Evidence map›Paper›PMID 28096672›Full record

ReviewNeuropsychiatric disease and treatment2017

A biopsychological review of gambling disorder.

Gabriel C Quintero

Abstract readReview
In one paragraph

Review in Neuropsychiatric disease and treatment, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed, 1 pooled it
–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

6 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Article
  4. Gambling Phenotypes in Older Adults.Journal of gambling studies · 2020
    Article
  5. Review
  6. Article
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.

Gabriel C QuinteroFlorida State University - Republic of Panama, Panama City, Panama.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The present review is an overview of previous experimental work on biopsychological aspects of gambling disorder. It includes the topics 1) gambling disorder from the neuroimaging and electroencephalography (EEG) perspective, 2) cognitive, executive functioning, and neuropsychological aspects of gambling disorder, and 3) rodent models of gambling disorder. Penalties and losses in gambling can differ in terms of brain activity. Also, specific patterns of brain activity, brain anatomical traits, EEG responses, and cognitive and executive performance can discriminate pathological gamblers from nonpathological gamblers. Also, pathological gamblers can display dysfunction in such brain areas as the insula, frontal lobe, and orbitofrontal cortex. Pathological gambling is a heterogeneous disorder that can vary depending on the severity of cognition, the style of gambling (strategic or not), the prospect of recovery, proneness to relapse, and proneness to treatment withdrawal. Finally, based on rodent models of gambling, the appropriateness of gambling decision is influenced by the presence of cues, the activity of dopamine receptors, and the activity of some brain areas (infralimbic, prelimbic, or rostral agranular insular cortex). Pathological gamblers differed in terms of frontoparietal brain activation compared to nonpathological gamblers (if winning or losing a game). Pathological gamblers had dysfunctional EEG activity. The severity of gambling was linked to the magnification and content of cognitive distortions. The insula was fundamental in the distortion of cognitions linked to result analysis during gambling activity.

Indexed as

biopsychologyhumanpathological gamblingrodent

Identifiers

PMID28096672
PMCPMC5207471

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.