DeeptDCS: Deep Learning-Based Estimation of Currents Induced During Transcranial Direct Current Stimulation
This work addresses the need for fast, accurate current density estimation in tDCS applications, enabling repetitive tasks like uncertainty quantification and optimization, though it is incremental as it builds on existing U-net architectures with attention mechanisms.
The paper tackled the problem of rapidly estimating current density induced during transcranial direct current stimulation (tDCS) by proposing DeeptDCS, a deep learning-based emulator that uses Attention U-net to output 3D current density distributions from head tissue models, achieving computational speeds at least two orders of magnitude faster than a physics-based simulator while maintaining satisfactory accuracy.
Objective: Transcranial direct current stimulation (tDCS) is a non-invasive brain stimulation technique used to generate conduction currents in the head and disrupt brain functions. To rapidly evaluate the tDCS-induced current density in near real-time, this paper proposes a deep learning-based emulator, named DeeptDCS. Methods: The emulator leverages Attention U-net taking the volume conductor models (VCMs) of head tissues as inputs and outputting the three-dimensional current density distribution across the entire head. The electrode configurations are also incorporated into VCMs without increasing the number of input channels; this enables the straightforward incorporation of the non-parametric features of electrodes (e.g., thickness, shape, size, and position) in the training and testing of the proposed emulator. Results: Attention U-net outperforms standard U-net and its other three variants (Residual U-net, Attention Residual U-net, and Multi-scale Residual U-net) in terms of accuracy. The generalization ability of DeeptDCS to non-trained electrode configurations can be greatly enhanced through fine-tuning the model. The computational time required by one emulation via DeeptDCS is a fraction of a second. Conclusion: DeeptDCS is at least two orders of magnitudes faster than a physics-based open-source simulator, while providing satisfactorily accurate results. Significance: The high computational efficiency permits the use of DeeptDCS in applications requiring its repetitive execution, such as uncertainty quantification and optimization studies of tDCS.