Eigenvector Centrality Mapping Reveals Volatility of Functional Brain Dynamics in Anti-NMDA Receptor Encephalitis

Abstract

BackgroundAnti-NMDA receptor encephalitis (NMDARE) causes long-lasting cognitive deficits associated with altered functional connectivity. Eigenvector centrality (EC) mapping represents a powerful new method for data-driven voxelwise and time-resolved estimation of network importance—beyond changes in classical static functional connectivity. MethodsTo assess changes in functional brain network organization, we applied EC mapping in 73 patients with NMDARE and 73 matched healthy control participants. Areas with significant group differences were further investigated using 1) spatial clustering analyses, 2) time series correlation to assess synchronicity between the hippocampus and cortical brain regions, and 3) correlation with cognitive and clinical parameters. ResultsDynamic, time-resolved EC showed significantly higher variability in 13 cortical areas (familywise error p < .05) in patients with NMDARE compared with healthy control participants. Areas with dynamic EC group differences were spatially organized in centrality clusters resembling resting-state networks. Importantly, variability of dynamic EC in the frontotemporal cluster was associated with impaired verbal episodic memory in patients (r = −0.25, p = .037). EC synchronicity between the hippocampus and the medial prefrontal cortex was reduced in patients compared with healthy control participants (familywise error p < .05, tmax = 3.76) and associated with verbal episodic memory in patients (r = 0.28, p = .019). Static EC analyses showed group differences in only one brain region (left intracalcarine cortex). ConclusionsWidespread changes in network dynamics and reduced hippocampal-medial prefrontal synchronicity were associated with verbal episodic memory deficits and may thus represent a functional neural correlate of cognitive dysfunction in NMDARE. Importantly, dynamic EC detected substantially more network alterations than traditional static approaches, highlighting the potential of this method to explain long-term deficits in NMDARE.