Evidence map›Paper›PMID 33875934›Full record

ReviewCurrent behavioral neuroscience reports2021

Population Modelling in Affective Disorders.

Erdem Pulcu

Open access · hybridAbstract readReview
In one paragraph

Review in Current behavioral neuroscience reports, 2021. 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, top 96% 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

0 citing papers in PubMed, 0 citations in OpenAlex.

No citing paper in PubMed yet.

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.

Erdem PulcuUniversity of Oxford Department of Psychiatry, Warneford Hospital, Oxford, OX3 7JX UK.ORCID 0000-0002-2170-0677
Warneford Hospital · GB

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purpose of reviewThe prevalence of affective disorders is on the rise. This upward trajectory leads to a substantial personal and societal cost. There is growing body of literature demonstrating decision-making impairments associated with affective disorders, and more studies are using computational modelling methods to infer underlying mechanisms of these impairments from participant choice behaviour. However, lack of population modelling suggests that data resources may still be underutilised. RECENT

findingsA number of recent studies associated major depression with abnormal risky decision-making as well as impairments in temporal discounting and social decision-making. These domains capture relevant aspects of real-life decision-making. Consequently, data from these studies can be used to define behavioural phenotypes for major depression. SUMMARY: The manuscript describes a detailed proposal for population modelling to capture changes in the prevalence rate of major depression. The population modelling approach can also identify which decision-making domains can account for a larger part of impairments in psychosocial functioning and how behavioural interventions built on computational principles can target these to improve real-life psychosocial functioning in patient groups.

Indexed as

Computational modellingEvolutionary biologyMajor depressionRiskSocial decision-makingTemporal discounting

Identifiers

PMID33875934
PMCPMC8047557
OpenAlexW3153080589

What OpenQuestion holds

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LicenceCC BY
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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.