Deep neural networks constrained by neural mass models improve electrophysiological source imaging of spatiotemporal brain dynamics.
Deep neural networks constrained by neural mass models improve electrophysiological source imaging of spatiotemporal brain dynamics.
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神经团模型约束下的深度神经网络改善了脑时空动力学的电生理源成像。
DOI：
10.1073/pnas.2201128119
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发表时间：
2022-08-02
影响因子：
11.1
通讯作者：
中科院分区：
文献类型：
--
作者：
电生理源成像（ESI）是无创研究脑功能和功能障碍不可或缺的工具。脑电图/脑磁图记录的中到低空间分辨率一直是其在该领域广泛应用的主要障碍。尽管ESI技术显著缓解了这一挑战，但没有相关培训的个人很难为这些ESI求解器选择和优化超参数。我们提出了一种基于深度学习的源成像方法，该方法将生物物理计算模型和神经网络的当前进展纳入ESI框架，并且在模型训练后需要最少的用户干预。我们的工作有望使精确和强大的高分辨率时空功能脑成像的各种神经科学研究和临床应用。已经做出了许多努力来对大脑的时空电活动进行成像，目的是映射其功能和功能障碍以及帮助管理大脑疾病。在这里，我们提出了一个非传统的基于深度学习的源成像框架（DeepSIF），该框架可以从非侵入性高密度脑电图（EEG）记录中提供对潜在大脑动力学的鲁棒和精确的时空估计。DeepSIF采用由生物物理模型生成的合成训练数据，能够模拟中尺度大脑动力学。底层大脑源的丰富特征嵌入在现实训练数据中，并通过DeepSIF网络隐式学习，避免了与优化问题中显式制定和调整先验相关的复杂性，这在传统的源成像方法中经常发生。DeepSIF的性能通过以下方式进行评估：1）一系列数值实验，2）对来自三个公共数据集的总共20名健康受试者的感觉和认知脑反应进行成像，以及3）通过将DeepSIF结果与侵入性测量和手术切除结果进行比较，严格验证DeepSIF在20名耐药性癫痫患者队列中识别致癫痫区域的能力。DeepSIF表现出强大而出色的性能，产生的结果与有关感觉和认知信息处理的常见神经科学知识以及有关致癫痫组织位置和范围的临床发现一致，并优于传统的源成像方法。DeepSIF方法作为一种数据驱动的成像框架，能够实现时空脑动力学的高效和有效的高分辨率功能成像，表明其对神经科学研究和临床应用的广泛适用性和价值。
Electrophysiological source imaging (ESI) is an indispensable tool for noninvasively studying brain function and dysfunction. The moderate to low spatial resolution of electroencephalography/magnetoencephalography recordings has been a major hindrance to their wide utility in the field. Although this challenge is mitigated significantly by ESI techniques, it is difficult for individuals without relevant training to select and optimize hyperparameters for these ESI solvers. We propose a deep learning–based source imaging methodology that incorporates current advances in biophysical computational models and neural networks into the ESI framework, and it requires minimal user intervention after the model is trained. Our work promises to enable precise and robust high-resolution spatiotemporal functional brain imaging for a variety of neuroscience research studies and clinical applications. Many efforts have been made to image the spatiotemporal electrical activity of the brain with the purpose of mapping its function and dysfunction as well as aiding the management of brain disorders. Here, we propose a non-conventional deep learning–based source imaging framework (DeepSIF) that provides robust and precise spatiotemporal estimates of underlying brain dynamics from noninvasive high-density electroencephalography (EEG) recordings. DeepSIF employs synthetic training data generated by biophysical models capable of modeling mesoscale brain dynamics. The rich characteristics of underlying brain sources are embedded in the realistic training data and implicitly learned by DeepSIF networks, avoiding complications associated with explicitly formulating and tuning priors in an optimization problem, as often is the case in conventional source imaging approaches. The performance of DeepSIF is evaluated by 1) a series of numerical experiments, 2) imaging sensory and cognitive brain responses in a total of 20 healthy subjects from three public datasets, and 3) rigorously validating DeepSIF’s capability in identifying epileptogenic regions in a cohort of 20 drug-resistant epilepsy patients by comparing DeepSIF results with invasive measurements and surgical resection outcomes. DeepSIF demonstrates robust and excellent performance, producing results that are concordant with common neuroscience knowledge about sensory and cognitive information processing as well as clinical findings about the location and extent of the epileptogenic tissue and outperforming conventional source imaging methods. The DeepSIF method, as a data-driven imaging framework, enables efficient and effective high-resolution functional imaging of spatiotemporal brain dynamics, suggesting its wide applicability and value to neuroscience research and clinical applications.
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DOI：
10.1073/pnas.2011130118
发表时间：
2021-04-27
影响因子：
11.1
作者：
Cai Z;Sohrabpour A;Jiang H;Ye S;Joseph B;Brinkmann BH;Worrell GA;He B
通讯作者：
He B
影响因子：
4.3
作者：
Gramfort A;Luessi M;Larson E;Engemann DA;Strohmeier D;Brodbeck C;Goj R;Jas M;Brooks T;Parkkonen L;Hämäläinen M
通讯作者：
Hämäläinen M
DOI：
10.1016/s1474-4422(15)00383-x
发表时间：
2016-04
期刊：
The Lancet. Neurology
影响因子：
--
作者：
Duncan JS;Winston GP;Koepp MJ;Ourselin S
通讯作者：
Ourselin S
影响因子：
5.7
作者：
Fischl, Bruce
通讯作者：
Fischl, Bruce
影响因子：
4.6
作者：
HE, B;MUSHA, T;SATO, T
通讯作者：
SATO, T