Sensory representation and detection mechanisms of gut osmolality change

Ichiki, Takako; Wang, Tongtong; Kennedy, Ann; Pool, Allan-Hermann; Ebisu, Haruka; Anderson, David J.; Oka, Yuki

doi:10.1038/s41586-021-04359-5

Cited by 37 publications

(32 citation statements)

References 43 publications

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“…All replicates are biological, and all statistical tests are two-tailed. No statistical methods were used to predetermine sample sizes, which were based on previous expertise and publications in our field 8 , 14 , 34 . Investigators were not blinded to group allocation, except for manual determination of axon innervation (Extended Data Fig.…”

Section: Methodsmentioning

confidence: 99%

“…Next, we compared NTS representations of intestinal stretch and intestinal nutrients, two stimuli that induce the sensation of satiety 9 , 10 , 13 , 15 , 17 , 34 . Acute glucose application into the proximal duodenum (300 mM in Hank’s balanced salt solution (HBSS), 100 μl, exit port at 1.5 cm distally) consistently activated NTS neurons across animals (Extended Data Fig.…”

Section: Neuronal Tuning To Internal Stimulimentioning

confidence: 99%

“…This result indicates that signal convergence is not random, but instead displays some organ selectivity. Mechanical and chemical signals in the duodenum activate different vagal sensory neurons but overlapping NTS neurons 8 , 14 , 34 , which indicates high-level convergence that arises in the brainstem. Such partial signal convergence may reflect an economical design whereby polymodal neurons mediate common physiological and behavioural effects induced by both stimuli, whereas more selective NTS neurons preserve modality discrimination.…”

Section: Neuronal Tuning To Internal Stimulimentioning

confidence: 99%

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A brainstem map for visceral sensations

Chen

Boettcher

Kaye

et al. 2022

Nature

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The nervous system uses various coding strategies to process sensory inputs. For example, the olfactory system uses large receptor repertoires and is wired to recognize diverse odours, whereas the visual system provides high acuity of object position, form and movement1–5. Compared to external sensory systems, principles that underlie sensory processing by the interoceptive nervous system remain poorly defined. Here we developed a two-photon calcium imaging preparation to understand internal organ representations in the nucleus of the solitary tract (NTS), a sensory gateway in the brainstem that receives vagal and other inputs from the body. Focusing on gut and upper airway stimuli, we observed that individual NTS neurons are tuned to detect signals from particular organs and are topographically organized on the basis of body position. Moreover, some mechanosensory and chemosensory inputs from the same organ converge centrally. Sensory inputs engage specific NTS domains with defined locations, each containing heterogeneous cell types. Spatial representations of different organs are further sharpened in the NTS beyond what is achieved by vagal axon sorting alone, as blockade of brainstem inhibition broadens neural tuning and disorganizes visceral representations. These findings reveal basic organizational features used by the brain to process interoceptive inputs.

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Section: Methodsmentioning

confidence: 99%

Section: Neuronal Tuning To Internal Stimulimentioning

confidence: 99%

Section: Neuronal Tuning To Internal Stimulimentioning

confidence: 99%

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A brainstem map for visceral sensations

Chen

Boettcher

Kaye

et al. 2022

Nature

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“…In addition, the implementation of “internal state” in the current model was not sufficient to represent diverse time constants, i.e., it could not represent different timescales for taste, gut, blood concentration, etc. ( Ichiki et al, 2022 ). Moreover, although this study only replicated existing animal studies, it would be useful to propose a working hypothesis for actual animal experiments, for example, by using the combination of operant conditioning learning tasks and optogenetics method.…”

Section: Discussionmentioning

confidence: 99%

Computational Mechanisms of Osmoregulation: A Reinforcement Learning Model for Sodium Appetite

2022

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Homeostatic control with oral nutrient intake is a vital complex system involving the orderly interactions between the external and internal senses, behavioral control, reward learning, and decision-making. Sodium appetite is a representative system and has been intensively investigated in animal models of homeostatic systems and oral nutrient intake. However, the system-level mechanisms for regulating sodium intake behavior and homeostatic control remain unclear. In the current study, we attempted to provide a mechanistic understanding of sodium appetite behavior by using a computational model, the homeostatic reinforcement learning model, in which homeostatic behaviors are interpreted as reinforcement learning processes. Through simulation experiments, we confirmed that our homeostatic reinforcement learning model successfully reproduced homeostatic behaviors by regulating sodium appetite. These behaviors include the approach and avoidance behaviors to sodium according to the internal states of individuals. In addition, based on the assumption that the sense of taste is a predictor of changes in the internal state, the homeostatic reinforcement learning model successfully reproduced the previous paradoxical observations of the intragastric infusion test, which cannot be explained by the classical drive reduction theory. Moreover, we extended the homeostatic reinforcement learning model to multimodal data, and successfully reproduced the behavioral tests in which water and sodium appetite were mediated by each other. Finally, through an experimental simulation of chemical manipulation in a specific neural population in the brain stem, we proposed a testable hypothesis for the function of neural circuits involving sodium appetite behavior. The study results support the idea that osmoregulation via sodium appetitive behavior can be understood as a reinforcement learning process, and provide a mechanistic explanation for the underlying neural mechanisms of decision-making related to sodium appetite and homeostatic behavior.

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“…recognizing the sugar-preference signals from any other activity). Third, by using very long sugar stimuli (1 min) under conditions known to activate large populations of vagal neurons that mask the response of sugar/nutrient preference neurons 4,22,58 (note also that the whole vagal nerve responses, unlike sugar/nutrient responses, never decayed after termination of the sugar stimulus). 6f).…”

Section: Extendedmentioning

confidence: 99%

Gut–brain circuits for fat preference

Tan

et al. 2022

Nature

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This is a PDF file of a peer-reviewed paper that has been accepted for publication. Although unedited, the content has been subjected to preliminary formatting. Nature is providing this early version of the typeset paper as a service to our authors and readers. The text and figures will undergo copyediting and a proof review before the paper is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers apply.

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Sensory representation and detection mechanisms of gut osmolality change

Cited by 37 publications

References 43 publications

A brainstem map for visceral sensations

A brainstem map for visceral sensations

Computational Mechanisms of Osmoregulation: A Reinforcement Learning Model for Sodium Appetite

Gut–brain circuits for fat preference

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