Evidence map›Paper›PMID 34668603›Full record

ReviewHuman brain mapping2022

Brain functional and effective connectivity based on electroencephalography recordings: A review.

Jun Cao, Yifan Zhao, Xiaocai Shan, Hua-Liang Wei, Yuzhu Guo, Liangyu Chen, John Ahmet Erkoyuncu, Ptolemaios Georgios Sarrigiannis

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Human brain mapping, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 102 papers, 6 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
102citing papers in PubMed, 6 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

102 citing papers in PubMed, 6 syntheses or guidelines pooled it.

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  3. Brain connectivity changes associated with acute sleep deprivation: a systematic review.Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology · 2025
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42 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

8 authors.

Jun CaoSchool of Aerospace, Transport and Manufacturing, Cranfield University, Cranfield.ORCID 0000-0003-2121-7631
Yifan ZhaoSchool of Aerospace, Transport and Manufacturing, Cranfield University, Cranfield.ORCID 0000-0003-2383-5724
Xiaocai ShanSchool of Aerospace, Transport and Manufacturing, Cranfield University, Cranfield.ORCID 0000-0003-0817-0357
Hua-Liang WeiDepartment of Automatic Control and Systems Engineering, University of Sheffield, Sheffield, UK.ORCID 0000-0002-4704-7346
Yuzhu GuoSchool of Automation Science and Electrical Engineering, Beihang University, Beijing, China.ORCID 0000-0002-8588-5172
Liangyu ChenDepartment of Neurosurgery, Shengjing Hospital of China Medical University, Shenyang, China.ORCID 0000-0002-6817-8644
John Ahmet ErkoyuncuSchool of Aerospace, Transport and Manufacturing, Cranfield University, Cranfield.ORCID 0000-0002-8046-9911
Ptolemaios Georgios SarrigiannisRoyal Devon and Exeter NHS Foundation Trust, Exeter, UK.ORCID 0000-0002-8380-8755

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Functional connectivity and effective connectivity of the human brain, representing statistical dependence and directed information flow between cortical regions, significantly contribute to the study of the intrinsic brain network and its functional mechanism. Many recent studies on electroencephalography (EEG) have been focusing on modeling and estimating brain connectivity due to increasing evidence that it can help better understand various brain neurological conditions. However, there is a lack of a comprehensive updated review on studies of EEG-based brain connectivity, particularly on visualization options and associated machine learning applications, aiming to translate those techniques into useful clinical tools. This article reviews EEG-based functional and effective connectivity studies undertaken over the last few years, in terms of estimation, visualization, and applications associated with machine learning classifiers. Methods are explored and discussed from various dimensions, such as either linear or nonlinear, parametric or nonparametric, time-based, and frequency-based or time-frequency-based. Then it is followed by a novel review of brain connectivity visualization methods, grouped by Heat Map, data statistics, and Head Map, aiming to explore the variation of connectivity across different brain regions. Finally, the current challenges of related research and a roadmap for future related research are presented.

Indexed as

ConnectomeMachine LearningBrainElectroencephalographyHumansNerve Netartificial intelligencebrain associationelectroencephalogrammachine learningsurvey

Identifiers

PMID34668603
PMCPMC8720201

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.