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Record W4211076345 · doi:10.3410/f.734895542.793582007

Faculty Opinions recommendation of A brain-wide functional map of the serotonergic responses to acute stress and fluoxetine.

2021· dataset· en· W4211076345 on OpenAlexaff
Emmanuel Darcq

Bibliographic record

VenueFaculty Opinions – Post-Publication Peer Review of the Biomedical Literature · 2021
Typedataset
Languageen
FieldNeuroscience
TopicFunctional Brain Connectivity Studies
Canadian institutionsMcGill UniversityDouglas Mental Health University Institute
Fundersnot available
KeywordsSerotonergicNeuroscienceOptogeneticsPhotostimulationPsychologySerotoninFluoxetineMedicineReceptorInternal medicine

Abstract

fetched live from OpenAlex

Central serotonin (5-HT) orchestrates myriad cognitive processes and lies at the core of many stress-related psychiatric illnesses. However, the basic relationship between its brain-wide axonal projections and functional dynamics is not known. Here we combine optogenetics and fMRI to produce a brain-wide 5-HT evoked functional map. We find that DRN photostimulation leads to an increase in the hemodynamic response in the DRN itself, while projection areas predominately exhibit a reduction of cerebral blood volume mirrored by suppression of cortical delta oscillations. We find that the regional distribution of post-synaptically expressed 5-HT receptors better correlates with DRN 5-HT functional connectivity than anatomical projections. Our work suggests that neuroarchitecture is not the primary determinant of function for the DRN 5-HT. With respect to two 5-HT elevating stimuli, we find that acute stress leads to circuit-wide blunting of the DRN output, while the SSRI fluoxetine noticeably enhances DRN functional connectivity. These data provide fundamental insight into the brain-wide functional dynamics of the 5-HT projection system. PMID: 30664643

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)0.001
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.063
Threshold uncertainty score0.210

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.005
Science and technology studies0.0000.000
Scholarly communication0.0020.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0630.045

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 head0.347
Teacher spread0.300 · 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 designObservational
Domainnot available
GenreDataset

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
Published2021
Admission routes1
Has abstractyes

Explore more

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