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 four 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); a combination of these parameters with the size of the place field (Combination method); and the spatial information held by the cells (Information method). The methods performed differently from each other on both model and real data. In real datasets, vastly different numbers of place cells were identified using the four 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. Ultimately, we recommend the Peak method be used in future studies to identify place cell populations, as this method is robust to moderate variations in place field within a session, and makes no inherent assumptions about the spatial information in place fields, 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 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 6 B). 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.…”

BTSP, not STDP, Drives Shifts in Hippocampal Representations During Familiarization

“…Motion correction of the raw movies, cell detection and signal extraction were performed as previously published using custom MATLAB scripts (Dong et al, 2021). Place fields (PFs) were identified and defined as in Dong et al 2021 using a method combining criteria about the peak fluorescence, stability and size of the PF compared against chance levels (Dombeck et al, 2010; Dong et al, 2021; Grijseels et al, 2021; Sheffield et al, 2017). Note that the criteria previously established were loose enough to not exclude shifting PFs.…”

Learning produces a hippocampal cognitive map in the form of an orthogonalized state machine

“…To identify significant place cells, we utilized an approach based on Dombeck et al, 2010 137 (but see also Grijseels et al, 2021 138 for overall caveats with such approaches). Place fields were determined during active trials, indicated by active licking within reward zones, and at running speeds greater than 5cm/s.…”