Choice of method of place cell classification determines the population of cells identified

Abstract

Place cells, spatially responsive hippocampal cells, provide the neural substrate supporting navigation and spatial memory. Historically most studies of these neurons have used electrophysiological recordings from implanted electrodes but optical methods, measuring intracellular calcium, are becoming increasingly common. Several methods have been proposed as a means to identify place cells based on their calcium activity but there is no common standard and it is unclear how reliable different approaches are. Here we tested three methods that have previously been applied to two-photon hippocampal imaging or electrophysiological data, using both model datasets and real imaging data. These methods use different parameters to identify place cells, including the peak activity in the place field, compared to other locations (the Peak method); the stability of cells’ activity over repeated traversals of an environment (Stability method); and a combination of these parameters with the size of the place field (Combination method). The three methods performed differently from each other on both model and real data. The Peak method showed high sensitivity and specificity for detecting model place cells and was the most robust to variations in place field width, reliability and field location. In real datasets, vastly different numbers of place cells were identified using the three methods, with little overlap between the populations identified as place cells. Therefore, choice of place cell detection method dramatically affects the number and properties of identified cells. We recommend the Peak method be used in future studies to identify place cell populations, unless there is an explicit theoretical reason for detecting cells with more narrowly defined properties.

Tracking Longitudinal Population Dynamics of Single Neuronal Calcium Signal Using SCOUT

“…Computing statistics for average JS divergence between place fields, percentiles exceeding 0.95 and information score thresholds in range ½0:8; 1:5 result in statistically lower JS divergence between place fields exclusively identified by SCOUT compared with those identified exclusively by cellReg (min p = 0.0079, max p = 0.0488, linear mixed effects model with fixed cell-tracking method grouped by recording, Figure 6B). This range of values matches that of a previous study (Grijseels et al, 2021) that suggests a percentile threshold of 0.95-0.99 of place cells, while experimentation showed that neurons with information score threshold exceeding 1.0 showed strong spatially localized firing activity.…”

Beyond correlation: optimal transport metrics for characterizing representational stability and remapping in neurons encoding spatial memory

“…As such, we offer examples of the EMD's increased spatial sensitivity using activity maps from recorded cells. We offer a combination of individual examples that depict specific cell types, or representative maps, as well as an open-source place cell dataset from the hippocampus (HPC; Grijseels D. et al, 2021 ).…”

“…Place cells from hippocampal CA1 two-photon microscopy recordings in mice running across a virtual linear track were obtained from a published dataset (Grijseels D. et al, 2021 ). Deconvolved spike trains from Suite2P outputs were used directly as cumulative spike counts and converted to 1D firing rates.…”

“…Deconvolved spike trains from Suite2P outputs were used directly as cumulative spike counts and converted to 1D firing rates. Δ F / F 0 fluorescence values were computed and preprocessed in the way described by the authors (Grijseels D. et al, 2021 ). The authors included a normalization step involving 15 s intervals.…”

APOE4 expression confers a mild, persistent reduction in neurovascular function in the visual cortex and hippocampus of awake mice

“…Net haemodynamic measurements were recorded using the Oxy-CBF probe and individual vessels and neurons were imaged using 2 photon microscopy. Figure adapted from Grijseels et al (2021); (D) Example output from a capillary line scan after preprocessing (left) and the same image binarised (right) for haematocrit measurements. No genotype differences were found in RBCV (E) or in haematocrit (G) measures but a trend towards increased RBCV coefficient of variation (CV) was observed in APOE4 animals (F) .…”