模型优化经皮迷走神经刺激或改善暗适应期间的视觉运动反应

Sudden reductions in ambient illuminance temporarily impair visuomotor performance due to the inherent latency of dark adaptation, posing significant risks in operational environments such as tunnel entry. To mitigate this degradation, we propose a model-based neuromodulation framework using transcutaneous vagus nerve stimulation (tVNS), which is known to accelerate and augment pupil dilation and potentially enhance sensorimotor processing by activating the locus coeruleus-norepinephrine (LC-NE) system. First, we characterized the temporal dynamics of the tVNS-induced pupillary response and developed a predictive model using a long short-term memory (LSTM) network in standard lighting. The model demonstrated efficacy in reproducing nonlinear physiological characteristics even under sudden dark exposure. Second, we derived optimized stimulation sequences using a genetic algorithm to maximize pupil dilation during immediate adaptation to darkness. Pupillary evaluations during bright-to-dark transitions revealed that the optimized tVNS additively increased pupil diameter. The improvement in visuomotor choice-reaction performance was highly correlated with the amount of tVNS-induced increase in pupil diameter during dark transition across healthy young adults. These findings suggest that the model-optimized tVNS approach may effectively mitigate the degradation of visuomotor performance during dark transitions via LC-NE activation as well as subsequent sensorimotor enhancement. Furthermore, the experimental results demonstrate the potential of the transient pupil response to tVNS to serve as a robust physiological indicator for predicting behavioral improvement.

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