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한빛사논문
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npj Digit. Med., Apr 23 2025, 8 (1) 220 | https://doi.org/10.1038/s41746-025-01635-w
Predictive modeling of hemodynamics during viscerosensory neurostimulation via neural computation mechanism in the brainstem
Jiho Lee1,2, Junseung Mun1,2, Minhye Choo1,2 & Sung-Min Park1,2,3,4,5
1Department of Convergence IT Engineering, Pohang University of Science and Technology (POSTECH), Pohang, Republic of Korea.
2Medical Device Innovation Center, Pohang University of Science and Technology (POSTECH), Pohang, Republic of Korea.
3Department of Electrical Engineering, Pohang University of Science and Technology (POSTECH), Pohang, Republic of Korea.
4Department of Mechanical Engineering, Pohang University of Science and Technology (POSTECH), Pohang, Republic of Korea.
5Institute of Convergence Science, Yonsei University, Seoul, Republic of Korea.
Corresponding author
Correspondence to Sung-Min Park.
1Department of Convergence IT Engineering, Pohang University of Science and Technology (POSTECH), Pohang, Republic of Korea.
2Medical Device Innovation Center, Pohang University of Science and Technology (POSTECH), Pohang, Republic of Korea.
3Department of Electrical Engineering, Pohang University of Science and Technology (POSTECH), Pohang, Republic of Korea.
4Department of Mechanical Engineering, Pohang University of Science and Technology (POSTECH), Pohang, Republic of Korea.
5Institute of Convergence Science, Yonsei University, Seoul, Republic of Korea.
Corresponding author
Correspondence to Sung-Min Park.
Abstract
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.
논문정보
- Research article
- 2025년 04월 (BRIC 등록일 2025-04-29)
- 국내 연구진
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