Integrating electric field modeling and pre-tDCS behavioral performance to predict the individual tDCS effect on visual crowding

Abstract

Abstract Objective. Transcranial direct current stimulation (tDCS) has been broadly used to modulate brain activity with both bipolar and high-definition montages. However, tDCS effects can be highly variable. In this work, we investigated whether the variability in the tDCS effects could be predicted by integrating individualized electric field modeling and individual pre-tDCS behavioral performance. Approach. Here, we first compared the effects of bipolar tDCS and 4 × 1 high-definition tDCS (HD-tDCS) with respect to the alleviation of visual crowding, which is the inability to identify targets in the presence of nearby flankers and considered to be an essential bottleneck of object recognition and visual awareness. We instructed subjects to perform an orientation discrimination task with both isolated and crowded targets in the periphery and measured their orientation discrimination thresholds before and after receiving 20 min of bipolar tDCS, 4 × 1 HD-tDCS, or sham stimulation over the visual cortex. Individual anatomically realistic head models were constructed to simulate tDCS-induced electric field distributions and quantify tDCS focality. Finally, a multiple linear regression model that used pre-tDCS behavioral performance and tDCS focality as factors was used to predict post-tDCS behavioral performance. Main results. We found that HD-tDCS, but not bipolar tDCS, could significantly alleviate visual crowding. Moreover, the variability in the tDCS effect could be reliably predicted by subjects’ pre-tDCS behavioral performance and tDCS focality. This prediction model also performed well when generalized to other two tDCS protocols with a different electrode size or a different stimulation intensity. Significance. Our study links the variability in the tDCS-induced electric field and the pre-tDCS behavioral performance in a visual crowding task to the variability in post-tDCS performance. It provides a new approach to predicting individual tDCS effects and highlights the importance of understanding the factors that determine tDCS effectiveness while developing more robust protocols.