A systematic data‐driven approach to analyze sensor‐level EEG connectivity: Identifying robust phase‐synchronized network components using surface Laplacian with spectral‐spatial PCA

• Published in: Psychophysiology 2022-10, Vol.59 (10), p.e14080-n/a
• Main Authors: Smith, Ezra E., Bel‐Bahar, Tarik S., Kayser, Jürgen
• Format: Article
• Language: English
• Subjects: alpha/theta networks; current source density (CSD); EEG; Eye; functional connectivity (FC); Neural networks; principal components analysis (PCA); resting EEG; Sensorimotor system
• ISSN: 0048-5772; 1469-8986; 1540-5958
• DOI: 10.1111/psyp.14080

Although conventional averaging across predefined frequency bands reduces the complexity of EEG functional connectivity (FC), it obscures the identification of resting‐state brain networks (RSN) and impedes accurate estimation of FC reliability. Extending prior work, we combined scalp current source density (CSD; spherical spline surface Laplacian) and spectral‐spatial PCA to identify FC components. Phase‐based FC was estimated via debiased‐weighted phase‐locking index from CSD‐transformed resting EEGs (71 sensors, 8 min, eyes open/closed, 35 healthy adults, 1‐week retest). Spectral PCA extracted six robust alpha and theta components (86.6% variance). Subsequent spatial PCA for each spectral component revealed seven robust regionally focused (posterior, central, and frontal) and long‐range (posterior–anterior) alpha components (peaks at 8, 10, and 13 Hz) and a midfrontal theta (6 Hz) component, accounting for 37.0% of FC variance. These spatial FC components were consistent with well‐known networks (e.g., default mode, visual, and sensorimotor), and four were sensitive to eyes open/closed conditions. Most FC components had good‐to‐excellent internal consistency (odd/even epochs, eyes open/closed) and test–retest reliability (ICCs ≥ .8). Moreover, the FC component structure was generally present in subsamples (session × odd/even epoch, or smaller subgroups [n = 7–10]), as indicated by high similarity of component loadings across PCA solutions. Apart from systematically reducing FC dimensionality, our approach avoids arbitrary thresholds and allows quantification of meaningful and reliable network components that may prove to be of high relevance for basic and clinical research applications. This study used current source density (CSD) and principal components analysis (PCA) to summarize sensor‐level phase‐lag EEG connectivity (3–16 Hz) at rest. We identified spectral‐spatial network components (7 alpha, 1 theta) with good‐to‐excellent internal consistency and test‐retest reliability, replicating and extending prior work examining EEG resting state networks (RSNs).