Efficient, Robust, and Accurate CNN Predictor for Neuronal Activation in Directional Deep Brain Stimulation
The programming of clinical deep brain stimulation (DBS) systems involves numerous combinations of stimulation parameters, such as stimulus amplitude, pulse width, and frequency. As more complex electrode designs, such as directional electrodes, are introduced, the traditional trial-and-error approa...
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IEEE
2025-01-01
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| Series: | IEEE Transactions on Neural Systems and Rehabilitation Engineering |
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| Online Access: | https://ieeexplore.ieee.org/document/10965875/ |
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| author | Shunjing Wang Ru Ma Qunran Yuan Hongda Li Changqing Jiang |
| author_facet | Shunjing Wang Ru Ma Qunran Yuan Hongda Li Changqing Jiang |
| author_sort | Shunjing Wang |
| collection | DOAJ |
| description | The programming of clinical deep brain stimulation (DBS) systems involves numerous combinations of stimulation parameters, such as stimulus amplitude, pulse width, and frequency. As more complex electrode designs, such as directional electrodes, are introduced, the traditional trial-and-error approach to manual DBS programming becomes increasingly impractical. Visualization of the volume of tissue activated (VTA) can assist in selecting stimulation parameters by showing the direct effects of DBS on neural tissue. However, the standard method for VTA calculation, which involves modeling biological nerve fibers, is highly time-consuming and limits clinical applicability. In this study, we used finite element models (FEM) of implanted DBS systems to compute electric fields and obtained a large dataset of axonal responses under electrical stimulation using multicompartment cable models. We then trained a convolutional neural network (CNN) to replace the cable models. The CNN model’s performance in calculating VTA was evaluated across various electrode configurations and stimulation parameters, and compared with existing activation function (AF) methods. The CNN model achieved a mean absolute error (MAE) of 0.032V in predicting nerve fiber activation thresholds, demonstrating greater stability and accuracy in VTA prediction compared to the AF method. Additionally, the CNN reduced computation time by five orders of magnitude compared to standard axonal modeling methods. We demonstrate that the CNN-based neural fiber predictor can quickly, accurately, and robustly predict neural activation responses to DBS, thereby improving the efficiency of DBS programming. |
| format | Article |
| id | doaj-art-21c8d29d566a4e77b536260751af948c |
| institution | Kabale University |
| issn | 1534-4320 1558-0210 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Transactions on Neural Systems and Rehabilitation Engineering |
| spelling | doaj-art-21c8d29d566a4e77b536260751af948c2025-08-20T03:53:17ZengIEEEIEEE Transactions on Neural Systems and Rehabilitation Engineering1534-43201558-02102025-01-01331685169410.1109/TNSRE.2025.356112210965875Efficient, Robust, and Accurate CNN Predictor for Neuronal Activation in Directional Deep Brain StimulationShunjing Wang0https://orcid.org/0009-0002-2473-9363Ru Ma1Qunran Yuan2https://orcid.org/0009-0006-8668-0642Hongda Li3Changqing Jiang4https://orcid.org/0000-0003-1666-8120National Engineering Research Center of Neuromodulation, School of Aerospace Engineering, Tsinghua University, Beijing, ChinaBeijing PINS Medical Company Ltd., Beijing, ChinaNational Engineering Research Center of Neuromodulation, School of Aerospace Engineering, Tsinghua University, Beijing, ChinaNational Engineering Research Center of Neuromodulation, School of Aerospace Engineering, Tsinghua University, Beijing, ChinaNational Engineering Research Center of Neuromodulation, School of Aerospace Engineering, Tsinghua University, Beijing, ChinaThe programming of clinical deep brain stimulation (DBS) systems involves numerous combinations of stimulation parameters, such as stimulus amplitude, pulse width, and frequency. As more complex electrode designs, such as directional electrodes, are introduced, the traditional trial-and-error approach to manual DBS programming becomes increasingly impractical. Visualization of the volume of tissue activated (VTA) can assist in selecting stimulation parameters by showing the direct effects of DBS on neural tissue. However, the standard method for VTA calculation, which involves modeling biological nerve fibers, is highly time-consuming and limits clinical applicability. In this study, we used finite element models (FEM) of implanted DBS systems to compute electric fields and obtained a large dataset of axonal responses under electrical stimulation using multicompartment cable models. We then trained a convolutional neural network (CNN) to replace the cable models. The CNN model’s performance in calculating VTA was evaluated across various electrode configurations and stimulation parameters, and compared with existing activation function (AF) methods. The CNN model achieved a mean absolute error (MAE) of 0.032V in predicting nerve fiber activation thresholds, demonstrating greater stability and accuracy in VTA prediction compared to the AF method. Additionally, the CNN reduced computation time by five orders of magnitude compared to standard axonal modeling methods. We demonstrate that the CNN-based neural fiber predictor can quickly, accurately, and robustly predict neural activation responses to DBS, thereby improving the efficiency of DBS programming.https://ieeexplore.ieee.org/document/10965875/Convolutional neural networkdirectional deep brain stimulationneuronal cable modelvolume of tissue activated |
| spellingShingle | Shunjing Wang Ru Ma Qunran Yuan Hongda Li Changqing Jiang Efficient, Robust, and Accurate CNN Predictor for Neuronal Activation in Directional Deep Brain Stimulation IEEE Transactions on Neural Systems and Rehabilitation Engineering Convolutional neural network directional deep brain stimulation neuronal cable model volume of tissue activated |
| title | Efficient, Robust, and Accurate CNN Predictor for Neuronal Activation in Directional Deep Brain Stimulation |
| title_full | Efficient, Robust, and Accurate CNN Predictor for Neuronal Activation in Directional Deep Brain Stimulation |
| title_fullStr | Efficient, Robust, and Accurate CNN Predictor for Neuronal Activation in Directional Deep Brain Stimulation |
| title_full_unstemmed | Efficient, Robust, and Accurate CNN Predictor for Neuronal Activation in Directional Deep Brain Stimulation |
| title_short | Efficient, Robust, and Accurate CNN Predictor for Neuronal Activation in Directional Deep Brain Stimulation |
| title_sort | efficient robust and accurate cnn predictor for neuronal activation in directional deep brain stimulation |
| topic | Convolutional neural network directional deep brain stimulation neuronal cable model volume of tissue activated |
| url | https://ieeexplore.ieee.org/document/10965875/ |
| work_keys_str_mv | AT shunjingwang efficientrobustandaccuratecnnpredictorforneuronalactivationindirectionaldeepbrainstimulation AT ruma efficientrobustandaccuratecnnpredictorforneuronalactivationindirectionaldeepbrainstimulation AT qunranyuan efficientrobustandaccuratecnnpredictorforneuronalactivationindirectionaldeepbrainstimulation AT hongdali efficientrobustandaccuratecnnpredictorforneuronalactivationindirectionaldeepbrainstimulation AT changqingjiang efficientrobustandaccuratecnnpredictorforneuronalactivationindirectionaldeepbrainstimulation |