Distinct retrosplenial cortex cell populations and their spike dynamics during ketamine-induced unconscious state

Abstract

Ketamine is known to induce psychotic-like symptoms, including delirium and visual hallucinations. It also causes neuronal damage and cell death in the retrosplenial cortex (RSC), an area that is thought to be a part of high visual cortical pathways and at least partially responsible for ketamine’s psychotomimetic activities. However, the basic physiological properties of RSC cells as well as their response to ketamine in vivo remained largely unexplored. Here, we combine a computational method, the Inter-Spike Interval Classification Analysis (ISICA), and in vivo recordings to uncover and profile excitatory cell subtypes within layers 2&3 and 5&6 of the RSC in mice within both conscious, sleep, and ketamine-induced unconscious states. We demonstrate two distinct excitatory principal cell sub-populations, namely, high-bursting excitatory principal cells and low-bursting excitatory principal cells, within layers 2&3, and show that this classification is robust over the conscious states, namely quiet awake, and natural unconscious sleep periods. Similarly, we provide evidence of high-bursting and low-bursting excitatory principal cell sub-populations within layers 5&6 that remained distinct during quiet awake and sleep states. We further examined how these subtypes are dynamically altered by ketamine. During ketamine-induced unconscious state, these distinct excitatory principal cell subtypes in both layer 2&3 and layer 5&6 exhibited distinct dynamics. We also uncovered different dynamics of local field potential under various brain states in layer 2&3 and layer 5&6. Interestingly, ketamine administration induced high gamma oscillations in layer 2&3 of the RSC, but not layer 5&6. Our results show that excitatory principal cells within RSC layers 2&3 and 5&6 contain multiple physiologically distinct sub-populations, and they are differentially affected by ketamine.

Scaling of Optogenetically Evoked Signaling in a Higher-Order Corticocortical Pathway in the Anesthetized Mouse

“…Consistent with this possibility, ketamine anesthesia (used here) blocks NMDA receptors, HCN channels, and other molecules involved in highly non-linear forms of signaling in cortical neurons (Chen et al, 2009 ; Antic et al, 2010 ; Sleigh et al, 2014 ). Indeed, a recent study comparing RSC activity in awake vs. ketamine-anesthetized mice found diverse ketamine-associated changes (Fox et al, 2017 ). Thus, a remaining question is the extent to which the patterns of corticocortical signaling described depend on brain state and anesthetic.…”

Neural Coding of Cell Assemblies via Spike-Timing Self-Information

“…The importance of spike variability is evident from the fact the diminished variability (i.e., rhythmic firing) underlies anesthesia-induced unconsciousness ( Fig. S2 ) ( Fox et al 2017 ; Kuang et al 2010 ; Li et al 2018 ). Third, the robustness of this ISI-based surprisal code also comes from its ternary nature of coding (positive or negative surprisals, plus the ground state).…”

Neuregulin 1 and ErbB4 Kinase Actively Regulate Sharp Wave Ripples in the Hippocampus

“…For in vivo recording, 8-to 12-week male animals were used. Electrodes (eight) were made by first constructing tetrodes folded, twisted, and fastened together with low heat (Lin et al, 2006;Fox et al, 2017;Tan et al, 2018;10% iridium, 90% platinum, California Fine Wire Company). Tetrodes (eight tetrodes) were inserted into polyimide tubing (inner diameter 200.5 mm; outer diameter 254.1 mm, Polymicro Technologies) that were directly attached to microdrivable system (Lin et al, 2006;Tan et al, 2018).…”

“…The dorsal hippocampus (dHPC; 2.0 mm anterior, 1.5 mm lateral, 1.15 mm ventral) was identified and a circular 1-mm diameter burr hole made in the skull. Before the removal of the dura, five screws were implanted around the burr hole, including one in the center of the cerebellum that was used to attach the reference via soldering (Gillespie et al, 2016;Fox et al, 2017;Tan et al, 2018;Oliva et al, 2020). The electrode for surgical implantation was more dorsal to the intended region in order that the electrodes might be advanced to location before recording.…”

“…Offline, spikes were sorted by first loading into MClust (MClust-4.4;Redish and Schmitzer-Torbert, 2002) and specifically sorted using the unsupervised clustering algorithm KlustaKwik (Harris et al, 2000). Spikes were sorted for energy, peak and valley (Spellman et al, 2015;Tan et al, 2018), then verified by visual inspection, interspike interval (ISI) distribution and isolation distance (Harris et al, 2001;Schmitzer-Torbert and Redish, 2004;Schmitzer-Torbert et al, 2005;Fox et al, 2017).…”