Evidence map›Paper›PMID 26393868›Full record

ReviewAnnual review of psychology2016

Modular Brain Networks.

Olaf Sporns, Richard F Betzel

Registry-linked trialAbstract readReview
In one paragraph

Review in Annual review of psychology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT03720821 (Cognitive Flexibility Training for Persistent Pain), which is not on this map. Cited by 638 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
638citing 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.

NCT03720821 nacompletednot on this mapstarted 2019, after this paper: background citation

Cognitive Flexibility Training for Persistent Pain: Neuroimaging Pilot (COFLEX-i)

TypeinterventionalSponsorWashington University School of MedicineRan2019 to 2022Enrolled46ConditionsChronic Pain, Knee Pain Chronic, Hip Pain Chronic, Back PainArmsCognitive Training + fMRI
3 · Its place in the literature

Who cites it

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

  1. Catalyst for change: Psilocybin's antidepressant mechanisms-A systematic review.Journal of psychopharmacology (Oxford, England) · 2025
    Pooled it
  2. Article
  3. Heterochronicity shapes brain network formation.Proceedings of the National Academy of Sciences of the United States of America · 2026
    Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. A solution to generalized learning from small training sets found in infants' repeated visual experiences of individual objects.Proceedings of the National Academy of Sciences of the United States of America · 2026
    Article
  9. Article
  10. Article
  11. Article
  12. Frontoparietal Hub Connectivity Integrates Information from Multiple Sources.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2026
    Article
  13. The Cerebellar Connectome.Cerebellum (London, England) · 2026
    Article
  14. Article
  15. Article
  16. Article
  17. Article
  18. Human striatal population state dynamics.bioRxiv : the preprint server for biology · 2026
    Article
  19. Article
  20. Article

578 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

2 authors.

Olaf SpornsDepartment of Psychological and Brain Sciences, Indiana University, Bloomington, Indiana 47405; email: osporns@indiana.edu.
Richard F BetzelDepartment of Psychological and Brain Sciences, Indiana University, Bloomington, Indiana 47405; email: osporns@indiana.edu.

Funding

CRCNS: Linking connectomic and large-Scale Dynamics of the Human BrainR01AT009036 · NCCIH · TRUSTEES OF INDIANA UNIVERSITY · PI SPORNS, OLAF · 2015 to 2017
$443k
NCCIH NIH HHS R01 AT009036NCCIH NIH HHS R01 AT009036-01
6 · The paper itself

Abstract

The development of new technologies for mapping structural and functional brain connectivity has led to the creation of comprehensive network maps of neuronal circuits and systems. The architecture of these brain networks can be examined and analyzed with a large variety of graph theory tools. Methods for detecting modules, or network communities, are of particular interest because they uncover major building blocks or subnetworks that are particularly densely connected, often corresponding to specialized functional components. A large number of methods for community detection have become available and are now widely applied in network neuroscience. This article first surveys a number of these methods, with an emphasis on their advantages and shortcomings; then it summarizes major findings on the existence of modules in both structural and functional brain networks and briefly considers their potential functional roles in brain evolution, wiring minimization, and the emergence of functional specialization and complex dynamics.

Indexed as

Brain MappingBrainHumansNerve Netclusteringconnectomefunctional connectivitygraph theoryhubsresting state

Identifiers

PMID26393868
PMCPMC4782188

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

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.