Evidence map›Paper›PMID 24624229›Full record

ReviewCognitive neurodynamics2014

A review of EEG and MEG for brainnetome research.

Xin Zhang, Xu Lei, Ting Wu, Tianzi Jiang

Abstract readReview
In one paragraph

Review in Cognitive neurodynamics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed
1.2field-weighted citation impact, top 22% of its field
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

14 citing papers in PubMed, 39 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Brain Functional Connectivity inFrontiers in neurology · 2022
    Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. IFCN-endorsed practical guidelines for clinical magnetoencephalography (MEG).Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology · 2018
    Review
  11. Article
  12. Article
  13. Article
  14. 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

4 authors at 3 institutions in 1 country.

Xin ZhangBrainnetome Center, Institute of Automation, The Chinese Academy of Sciences, Beijing, 100190 China ; National Laboratory of Pattern Recognition, Institute of Automation, The Chinese Academy of Sciences, Beijing, 100190 China.
Xu LeiKey Laboratory of Cognition and Personality (Ministry of Education) and School of Psychology, Southwest University, Chongqing, China ; Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054 China.
Ting WuKey Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054 China ; Department of Magnetoencephalography, Nanjing Brain Hospital Affiliated to Nanjing Medical University, Nanjing, 210029 China.
Tianzi JiangBrainnetome Center, Institute of Automation, The Chinese Academy of Sciences, Beijing, 100190 China ; National Laboratory of Pattern Recognition, Institute of Automation, The Chinese Academy of Sciences, Beijing, 100190 China ; Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054 China ; The Queensland Brain Institute, The University of Queensland, Brisbane, QLD 4072 Australia.
Institute of Automation · CNSouthwest University · CNUniversity of Electronic Science and Technology of China · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The majority of brain activities are performed by functionally integrating separate regions of the brain. Therefore, the synchronous operation of the brain's multiple regions or neuronal assemblies can be represented as a network with nodes that are interconnected by links. Because of the complexity of brain interactions and their varying effects at different levels of complexity, one of the corresponding authors of this paper recently proposed the brainnetome as a new -ome to explore and integrate the brain network at different scales. Because electroencephalography (EEG) and magnetoencephalography (MEG) are noninvasive and have outstanding temporal resolution and because they are the primary clinical techniques used to capture the dynamics of neuronal connections, they lend themselves to the analysis of the neural networks comprising the brainnetome. Because of EEG/MEG's applicability to brainnetome analyses, the aim of this review is to identify the procedures that can be used to form a network using EEG/MEG data in sensor or source space and to promote EEG/MEG network analysis for either neuroscience or clinical applications. To accomplish this aim, we show the relationship of the brainnetome to brain networks at the macroscale and provide a systematic review of network construction using EEG and MEG. Some potential applications of the EEG/MEG brainnetome are to use newly developed methods to associate the properties of a brainnetome with indices of cognition or disease conditions. Associations based on EEG/MEG brainnetome analysis may improve the comprehension of the functioning of the brain in neuroscience research or the recognition of abnormal patterns in neurological disease.

Indexed as

BrainnetomeBrain networkEEGMEG

Identifiers

PMID24624229
PMCPMC3945460
OpenAlexW2031312517

What OpenQuestion holds

Textmetadata
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.