From Fast Oscillations to Circadian Rhythms: Coupling at Multiscale Frequency Bands in the Rodent Subcortical Visual System

Metabolic cues impact non-oscillatory intergeniculate leaflet and ventral lateral geniculate nucleus: standard versus high-fat diet comparative study

The intergeniculate leaflet and ventral lateral geniculate nucleus (IGL/VLG) are subcortical structures involved in entrainment of the brain's circadian system to photic and non-photic (e.g. metabolic and arousal) cues. Both receive information about environmental light from photoreceptors, exhibit infra-slow oscillations (ISO) in vivo, and connect to the master circadian clock. Although current evidence demonstrates that the IGL/VLG communicate metabolic information and are crucial for entrainment of circadian rhythms to time-restricted feeding, their sensitivity to food intake-related peptides has not been investigated yet. We examined the effect of metabolically relevant peptides on the spontaneous activity of IGL/VLG neurons. Using ex vivo and in vivo electrophysiological recordings as well as in situ hybridisation, we tested potential sensitivity of the IGL/VLG to anorexigenic and orexigenic peptides, such as cholecystokinin, glucagon-like peptide 1, oxyntomodulin, peptide YY, orexin A and ghrelin. We explored neuronal responses to these drugs during day and night, and in standard vs. high-fat diet conditions. We found that IGL/VLG neurons responded to all the substances tested, except peptide YY. Moreover, more neurons responded to anorexigenic drugs at night, while a high-fat diet affected the IGL/VLG sensitivity to orexigenic peptides. Interestingly, ISO neurons responded to light and orexin A, but did not respond to the other food intake-related peptides. In contrast, non-ISO cells were activated by metabolic peptides, with only some being responsive to light. Our results show for the first time that peptides involved in the body's energy homeostasis stimulate the thalamus and suggest functional separation of the IGL/VLG cells. KEY POINTS: The intergeniculate leaflet and ventral lateral geniculate nucleus (IGL/VLG) of the rodent thalamus process various signals and participate in circadian entrainment. In both structures, cells exhibiting infra-slow oscillatory activity as well as non-rhythmically firing neurons being observed. Here, we reveal that only one of these two groups of cells responds to anorexigenic (cholecystokinin, glucagon-like peptide 1 and oxyntomodulin) and orexigenic (ghrelin and orexin A) peptides. Neuronal responses vary depending on the time of day (day vs. night) and on the diet (standard vs. high-fat diet). Additionally, we visualised receptors to the tested peptides in the IGL/VLG using in situ hybridisation. Our results suggest that two electrophysiologically different subpopulations of IGL/VLG neurons are involved in two separate functions: one related to the body's energy homeostasis and one associated with the subcortical visual system.

Electrophysiology as a Tool to Decipher the Network Mechanism of Visceral Pain in Functional Gastrointestinal Disorders

Abdominal pain, including visceral pain, is prevalent in functional gastrointestinal (GI) disorders (FGIDs), affecting the overall quality of a patient's life. Neural circuits in the brain encode, store, and transfer pain information across brain regions. Ascending pain signals actively shape brain dynamics; in turn, the descending system responds to the pain through neuronal inhibition. Pain processing mechanisms in patients are currently mainly studied with neuroimaging techniques; however, these techniques have a relatively poor temporal resolution. A high temporal resolution method is warranted to decode the dynamics of the pain processing mechanisms. Here, we reviewed crucial brain regions that exhibited pain-modulatory effects in an ascending and descending manner. Moreover, we discussed a uniquely well-suited method, namely extracellular electrophysiology, that captures natural language from the brain with high spatiotemporal resolution. This approach allows parallel recording of large populations of neurons in interconnected brain areas and permits the monitoring of neuronal firing patterns and comparative characterization of the brain oscillations. In addition, we discussed the contribution of these oscillations to pain states. In summary, using innovative, state-of-the-art methods, the large-scale recordings of multiple neurons will guide us to better understanding of pain mechanisms in FGIDs.

Racing and Pacing in the Reward System: A Multi-Clock Circadian Control Over Dopaminergic Signalling

Level of motivation, responsiveness to rewards and punishment, invigoration of exploratory behaviours, and motor performance are subject to daily fluctuations that emerge from circadian rhythms in neuronal activity of the midbrain’s dopaminergic system. While endogenous circadian rhythms are weak in the ventral tegmental area and substantia nigra pars compacta, daily changes in expression of core clock genes, ion channels, neurotransmitter receptors, dopamine-synthesising enzymes, and dopamine transporters, accompanied by changes in electrical activity, are readily observed in these nuclei. These processes cause dopamine levels released in structures innervated by midbrain dopaminergic neurons (e.g., the striatum) to oscillate in a circadian fashion. Additionally, growing evidence show that the master circadian clock located in the suprachiasmatic nucleus of the hypothalamus (SCN) rhythmically influences the activity of the dopaminergic system through various intermediate targets. Thus, circadian changes in the activity of the dopaminergic system and concomitant dopamine release observed on a daily scale are likely to be generated both intrinsically and entrained by the master clock. Previous studies have shown that the information about the value and salience of stimuli perceived by the animal is encoded in the neuronal activity of brain structures innervating midbrain dopaminergic centres. Some of these structures themselves are relatively autonomous oscillators, while others exhibit a weak endogenous circadian rhythm synchronised by the SCN. Here, we place the dopaminergic system as a hub in the extensive network of extra-SCN circadian oscillators and discuss the possible consequences of its daily entrainment for animal physiology and behaviour.

Time-Frequency Representations of Brain Oscillations: Which One Is Better?

Brain oscillations are thought to subserve important functions by organizing the dynamical landscape of neural circuits. The expression of such oscillations in neural signals is usually evaluated using time-frequency representations (TFR), which resolve oscillatory processes in both time and frequency. While a vast number of methods exist to compute TFRs, there is often no objective criterion to decide which one is better. In feature-rich data, such as that recorded from the brain, sources of noise and unrelated processes abound and contaminate results. The impact of these distractor sources is especially problematic, such that TFRs that are more robust to contaminants are expected to provide more useful representations. In addition, the minutiae of the techniques themselves impart better or worse time and frequency resolutions, which also influence the usefulness of the TFRs. Here, we introduce a methodology to evaluate the "quality" of TFRs of neural signals by quantifying how much information they retain about the experimental condition during visual stimulation and recognition tasks, in mice and humans, respectively. We used machine learning to discriminate between various experimental conditions based on TFRs computed with different methods. We found that various methods provide more or less informative TFRs depending on the characteristics of the data. In general, however, more advanced techniques, such as the superlet transform, seem to provide better results for complex time-frequency landscapes, such as those extracted from electroencephalography signals. Finally, we introduce a method based on feature perturbation that is able to quantify how much time-frequency components contribute to the correct discrimination among experimental conditions. The methodology introduced in the present study may be extended to other analyses of neural data, enabling the discovery of data features that are modulated by the experimental manipulation.