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Record W4387536281 · doi:10.1101/2023.10.09.560813

Striatal calcium transients detected by fiber photometry propagate to axons

2023· preprint· en· W4387536281 on OpenAlexaff
David M. Lipton, Mohammad Tamimi, Itay Shalom, Tomer Sheinfeld, Ben J. Gonzales, Maya Groysman, Ami Citri

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2023
Typepreprint
Languageen
FieldNeuroscience
TopicNeural dynamics and brain function
Canadian institutionsCanadian Institute for Advanced Research
Fundersnot available
KeywordsPhotometry (optics)NeuroscienceNeuropilSomaNeuronal firingCalcium imagingCalciumPhysicsBiologyElectrophysiologyChemistryCentral nervous systemAstrophysics

Abstract

fetched live from OpenAlex

ARISING FROM: A. Legaria et al., Nature Neuroscience https://doi.org/10.1038/s41593-022-01152-z (2022). Calcium fiber photometry is a popular technique for recording the activity of neuronal populations defined by their gene expression or connectivity. In a recent study, Legaria et al., present evidence that the calcium signal recorded with fiber photometry primarily reports local fluctuations in neuropil Ca 2+ , rather than somatic Ca 2+ influx corresponding to neural firing, as has been assumed by the field. This raises the question of whether fiber photometry transients are a valid measure of the propagation of information from neural soma to their axons. We addressed this question directly, recording coincident activity from both the somato-dendritic region and downstream axons of striatal neural populations. Our findings demonstrate that calcium events are reliably propagated to axons, supporting the interpretation that these events reflect neuronal firing.

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 score0.002
Threshold uncertainty score0.007

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 communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.034
GPT teacher head0.248
Teacher spread0.214 · 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
Published2023
Admission routes1
Has abstractyes

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