Deep-Learning-Based Prediction of TMS-Induced Motor Evoked Responses from Flattened Individualized Cortical Electric Field Maps

Background: Transcranial magnetic stimulation (TMS) enables functional brain mapping by linking stimulation (electric field) to motor evoked responses (MEPs), but accurate mapping often requires many empirical samples. Prediction models based only on TMS setup parameters have limited ability to account for subject-specific anatomy, whereas 3D intracranial E-Fields provide richer biophysical information but may suffer from sparse volumetric representations. This study aimed to predict MEPs from individualized cortical E-Field distributions using flattened two-dimensional cortical maps. Methods: TMS–MEP experiments were conducted in seven healthy participants. MRI-derived head models were used to simulate FEM-based intracranial E-Fields for each stimulation condition. Primary motor cortex E-Fields were transformed into two-dimensional cortical maps, and multiple deep learning models were evaluated using nested cross-validation with held-out-subject testing with subject-wise inner validation (HOS-SIV) as a stricter setting and held-out-subject testing with random inner validation (HOS-RIV). Results: ROC-AUC values ranged from 0.710 to 0.811 and from 0.840 to 0.872 across muscles under the held-out-subject testing with subject-wise inner validation (HOS-SIV) and held-out-subject testing with random inner validation (HOS-RIV) settings, respectively. Under the primary HOS-SIV setting, conventional classifiers and CNN-based models showed broadly comparable performance, whereas CNN- and ResNet-based models tended to perform better under the HOS-RIV setting. Predicted spatial MEP maps partially reproduced the measured response distributions. Conclusions: The presence of TMS-induced MEPs can be predicted from flattened maps of subject-specific cortical E-fields, providing proof-of-concept support for future approaches to improve the efficiency of TMS motor mapping.