具有轴突结构定制能力的多功能神经激活预测器,实现个性化神经调节计算。

IF 5.2 2区 医学 Q2 ENGINEERING, BIOMEDICAL
Hongda Li, Shunjing Wang, Xuesong Luo, Changqing Jiang, Boyang Zhang
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引用次数: 0

摘要

神经调节疗法正在向越来越智能和个性化的方向发展,这推动了对更精确和有效的刺激策略的需求。生物物理上详细的计算模型与解剖学上精确的神经结构相结合,可以提供对各种刺激条件下神经激活模式的关键见解,这对优化治疗至关重要。然而,求解这些包含大量神经纤维的模型需要大量的计算,特别是当神经目标包含异质轴突时,例如,具有不同的几何形状。此外,目前的方法在各种神经调节场景中缺乏通用性,限制了这些模型的可扩展性和临床实用性。在这项研究中,我们提出了一个基于卷积神经网络(CNN)的框架,作为传统逐案野蛮力计算方法的通用、快速和准确的替代方案。我们的方法在不同的细胞外刺激情景下实现了6.91 × 10-3 mV的平均绝对误差(MAE)和超过95%的预测精度,有助于个性化模拟和定制神经调节治疗。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Versatile Neural Activation Predictor with Axon Structure Tailoring Capability Enabling Personalized Neuromodulation Computation.

Neuromodulation therapies are evolving to be more and more intelligent and personalized, driving the need for more precise and efficient stimulation strategies. Biophysically detailed computational models integrated with anatomically accurate neural structures could offer critical insights into neural activation patterns under various stimulation conditions, which are essential to optimize the treatment. However, solving these models containing a large number of nerve fibers is computationally intensive, especially when the neural targets comprise heterogenous axons, e.g., with varying geometries. Also, current methods lack generalizability across various neuromodulation scenarios, limiting the scalability and clinical utility of such models. In this study, we present a convolutional neural network (CNN)-based framework as a universal, rapid, and accurate alternative to conventional case-by-case brutal force computation methods. Our approach achieves a mean absolute error (MAE) of 6.91 × 10-3 mV and over 95% prediction accuracy under diverse extracellular stimulation scenarios, facilitating personalized simulations and tailored neuromodulation treatments.

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来源期刊
CiteScore
8.60
自引率
8.20%
发文量
479
审稿时长
6-12 weeks
期刊介绍: Rehabilitative and neural aspects of biomedical engineering, including functional electrical stimulation, acoustic dynamics, human performance measurement and analysis, nerve stimulation, electromyography, motor control and stimulation; and hardware and software applications for rehabilitation engineering and assistive devices.
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