Evidence map›Paper›PMID 39295107›Full record

ReviewCNS neuroscience & therapeutics2024

Modulating reward and aversion: Insights into addiction from the paraventricular nucleus.

Shihao Huang, Cuijie Shi, Dan Tao, Chang Yang, Yixiao Luo

Abstract readReview
In one paragraph

Review in CNS neuroscience & therapeutics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
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

4 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. 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

5 authors.

Shihao HuangHunan Province People's Hospital, The First-Affiliated Hospital of Hunan Normal University, Changsha, China.ORCID 0009-0006-9846-4090
Cuijie ShiCollege of Forensic Medicine, Hebei Medical University, Shijiazhuang, China.
Dan TaoSchool of Medicine, Hunan Normal University, Changsha, China.
Chang YangSchool of Medicine, Hunan Normal University, Changsha, China.
Yixiao LuoHunan Province People's Hospital, The First-Affiliated Hospital of Hunan Normal University, Changsha, China.ORCID 0000-0001-5013-1998

Funding

National Natural Science Foundation of China 82271534
6 · The paper itself

Abstract

backgroundDrug addiction, characterized by compulsive drug use and high relapse rates, arises from complex interactions between reward and aversion systems in the brain. The paraventricular nucleus (PVN), located in the anterior hypothalamus, serves as a neuroendocrine center and is a key component of the hypothalamic-pituitary-adrenal axis.

objectiveThis review aimed to explore how the PVN impacts reward and aversion in drug addiction through stress responses and emotional regulation and to evaluate the potential of PVN as a therapeutic target for drug addiction.

methodsWe review the current literature, focusing on three main neuron types in the PVN-corticotropin-releasing factor, oxytocin, and arginine vasopressin neurons-as well as other related neurons, to understand their roles in modulating addiction.

resultsExisting studies highlight the PVN as a key mediator in addiction, playing a dual role in reward and aversion systems. These findings are crucial for understanding addiction mechanisms and developing targeted therapies.

conclusionThe role of PVN in stress response and emotional regulation suggests its potential as a therapeutic target in drug addiction, offering new insights for addiction treatment.

Indexed as

Paraventricular Hypothalamic NucleusRewardSubstance-Related DisordersAnimalsAvoidance LearningBehavior, AddictiveCorticotropin-Releasing HormoneHumansOxytocinStress, PsychologicalCorticotropin-Releasing HormoneOxytocinaddictionAVPCRFOXTparaventricular nucleus

Identifiers

PMID39295107
PMCPMC11410887

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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.