EEG source connectivity analysis: from dense array recordings to brain networks

Abstract

The recent past years have seen a noticeable increase of interest for electroencephalography (EEG) to analyze functional connectivity through brain sources reconstructed from scalp signals. Although considerable advances have been done both on the recording and analysis of EEG signals, a number of methodological questions are still open regarding the optimal way to process the data in order to identify brain networks. In this paper, we analyze the impact of three factors that intervene in this processing: i) the number of scalp electrodes, ii) the combination between the algorithm used to solve the EEG inverse problem and the algorithm used to measure the functional connectivity and iii) the frequency bands retained to estimate the functional connectivity among neocortical sources. Using High-Resolution (hr) EEG recordings in healthy volunteers, we evaluated these factors on evoked responses during picture recognition and naming task. The main reason for selection this task is that a solid literature background is available about involved brain networks (ground truth). From this a priori information, we propose a performance criterion based on the number of connections identified in the regions of interest (ROI) that belong to potentially activated networks. Our results show that the three studied factors have a dramatic impact on the final result (the identified network in the source space) as strong discrepancies were evidenced depending on the methods used. They also suggest that the combination of weighted Minimum Norm Estimator (wMNE) and the Phase Synchronization (PS) methods applied on High-Resolution EEG in beta/gamma bands provides the best performance in term of topological distance between the identified network and the expected network in the above-mentioned cognitive task.

Figure 1. The different steps of the comparative study.

hr-EEG: high-resolution EEG, MNE: Minimum norm estimate, wMNE: Weighted Minimum norm estimate, LORETA: Low resolution Brain Electromagnetic Tomography, sLORETA: Standardized Low resolution Brain Electromagnetic Tomography, sPLV: single-trial Phase Locking Value, PE: Phase Entropy, R²: linear correlation coefficient, MI: Mutual Information, ImC: Imaginary Coherence ROIs: Regions of Interest, LO: Left Occipital; RO: Right Occipital, LT: Left Temporal, RT: Right Temporal, LF: Left Frontal, RF: Right Frontal, LP: Left Parietal.

Figure 2. EEG signals and its reconstructed sources.

A) The recorded evoked responses for a given subject, B) the corresponding reconstructed sources and C) an example of the sources in each of the ROIs. The window of analysis is illustrated in transparent blue rectangle.

Figure 3. Connectivity graphs obtained by using the different inverse and connectivity methods for hr-EEG (Up) and classical EEG montage (Bottom).

Red and blue lines denote the functional connectivity as measured in the gamma (>30 Hz) and beta (14–30 Hz) frequency band respectively.

Figure 4. Comparison between the 32, 64, 128 and hr-montage for different inverse and connectivity methods.

Asterisk above boxes indicates significant difference (p<0.05). LI: Localization Index.

Figure 5. The mean and standard variations of the R (percentage of identified edges for within each ROI) values obtained for the different functional connectivity methods (computed over the 12 subjects).

Figure 6. Typical example of the brain network identified using wMNE and sPLV in the picture recognition and naming task.

A: lateral view B: Top view C: frontal view.