Details

Autor(en) / Beteiligte

• Aberra, Aman S.
• Lopez, Adrian
• Grill, Warren M.
• Peterchev, Angel V.

Titel

Rapid estimation of cortical neuron activation thresholds by transcranial magnetic stimulation using convolutional neural networks

Ist Teil von

• NeuroImage (Orlando, Fla.), 2023-07, Vol.275, p.120184-120184, Article 120184

Ort / Verlag

United States: Elsevier Inc

Erscheinungsjahr

2023

Quelle

MEDLINE

Beschreibungen/Notizen

• •Computational cost of simulating neural response to transcranial magnetic stimulation is high.•Developed method for estimation of neural activation thresholds by TMS-induced E-fields using convolutional neural networks.•CNNs achieved under 2.5% mean absolute percent error with 2 – 5 orders of magnitude speedup in 15 cortical neuron models.•On single CPU, CNN estimators reduce time to simulate thresholds of 100,000 neurons from ∼1 week to ∼1min∼1 min. 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. To develop computationally efficient estimators of the activation thresholds of multi-compartmental cortical neuron models in response to TMS-induced E-field distributions. 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. 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. 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.