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Record W7139253771

The Role of Phasic Cholinergic Modulation in the Medial Prefrontal Cortex During Threat Learning

2025· dissertation· W7139253771 on OpenAlexfundno aff
Gaqi Tu

Bibliographic record

VenueTSpace (University of Toronto) · 2025
Typedissertation
Language
FieldNeuroscience
TopicMemory and Neural Mechanisms
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsCholinergicBasal forebrainCholinergic neuronPrefrontal cortexAcetylcholineAssociative learningForebrainCholinergic Fibers
DOInot available

Abstract

fetched live from OpenAlex

To survive, animals learn the predictive relationships between threats and preceding cues or actions and later use these associations to guide adaptive decisions. This ability relies on the prelimbic region (PL) of the medial prefrontal cortex, which is critical for evaluating predictive relationships to inform learning and decision-making. Concurrently, the PL receives strong neuromodulatory signals, specifically acetylcholine from the basal forebrain (BF) cholinergic neurons. These neurons release precisely timed signals upon threats during cognitive tasks. However, the function of these signals in the PL during threat learning remains unknown. To address this, I first investigated the real-time dynamics of phasic cholinergic signals in the PL during an aversive associative learning task. Fiber photometric recordings of BF cholinergic terminals revealed that innate threat-locked phasic cholinergic activity diminished with learning, while a strong response to the cue developed as mice formed the cue-threat association. Using optogenetics, I further demonstrated that phasic cholinergic signals exerted opposite effects on learning depending on their temporal alignment: suppression of threat-locked phasic cholinergic signals facilitated learning, while stimulation of these signals impeded learning. In contrast, inhibition of cue-locked phasic cholinergic signals impaired learning, while their excitation had no effect. Next, using an aversive spatial learning task, I examined how threat-locked phasic cholinergic signals influenced learning when mice needed to discriminate between two maze paths that differed in threat probabilities. I found that optogenetically enhancing threat-locked phasic cholinergic signals impaired optimal decisions by making mice abandon correct choices when confronted with occasional, surprising threats. Importantly, this impairment was only seen when threats were delivered probabilistically but spared learning when threats were delivered deterministically. To uncover the underlying mechanism, I combined in vivo one-photon calcium imaging of PL cells with optogenetic stimulation of BF cholinergic terminals. I found that enhanced threat-locked phasic cholinergic signals modified PL coding of prediction errors by increasing its selectivity to signed but not unsigned prediction errors. Together, these findings suggest that threat-locked phasic cholinergic signals transmitted via the BF-PL pathways are crucial for threat learning and modulate PL computation of prediction error coding, thereby influencing how much outcomes dictate future choices under uncertainty.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

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 integrity0.0000.001
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 head0.022
GPT teacher head0.268
Teacher spread0.246 · 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
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
Published2025
Admission routes1
Has abstractyes

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