KR-20260063919-A - Neural-hemodynamic outcome prediction digital twin system and blood pressure prediction method using the same

Abstract

A digital twin system for predicting NTS-based neuro-hemodynamic results performed on a computable processor, wherein the hemodynamic value (BP T ) obtained at time T is normalized into a normalized NTS latent space ( An encoding unit that converts the latent space state converted by the encoding unit into the next latent state ( A digital twin system is provided, characterized by comprising: a calculation unit for calculating ); and a decoding unit for converting the next potential state calculated by the stimulus-based dynamics calculation unit into an updated hemodynamic value (BP T+1 ).

Inventors

• 박성민
• 추민혜
• 이지호

Assignees

• 포항공과대학교 산학협력단

Dates

Publication Date

20260507

Application Date

20241031

Claims (14)

1. A digital twin system for predicting neuro-hemodynamic results based on the nucleus isolate (NTS) performed on a computationally capable processor, The hemodynamic value (BP T ) obtained at time T is normalized into the NTS latent space ( Encoding unit that converts to the state of ); The latent space state converted by the above encoding unit is used to create the next latent state ( A calculation unit that calculates ); and A digital twin system characterized by including a decoding unit that converts the next potential state calculated by the stimulus-based dynamics calculation unit into an updated hemodynamic value (BP T+1 ).
2. In Article 1, A digital twin system characterized by the above-mentioned computational unit using neural circuit modeling utilizing a recurrent neural network (RNN).
3. In Paragraph 2, A digital twin system characterized by the above-mentioned recurrent neural network (RNN) successfully modeling the collective dynamics of the above-mentioned NTS neural circuit to replicate measured single neural activities and neural trajectories.
4. In Article 1, A digital twin system characterized by the above encoding unit converting hemodynamic values (BP measurements) into a vector form representing the potential state of an NTS neuron population.
5. In Article 1, A digital twin system characterized by the above-described decoding unit linearly combining each element of a latent space vector using a predefined decoding vector.
6. In Paragraph 5, The above decoding unit A digital twin system characterized by multiplying each dimension value by the weight of a decoding vector and summing them all to calculate a Δ-predicted value.
7. In Article 1, A digital twin system characterized in that the above hemodynamic value is blood pressure.
8. A blood pressure prediction method for predicting neuro-hemorrhagic results based on a nucleus isolate (NTS) performed on a computationally capable processor, The hemodynamic value (BP T ) obtained at time T is normalized into the NTS latent space ( Encoding step that converts to the state of ); The latent space state transformed in the above encoding step is used with an artificial neural network to create the next latent state Calculation step for calculating; A blood pressure prediction method characterized by including a decoding step that converts the next potential state calculated in the above calculation step into an updated hemodynamic value (BP T+1 ).
9. In Paragraph 8, A blood pressure prediction method characterized by using neural circuit modeling utilizing a recurrent neural network (RNN) in the above calculation step.
10. In Article 9, A blood pressure prediction method characterized by the above-mentioned recurrent neural network (RNN) successfully modeling the collective dynamics of the above-mentioned NTS neural circuit to replicate the measured single neural activity and neural trajectory.
11. In Paragraph 8, A blood pressure prediction method characterized by the above encoding step converting hemodynamic values (BP measurements) into a vector form representing the potential state of an NTS neuron population.
12. In Paragraph 8, A blood pressure prediction method characterized by the above-mentioned decoding step of linearly combining each element of a latent space vector using a predefined decoding vector.
13. In Paragraph 12, The above decoding unit A blood pressure prediction method characterized by multiplying each dimension value by the weight of a decoding vector and summing them all to calculate a Δ-predicted value.
14. In Paragraph 8, A blood pressure prediction method characterized in that the above hemodynamic value is blood pressure.

