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Record W4403036381 · doi:10.1101/2024.09.29.615662

Serotonergic neurons in the dorsal raphe regulate visual attention

2024· preprint· en· W4403036381 on OpenAlexaff
Jonas Lehnert, Kuwook Cha, Julia Forestell, Kerry Yang, Xinyue Ma, Jonathan P. Britt, Anmar Khadra, Erik P. Cook, Arjun Krishnaswamy

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2024
Typepreprint
Languageen
FieldNeuroscience
TopicOlfactory and Sensory Function Studies
Canadian institutionsMcGill University
Fundersnot available
KeywordsDorsal raphe nucleusSerotonergicDorsumNeuroscienceRapheSerotonergic cell groupsSerotoninRaphe nucleiPsychologyBiologyAnatomy

Abstract

fetched live from OpenAlex

Abstract Visual attention enhances the neural representation of salient stimuli within the visual cortex. It is generally thought that this enhancement is driven by glutamatergic feedback from frontal cortical areas. Here we report the unexpected observation that dorsal raphe (DR) derived serotonin (5HT) controls visual attention. We developed a behavioral model that captured the way mice allocated attention to cued and uncued visual locations and features. Simultaneous photometry showed reduced DR activity when mice deployed attention to the cued locations and features, whereas high DR activity was observed when mice were less attentive. Optogenetic excitation of DR-5HT neurons impaired attention to the cue and degraded behavioral performance, while optogenetic suppression improved attention and performance. A genetically encoded sensor of 5HT release showed reduced 5HT levels in visual cortex when mice attend and detect stimuli. These results demonstrate that DR-5HT neurons are members of the brain’s attentional circuit and suggest that 5HT is a novel biological carrier of visual attention.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.056
GPT teacher head0.259
Teacher spread0.203 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2024
Admission routes1
Has abstractyes

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