Calcium Imaging Reveals Fast Tuning Dynamics of Hippocampal Place Cells and CA1 Population Activity during Free Exploration Task in Mice

Abstract

Hippocampal place cells are a well-known object in neuroscience, but their place field formation in the first moments of navigating in a novel environment remains an ill-defined process. To address these dynamics, we performed in vivo imaging of neuronal activity in the CA1 field of the mouse hippocampus using genetically encoded green calcium indicators, including the novel NCaMP7 and FGCaMP7, designed specifically for in vivo calcium imaging. Mice were injected with a viral vector encoding calcium sensor, head-mounted with an NVista HD miniscope, and allowed to explore a completely novel environment (circular track surrounded by visual cues) without any reinforcement stimuli, in order to avoid potential interference from reward-related behavior. First, we calculated the average time required for each CA1 cell to acquire its place field. We found that 25% of CA1 place fields were formed at the first arrival in the corresponding place, while the average tuning latency for all place fields in a novel environment equaled 247 s. After 24 h, when the environment was familiar to the animals, place fields formed faster, independent of retention of cognitive maps during this session. No cumulation of selectivity score was observed between these two sessions. Using dimensionality reduction, we demonstrated that the population activity of rapidly tuned CA1 place cells allowed the reconstruction of the geometry of the navigated circular maze; the distribution of reconstruction error between the mice was consistent with the distribution of the average place field selectivity score in them. Our data thus show that neuronal activity recorded with genetically encoded calcium sensors revealed fast behavior-dependent plasticity in the mouse hippocampus, resulting in the rapid formation of place fields and population activity that allowed the reconstruction of the geometry of the navigated maze.

Low-cost platform for multi-animal chronic local field potential video monitoring with graphical user interface (GUI) for seizure detection and behavioral scoring

“…For example, we could imagine real-time behavioral scoring of seizures by adding behavioral tracking and pose estimation analysis to the video stream, as has been already implemented in Bonsai in other workflows (Dreosti et al, 2015). Furthermore, our workflow could easily be updated to realize chronic monitoring of other data types such as fiber photometry (Soares, Atallah & Paton, 2016), calcium imaging (Sotskov et al, 2022), and high-density electrophysiology (Karalis & Sirota, 2022). Finally, Bonsai has been used in different animal models, including zebrafish and drosophila, suggesting that our platform may be translatable to other animal models of epilepsy (Itskov et al, 2014; Hortopan, Dinday, & Baraban, 2010; Sun et al, 2012).…”

Low-Cost Platform for Multianimal Chronic Local Field Potential Video Monitoring with Graphical User Interface (GUI) for Seizure Detection and Behavioral Scoring

“…For example, we could imagine real-time behavioral scoring of seizures by adding behavioral tracking and pose estimation analysis to the video stream, as has been already implemented in Bonsai in other workflows ( Dreosti et al, 2015 ). Furthermore, our workflow could easily be updated to realize chronic monitoring of other data types such as fiber photometry ( Soares et al, 2016 ), calcium imaging ( Sotskov et al, 2022 ), and high-density electrophysiology ( Karalis and Sirota, 2022 ). Finally, Bonsai has been used in different animal models, including zebrafish and Drosophila , suggesting that our platform may be translatable to other animal models of epilepsy ( Hortopan et al, 2010 ; Sun et al, 2012 ; Itskov et al, 2014 ).…”

Non-Negative Matrix Factorization for Analyzing State Dependent Neuronal Network Dynamics in Calcium Recordings

“…DR approaches have many applications in the analysis of neural data, including analysis of electrophysiological recordings (9,12,13), automation of trace extraction of neuronal activity in fluorescent recordings (1,(14)(15)(16)(17)(18), and as a pre-processing step to more complex behavioral decoding (19). To analyze network dynamics in calcium recordings, non-linear DR methods have emerged to map dynamics to low dimensional manifolds (20)(21)(22)(23). While excellent for very low dimensional visualization (24,25), the dynamics are difficult to interpret beyond the manifold being modeled, with manifolds being difficult to interpret beyond three dimensions.…”