Evidence map›Paper›PMID 28220071›Full record

ArticleFrontiers in neuroinformatics2017

Efficient Computation of Functional Brain Networks: toward Real-Time Functional Connectivity.

Juan García-Prieto, Ricardo Bajo, Ernesto Pereda

Abstract read
In one paragraph

Article in Frontiers in neuroinformatics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing 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

13 citing papers in PubMed.

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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

3 authors.

Juan García-PrietoDepartment of Industrial Engineering, Laboratory of Electrical Engineering and Bioengineering, Universidad de La LagunaTenerife, Spain; Laboratory of Computational and Cognitive Neuroscience, Centre of Biomedical Technology, UPMMadrid, Spain.
Ricardo BajoLaboratory of Computational and Cognitive Neuroscience, Centre of Biomedical Technology, UPMMadrid, Spain; Department of Statistics and Operative Research, Faculty of Medicine, Universidad Complutense de MadridMadrid, Spain.
Ernesto PeredaDepartment of Industrial Engineering, Laboratory of Electrical Engineering and Bioengineering, Universidad de La LagunaTenerife, Spain; Laboratory of Computational and Cognitive Neuroscience, Centre of Biomedical Technology, UPMMadrid, Spain; Institute of Biomedical Technology (CIBICAN), Universidad de La LagunaTenerife, Spain.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Functional Connectivity has demonstrated to be a key concept for unraveling how the brain balances functional segregation and integration properties while processing information. This work presents a set of open-source tools that significantly increase computational efficiency of some well-known connectivity indices and Graph-Theory measures. PLV, PLI, ImC, and wPLI as Phase Synchronization measures, Mutual Information as an information theory based measure, and Generalized Synchronization indices are computed much more efficiently than prior open-source available implementations. Furthermore, network theory related measures like Strength, Shortest Path Length, Clustering Coefficient, and Betweenness Centrality are also implemented showing computational times up to thousands of times faster than most well-known implementations. Altogether, this work significantly expands what can be computed in feasible times, even enabling whole-head real-time network analysis of brain function.

Indexed as

brain-computer interface (BCI)complex networkselectroencephalogram (EEG)Functional Connectivitygraph-theorymagnetoencephalography (MEG)neuro-feedback

Identifiers

PMID28220071
PMCPMC5292573

What OpenQuestion holds

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LicenceCC BY
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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.