Regional Excitatory-Inhibitory Balance Relates to Self-Reference Effect on Recollection via the Precuneus/Posterior
Ying He1, Hilary Sweatman2, Alice R Thomson3,4
1Department of Neurology and Neurosurgery, McGill University, Montreal, Quebec H3A2B4, Canada ying.he3@mcgill.ca.
Abstract:
Self-related representation can enhance perception and memory-a phenomenon known as the self-referential effect (SRE). While SRE has been linked to the activation of the default mode network (DMN), including the precuneus/posterior cingulate cortex (Pcu/PCC) and the medial prefrontal cortex (mPFC), the underlying neurochemical processes of DMN activations remain unclear. The balance of excitation and inhibition (E/I balance) within brain circuits is crucial for cognition and may play a role in the SRE. We examine whether the ratio of glutamate/glutamine (Glx) to γ-aminobutyric acid (GABA) concentrations, measured by 1H-magnetic resonance spectroscopy (1H-MRS) as a proxy measure for E/I balance, is associated with DMN neural processes involved in self-referential encoding. Fifty-four healthy participants aged 7-35 (25 female) underwent MRS to measure levels of Glx and GABA in Pcu/PCC and completed an fMRI scan during an encoding task that involved self-referential and semantic judgments. We found that the self-related condition led to better subsequent memory and greater activation in the Pcu/PCC compared with the semantic condition. Activations in the Pcu/PCC were positively correlated with the Glx/GABA+ ratio. Task-dependent functional connectivity analysis revealed that connectivity between the Pcu/PCC and medial prefrontal cortex (mPFC) was positively associated with both the Glx/GABA+ ratio and the SRE effect on recollection accuracy. Furthermore, mediation analysis showed that a higher Glx/GABA+ ratio correlated with better SRE on memory recollection through increased Pcu/PCC-mPFC connectivity. Our study provides valuable insights into how neurochemical activity is associated with self-related cognition via functional connectivity of large-scale brain networks.
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