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

Abstract

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 HFAmemory 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. AbbreviationsHFA -High-frequency activity MEM -Brain regions showing HFA associated with verbal-memory encoding CN -Controls Nodes P.REC -Percentage of words recalled during IEEG memory testing CVLT -California Verbal Learning Test IEEG -Invasive Electroencephalography BC -Betweenness Centrality CC-Clustering Coefficient PC -Participation Coefficient Highlights 1. High frequency memory activity in IEEG corresponds to specific BOLD changes in resting-state data.2. HFA-memory regions had lower hubness relative to control brain nodes in both epilepsy patients and healthy controls.3. HFA-memory network displayed hubness and participation (interaction) values distinct from other cognitive networks.4. HFA-memory network shared regional membership and interacted with other cognitive networks for successful memory encoding. 5. HFA-memory network hubness predicted both concurrent task (phasic) and baseline (tonic) verbal-memory encoding success.resting-state fMRI (rsfMRI) data to characterize the network architecture of a task-defined HFAmemory network and understand its relationship to the functional connectivity of a broader set of intrinsic resting-state networks. Graph theory has been a useful tool for mapping functional networks and characterizing their properties during task and at rest. Three important graph indices that are essential in characterizing brain regions and their functionality include: (1) clustering coefficient (CC), which captures network segregation and local information processing and (2) betweenness centrality (BC), which captures hubness and the degree of importance held by a region that connects two or more modules and (3) participation coefficient (PC) which measures the extent to which regions within a network connect to networks other than its own, with a higher participation coefficient indicating that the regions connect with a variety of o...