Simulation of evoked responses to transcranial magnetic stimulation using a multiscale cortical circuit model

Transcranial magnetic stimulation (TMS) is a widely used non-invasive brain stimulation technique, but the neural circuits activated by TMS remain poorly understood. Previous modeling approaches have been limited to either simplified point-neuron networks or isolated single-cell models that lack synaptic connectivity.

To develop and validate a multiscale cortical circuit model that integrates morphologically-realistic neurons with accurate TMS-induced electric field distributions and to investigate mechanisms underlying cortical responses to stimulation.

We constructed a network model of a cortical column comprising 10,000 neurons across layers 2/3, 5, and 6 with over 10 million synaptic connections. The model incorporated thalamic and non-specific corticocortical inputs to generate physiological firing rates. TMS-induced electric fields were calculated using finite element modeling and coupled to individual neurons through the extracellular mechanism. We validated model predictions against experimental recordings of TMS-evoked local field potentials (LFPs) and multiunit activity.

The model reproduced key experimental observations including the dose-dependent N50 LFP component and multiphasic multi-unit responses, consisting of an (excitatory) increase followed by a (inhibitory) decrease in firing rates. The early excitatory response exhibited dual-peak dynamics reflecting distinct contributions from directly and indirectly activated neuronal populations, and the subsequent inhibitory phase reflected activation of feedback GABAergic circuits through both GABA_A and GABA_B conductances. Spatial analysis across 30 cortical columns distributed across the precentral gyrus revealed orientation-dependent evoked responses.

This validated multiscale model provides mechanistic insights into TMS-evoked cortical dynamics, demonstrating how direct neuronal activation cascades through synaptic networks to generate characteristic population responses. The framework establishes a computational platform for optimizing stimulation protocols in research and clinical applications.