Evidence map›Paper›PMID 24379387›Full record

ArticleProceedings of the National Academy of Sciences of the United States of America2014

Resting-brain functional connectivity predicted by analytic measures of network communication.

Joaquín Goñi, Martijn P van den Heuvel, Andrea Avena-Koenigsberger, Nieves Velez de Mendizabal, Richard F Betzel, Alessandra Griffa, Patric Hagmann, Bernat Corominas-Murtra, Jean-Philippe Thiran, Olaf Sporns

Abstract read
In one paragraph

Article in Proceedings of the National Academy of Sciences of the United States of America, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 313 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
313citing 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

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

  1. A systematic review on the quantitative relationship between structural and functional network connectivity strength in mammalian brains.Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism · 2019
    Pooled it
  2. Trial
  3. Trial
  4. Heterochronicity shapes brain network formation.Proceedings of the National Academy of Sciences of the United States of America · 2026
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  9. The Cerebellar Connectome.Cerebellum (London, England) · 2026
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  17. Variation in high-amplitude events across the human lifespan.Network neuroscience (Cambridge, Mass.) · 2026
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253 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

10 authors.

Joaquín GoñiDepartment of Psychological and Brain Sciences, Indiana University, Bloomington, IN 47405.
Martijn P van den Heuvel
Andrea Avena-Koenigsberger
Nieves Velez de Mendizabal
Richard F Betzel
Alessandra Griffa
Patric Hagmann
Bernat Corominas-Murtra
Jean-Philippe Thiran
Olaf Sporns

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The complex relationship between structural and functional connectivity, as measured by noninvasive imaging of the human brain, poses many unresolved challenges and open questions. Here, we apply analytic measures of network communication to the structural connectivity of the human brain and explore the capacity of these measures to predict resting-state functional connectivity across three independently acquired datasets. We focus on the layout of shortest paths across the network and on two communication measures--search information and path transitivity--which account for how these paths are embedded in the rest of the network. Search information is an existing measure of information needed to access or trace shortest paths; we introduce path transitivity to measure the density of local detours along the shortest path. We find that both search information and path transitivity predict the strength of functional connectivity among both connected and unconnected node pairs. They do so at levels that match or significantly exceed path length measures, Euclidean distance, as well as computational models of neural dynamics. This capacity suggests that dynamic couplings due to interactions among neural elements in brain networks are substantially influenced by the broader network context adjacent to the shortest communication pathways.

Indexed as

ConnectomeModels, NeurologicalBrainCell CommunicationDiffusion Magnetic Resonance ImagingFemaleHumansLinear ModelsMaleNerve Netbrain connectivityconnectomegraph theorynetwork theory

Identifiers

PMID24379387
PMCPMC3896172

What OpenQuestion holds

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Registered trials

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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.