Characterization of neuronal dynamics in working memory: exploring phase synchronization across spatial, spectral, and temporal dimensions

Abstract

Working memory (WM) is an essential neuronal function for higher-order cognitive tasks such as reasoning, language comprehension, problem-solving, and planning. Understanding the mechanisms underlying WM requires examining the communication between brain networks at high spatial, temporal, and spectral resolutions. For this examination, phase synchronization of brain oscillations is often studied in neuroscience to characterize neuronal coordination. Nevertheless, the patterns of synchronization across time, frequency, and space in the context of WM are not fully understood. This thesis aims to provide a comprehensive analysis of phase synchronization of magnetoencephalography (MEG) signals from subjects performing so-called n-back WM tasks. We first use bivariate mean phase coherence to identify synchronized neural subnetworks. Subsequently, we apply the re-normalized mean resultant length to explore the multivariate phase synchrony of subnetworks across frequencies and time. Our findings reveal distinct patterns in nine spectral ranges, spanning from delta to gamma bands. Lower frequencies show greater interhemispheric and long-range connectivity, while higher frequencies emphasize local synchrony within specific brain regions. The analysis is complemented with surrogate signals to confirm that the observed patterns of synchrony are intrinsic to neuronal dynamics. Desynchronization between the sensorimotor cortex and angular gyri suggests the existence of an alpha-mediated attentional control mechanism that inhibits response during the n-back task. In addition, the event-related desynchronization identified in the sensorimotor cortex could indicate the allocation of neural resources to more demanding cognitive processes in memory encoding. Future studies integrating resting-state data may potentially improve our understanding of the neuronal mechanisms underlying WM.