» Articles » PMID: 24624229

A Review of EEG and MEG for Brainnetome Research

Overview
Journal Cogn Neurodyn
Publisher Springer
Specialty Neurology
Date 2014 Mar 14
PMID 24624229
Citations 11
Authors
Affiliations
Soon will be listed here.
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.

Citing Articles

Low-Rank Tensor Fusion for Enhanced Deep Learning-Based Multimodal Brain Age Estimation.

Liu X, Zheng G, Beheshti I, Ji S, Gou Z, Cui W Brain Sci. 2025; 14(12.

PMID: 39766451 PMC: 11674316. DOI: 10.3390/brainsci14121252.


High-altitude exposure leads to increased modularity of brain functional network with the increased occupation of attention resources in early processing of visual working memory.

Zhou J, Wang N, Huang X, Su R, Li H, Ma H Cogn Neurodyn. 2024; 18(5):1-20.

PMID: 39555295 PMC: 11564581. DOI: 10.1007/s11571-024-10091-3.


Brain Functional Connectivity in Parkinson's Disease Patients Based on Clinical EEG.

Conti M, Bovenzi R, Garasto E, Schirinzi T, Placidi F, Mercuri N Front Neurol. 2022; 13:844745.

PMID: 35370899 PMC: 8964594. DOI: 10.3389/fneur.2022.844745.


Resting-State Electroencephalography Functional Connectivity Networks Relate to Pre- and Postoperative Language Functioning in Low-Grade Glioma and Meningioma Patients.

Wolthuis N, Satoer D, Veenstra W, Smits M, Wagemakers M, Vincent A Front Neurosci. 2021; 15:785969.

PMID: 34955732 PMC: 8693574. DOI: 10.3389/fnins.2021.785969.


Ant Colony System Optimization for Spatiotemporal Modelling of Combined EEG and MEG Data.

Opoku E, Ahmed S, Song Y, Nathoo F Entropy (Basel). 2021; 23(3).

PMID: 33799662 PMC: 7999289. DOI: 10.3390/e23030329.


References
1.
Jafri M, Pearlson G, Stevens M, Calhoun V . A method for functional network connectivity among spatially independent resting-state components in schizophrenia. Neuroimage. 2007; 39(4):1666-81. PMC: 3164840. DOI: 10.1016/j.neuroimage.2007.11.001. View

2.
Xu P, Tian Y, Lei X, Yao D . Neuroelectric source imaging using 3SCO: a space coding algorithm based on particle swarm optimization and l0 norm constraint. Neuroimage. 2010; 51(1):183-205. DOI: 10.1016/j.neuroimage.2010.01.106. View

3.
Cheung B, Riedner B, Tononi G, Van Veen B . Estimation of cortical connectivity from EEG using state-space models. IEEE Trans Biomed Eng. 2010; 57(9):2122-34. PMC: 2923689. DOI: 10.1109/TBME.2010.2050319. View

4.
Darvas F, Pantazis D, Kucukaltun-Yildirim E, Leahy R . Mapping human brain function with MEG and EEG: methods and validation. Neuroimage. 2004; 23 Suppl 1:S289-99. DOI: 10.1016/j.neuroimage.2004.07.014. View

5.
Varela F, Lachaux J, Rodriguez E, Martinerie J . The brainweb: phase synchronization and large-scale integration. Nat Rev Neurosci. 2001; 2(4):229-39. DOI: 10.1038/35067550. View