An EEG-based intrinsic brain network perspective on chronic pain – a multi-dataset study

To assess the function of IBNs, we computed cross-network functional connectivity from resting state EEG data. We related connectivity to pain intensity in patients with different types of chronic pain using both univariate statistical and multivariate machine learning methods. We first analyzed a discovery dataset recorded in our research group (n = 119) and then tested the reliability of findings in six replication datasets (n = 418). As a benchmark, we also assessed the association between pain intensity and other, established features of EEG. Moreover, since age has been shown to robustly covary with many EEG features, we validated our approach by considering age instead of pain intensity as a dependent variable. In exploratory analyses, we expanded the focus and investigated how brain activity and connectivity, beyond the four IBNs of interest, relate to pain intensity.