Advances in the instrumentation and signal processing for simultaneously acquired electroencephalography and functional magnetic resonance imaging (EEG-fMRI) have enabled new ways to observe the spatiotemporal neural dynamics of the human brain. Central to the utility of EEG-fMRI neuroimaging systems are the methods for fusing the two data streams, with machine learning playing a key role. These methods can be dichotomized into those that are symmetric and asymmetric in terms of how the two modalities inform the fusion. Studies using these methods have shown that fusion yields new insights into brain function that are not possible when each modality is acquired separately. As technology improves and methods for fusion become more sophisticated, the future of EEG-fMRI for noninvasive measurement of brain dynamics includes mesoscale mapping at ultrahigh magnetic resonance fields, targeted perturbation-based neuroimaging, and using deep learning to uncover nonlinear representations that link the electrophysiological and hemodynamic measurements. Expected final online publication date for the Annual Review of Neuroscience, Volume 44 is July 2021. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

中文翻译：

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通过同时EEG-fMRI融合推断宏观大脑动力学。

同时获取的脑电图和功能磁共振成像（EEG-fMRI）的仪器和信号处理技术的进步为观察人脑时空神经动力学提供了新的途径。EEG-fMRI神经成像系统实用程序的核心是融合两个数据流的方法，其中机器学习起着关键作用。就这两种方式如何告知融合而言，可以将这些方法分为对称和不对称的方法。使用这些方法的研究表明，融合产生了对脑功能的新见解，而当每种方式单独获得时，融合是不可能的。随着技术的进步和融合方法的日益完善，用于无创性脑动力学测量的EEG-fMRI的未来包括超高磁共振场的中尺度测绘，基于目标扰动的神经成像，以及使用深度学习来发现将电生理学和血液动力学测量联系起来的非线性表示。《神经科学年度评论》（第44卷）的最终最终在线发布日期为2021年7月。有关修订的估算，请参见http://www.annualreviews.org/page/journal/pubdates。