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Tonic resting-state hubness supports high-frequency activity defined verbal-memory encoding network in epilepsy

High-frequency gamma activity of verbal-memory encoding using invasive-electroencephalogram coupled has laid the foundation for numerous studies testing the integrity of memory in diseased populations. Yet, the functional connectivity characteristics of networks subserving these HFA-memory linkages remains uncertain. By integrating this electrophysiological biomarker of memory encoding from IEEG with resting-state BOLD fluctuations, we estimated the segregation and hubness of HFA-memory regions in drug-resistant epilepsy patients and matched healthy controls. HFA-memory regions express distinctly different hubness compared to neighboring regions in health and in epilepsy, and this hubness was more relevant than segregation in predicting verbal memory encoding. The HFA-memory network comprised regions from both the cognitive control and primary processing networks, validating that effective verbal-memory encoding requires multiple functions, and is not dominated by a central cognitive core. Our results demonstrate a tonic intrinsic set of functional connectivity, which provides the necessary conditions for effective, phasic, task-dependent memory encoding. Highlights High frequency memory activity in IEEG corresponds to specific BOLD changes in resting-state data. HFA-memory regions had lower hubness relative to control brain nodes in both epilepsy patients and healthy controls. HFA-memory network displayed hubness and participation (interaction) values distinct from other cognitive networks. HFA-memory network shared regional membership and interacted with other cognitive networks for successful memory encoding. HFA-memory network hubness predicted both concurrent task (phasic) and baseline (tonic) verbal-memory encoding success.