Rapid whole-brain electric field mapping in transcranial magnetic stimulation using deep learning.
Rapid whole-brain electric field mapping in transcranial magnetic stimulation using deep learning.
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DOI：
10.1371/journal.pone.0254588
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发表时间：
2021
期刊：
影响因子：
3.7
通讯作者：
Ning L
中科院分区：
文献类型：
--
作者：
Xu G;Rathi Y;Camprodon JA;Cao H;Ning L
经颅磁刺激(TMS)是一种非侵入性的神经刺激技术，越来越多地应用于神经精神障碍的治疗和神经科学研究。由于大脑的复杂结构和不同受试者之间的电导率差异，识别受试者特定的脑区对于提高治疗效果和了解治疗反应的机制具有重要意义。数值计算被用来估计TMS在脑组织中的刺激电场(E-场)。但相对较长的计算时间限制了该方法的应用。在本文中，我们提出了一种基于深度神经网络的方法，利用3D-MSResUnet和多模式成像数据的神经网络结构来加快全脑电场的估计。3D-MSResUnet网络集成了3D U-Net架构、残差模块和组合多比例要素地图的机制。它是使用大型数据集进行训练的，其中包含基于电场和基于各向异性体积电导率或解剖图像的扩散磁共振成像(MRI)。3D-MSResUnet的性能使用几个评估指标以及成像模式和线圈的不同组合进行评估。实验结果表明，3D-MSResUnet的输出电场对目前最先进的有限元方法估计的电场提供了可靠的估计，预测时间显著缩短至约0.24秒。因此，本研究证明神经网络在加速TMS靶向电场预测方面具有潜在的实用价值。
Transcranial magnetic stimulation (TMS) is a non-invasive neurostimulation technique that is increasingly used in the treatment of neuropsychiatric disorders and neuroscience research. Due to the complex structure of the brain and the electrical conductivity variation across subjects, identification of subject-specific brain regions for TMS is important to improve the treatment efficacy and understand the mechanism of treatment response. Numerical computations have been used to estimate the stimulated electric field (E-field) by TMS in brain tissue. But the relative long computation time limits the application of this approach. In this paper, we propose a deep-neural-network based approach to expedite the estimation of whole-brain E-field by using a neural network architecture, named 3D-MSResUnet and multimodal imaging data. The 3D-MSResUnet network integrates the 3D U-net architecture, residual modules and a mechanism to combine multi-scale feature maps. It is trained using a large dataset with finite element method (FEM) based E-field and diffusion magnetic resonance imaging (MRI) based anisotropic volume conductivity or anatomical images. The performance of 3D-MSResUnet is evaluated using several evaluation metrics and different combinations of imaging modalities and coils. The experimental results show that the output E-field of 3D-MSResUnet provides reliable estimation of the E-field estimated by the state-of-the-art FEM method with significant reduction in prediction time to about 0.24 second. Thus, this study demonstrates that neural networks are potentially useful tools to accelerate the prediction of E-field for TMS targeting.
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