Lee Jiho, Mun Junseung, Choo Minhye, Park Sung-Min
Department of Convergence IT Engineering, Pohang University of Science and Technology (POSTECH), Pohang, Republic of Korea.
Medical Device Innovation Center, Pohang University of Science and Technology (POSTECH), Pohang, Republic of Korea.
NPJ Digit Med. 2025 Apr 23;8(1):220. doi: 10.1038/s41746-025-01635-w.
Neurostimulation for cardiovascular control faces challenges due to the lack of predictive modeling for stimulus-driven dynamic responses, which is crucial for precise neuromodulation via quality feedback. We address this by employing a digital twin approach that leverages computational mechanisms underlying neuro-hemodynamic responses during neurostimulation. Our results emphasize the computational role of the nucleus tractus solitarius (NTS) in the brainstem in determining these responses. The intrinsic neural circuit within the NTS harbors collective dynamics residing in a low-dimensional latent space, which effectively captures stimulus-driven hemodynamic perturbations. Building on this, we developed a digital twin framework for individually optimized predictive modeling of neuromodulatory outcomes. This framework potentially enables the design of closed-loop neurostimulation systems for precise hemodynamic control. Consequently, our digital twin based on neural computation mechanisms marks an advancement in the artificial regulation of internal organs, paving the way for precise translational medicine to treat chronic diseases.
由于缺乏对刺激驱动的动态反应的预测模型，神经刺激用于心血管控制面临挑战，而这对于通过质量反馈进行精确神经调节至关重要。我们通过采用数字孪生方法来解决这一问题，该方法利用神经刺激期间神经血液动力学反应背后的计算机制。我们的结果强调了脑干中孤束核（NTS）在确定这些反应中的计算作用。NTS内的内在神经回路具有存在于低维潜在空间中的集体动力学，可有效捕获刺激驱动的血液动力学扰动。在此基础上，我们开发了一个数字孪生框架，用于对神经调节结果进行个体优化的预测建模。该框架有可能实现用于精确血液动力学控制的闭环神经刺激系统的设计。因此，我们基于神经计算机制的数字孪生标志着内脏人工调节的进步，为治疗慢性病的精确转化医学铺平了道路。
