortex and receive notable input from the hippocampus. Grid cells increase and decrease their firing rate as an animal moves through space. They are so named because when the activity is mapped out into space, it forms a hexagonal grid of peaks of activity.
The pre-print covers further analysis using data from an earlier paper from the same lab (Bonnevie et al. 2013). The Bonnevie paper showed that blocking activity in the hippocampus disrupted the grid pattern firing of grid cells. The current pre-print looks at whether disrupting the grid pattern also disrupts the synchrony between activity of pairs of neurons in time and space.
Several main measures were used:
- The gridness of a given cell, which can range from -2 to 2. Described in detail in Tocker et al. (2015). Gridness measures how selectively the spatial autocorrelation map of the cell’s firing fits the hexagonal pattern common to grid cells. An autocorrelation map is produced by correlating the firing rate of a neuron when an animal is in one position in an arena with the neuron’s activity in positions in locations displaced from this point.
- The spatial correlation of a pair of cells. Similar to the autocorrelation map, but this measure correlates the activity of one neuron with another. The value used for this variable seems to be a single value, and I understand it to be the correlation at point [0,0] in the map, but I was not sure.
- The temporal correlation of a pair of cells. It is described as a “Pearson correlation of their spike trains”, which I assume to be a correlation of a binned spike rate.
- Rayleigh score. A measure of direction selectivity to measure firing preference for head directions. Described in Tocker et al. (2015).
Pairs of cells were selected from the Bonnevie et al. (2013) data where gridness score was at least 0.5 before inhibition and at most 0.2 during inhibition i.e. where gridness was lost. The authors state that “different thresholds…did not change the central finding of the analysis.
The paper aims to look at:
- Whether temporal synchrony i.e. correlation persists during loss of gridness.
- Whether spatial synchrony i.e. correlation persists during loss of gridness.
- Whether their is a difference in the above between grid cells that become head-direction selective when gridness is lost, and those that don’t.
In order to measure whether temporal or spatial sychrony (correlation) persists, the authors examine the correlation coefficients representing temporal and spatial synchrony before and during inhibition of the hippocampus. The authors do this by calculating the correlation betwen the pre-inhibition correlation coefficients and the during inhibition correlation coefficients. The pre- vs during-inhibition measures were positively correlated (r = 0.57 for temporal correlation, r = 0.34 for temporal correlation), which the authors take as a demonstration that the synchrony persists even when the gridness is lost. Temporal and spatial correlations were strongly correlated with each other.
I think that correlation of pre and during scores was an unusual way to demonstrate that the synchrony persists – out of the pairs of neurons analysed, over 40% were not significantly temporally synchronised and 60% were not significantly spatially synchronised. Without knowing if the pre and during synchrony scores of these neurons is correlated, it is difficult to know the extent to which correlation between pre and during sychrony scores for the whole sample represents ‘persistence’ of synchrony. Indeed, the percentage of neurons that were significantly synchronised fell from 57% to 26% (temporal) and from 30% to 7% (spatial), suggesting that a synchrony was not persistent to a substantial extent. Perhaps a better measure might have been to subtract pre-inhibition scores from post-inhibition scores, then to assess whether the residual scores are significantly different from zero.
In the Bonnevie et al. (2013) study, it was found that inhibiting hippocampal input caused a subset of grid cells to become sensitive to the direction that the head of the animal was pointing. The authors of the present paper looked at whether this subset of cells differed in the persistence of synchrony. They found that pre and during scores of temporal synchrony of cells that did not become head-direction sensitive more correlated (r = 0.71) than direction sensitive cells (r = 0.52, both p <0.01). For spatial corrrelations, non-direction sensitive cells were significantly correlate (r = 0.46, p <0.01) while direction sensitive cells were not (r = 0.22, p = 0.19).
Why it is important
The authors argue that the persistence of some temporal and spatial sychrony between pairs of entorhinal grid cells even when a major external input from the hippocampus is blocked supports a particular model of entorhinal acitivty. That is, the idea that the structure of grid cell activity is not the result of feedforward input from the hippocampus, but instead the product of circuit dynamics within entorhinal cortex.
The paper also provides an example of reuse and reanalysis of existing data to answer further questions. This is efficient in terms of time, money, and impact on the animals used. It is worthwhile highlighting this admirable research approach. Further, the authors have made another admirable move, by making the MATLAB code for their paper available online via GitHub (github.com/noamza/muscimol).
To what extent does synchrony persist beyond remaining broadly correlated?
What distinguishes cells that maintain pair synchrony during inhibition vs those that do not? The relationship between gridness pre or during inhibition and synchrony pre and during inhibition was examined. Is there a relationship between the extent of loss of gridness and the persistence or not of synchrony?
What is the physical arrangement of the circuit that drives these dynamics? Grid cells are topographically organised, e.g. cells near to each other have similar sized grids, and the scale of the grid increases for cells more ventrally. Does physical proximity affect how likely cells are to be or remain synchronised?
Posted on: 30 August 2019 , updated on: 6 September 2019Read preprint
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