Phase-Dependent Stimulation of the Hippocampus: A Computational Modeling Approach
Phase-amplitude coupling (PAC) between brain oscillations of different frequencies plays a fundamental role in neural processing, and phase-dependent neuromodulation has emerged as a promising strategy to modulate PAC. In the hippocampus, theta-gamma PAC is critically involved in memory-related functions and information propagation. Computational models provide a valuable platform for investigating the neurobiological mechanisms underlying phase-dependent effects, bypassing the limitations of in vivo and in vitro experiments. In this study, we extended a previously published computational model of the hippocampal CA3 region using the NEURON and Python environments. A closed-loop autoregressive (AR) forward prediction model was employed to sample the network's local field potential (LFP) in real time, enabling the precise calculation of phase-locked stimulus time points. Our results demonstrated the successful delivery of phase-locked current injections to all neuronal populations at both the peak and trough of theta oscillations. Phase-specific alterations in the theta band were observed during stimulation, along with enhanced theta-gamma coupling induced by peak-phase stimulation. Single neuron activity analysis highlighted the critical role of oriens lacunosum-moleculare (OLM) cells in modulating phase-dependent network dynamics. These findings underscore the potential of closed-loop stimulation systems to modulate PAC, with significant implications for the treatment of neurological disorders characterized by abnormal oscillatory activity, such as Alzheimer's disease and other memory-related disorders.