Daily Rhythms of Spike Coding in the Rat Supraoptic Nucleus

Abstract

Novel measures of coding based on interspike intervals were used to characterise the rhythms of single unit activity in the supraoptic nucleus during the day/night cycle in urethane-anaesthetised rats in vivo. Both continuously firing and phasic cells showed significant (P < 0.001) diurnal rhythms of spike frequency and in the irregularity of firing, as quantified by the log interval entropy (ENT). Comparison of rhythms in log interval ENT showed that the amplitude of the rhythms was greater for the continuously firing cells than for the phasic cells (P = 0.002). Rhythms persisted after hypertonic stimulation or pinealectomy and both treatments reduced the amplitude significantly only for the continuously firing cell group. By contrast, the mesor (i.e. mid-point of the rhythm) was reduced only for the phasic cell group. A similar analysis applied to the activity of cells of the suprachiasmatic nucleus showed that, after pinealectomy, there was a significant rhythm in ENT (P < 0.001) but not firing rate; however, the amplitude of the rhythm in ENT was attenuated (P = 0.047). Diurnal changes in the electrical activity of supraoptic cells are consistent with previously reported circadian changes in magnocellular neuropeptide release. Differences between continuous and phasic cell groups in the effects of osmotic stimulation on rhythmic activity indicate that the two cell types differ in their coding of osmolality and zeitgeber time information. The different effects of pinealectomy on the supraoptic and suprachiasmatic nuclei suggest that removal of endogenous melatonin unmasks a difference in circadian coding between the two nuclei.

Circadian regulation of membrane physiology in neural oscillators throughout the brain

“…This frees the SON to respond with greater effect to input from the OVLT, thereby increasing AVP secretion (Trudel & Bourque, ). Consistent with this idea is the prior report that SON neurons have increased firing frequency in vivo during the late day (Bhumbra, Lombardelli, Gonzalez, Parsy, & Dyball, ). The physiological effect of this pathway is to allow for a late sleep, non‐osmotically driven increases in AVP secretion to prevent nocturnal polyuria and interrupted sleep.…”

Systematic review: pain, cognition, and cardioprotection—unpacking oxytocin’s contributions in a sport context

“…Additionally, oxytocin’s recognized anti-inflammatory properties and its ability to facilitate tissue regeneration against oxidative stress have been recognized in rat studies ( Wang et al, 2020 ; Frey Law et al, 2008 ; González-Hernández et al, 2017 ; Arabacı Tamer et al, 2020 ). Furthermore, the degree of inflammation seems to be linked directly to oxytocin in a dose-dependent manner in rats ( Juif and Poisbeau, 2013 ; Bhumbra et al, 2009 ; Erbas et al, 2014 ; Indrayan et al, 2023 ). Exogenous oxytocin may have the ability to lower muscle temperature in mice and rats ( Watts et al, 2022 ), inevitably highlighting the importance of temperature and other variables.…”

Reading Between the Spikes of the Hypothalamic Neural Code

“…It has been used subsequently to show that supraoptic responses to osmotic stimulation demonstrate an increase in the regularity of the spike activity of continuously firing cells but not phasic cells (10). Day/night rhythms in spike irregularity have been demonstrated for neurones recorded in the suprachiasmatic (9) and supraoptic (77) nuclei by monitoring diurnal changes in log interval entropy.…”

“…Although we have used nonparametric C-association test statistics to detect the presence of a significant rhythm (77), log interval entropies tend to conform to a Gaussian distribution that is amenable to parametric analysis. It is therefore possible to undertake cosinor multiple regression (78) to estimate the amplitude, acrophase and mesor of the rhythm of log interval entropy and assign confidence limits to the estimates.…”

“…It is therefore possible to undertake cosinor multiple regression (78) to estimate the amplitude, acrophase and mesor of the rhythm of log interval entropy and assign confidence limits to the estimates. Parametric modelling of log interval entropy thus permits statistically valid comparisons between different rhythms of activity, which can be used, for example, to detect phase shifts and attenuations in rhythm (77). Such comparisons of rhythms could not be undertaken with statistical validity without the introduction of measures of neural activity amenable to parametric modelling, such as the log interval entropy.…”