Cao Jiaming, Huppert Theodore J, Grover Pulkit, Kainerstorfer Jana M
Carnegie Mellon University, Department of Biomedical Engineering, Pittsburgh, Pennsylvania, United States.
University of Pittsburgh, Department of Electrical and Computer Engineering Pittsburgh, Pennsylvania, United States.
Neurophotonics. 2021 Jan;8(1):015002. doi: 10.1117/1.NPh.8.1.015002. Epub 2021 Jan 1.
Electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) are both commonly used methodologies for neuronal source reconstruction. While EEG has high temporal resolution (millisecond-scale), its spatial resolution is on the order of centimeters. On the other hand, in comparison to EEG, fNIRS, or diffuse optical tomography (DOT), when used for source reconstruction, can achieve relatively high spatial resolution (millimeter-scale), but its temporal resolution is poor because the hemodynamics that it measures evolve on the order of several seconds. This has important neuroscientific implications: e.g., if two spatially close neuronal sources are activated sequentially with only a small temporal separation, single-modal measurements using either EEG or DOT alone would fail to resolve them correctly. We attempt to address this issue by performing joint EEG and DOT neuronal source reconstruction. We propose an algorithm that utilizes DOT reconstruction as the spatial prior of EEG reconstruction, and demonstrate the improvements using simulations based on the ICBM152 brain atlas. We show that neuronal sources can be reconstructed with higher spatiotemporal resolution using our algorithm than using either modality individually. Further, we study how the performance of the proposed algorithm can be affected by the locations of the neuronal sources, and how the performance can be enhanced by improving the placement of EEG electrodes and DOT optodes. We demonstrate using simulations that two sources separated by 2.3-3.3 cm and 50 ms can be recovered accurately using the proposed algorithm by suitably combining EEG and DOT, but not by either in isolation. We also show that the performance can be enhanced by optimizing the electrode and optode placement according to the locations of the neuronal sources.
脑电图（EEG）和功能近红外光谱（fNIRS）都是常用于神经元源重建的方法。虽然EEG具有高时间分辨率（毫秒级），但其空间分辨率在厘米量级。另一方面，与EEG相比，fNIRS或漫射光学断层扫描（DOT）在用于源重建时，可以实现相对较高的空间分辨率（毫米级），但其时间分辨率较差，因为它所测量的血液动力学变化在几秒量级。这具有重要的神经科学意义：例如，如果两个空间上接近的神经元源以很小的时间间隔依次被激活，单独使用EEG或DOT的单模态测量将无法正确分辨它们。我们试图通过进行联合EEG和DOT神经元源重建来解决这个问题。我们提出一种算法，该算法利用DOT重建作为EEG重建的空间先验，并基于ICBM152脑图谱通过模拟来证明改进效果。我们表明，使用我们的算法比单独使用任何一种模态都能以更高的时空分辨率重建神经元源。此外，我们研究了所提出算法的性能如何受到神经元源位置的影响，以及如何通过改进EEG电极和DOT光极的布置来提高性能。我们通过模拟证明，通过适当地结合EEG和DOT，所提出的算法可以准确地恢复相距2.3 - 3.3厘米且时间间隔为50毫秒的两个源，但单独使用任何一种都不行。我们还表明，根据神经元源的位置优化电极和光极布置可以提高性能。
