Evidence map›Paper›PMID 40461515›Full record

ArticleScientific reports2025

Navigation in brain networks is possible using only local measures.

Alex Hocking, Lena Vogelsang, Frank Jiang, Hung Le, Kerri Morgan, Nicholas Parsons, Govinda Poudel, Sergiy Shelyag, Julien Ugon

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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.

Alex HockingSchool of IT, Faculty of Science, Engineering & Built Environment, Deakin University, Melbourne, Australia.
Lena VogelsangSchool of IT, Faculty of Science, Engineering & Built Environment, Deakin University, Melbourne, Australia.
Frank JiangSchool of IT, Faculty of Science, Engineering & Built Environment, Deakin University, Melbourne, Australia.
Hung LeApplied AI Institute, Deakin University, Geelong, Australia.
Kerri MorganSchool of Science (Mathematical Sciences), RMIT University, Melbourne, Australia.
Nicholas ParsonsDepartment of Neuroscience, School of Translational Medicine, Monash University, Melbourne, Australia.
Govinda PoudelMary Mackillop Institute for Health Research, Australian Catholic University, Melbourne, Australia.
Sergiy ShelyagCollege of Science and Engineering, Flinders University, Tonsley, Adelaide, South Australia, 5042, Australia. sergiy.shelyag@flinders.edu.au.
Julien UgonSchool of IT, Faculty of Science, Engineering & Built Environment, Deakin University, Melbourne, Australia.

Funding

SEBE Deakin University MAAP
6 · The paper itself

Abstract

A major question in neuroscience is how the brain structure facilitates its complex functions via the synchronous activity of separate parts of the brain. Recent research has indicated that the mechanisms underlying this facilitation are based on local interactions between neurons. We investigated whether it is possible in a network structured like the human brain to facilitate communication between functionally connected parts of the brain. We observed that using a combination of local criteria to direct it, a simple greedy algorithm can achieve similar efficiency (within 80% and 90% of the shortest paths) to the propagation mechanisms proposed in the literature, without relying on global explanations.

Indexed as

BrainModels, NeurologicalNerve NetAlgorithmsHumansNeuronsBiological plausibilityConnectomeDeep learningGraph theoryNetwork navigationOptimisationPolysynaptic connections

Identifiers

PMID40461515
PMCPMC12134377

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.