Psychophysiology, 2020, in press. [Presented at the 60th Virtual Annual Meeting of the Society of Psychophysiological Research (SPR), October 5 - 9, 2020.]
A systematic data-driven approach to analyze sensor-level EEG connectivity: Surface Laplacian with spectral-spatial PCA identifies reliable alpha and theta network components
Ezra E. Smith1, Tarik S. Bel-Bahar1, Jürgen Kayser1,2
1New York State Psychiatric Institute, New York, NY, USA; 2Vagelos College of Physicians & Surgeons, Columbia University, New York, NY, USA
Abstract
Heterogeneous approaches to reducing the complexity of EEG functional connectivity (FC) undercut confidence in their validity and 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 sites, 8 min, eyes open/closed, 35 healthy adults, 1-week retest) to mitigate volume conduction and improve spatial resolution. Spectral PCA extracted 6 robust alpha and theta factors (86% variance). Subsequent spatial PCA for each spectral factor revealed regionally-focused (posterior, central, frontal) and long-range (anterior-posterior, frontal-temporal) alpha components (peaks at 8, 10 and 13 Hz) and less robust midfrontal theta (5 and 6 Hz) components. These spatial FC components overlapped with well-known networks (e.g., default mode, visual, sensorimotor), with some being 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 (up to single-subject level), as indicated by similarity of factor 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.
Key Words: functional connectivity, resting EEG, alpha/theta networks
[Supported by National Institute of Mental Health (MH115299; MH092221; MH092250)].