Background:Transcranial magnetic stimulation (TMS) can modulate neural activity
by evoking action potentials in cortical neurons. TMS neural activation can
be predicted by coupling subject-specific head models of the TMS-induced
electric field (E-field) to populations of biophysically realistic neuron
models; however, the significant computational cost associated with these
models limits their utility and eventual translation to clinically relevant
applications.Objective:To develop computationally efficient estimators of the activation
thresholds of multi-compartmental cortical neuron models in response to
TMS-induced E-field distributions.Methods:Multi-scale models combining anatomically accurate finite element
method (FEM) simulations of the TMS E-field with layer-specific
representations of cortical neurons were used to generate a large dataset of
activation thresholds. 3D convolutional neural networks (CNNs) were trained
on these data to predict thresholds of model neurons given their local
E-field distribution. The CNN estimator was compared to an approach using
the uniform E-field approximation to estimate thresholds in the non-uniform
TMS-induced E-field.Results:The 3D CNNs estimated thresholds with mean absolute percent error
(MAPE) on the test dataset below 2.5% and strong correlation between the CNN
predicted and actual thresholds for all cell types(R2>0.96). The CNNs estimated thresholds with a
2–4 orders of magnitude reduction in the computational cost of the
multi-compartmental neuron models. The CNNs were also trained to predict the
median threshold of populations of neurons, speeding up computation
further.Conclusion:3D CNNs can estimate rapidly and accurately the TMS activation
thresholds of biophysically realistic neuron models using sparse samples of
the local E-field, enabling simulating responses of large neuron populations
or parameter space exploration on a personal computer.