Modeling plasticity for neural mass models under tES

This thesis introduces a novel methodology for integrating plasticity mechanisms within Neural Mass Models (NMMs) to simulate the lasting effects of transcranial electrical stimulation (tES). To do so, we incorporate homeostatic plasticity into two NMMs: the PING model, lacking multistability, and the Jansen and Rit model, with more complex multistable dynamics. Our findings indicate that homeostatic plasticity, which adjusts connectivity to maintain firing rates, does not induce permanent changes post-tES. To overcome this, we introduced a proxy for Hebbian plasticity for a proof-of concept demonstration that the combination with homeostatic mechanisms results in persistent post stimulation effects. Future research should explore further the mechanisms of Hebbian plasticity and network interactions to enable comprehensive whole-brain simulations. These advancements could enhance personalized tES treatments, potentially transforming therapeutic strategies for neurological conditions.
Outgoing