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Record W3003257487 · doi:10.1038/s41467-020-14281-5

The anterior insular cortex unilaterally controls feeding in response to aversive visceral stimuli in mice

2020· article· en· W3003257487 on OpenAlexafffund
Yu Wu, Changwan Chen, Ming Chen, Kai Qian, Xinyou Lv, Haiting Wang, Lina Yu, Min Zhuo, Shuang Qiu

Bibliographic record

VenueNature Communications · 2020
Typearticle
Languageen
FieldNeuroscience
TopicSleep and Wakefulness Research
Canadian institutionsCanada Research ChairsUniversity of Toronto
FundersFundamental Research Funds for the Central UniversitiesZhejiang UniversityCanadian Institutes of Health ResearchChina Postdoctoral Science FoundationNational Natural Science Foundation of ChinaFondation Brain Canada
KeywordsHyperactivationInsular cortexHypothalamusNeuroscienceAnorexiaCortex (anatomy)Lateral hypothalamusAversive StimulusPathologicalBiologyPsychologyMedicineInternal medicine

Abstract

fetched live from OpenAlex

Abstract Reduced food intake is common to many pathological conditions, such as infection and toxin exposure. However, cortical circuits that mediate feeding responses to these threats are less investigated. The anterior insular cortex (aIC) is a core region that integrates interoceptive states and emotional awareness and consequently guides behavioral responses. Here, we demonstrate that the right-side aIC CamKII + (aIC CamKII ) neurons in mice are activated by aversive visceral signals. Hyperactivation of the right-side aIC CamKII neurons attenuates food consumption, while inhibition of these neurons increases feeding and reverses aversive stimuli-induced anorexia and weight loss. Similar manipulation at the left-side aIC does not cause significant behavioral changes. Furthermore, virus tracing reveals that aIC CamKII neurons project directly to the vGluT2 + neurons in the lateral hypothalamus (LH), and the right-side aIC CamKII -to-LH pathway mediates feeding suppression. Our studies uncover a circuit from the cortex to the hypothalamus that senses aversive visceral signals and controls feeding behavior.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.912
Threshold uncertainty score0.747

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0020.001
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.047
GPT teacher head0.349
Teacher spread0.303 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations81
Published2020
Admission routes2
Has abstractyes

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