title: A review of EEG and MEG for brainnetome research.
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.
authors.affiliation: Brainnetome 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.
authors.forename: Xin
authors.initials: X
authors.lastname: Zhang
authors.affiliation: Key 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.
authors.forename: Xu
authors.initials: X
authors.lastname: Lei
authors.affiliation: 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 ; Department of Magnetoencephalography, Nanjing Brain Hospital Affiliated to Nanjing Medical University, Nanjing, 210029 China.
authors.forename: Ting
authors.initials: T
authors.lastname: Wu
authors.affiliation: Brainnetome 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.
authors.forename: Tianzi
authors.initials: T
authors.lastname: Jiang
keywords: Brain network; Brainnetome; EEG; MEG