Evidence map›Paper›PMID 34456665›Full record

ReviewFrontiers in neuroscience2021

Dynamic Causal Modeling Self-Connectivity Findings in the Functional Magnetic Resonance Imaging Neuropsychiatric Literature.

Andrew D Snyder, Liangsuo Ma, Joel L Steinberg, Kyle Woisard, Frederick G Moeller

Abstract readReview
In one paragraph

Review in Frontiers in neuroscience, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
15citing 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

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

  1. Pooled it
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  5. Abnormal Connectivity of the Head Neural Integrator in Cervical Dystonia.Movement disorders : official journal of the Movement Disorder Society · 2025
    Observational
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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

5 authors.

Andrew D SnyderInstitute for Drug and Alcohol Studies, Virginia Commonwealth University School of Medicine, Richmond, VA, United States.
Liangsuo MaInstitute for Drug and Alcohol Studies, Virginia Commonwealth University School of Medicine, Richmond, VA, United States.
Joel L SteinbergInstitute for Drug and Alcohol Studies, Virginia Commonwealth University School of Medicine, Richmond, VA, United States.
Kyle WoisardInstitute for Drug and Alcohol Studies, Virginia Commonwealth University School of Medicine, Richmond, VA, United States.
Frederick G MoellerInstitute for Drug and Alcohol Studies, Virginia Commonwealth University School of Medicine, Richmond, VA, United States.

Funding

Research Supplement to Promote Diversity in Health Related ResearchUL1TR002649 · NCATS · VIRGINIA COMMONWEALTH UNIVERSITY · PI MOELLER, FREDERICK GERARD · 2018 to 2022
$19.1M
Preclinical Assessment of Compounds for Cocaine Use Disorder (CocUD)U54DA038999 · NIDA · VIRGINIA COMMONWEALTH UNIVERSITY · PI MOELLER, FREDERICK GERARD · 2014 to 2018
$6.8M
NCATS NIH HHS UL1 TR002649NIDA NIH HHS U54 DA038999
6 · The paper itself

Abstract

Dynamic causal modeling (DCM) is a method for analyzing functional magnetic resonance imaging (fMRI) and other functional neuroimaging data that provides information about directionality of connectivity between brain regions. A review of the neuropsychiatric fMRI DCM literature suggests that there may be a historical trend to under-report self-connectivity (within brain regions) compared to between brain region connectivity findings. These findings are an integral part of the neurologic model represented by DCM and serve an important neurobiological function in regulating excitatory and inhibitory activity between regions. We reviewed the literature on the topic as well as the past 13 years of available neuropsychiatric DCM literature to find an increasing (but still, perhaps, and inadequate) trend in reporting these results. The focus of this review is fMRI as the majority of published DCM studies utilized fMRI and the interpretation of the self-connectivity findings may vary across imaging methodologies. About 25% of articles published between 2007 and 2019 made any mention of self-connectivity findings. We recommend increased attention toward the inclusion and interpretation of self-connectivity findings in DCM analyses in the neuropsychiatric literature, particularly in forthcoming effective connectivity studies of substance use disorders.

Indexed as

dynamic causal modelingeffective connectivityextrinsic connectivityinhibitory interneuronintrinsic connectivityself-connectivity

Identifiers

PMID34456665
PMCPMC8385130

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.