Evidence map›Paper›PMID 30302538›Full record

ArticleBrain structure & function2019

Linking structural and effective brain connectivity: structurally informed Parametric Empirical Bayes (si-PEB).

Arseny A Sokolov, Peter Zeidman, Michael Erb, Philippe Ryvlin, Marina A Pavlova, Karl J Friston

Abstract read
In one paragraph

Article in Brain structure & function, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
32citing 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

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

  1. Pooled it
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Review
  8. Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Article
  14. Article
  15. Article
  16. Review
  17. Article
  18. Article
  19. Article
  20. 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

6 authors.

Arseny A SokolovWellcome Centre for Human Neuroimaging, Institute of Neurology, University College London (UCL), London, WC1N 3BG, UK. arseny.sokolov@chuv.ch.ORCID http://orcid.org/0000-0002-8982-2339
Peter ZeidmanWellcome Centre for Human Neuroimaging, Institute of Neurology, University College London (UCL), London, WC1N 3BG, UK.
Michael ErbDepartment of Biomedical Magnetic Resonance, Department of Radiology, University of Tübingen Medical School, 72076, Tübingen, Germany.
Philippe RyvlinService de Neurologie, Département des Neurosciences Cliniques, Centre Hospitalier Universitaire Vaudois (CHUV), 1011, Lausanne, Switzerland.
Marina A PavlovaDepartment of Psychiatry and Psychotherapy, University of Tübingen Medical School, 72076, Tübingen, Germany.
Karl J FristonWellcome Centre for Human Neuroimaging, Institute of Neurology, University College London (UCL), London, WC1N 3BG, UK.

Funding

Wellcome TrustWellcome Trust Sponsored Research Grant number 539208
6 · The paper itself

Abstract

Despite the potential for better understanding functional neuroanatomy, the complex relationship between neuroimaging measures of brain structure and function has confounded integrative, multimodal analyses of brain connectivity. This is particularly true for task-related effective connectivity, which describes the causal influences between neuronal populations. Here, we assess whether measures of structural connectivity may usefully inform estimates of effective connectivity in larger scale brain networks. To this end, we introduce an integrative approach, capitalising on two recent statistical advances: Parametric Empirical Bayes, which provides group-level estimates of effective connectivity, and Bayesian model reduction, which enables rapid comparison of competing models. Crucially, we show that structural priors derived from high angular resolution diffusion imaging on a dynamic causal model of a 12-region network-based on functional MRI data from the same subjects-substantially improve model evidence (posterior probability 1.00). This provides definitive evidence that structural and effective connectivity depend upon each other in mediating distributed, large-scale interactions in the brain. Furthermore, this work offers novel perspectives for understanding normal brain architecture and its disintegration in clinical conditions.

Indexed as

Diffusion Magnetic Resonance ImagingModels, NeurologicalSynaptic TransmissionVisual PerceptionAdultBayes TheoremBrainBrain MappingHumansMaleModels, StatisticalNeural PathwaysPhotic StimulationTime FactorsDynamic causal modelling (DCM)Effective connectivityFunctional MRIStructural connectivity

Identifiers

PMID30302538
PMCPMC6373362

What OpenQuestion holds

Textmetadata
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.