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.

Power spectral density and coherence analysis of Alzheimer’s EEG

“…Investigation of neuropsychiatric disorder using neuroscientific approaches, such as functional magnetic resonance imaging (fMRI) (Chang and Glover 2010;Vemuri et al 2012;Zhou et al 2010), magnetoencephalography (MEG) (Stam et al 2006;Zhang et al 2014), electroencephalography (EEG) (Dauwels et al 2010a(Dauwels et al , b, 2013Jelles et al 2008;Vialatte et al 2012) and so on, attracts more interests. As a noninvasive, simple, and relatively low cost approach, resting-state EEG indirectly measures brain neural electric activity from the scalp of the head.…”

The graph theoretical analysis of the SSVEP harmonic response networks

“…The graph theory serves as a powerful tool to investigate the brain network topology (Bullmore and Sporns 2009;Rubinov and Sporns 2010). Meanwhile, EEG is often adopted to construct brain network because it is noninvasive and has high temporal resolution (Bonita et al 2014;De Vico Fallani et al 2008;Han et al 2013;Zhang et al 2014). In the current study, we still focused on the EEG-based network to probe the association between SSVEP harmonic responses and the corresponding network topology of the harmonic response entrained by the flickering stimulus using the graph theoretical analysis.…”

The path from big data to precision medicine

“…In common to these technologies is the need to answer three questions -what is the specific question/hypothesis being asked/tested; what data must be collected to answer it; and what quality of data will ensure that signal can be differentiated from noise, avoiding inconclusive or misleading 'answers'. As technologies improve, we can measure phenotype and environment in the normal and disease states on both small and large scales, spatially and temporally, from single cell sequencing [14,15] to global sensor networks [16]; from millisecond scale [17] to over decades [18]. However, much of the data collected is redundant and non-informative, and ideally does not need to be retained.…”