Cell-Type and State-Dependent Synchronization among Rodent Somatosensory, Visual, Perirhinal Cortex, and Hippocampus CA1

Abstract

Beta and gamma rhythms have been hypothesized to be involved in global and local coordination of neuronal activity, respectively. Here, we investigated how cells in rodent area S1BF are entrained by rhythmic fluctuations at various frequencies within the local area and in connected areas, and how this depends on behavioral state and cell type. We performed simultaneous extracellular field and unit recordings in four connected areas of the freely moving rat (S1BF, V1M, perirhinal cortex, CA1). S1BF spiking activity was strongly entrained by both beta and gamma S1BF oscillations, which were associated with deactivations and activations, respectively. We identified multiple classes of fast spiking and excitatory cells in S1BF, which showed prominent differences in rhythmic entrainment and in the extent to which phase locking was modulated by behavioral state. Using an additional dataset acquired by whole-cell recordings in head-fixed mice, these cell classes could be compared with identified phenotypes showing gamma rhythmicity in their membrane potential. We next examined how S1BF cells were entrained by rhythmic fluctuations in connected brain areas. Gamma-synchronization was detected in all four areas, however we did not detect significant gamma coherence among these areas. Instead, we only found long-range coherence in the theta-beta range among these areas. In contrast to local S1BF synchronization, we found long-range S1BF-spike to CA1–LFP synchronization to be homogeneous across inhibitory and excitatory cell types. These findings suggest distinct, cell-type contributions of low and high-frequency synchronization to intra- and inter-areal neuronal interactions.

Millisecond precision temporal encoding of stimulus features during cortically generated gamma oscillations in the rat somatosensory cortex

“…In cortical networks, sensory processing results from the interaction between sensory inputs relayed from the thalamus and internally generated activity (Destexhe & Contreras, 2006;Hasenstaub et al 2007;Buonomano & Maass, 2009;Alenda et al 2010). However, how emergent activity such as gamma oscillations that have been observed in the barrel cortex of both anaesthetized and awake animals interact with high-frequency sensory inputs at the cellular level is not fully understood (Ewert et al 2008(Ewert et al , 2015Vinck et al 2015).…”

Long‐range cortical dynamics: a perspective from the mouse sensorimotor whisker system

“…Studies in the primate visual and somatosensory system have implicated the synchronization of activity across cortical areas through oscillations as means to facilitate information transfer between areas (Brovelli et al, 2004;Fries, 2005;Haegens et al, 2011;Ni et al, 2016). Spiking activity in barrel cortex is strongly locked to beta and gamma band LFP (Vinck et al, 2015). Inter-areal LFP coherence between S1 and remote areas such as perirhinal cortex, hippocampus CA1, and visual cortex peaked between theta-beta band, consistent with idea of low-frequency band neural oscillation serves as top-down communication channel (Wang, 2010;van Kerkoerle et al, 2014;Bastos et al, 2015).…”

Functional determinants of enhanced and depressed inter-areal information flow in NREM sleep between neuronal ensembles in rat cortex and hippocampus

“…The data utilized for this study was collected during experiments described in recently published manuscripts (4,62,63). For this reason, while all analyses presented here have been performed independently and do not overlap with the results presented in those articles, methods and data preprocessing will be only summarized here.…”

“…Visual stimuli ended when animals crossed an infrared beam located at the end of the area where sandpaper was present. The grain of the sandpaper predicted the amount of reward animals would receive after choosing the correct side arm (62,63). One pellet was also provided in the middle arm of the maze if animals had chosen the correct side arm in the previous trial.…”

“…All animals were highly trained on the task and performed 60.1 ± 18.7 (mean ± standard deviation) trials per recording session, with an average success rate of 59.2 ± 0.9 % (mean ± standard error), significantly different from chance (p=2.1×10 −8 ). For further details on the task, see (62,63).…”