Evidence map›Paper›PMID 29691468›Full record

ArticleScientific reports2018

Large-scale brain network associated with creative insight: combined voxel-based morphometry and resting-state functional connectivity analyses.

Takeshi Ogawa, Takatsugu Aihara, Takeaki Shimokawa, Okito Yamashita

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In one paragraph

Article in Scientific reports, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
20citing papers in PubMed, 2 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

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3 · Its place in the literature

Who cites it

20 citing papers in PubMed, 2 syntheses or guidelines pooled it.

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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

4 authors.

Takeshi OgawaAdvanced Telecommunication Research Institute International, Cognitive Mechanisms Laboratories, Kyoto, 619-0288, Japan. t.ogawa@atr.jp.
Takatsugu AiharaAdvanced Telecommunication Research Institute International, Neural Information Analysis Laboratories, Kyoto, 619-0288, Japan.
Takeaki ShimokawaAdvanced Telecommunication Research Institute International, Neural Information Analysis Laboratories, Kyoto, 619-0288, Japan.
Okito YamashitaAdvanced Telecommunication Research Institute International, Neural Information Analysis Laboratories, Kyoto, 619-0288, Japan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Creative insight occurs with an "Aha!" experience when solving a difficult problem. Here, we investigated large-scale networks associated with insight problem solving. We recruited 232 healthy participants aged 21-69 years old. Participants completed a magnetic resonance imaging study (MRI; structural imaging and a 10 min resting-state functional MRI) and an insight test battery (ITB) consisting of written questionnaires (matchstick arithmetic task, remote associates test, and insight problem solving task). To identify the resting-state functional connectivity (RSFC) associated with individual creative insight, we conducted an exploratory voxel-based morphometry (VBM)-constrained RSFC analysis. We identified positive correlations between ITB score and grey matter volume (GMV) in the right insula and middle cingulate cortex/precuneus, and a negative correlation between ITB score and GMV in the left cerebellum crus 1 and right supplementary motor area. We applied seed-based RSFC analysis to whole brain voxels using the seeds obtained from the VBM and identified insight-positive/negative connections, i.e. a positive/negative correlation between the ITB score and individual RSFCs between two brain regions. Insight-specific connections included motor-related regions whereas creative-common connections included a default mode network. Our results indicate that creative insight requires a coupling of multiple networks, such as the default mode, semantic and cerebral-cerebellum networks.

Indexed as

CreativityAdultAgedBrainBrain MappingCerebellumFemaleGray MatterGyrus CinguliHealthy VolunteersHumansMagnetic Resonance ImagingMaleMiddle AgedNerve Net

Identifiers

PMID29691468
PMCPMC5915578

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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.