Evidence map›Paper›PMID 33173515›Full record

ArticleFrontiers in psychiatry2020

Neural and Self-Report Markers of Reassurance: A Generalized Additive Modelling Approach.

Jeffrey J Kim, Trent Henderson, Talitha Best, Ross Cunnington, James N Kirby

Abstract read
In one paragraph

Article in Frontiers in psychiatry, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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

5 authors.

Jeffrey J KimCompassionate Mind Research Group, School of Psychology, The University of Queensland, Brisbane, QLD, Australia.
Trent HendersonOrbisant Analytics, Brisbane, QLD, Australia.
Talitha BestSchool of Health, Medical and Applied Sciences, Central Queensland University, Brisbane, QLD, Australia.
Ross CunningtonCompassionate Mind Research Group, School of Psychology, The University of Queensland, Brisbane, QLD, Australia.
James N KirbyCompassionate Mind Research Group, School of Psychology, The University of Queensland, Brisbane, QLD, Australia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Research has shown that engaging in self-reassurance, a compassionately motivated cognitive relating style, can down-regulate neural markers of threat and pain. Whilst important, the relationship between neural and self-report markers of reassurance are largely unknown. Here we analyzed previously published fMRI data which measured neural responses when participants engaged in self-reassurance toward a mistake, setback, or failure. Within the present paper, we identified correlations between regions of interest extracted during self-reassurance with fMRI and self-report data. Using generalized additive modelling, we show that participants with greater inadequate forms of self-criticism exhibited greater neural activation within the medial prefrontal cortex (MPFC) and anterior insula (AI). Furthermore, a relationship between greater fears of expressing compassion to the self and neural activation within the MPFC returned non-significant after correction for multiple comparisons. No significant relationships were observed between brain activation and hated and reassuring forms of self-criticism. Our results identify preliminary evidence for neural activity during self-reassurance as correlated with self-report markers, and we outline a method for modelling neural and self-report data which can be applied to future studies in compassion science, particularly with a clinical sample.

Indexed as

compassioncriticismfMRImodellingreassuranceself-report

Identifiers

PMID33173515
PMCPMC7538506

What OpenQuestion holds

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Registered trials

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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.