Evidence map›Paper›PMID 31226497›Full record

ArticleNeuroImage2019

A guide to group effective connectivity analysis, part 1: First level analysis with DCM for fMRI.

Peter Zeidman, Amirhossein Jafarian, Nadège Corbin, Mohamed L Seghier, Adeel Razi, Cathy J Price, Karl J Friston

Abstract read
In one paragraph

Article in NeuroImage, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 196 papers.

0numbers the graph read from it
0cells of the map it votes in
196citing 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

196 citing papers in PubMed.

  1. Rostral anterior cingulate network effective connectivity in depressed adolescents and associations with treatment response in a randomized controlled trial.Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology · 2022
    Trial
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  5. Distinct Brain Drivers and Shared Cerebello-Cortical Input in ADCY5 and SGCE Hyperkinetic Movements.Movement disorders : official journal of the Movement Disorder Society · 2026
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136 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

7 authors.

Peter ZeidmanWellcome Centre for Human Neuroimaging, 12 Queen Square, London, WC1N 3AR, UK. Electronic address: peter.zeidman@ucl.ac.uk.
Amirhossein JafarianWellcome Centre for Human Neuroimaging, 12 Queen Square, London, WC1N 3AR, UK.
Nadège CorbinWellcome Centre for Human Neuroimaging, 12 Queen Square, London, WC1N 3AR, UK.
Mohamed L SeghierCognitive Neuroimaging Unit, ECAE, Abu Dhabi, United Arab Emirates.
Adeel RaziMonash Institute of Cognitive & Clinical Neuroscience, Monash Biomedical Imaging, 18 Innovation Walk, Monash University, Clayton, VIC, 3800, Australia; Wellcome Centre for Human Neuroimaging, 12 Queen Square, London, WC1N 3AR, UK.
Cathy J PriceWellcome Centre for Human Neuroimaging, 12 Queen Square, London, WC1N 3AR, UK.
Karl J FristonWellcome Centre for Human Neuroimaging, 12 Queen Square, London, WC1N 3AR, UK.

Funding

Medical Research Council MR/M023672/1Wellcome Trust 203147/Z/16/ZWellcome Trust 205103/Z/16/Z
6 · The paper itself

Abstract

Dynamic Causal Modelling (DCM) is the predominant method for inferring effective connectivity from neuroimaging data. In the 15 years since its introduction, the neural models and statistical routines in DCM have developed in parallel, driven by the needs of researchers in cognitive and clinical neuroscience. In this guide, we step through an exemplar fMRI analysis in detail, reviewing the current implementation of DCM and demonstrating recent developments in group-level connectivity analysis. In the appendices, we detail the theory underlying DCM and the assumptions (i.e., priors) in the models. In the first part of the guide (current paper), we focus on issues specific to DCM for fMRI. This is accompanied by all the necessary data and instructions to reproduce the analyses using the SPM software. In the second part (in a companion paper), we move from subject-level to group-level modelling using the Parametric Empirical Bayes framework, and illustrate how to test for commonalities and differences in effective connectivity across subjects, based on imaging data from any modality.

Indexed as

Models, TheoreticalResearch DesignAdultBrainConnectomeDatasets as TopicGuidelines as TopicHumansMagnetic Resonance Imaging

Identifiers

PMID31226497
PMCPMC6711459

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

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.