NEUROCOMPUTATIONAL MODELING OF POTENTIATION AND DEPRESSION PATHWAYS TO ENHANCE PREDICTIVE EPILEPTIC NEUROMODULATION

Abstract

Objective: Neuroplasticity is the ability of the brain to reorganize itself by forming new neural connections, which is manifested through synaptic potentiation and depression, thus being the base of learning, memory, and recovery after injuries.

Objective: The goal of this study is to design and test an AI-driven computational model that is able to integrate the varying non-invasive electrical stimulation parameters over time with the biologically realistic synaptic plasticity mechanisms, namely spike-timing-dependent plasticity and reward-modulated plasticity, so that it can predict and simulate the changes in the dual synapses of the excitatory neuronal circuits and consequently provide personalized neuromodulation methods for foreseeing and controlling seizures.

Methods: Electrical stimulation methods applied at different times, such as patterned pulsed stimulation and temporal interference, are among the most potential noninvasive brain stimulation techniques for enhancing synaptic transmission; however, the variability among individuals and the intricacy of synaptic changes still stand as obstacles for the creation of tailored treatment plans. The paper puts forth a computational framework powered by artificial intelligence that harmonizes synaptic plasticity mechanisms that are biologically plausible, such as spike-timing dependent plasticity (STDP) and reward-modulated plasticity (RMP), with the time-related electrical stimulation parameters in order to emulate dual synaptic alterations in the circuits associated with the neuroma.

Results: Training and validating the model on publicly available intracranial EEG datasets from epilepsy patients are carried out to ensure that the model depicts biological reality and is clinically applicable across a variety of neural conditions. The evaluation of the performance of predictive models will be determined based on the criteria of precision, sensitivity, mean squared error, and sensitivity analyses will be conducted to evaluate the effect of various stimulation parameters on predictive performance.

Conclusion: The main objective of this research is to improve personalized neuromodulation methods for the prediction and suppression of seizures, which in turn will lead to precision neurotherapeutics, allowing for the application of digital health equity and gender-inclusive care principles.