Effects of spatial sampling on network alterations in idiopathic generalized epilepsy. Can routine EEG be enough?

Idiopathic generalized epilepsy (IGE) is characterized by marked brain network alterations as assessed using electrophysiology. Logistical challenges and the need for a volumetric MRI often hinder the clinical application of high-density EEG or MEG. This study investigates the influence of EEG channel density and the head model on brain metrics derived from 256-channel EEG and 19-channel routine EEG in two samples balanced for age and sex. First, we evaluated resting-state data from 35 individuals with IGE and 54 healthy controls collected using the 256-channel setup. Data were analyzed at full density and then iteratively down-sampled to lower densities. Source reconstruction was performed either using individual MRI data or a standard brain template and dynamic imaging of coherent sources (DICS). We assessed EEG power and connectivity (imaginary part of coherency) group differences at all channel compositions, head model types, and parcellations (cortical vertices, anatomical and network parcellations). Second, a routine sample recorded with 19 channels was analyzed to validate findings in a real epilepsy monitoring scenario (71 patients, 43 controls). We found that lower-density arrays reliably identified global group differences for both power and connectivity and in frequency bands for which the strongest effects were observed. The spatial similarity of the results for the 256 channels set and those with fewer channels were good to moderate for power (rspin [~]0.97 to 0.33), but dropped for connectivity with fewer than 64 channels (rspin [~]0.78 to-0.12). Comparing individual and canonical head models revealed consistent effects (rspin [~]0.77 to 0.5), with coarser brain parcellations increasing stability for low-density maps. In sum, low-density EEG arrays suffice for detecting global alterations in IGE, particularly in signal power. Our findings advocate for leveraging clinical EEG for brain-wide analyses in IGE while emphasizing the need for high-density coverage if spatial precision is needed. Canonical head models are a viable alternative if no individual MRI is available, especially for regional-or network-level assessments.