Detection of Resting-State Functional Connectivity from High-Density Electroencephalography Data: Impact of Head Modeling Strategies

Abstract

Recent technological advances have been permitted to use high-density electroencephalography (hdEEG) for the estimation of functional connectivity and the mapping of resting-state networks (RSNs). The reliable estimate of activity and connectivity from hdEEG data relies on the creation of an accurate head model, defining how neural currents propagate from the cortex to the sensors placed over the scalp. To the best of our knowledge, no study has been conducted yet to systematically test to what extent head modeling accuracy impacts on EEG-RSN reconstruction. To address this question, we used 256-channel hdEEG data collected in a group of young healthy participants at rest. We first estimated functional connectivity in EEG-RSNs by means of band-limited power envelope correlations, using neural activity estimated with an optimized analysis workflow. Then, we defined a series of head models with different levels of complexity, specifically testing the effect of different electrode positioning techniques and head tissue segmentation methods. We observed that robust EEG-RSNs can be obtained using a realistic head model, and that inaccuracies due to head tissue segmentation impact on RSN reconstruction more than those due to electrode positioning. Additionally, we found that EEG-RSN robustness to head model variations had space and frequency specificity. Overall, our results may contribute to defining a benchmark for assessing the reliability of hdEEG functional connectivity measures.

Keywords: electrode localization; electroencephalography; functional connectivity; head modelling; head tissue segmentation; resting-state networks.

Figure 1

Anatomical positions of the 21 seeds used for analysis, subdivided into the corresponding networks. Default mode network (DMN): posterior cingulate cortex (PCC), medial prefrontal cortex (MPFC), left/right angular gyrus (lANG/rANG); Dorsal attention network (DAN): left/right frontal eye field (lFEF/rFEF), left/right inferior parietal sulcus (lIPS/rIPS); Ventral attention network (VAN): right temporo-parietal junction (rTPJ), right inferior frontal gyrus (rIFG); Language network (LN, green): left temporo-parietal junction (lTPJ), left inferior frontal gyrus (lIFG); Somatomotor network (SMN): supplementary motor area (SMA), left/right primary somatosensory cortex (lS1/rS1), left/right secondary somatosensory cortex (lS2/rS2); Visual network (VN): left/right human ventral visual area 4 (lV4v/rV4v), left/right visual area 5 (lV5/rV5).

Figure 2

Example of EEG electrode positions and their simulated misplacement. From top to bottom: original electrode positions for a representative participant; positions with a systematic error of 1 cm; positions with a random error of 1 cm.

Figure 3

Example of head tissue segmentations used in the study. From left to right: T1-weighted individual MR image; 12-layer segmentation using the MR-TIM toolbox; 12-layer segmentation using WTS; 3-layer segmentation using WTS.

Figure 4

Functional connectivity matrices for the following frequency bands: delta (1–4 Hz), theta (4–8 Hz), alpha (8–13 Hz), beta (13–30 Hz) and gamma (30–80 Hz). In each matrix, the diagonal values represent the group average intra-network connectivity, whereas the off-diagonal values represent the group average inter-network connectivity.

Figure 5

Intra- and inter-network connectivity differences per network and frequency band. A comparison was performed by means of a Wilcoxon signed-rank paired test; the color bar represents the z-value obtained through the test, where the red colors stand for intra-network connectivity values higher than inter-network ones, and the blue colors vice-versa.

Figure 6

Correlation between the reference connectivity matrices and those obtained when introducing electrode localization errors. For each error, magnitude is tested in the range 0.25 to 1 cm, and distinction is made between systematic (dark grey) and random (light grey) errors. Black dots represent the outliers. The asterisks define the significance level of the two-tailed Wilcoxon signed-rank paired test between error types, for each error magnitude: * for p < 0.05, ** for p < 0.01, **** for p < 0.0001.

Figure 7

Correlation between the reference connectivity matrices and those obtained when introducing errors in the head model. These errors were related to the electrode localization technique (dark grey) or to the head tissue segmentation method (light grey). Black dots represent the outliers.

Figure 8

Impact of head modeling on the intra- and inter-network connectivity differences, for each RSN. This was assessed by calculating the correlation with values obtained using the reference analysis workflow. Two electrode localization techniques (3D Scan and Digitizer—on the left) and three head tissue segmentation methods (12-layer WTS, 3-layer WTS and 3-layer template—on the right) were tested.