Using the high-density, artifact-minimized EEG data, we conducted intracranial source analyses using eLORETA, a linear inverse solution to reconstruct cortical activity with scalp EEG data (Pascual-Marqui et al., 2011). The LORETA algorithm has been cross-validated in multiple studies combining EEG-based LORETA with fMRI (Worrell et al., 2000; Vitacco et al., 2002; Mulert et al., 2004; Mobascher et al., 2009; Olbrich et al., 2009), positron emission tomography (Dierks et al., 2000; Pizzagalli et al., 2004), and intracranial recordings (Zumsteg et al., 2005). The solution space consists of 6239 cortical gray matter voxels with a spatial resolution of 5 × 5 × 5 mm in a realistic head model. eLORETA is a suitable tool to investigate network activity and connectivity (Neuner et al., 2014; Thatcher et al., 2014; Liu et al., 2017; Samogin et al., 2019) and has provided important network insights into psychiatric disorders (Whitton et al., 2018; Imperatori et al., 2019; Samogin et al., 2019).
