Phase-Dependent Neuromodulation in a Computational Hippocampal Model

The critical role of phase-amplitude coupling (PAC) between oscillations of differing frequencies highlights the promise of phase-dependent neuromodulation as a therapeutic strategy for various neurological conditions. In the hippocampus, theta-gamma PAC is linked to key memory processes and information transfer. Computational models avoid technical challenges in in vivo and in vitro experiments and offer a practical alternative for exploring the mechanisms behind phase-dependent effects. In this study, we built on a published CA3 hippocampal computational model implemented in the NEURON-Python environment. We used a closed-loop autoregressive (AR) forward prediction model that sampled the network's local field potential (LFP) to achieve real-time calculation of stimulus time points locked to a target phase of the theta oscillation. Our approach enabled the delivery of current injections to all neuronal populations at either the peak or the trough of the theta rhythm. Analysis of the resulting network LFP showed distinct phase-dependent changes in the theta band during stimulation. The peak-phase stimulation significantly enhanced theta-gamma coupling. Further study on a large-scale human-based model is needed to better capture these phase-dependent effects. Overall, the results underscored the importance of closed-loop stimulation systems and the potential of phase-targeted neuromodulation to influence PAC. These findings offer new avenues for treating disorders marked by disrupted oscillations, including Alzheimer's disease and other memory disorders.Clinical Relevance- This study provides investigations of the origins of neuronal oscillations and the development of a brain stimulation technique for modulating the level of oscillations, possibly contributing to the development of novel treatment methods for neurological disorders associated with abnormal oscillations..