Optimizing the electric field strength in multiple targets for multichannel transcranial electric stimulation

Objective. Most approaches to optimize the electric field pattern generated by multichannel transcranial electric stimulation (TES) require the definition of a preferred direction of the electric field in the target region(s). However, this requires knowledge about how the neural effects depend on the field direction, which is not always available. Thus, it can be preferential to optimize the field strength in the target(s), irrespective of the field direction. However, this results in a more complex optimization problem. Approach. We introduce and validate a novel optimization algorithm that maximizes focality while controlling the electric field strength in the target to maintain a defined value. It obeys the safety constraints, allows limiting the number of active electrodes and allows also for multi-target optimization. Main results. The optimization algorithm outperformed naive search approaches in both quality of the solution and computational efficiency. Using the amygdala as test case, we show that it allows for reaching a reasonable trade-off between focality and field strength in the target. In contrast, simply maximizing the field strength in the target results in far more extended fields. In addition, by maintaining the pre-defined field strengths in the targets, the new algorithm allows for a balanced stimulation of two or more regions. Significance. The novel algorithm can be used to automatically obtain individualized, optimal montages for targeting regions without the need to define preferential directions. It will automatically select the field direction that achieves the desired field strength in the target(s) with the most focal stimulation pattern.

Automated summary

The novel optimization algorithm introduced in this study maximizes focality while controlling the electric field strength in the target regions, adhering to safety constraints and allowing for multi-target optimization. It outperformed naive search approaches in quality of solution and computational efficiency, providing a balanced stimulation for multiple regions without the need to define preferred directions.