{"title":"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。","authors":"Hongda Li, Shunjing Wang, Xuesong Luo, Changqing Jiang, Boyang Zhang","doi":"10.1109/TNSRE.2025.3614215","DOIUrl":null,"url":null,"abstract":"<p><p>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<sup>-3</sup> mV and over 95% prediction accuracy under diverse extracellular stimulation scenarios, facilitating personalized simulations and tailored neuromodulation treatments.</p>","PeriodicalId":13419,"journal":{"name":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","volume":"PP ","pages":""},"PeriodicalIF":5.2000,"publicationDate":"2025-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Versatile Neural Activation Predictor with Axon Structure Tailoring Capability Enabling Personalized Neuromodulation Computation.\",\"authors\":\"Hongda Li, Shunjing Wang, Xuesong Luo, Changqing Jiang, Boyang Zhang\",\"doi\":\"10.1109/TNSRE.2025.3614215\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>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<sup>-3</sup> mV and over 95% prediction accuracy under diverse extracellular stimulation scenarios, facilitating personalized simulations and tailored neuromodulation treatments.</p>\",\"PeriodicalId\":13419,\"journal\":{\"name\":\"IEEE Transactions on Neural Systems and Rehabilitation Engineering\",\"volume\":\"PP \",\"pages\":\"\"},\"PeriodicalIF\":5.2000,\"publicationDate\":\"2025-09-25\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Transactions on Neural Systems and Rehabilitation Engineering\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://doi.org/10.1109/TNSRE.2025.3614215\",\"RegionNum\":2,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"ENGINEERING, BIOMEDICAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1109/TNSRE.2025.3614215","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, BIOMEDICAL","Score":null,"Total":0}
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.
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.