Evidence map›Paper›PMID 38553865›Full record

ArticleHuman brain mapping2024

Community detection in the human connectome: Method types, differences and their impact on inference.

Skylar J Brooks, Victoria O Jones, Haotian Wang, Chengyuan Deng, Staunton G H Golding, Jethro Lim, Jie Gao, Prodromos Daoutidis, Catherine Stamoulis

Abstract read
In one paragraph

Article in Human brain mapping, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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

9 authors.

Skylar J BrooksBoston Children's Hospital, Department of Pediatrics, Boston, Massachusetts, USA.
Victoria O JonesUniversity of Minnesota, Department of Chemical Engineering and Material Science, Minneapolis, Minnesota, USA.
Haotian WangRutgers University, Department of Computer Science, Piscataway, New Jersey, USA.
Chengyuan DengRutgers University, Department of Computer Science, Piscataway, New Jersey, USA.
Staunton G H GoldingBoston Children's Hospital, Department of Pediatrics, Boston, Massachusetts, USA.
Jethro LimBoston Children's Hospital, Department of Pediatrics, Boston, Massachusetts, USA.
Jie GaoRutgers University, Department of Computer Science, Piscataway, New Jersey, USA.
Prodromos DaoutidisUniversity of Minnesota, Department of Chemical Engineering and Material Science, Minneapolis, Minnesota, USA.
Catherine StamoulisBoston Children's Hospital, Department of Pediatrics, Boston, Massachusetts, USA.ORCID 0000-0001-9814-1802

Funding

National Science Foundation 1649865National Science Foundation 1938914National Science Foundation 1940094National Science Foundation 2116707National Science Foundation 2126582National Science Foundation 2207733
6 · The paper itself

Abstract

Community structure is a fundamental topological characteristic of optimally organized brain networks. Currently, there is no clear standard or systematic approach for selecting the most appropriate community detection method. Furthermore, the impact of method choice on the accuracy and robustness of estimated communities (and network modularity), as well as method-dependent relationships between network communities and cognitive and other individual measures, are not well understood. This study analyzed large datasets of real brain networks (estimated from resting-state fMRI from

Indexed as

ConnectomeAdolescentBayes TheoremBrainHumansMagnetic Resonance ImagingReproducibility of Resultscommunity detectiondata‐driven synthetic graphsfMRIgraph Ricci flowhuman brain networksstochastic block modeling

Identifiers

PMID38553865
PMCPMC10980844

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.