Realistic head model for predicting electric field distribution and optimizing parameters in transcranial direct current stimulation

Transcranial direct current stimulation (tDCS) has been extensively utilized for cognitive enhancement and the treatment of neuropsychiatric disorders. Increasing evidence indicates that the spatial distribution of the electric field induced by tDCS is highly dependent on individual head anatomy. To address the need for anatomically realistic electric field simulations, this study establishes an efficient computational modeling pipeline and applies quantitative metrics to evaluate stimulation effects in the dorsolateral prefrontal cortex (DLPFC), which is a key cortical region implicated in higher-order cognitive functions. Structural magnetic resonance imaging (MRI) data were processed to perform multi-tissue segmentation, encompassing five major biological compartments: scalp, skull, cerebrospinal fluid (CSF), gray matter, and white matter. The segmented tissues were subsequently refined to reconstruct a three-dimensional head model. Based on this realistic geometry, a finite element modeling framework was developed to simulate the electric field distribution generated by tDCS. The stimulation effects targeting the DLPFC were evaluated using three quantitative metrics: maximum electric field intensity, shunting index, and focality index. Through parametric analysis, an optimized electrode configuration was identified that yielded a peak electric field intensity of 0.36 V/m. The model further characterized the electric field distribution across cortical tissues and revealed how electrode parameters influence field patterns within the target region, providing a theoretical basis for optimized electrode design in neuromodulation applications.