Tenzer Mark L, Lisinski Jonathan M, LaConte Stephen M
Fralin Biomedical Research Institute at VTC, Virginia Tech, Roanoke, VA, United States.
Department of Biomedical Engineering and Mechanics, Virginia Tech, Blacksburg, VA, United States.
Front Neuroimaging. 2022 Mar 17;1:815778. doi: 10.3389/fnimg.2022.815778. eCollection 2022.
Neural activity can be readily and non-invasively recorded from the scalp using electromagnetic and optical signals, but unfortunately all scalp-based techniques have depth-dependent sensitivities. We hypothesize, though, that the cortex's connectivity with the rest of the brain could serve to construct proxy signals of deeper brain activity. For example, functional magnetic resonance imaging (fMRI)-derived models that link surface connectivity to deeper regions could subsequently extend the depth capabilities of other modalities. Thus, as a first step toward this goal, this study examines whether or not surface-limited support vector regression of resting-state fMRI can indeed track deeper regions and distributed networks in independent data. Our results demonstrate that depth-limited fMRI signals can in fact be calibrated to report ongoing activity of deeper brain structures. Although much future work remains to be done, the present study suggests that scalp recordings have the potential to ultimately overcome their intrinsic physical limitations by utilizing the multivariate information exchanged between the surface and the rest of the brain.
利用电磁和光信号可以很容易且无创地从头皮记录神经活动，但不幸的是，所有基于头皮的技术都具有与深度相关的敏感性。不过，我们推测，皮层与大脑其他部分的连接性可用于构建深部脑活动的代理信号。例如，将表面连接性与深部区域联系起来的功能磁共振成像（fMRI）衍生模型随后可以扩展其他模态的深度探测能力。因此，作为朝着这个目标迈出的第一步，本研究考察了静息态fMRI的表面受限支持向量回归是否真的能够追踪独立数据中的深部区域和分布式网络。我们的结果表明，深度受限的fMRI信号实际上可以进行校准，以报告深部脑结构的持续活动。尽管未来还有很多工作要做，但本研究表明，头皮记录有可能最终通过利用表面与大脑其他部分之间交换的多变量信息来克服其固有的物理限制。
