Modulation of behavioral adaptation to aversive experience by accumbal circuits
Bibliographic record
Abstract
Chapter 1: Introduction……………………………………………………………………………………………………………. 1 1.1 Depression ……………………………………………………………………………………………………………… 2 1.1.1Major depressive disorder………………………………………………………………………… 2 1.1.2Stress and depression………………………………………………………………………………. 2 1.1.3Rodent models for depression…………………………………………………………………. 4 1.1.4Circuit based model of depression………………………………………………………… 10 1.2 Circuit Interrogation of depression-relevant states……………………………………………… 11 1.2.1 The optogenetic and chemogenetic toolbox ………………………………………… 11 1.2.2Strategies for targeting specific cell populations …………………………………… 13 1.2.3 Optogenetics and chemogenetics: advantages, limitations and caveats… 13 1.2.4In vivo imaging advances…………………………………………………………………………15 1.2.5 Probing circuitry underlying depression ………………………………………………… 17 1.3 Accumbal circuitry ……………………………………………………………………………………………… 18 II 1.3.1 Nucleus accumbens anatomy and connectivity……………………………………… 18 1.3.2Nucleus accumbens function………………………………………………………………….20 1.3.3Nucleus accumbens and stress adaptation……………………………………………… 22 1.3.4Contributions of afferent projections to behavior: focus on prefrontal cortical and ventral hippocampal projections………………………………………………….23 1.3.5 Hippocampal projections and stress adaptation …………………………………….24 1.3.6Prefrontal cortical projections and stress adaptation……………………………… 26 1.4 Rationale and Aims: Probing Vulnerability…………………………………………………………… 28 Framing the questions: Chapter 2 ………………………………………………………………………………………… 30 Chapter 2: In vivo fiber photometry reveals signature of future stress susceptibility in nucleus accumbens …………………………………………………………………………………………………………………………….31 Abstract……………………………………………………………………………………………………………………… 32 Introduction……………………………………………………………………………………………………………….
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".