Evidence map›Paper›PMID 38645212›Full record

ArticlebioRxiv : the preprint server for biology2024

Sex differences in oxycodone-taking behaviors are linked to disruptions in reward-guided, decision-making functions.

Kaitlyn LaRocco, Peroushini Villiamma, Justin Hill, Mara A Russell, Ralph J DiLeone, Stephanie M Groman

Open access · greenAbstract readPreprint
In one paragraph

Article in bioRxiv : the preprint server for biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed, 0 citations in OpenAlex.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

6 authors at 2 institutions in 1 country.

Kaitlyn LaRocco
Peroushini Villiamma
Justin Hill
Mara A Russell
Ralph J DiLeone
Stephanie M Groman
University of Minnesota · USYale University · US

Funding

Neuroscience Training at Wake ForestT32NS115704 · NINDS · WAKE FOREST UNIVERSITY HEALTH SCIENCES · PI CZOTY, PAUL W. · 2021 to 2025
$1.4M
NINDS NIH HHS T32 NS115704
6 · The paper itself

Abstract

Problematic opioid use that emerges in a subset of individuals may be due to pre-existing disruptions in the biobehavioral mechanisms that regulate drug use. The identity of these mechanisms is not known, but emerging evidence suggests that suboptimal decision-making that is observable prior to drug use may contribute to the pathology of addiction and, notably, serve as a powerful phenotype for interrogating biologically based differences in opiate-taking behaviors. The current study investigated the relationship between decision-making phenotypes and opioid-taking behaviors in male and female Long Evans rats. Adaptive decision-making processes were assessed using a probabilistic reversal-learning task and oxycodone- (or vehicle, as a control) taking behaviors assessed for 32 days using a saccharin fading procedure that promoted dynamic intake of oxycodone. Tests of motivation, extinction, and reinstatement were also performed. Computational analyses of decision-making and opioid-taking behaviors revealed that attenuated reward-guided decision-making was associated with greater self-administration of oxycodone and addiction-relevant behaviors. Moreover, pre-existing impairments in reward-guided decision-making observed in female rats was associated with greater oxycodone use and addiction-relevant behaviors when compared to males. These results provide new insights into the biobehavioral mechanisms that regulate opiate-taking behaviors and offer a novel phenotypic approach for interrogating sex differences in addiction susceptibility and opioid use disorders.

Identifiers

PMID38645212
PMCPMC11030399
OpenAlexW4394759781

What OpenQuestion holds

Textmetadata
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.