Description

Neural-hemodynamic outcome prediction digital twin system and blood pressure prediction method using the same The present invention relates to a digital twin system and method for predicting neuro-hemodynamic results, and more specifically, to a digital twin system based on the collective dynamics of a baroreflex-related NTS neuronal population involved in hemodynamic perturbation among visceral sensory stimuli, and as a result, to a digital twin system and method for predicting neuro-hemodynamic results capable of predicting hemodynamic responses based on visceral sensory stimuli. Recent advancements in Brain-Machine Interface (BMI) technology are revolutionizing neural stimulation practices, providing promising solutions for restoring neurological dysfunction and promoting brain function. In fact, advancements in BMI for neural stimulation have achieved significant success in various medical fields. In particular, applications for treating chronic diseases have been developed, such as deep brain stimulation to alleviate brain disorders like type 1 and type 2 epilepsy, and spinal cord stimulation for the rehabilitation of motor disorders resulting from spinal cord injury. Despite these achievements, the application of BMI technology to visceral sensory nerve stimulation therapy to artificially control the functions of internal organs, such as the hemodynamic function of the cardiovascular system, has not yet been realized. Although the treatment has shown potential to effectively regulate hemodynamic function, clinical interpretation is difficult due to the high interpersonal variability of the results following stimulation. For example, baroreflex activation therapy faces clinical limitations such as excessive blood pressure (BP) reduction and inefficiency due to the open-loop stimulation method and heuristic selection of stimulation parameters (see Non-patent Literature 1, Non-patent Literature 2). This has raised the need for personalized computational models, the concept of digital twins (patient-specific virtual replicas), as an aspect of BMI technology to individually predict stimulus-centered outcomes. Accurate predictive modeling provided by digital twins is, in fact, an essential prerequisite for modern closed-loop feedback control systems. However, digital twins for complex stimulus-centered responses, such as the nonlinear evolution of blood pressure observed in therapy through baroreflex activation, are still difficult to find. Designing digital twins to predictively model stimulus-based responses in internal organs, such as the cardiovascular system, requires a fundamental understanding of the anatomical and computational mechanisms by which stimulus inputs regulate the autonomic nervous system and internal organs. Classical and recent studies have established a neuroanatomical atlas of the autonomic nervous system. The autonomic nervous system hemodynamically regulates visceral sensory afferents from the cardiovascular system to the brainstem. The brainstem is an essential intermediate processor between afferent and efferent pathways. In particular, the nucleus tractus solitarius (NTS) in the brainstem plays an important role in integrating visceral sensory information and coordinating the rostral ventrolateral medulla (RVLM) and the dorsal motor nucleus of the vagus (DMV). Pre-autonomic nodes regulate the efferent pathways of the autonomic nervous system by regulating pre-autonomic neurons in spinal cord regions, such as the intermediolateral nucleus (IML), to provide feedback to peripheral viscera. Despite this neuroanatomical clarity, the computational mechanisms related to the stimulus-centered dynamics of neural activity and hemodynamic function are not yet known. Figure 1 is a diagram illustrating the structure of the neural population of the nucleus solitary (NTS) for predicting hemodynamic state. Figure 2 illustrates the interactions and cross-correlation between neurons within the NTS and explains the collective dynamics of neural responses to stimulation. Figure 3 is a diagram illustrating the modeling process and performance of predicting hemodynamic response (ΔBP) based on the activity of NTS nerve groups. Figure 4 is a diagram illustrating the process of analyzing consistent hemodynamic responses (Δ) in a common latent space by normalizing the neural trajectories of NTS neural populations observed in various individuals. Figure 5 is a diagram illustrating the process of predicting stimulus-based hemodynamic responses using H-BIND (Hierarchical Brain-Inspired Neural Decoder). FIG. 6 is a block diagram of a system for predicting blood flow information, such as blood pressure, from NTS neural information according to one embodiment of the present invention. FIG. 7 is a step diagram of a blood pressure prediction method for predicting neuro-hemorrhagic results based on a nucleus isolate (NTS) performed on a computationally capable processor according to one embodiment of the present