Tractography-based transcranial magnetic stimulation prediction using machine learning

Abstract

BackgroundIdentifying the stimulation brain target is fundamental for transcranial magnetic stimulation (TMS). Currently, this process is time-consuming and heavily dependent on the operators expertise. ObjectiveThis study evaluated a deep learning-based approach to structural brain connectivity for improving stimulation site prediction and real-time cortical excitability mapping during neuronavigation. ResultsTractography-derived connectivity features and distal myographic responses were used to train four neural network models across five subjects. Neural network inputs were incrementally varied using either tractography alone, coil coordinates alone, or hybrid combinations of both using different concatenation regimes. The best performance was observed in two subjects out of 5, where hybrid models integrating coil coordinates and fiber information achieved higher F1 scores and accuracy. ConclusionDespite these promising results, substantial inter-subject variability was observed. This approach may also be applied to other brain regions to investigate connectivity, and the use of machine learning could be extended to functional domains beyond the motor cortex, including sensory and cognitive areas.