Evidence map›Paper›PMID 36389177›Full record

ReviewFrontiers in neural circuits2022

Neural circuits provide insights into reward and aversion.

Wanqi Chen

Open access · goldAbstract readReview
In one paragraph

Review in Frontiers in neural circuits, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed
1.7field-weighted citation impact, top 16% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

13 citing papers in PubMed, 17 citations in OpenAlex.

  1. The efficacy and cerebral mechanism of intradermal acupuncture for major depressive disorder: a multicenter randomized controlled trial.Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology · 2025
    Trial
  2. Article
  3. The social life of Pavlovian learning.Cognitive, affective & behavioral neuroscience · 2026
    Review
  4. Article
  5. Article
  6. Article
  7. Review
  8. Review
  9. Review
  10. Adrenergic C1 neurons enhance anxiety via projections to PAG.bioRxiv : the preprint server for biology · 2024
    Article
  11. Review
  12. Article
  13. Neurotensin and Alcohol Use Disorders: Towards a Pharmacological Treatment.International journal of molecular sciences · 2023
    Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

1 author at 1 institution in 1 country.

Wanqi ChenSchool of Psychology, Shenzhen University, Shenzhen, China.
Shenzhen University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Maladaptive changes in the neural circuits associated with reward and aversion result in some common symptoms, such as drug addiction, anxiety, and depression. Historically, the study of these circuits has been hampered by technical limitations. In recent years, however, much progress has been made in understanding the neural mechanisms of reward and aversion owing to the development of technologies such as cell type-specific electrophysiology, neuronal tracing, and behavioral manipulation based on optogenetics. The aim of this paper is to summarize the latest findings on the mechanisms of the neural circuits associated with reward and aversion in a review of previous studies with a focus on the ventral tegmental area (VTA), nucleus accumbens (NAc), and basal forebrain (BF). These findings may inform efforts to prevent and treat mental illnesses associated with dysfunctions of the brain's reward and aversion system.

Indexed as

Basal ForebrainRewardNeuronsNucleus AccumbensVentral Tegmental Areaaversionbasal forebrainnucleus accumbensrewardventral tegmental area

Identifiers

PMID36389177
PMCPMC9650032
OpenAlexW4307706236

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.