Evidence map›Paper›PMID 24955232›Full record

ArticleMolecular autism2014

Default mode network in young male adults with autism spectrum disorder: relationship with autism spectrum traits.

Minyoung Jung, Hirotaka Kosaka, Daisuke N Saito, Makoto Ishitobi, Tomoyo Morita, Keisuke Inohara, Mizuki Asano, Sumiyoshi Arai, Toshio Munesue, Akemi Tomoda and 4 more

Registry-linked trialAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Molecular autism, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT03199937 (Behavioral and Neural Outcomes of a New Executive Function Treatment for Transition-age Youth With ASD), which is not on this map. Cited by 75 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
75citing 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

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.

NCT03199937 nacompletednot on this mapstarted 2015, after this paper: background citation

Behavioral and Neural Outcomes of a New Executive Function Treatment for Transition-age Youth With ASD

TypeinterventionalSponsorChildren's National Research InstituteRan2015 to 2019Enrolled68ConditionsAutism Spectrum DisorderArmsFlexible Futures, Current standard of care
3 · Its place in the literature

Who cites it

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

  1. Pooled it
  2. Pooled it
  3. Article
  4. Article
  5. Default Mode Network Functional Connectivity As a Transdiagnostic Biomarker of Cognitive Function.Biological psychiatry. Cognitive neuroscience and neuroimaging · 2025
    Review
  6. Article
  7. Article
  8. Article
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  11. Secondary analysis: Graph analysis of brain connectivity network in autism spectrum disorder.Journal of research in medical sciences : the official journal of Isfahan University of Medical Sciences · 2024
    Article
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  13. Article
  14. Article
  15. Article
  16. Maladaptive Laterality in Cortical Networks Related to Social Communication in Autism Spectrum Disorder.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2022
    Article
  17. Group-level comparison of brain connectivity networks.BMC medical research methodology · 2022
    Article
  18. Article
  19. Article
  20. Article

15 more citing papers are in PubMed but not listed here.

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

14 authors.

