A multi-objective optimisation approach for the linear modelling of cerebral autoregulation system

IF 2 4区生物学 Q2 BIOLOGY

Biosystems Pub Date : 2024-05-14 DOI:10.1016/j.biosystems.2024.105231

Felipe-Andrés Bello-Robles , Manuel Villalobos-Cid , Max Chacón , Mario Inostroza-Ponta

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引用次数: 0

Abstract

Objective:

Dynamic cerebral autoregulation (dCA) has been addressed through different approaches for discriminating between normal and impaired conditions based on spontaneous fluctuations in arterial blood pressure (ABP) and cerebral blood flow (CF). This work presents a novel multi-objective optimisation (MO) approach for finding good configurations of a cerebrovascular resistance-compliance model.

Methods:

Data from twenty-nine subjects under normo and hypercapnic (5% CO $_{2}$ in air) conditions was used. Cerebrovascular resistance and vessel compliance models with ABP as input and CF velocity as output were fitted using a MO approach, considering fitting Pearson’s correlation and error.

Results:

MO approach finds better model configurations than the single-objective (SO) approach, especially for hypercapnic conditions. In addition, the Pareto-optimal front from the multi-objective approach enables new information on dCA, reflecting a higher contribution of myogenic mechanism for explaining dCA impairment.

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大脑自动调节系统线性建模的多目标优化方法。

目的：根据动脉血压（ABP）和脑血流量（CF）的自发波动，动态脑自动调节（dCA）通过不同的方法来区分正常和受损状态。本研究提出了一种新颖的多目标优化（MO）方法，用于寻找脑血管阻力-顺应性模型的良好配置：方法：使用了 29 名受试者在正常和高碳酸血症（5% CO2 空气）条件下的数据。使用 MO 方法拟合了以 ABP 为输入、CF 速度为输出的脑血管阻力和血管顺应性模型，并考虑了拟合皮尔逊相关性和误差：结果：MO 方法比单目标（SO）方法找到了更好的模型配置，尤其是在高碳酸血症条件下。此外，多目标方法的帕累托-极值前沿为 dCA 提供了新的信息，反映了肌源性机制在解释 dCA 损伤方面的更大贡献。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Biosystems 生物-生物学

CiteScore

3.70

自引率

18.80%

发文量

129

审稿时长

34 days

期刊介绍： BioSystems encourages experimental, computational, and theoretical articles that link biology, evolutionary thinking, and the information processing sciences. The link areas form a circle that encompasses the fundamental nature of biological information processing, computational modeling of complex biological systems, evolutionary models of computation, the application of biological principles to the design of novel computing systems, and the use of biomolecular materials to synthesize artificial systems that capture essential principles of natural biological information processing.