Large-Scale Functional Networks Identified from Resting-State EEG Using Spatial ICA

Abstract

Several methods have been applied to EEG or MEG signals to detect functional networks. In recent works using MEG/EEG and fMRI data, temporal ICA analysis has been used to extract spatial maps of resting-state networks with or without an atlas-based parcellation of the cortex. Since the links between the fMRI signal and the electromagnetic signals are not fully established, and to avoid any bias, we examined whether EEG alone was able to derive the spatial distribution and temporal characteristics of functional networks. To do so, we propose a two-step original method: 1) An individual multi-frequency data analysis including EEG-based source localisation and spatial independent component analysis, which allowed us to characterize the resting-state networks. 2) A group-level analysis involving a hierarchical clustering procedure to identify reproducible large-scale networks across the population. Compared with large-scale resting-state networks obtained with fMRI, the proposed EEG-based analysis revealed smaller independent networks thanks to the high temporal resolution of EEG, hence hierarchical organization of networks. The comparison showed a substantial overlap between EEG and fMRI networks in motor, premotor, sensory, frontal, and parietal areas. However, there were mismatches between EEG-based and fMRI-based networks in temporal areas, presumably resulting from a poor sensitivity of fMRI in these regions or artefacts in the EEG signals. The proposed method opens the way for studying the high temporal dynamics of networks at the source level thanks to the high temporal resolution of EEG. It would then become possible to study detailed measures of the dynamics of connectivity.

Exploring Frequency-dependent Brain Networks from ongoing EEG using Spatial ICA during music listening

“…Brookes et al, 2011;Nugent et al, 2017;P. Ramkumar, Parkkonen, & Hyvarinen, 2014) and EEG data (Chen, Ros, & Gruzelier, 2013;Sockeel, Schwartz, Pelegrini-Issac, & Benali, 2016), in accordance with resting state fMRI networks. Additional studies have demonstrated that brain rhythms in a broad range of frequencies contributed to these networks.…”

“…Similar methods were proposed in (Sockeel et al, 2016). Besides, there are some other spatial ICA approaches including spatial Fourier ICA (SFICA) (P. Ramkumar, Parkkonen, Hari, & Hyvarinen, 2012) and envelope Fourier ICA (eFICA) (P. Ramkumar et al, 2014).…”

Revolution of Alzheimer Precision Neurology. Passageway of Systems Biology and Neurophysiology

“…This allows identifying a rich set of resting state networks in distinct frequency bands (e.g. [390, 392, 393, 396]). It was shown that AD patients show altered resting state network activity.…”

Algebraic Topology of Multi-Brain Connectivity Networks Reveals Dissimilarity in Functional Patterns during Spoken Communications

“…Much shorter time scales can characterise the brain connections related to different brain function, for instance, information processing, integration or segregation, cognitive control, empathy, and other. Therefore, electroencephalography (EEG) imaging has received an increased interest in functional brain research [8–12]. In this case, the functional connections are often reconstructed from EEG signals recorded at many scalp locations.…”

“…In this case, the functional connections are often reconstructed from EEG signals recorded at many scalp locations. In contrast to fMRI imaging, which measures spatially specific cortical or subcortical regions, the signal registered by an electrode at a particular scalp location (i.e., above a cortical region of interest) is spatially less specific, containing the average electric neuronal activities of all voxels belonging to that area [8, 12]. Nevertheless, regarding the generalised synchronisation, the recognisable patterns of positively correlated EEG signals suitably reflect the macroscopic organisation of the brain network [3].…”