Minyoung JungDevelopmental Emotional Intelligence, Division of Developmental Higher Brain Functions, Department of Child Development United Graduate School of Child Development, Osaka University, Kanazawa University, Hamamatsu University School of Medicine, Chiba University and University of Fukui, Fukui, Eiheiji 910-1193, Japan.
Hirotaka KosakaDevelopmental Emotional Intelligence, Division of Developmental Higher Brain Functions, Department of Child Development United Graduate School of Child Development, Osaka University, Kanazawa University, Hamamatsu University School of Medicine, Chiba University and University of Fukui, Fukui, Eiheiji 910-1193, Japan ; Research Center for Child Mental Development, University of Fukui, Fukui, Eiheiji 910-1193, Japan ; Department of Neuropsychiatry, Faculty of Medical Sciences, University of Fukui, Fukui, Eiheiji 910-1193, Japan ; Biomedical Imaging Research Center, University of Fukui, Fukui, Eiheiji 910-1193, Japan.
Daisuke N SaitoResearch Center for Child Mental Development, University of Fukui, Fukui, Eiheiji 910-1193, Japan ; Biomedical Imaging Research Center, University of Fukui, Fukui, Eiheiji 910-1193, Japan.
Makoto IshitobiDepartment of Neuropsychiatry, Faculty of Medical Sciences, University of Fukui, Fukui, Eiheiji 910-1193, Japan ; Department of Child and Adolescent Mental Health, National Institute of Mental Health, National Center of Neurology and Psychiatry, Kodaira, Tokyo 187-8553, Japan.
Tomoyo MoritaGraduate School of Engineering, Osaka University, Suita, Osaka 565-0871, Japan.
Keisuke InoharaDepartment of Neuropsychiatry, Faculty of Medical Sciences, University of Fukui, Fukui, Eiheiji 910-1193, Japan.
Mizuki AsanoResearch Center for Child Mental Development, University of Fukui, Fukui, Eiheiji 910-1193, Japan ; Department of Neuropsychiatry, Faculty of Medical Sciences, University of Fukui, Fukui, Eiheiji 910-1193, Japan.
Sumiyoshi AraiDevelopmental Emotional Intelligence, Division of Developmental Higher Brain Functions, Department of Child Development United Graduate School of Child Development, Osaka University, Kanazawa University, Hamamatsu University School of Medicine, Chiba University and University of Fukui, Fukui, Eiheiji 910-1193, Japan.
Toshio MunesueDevelopmental Emotional Intelligence, Division of Developmental Higher Brain Functions, Department of Child Development United Graduate School of Child Development, Osaka University, Kanazawa University, Hamamatsu University School of Medicine, Chiba University and University of Fukui, Fukui, Eiheiji 910-1193, Japan ; Research Center for Child Mental Development, Kanazawa University, Kanazawa, Ishikawa 920-8641, Japan.
Akemi TomodaDevelopmental Emotional Intelligence, Division of Developmental Higher Brain Functions, Department of Child Development United Graduate School of Child Development, Osaka University, Kanazawa University, Hamamatsu University School of Medicine, Chiba University and University of Fukui, Fukui, Eiheiji 910-1193, Japan ; Research Center for Child Mental Development, University of Fukui, Fukui, Eiheiji 910-1193, Japan.
Yuji WadaResearch Center for Child Mental Development, University of Fukui, Fukui, Eiheiji 910-1193, Japan ; Department of Neuropsychiatry, Faculty of Medical Sciences, University of Fukui, Fukui, Eiheiji 910-1193, Japan.
Norihiro SadatoBiomedical Imaging Research Center, University of Fukui, Fukui, Eiheiji 910-1193, Japan ; Department of Cerebral Research, National Institute for Physiological Sciences, Okazaki, Aichi 444-8585, Japan.
Hidehiko OkazawaResearch Center for Child Mental Development, University of Fukui, Fukui, Eiheiji 910-1193, Japan ; Biomedical Imaging Research Center, University of Fukui, Fukui, Eiheiji 910-1193, Japan.
Tetsuya IidakaDepartment of Psychiatry, Graduate School of Medicine, Nagoya University, Nagoya, Aichi 466-8550, Japan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundAutism spectrum traits are postulated to lie on a continuum that extends between individuals with autism and individuals with typical development (TD). Social cognition properties that are deeply associated with autism spectrum traits have been linked to functional connectivity between regions within the brain's default mode network (DMN). Previous studies have shown that the resting-state functional connectivities (rs-FCs) of DMN are low and show negative correlation with the level of autism spectrum traits in individuals with autism spectrum disorder (ASD). However, it is unclear whether individual differences of autism spectrum traits are associated with the strength of rs-FCs of DMN in participants including the general population.

methodsUsing the seed-based approach, we investigated the rs-FCs of DMN, particularly including the following two core regions of DMN: the anterior medial prefrontal cortex (aMPFC) and posterior cingulate cortex (PCC) in 19 young male adults with high-functioning ASD (mean age = 25.3 ± 6.9 years; autism-spectrum quotient (AQ) = 33.4 ± 4.2; full scale IQ (F-IQ) = 109.7 ± 12.4) compared with 21 age- and IQ-matched young male adults from the TD group (mean age = 24.8 ± 4.3 years; AQ = 18.6 ± 5.7; F-IQ = 109.5 ± 8.7). We also analyzed the correlation between the strength of rs-FCs and autism spectrum traits measured using AQ score.

resultsThe strengths of rs-FCs from core regions of DMN were significantly lower in ASD participants than TD participants. Under multiple regression analysis, the strengths of rs-FCs in brain areas from aMPFC seed showed negative correlation with AQ scores in ASD participants and TD participants.

conclusionsOur findings suggest that the strength of rs-FCs in DMN is associated with autism spectrum traits in the TD population as well as patients with ASD, supporting the continuum view. The rs-FCs of DMN may be useful biomarkers for the objective identification of autism spectrum traits, regardless of ASD diagnosis.

Indexed as

Anterior medial prefrontal cortex (aMPFC)Autism spectrum disorder (ASD)Autism-spectrum quotient (AQ)Autism spectrum traitsDefault mode network (DMN)Posterior cingulate cortex (PCC)Resting-state functional connectivities (rs-FCs)

Identifiers

PMID24955232
PMCPMC4064274

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Registered trials

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.