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基于经颅脑图谱的功能近红外光谱光学探头排布优化:理论、算法与应用。

Transcranial brain atlas-based optimization for functional near-infrared spectroscopy optode arrangement: Theory, algorithm, and application.

机构信息

State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China.

Neuroimaging Research Branch, National Institute on Drug Abuse, National Institutes of Health, Baltimore, Maryland, USA.

出版信息

Hum Brain Mapp. 2021 Apr 15;42(6):1657-1669. doi: 10.1002/hbm.25318. Epub 2020 Dec 17.

Abstract

The quality of optode arrangement is crucial for group imaging studies when using functional near-infrared spectroscopy (fNIRS). Previous studies have demonstrated the promising effectiveness of using transcranial brain atlases (TBAs), in a manual and intuition-based way, to guide optode arrangement when individual structural MRI data are unavailable. However, the theoretical basis of using TBA to optimize optode arrangement remains unclear, which leads to manual and subjective application. In this study, we first describe the theoretical basis of TBA-based optimization of optode arrangement using a mathematical framework. Second, based on the theoretical basis, an algorithm is proposed for automatically arranging optodes on a virtual scalp. The resultant montage is placed onto the head of each participant guided by a low-cost and portable navigation system. We compared our method with the widely used 10/20-system-assisted optode arrangement procedure, using finger-tapping and working memory tasks as examples of both low- and high-level cognitive systems. Performance, including optode montage designs, locations on each participant's scalp, brain activation, as well as ground truth indices derived from individual MRI data were evaluated. The results give convergent support for our method's ability to provide more accurate, consistent and efficient optode arrangements for fNIRS group imaging than the 10/20 method.

摘要

当使用功能近红外光谱 (fNIRS) 进行组成像研究时,光极排列的质量至关重要。先前的研究已经证明,在无法获得个体结构 MRI 数据的情况下,使用基于经颅脑图谱 (TBA) 的手动和基于直觉的方法来指导光极排列具有很大的潜力。然而,使用 TBA 来优化光极排列的理论基础尚不清楚,这导致了手动和主观的应用。在这项研究中,我们首先用数学框架描述了基于 TBA 的光极排列优化的理论基础。其次,基于该理论基础,提出了一种在虚拟头皮上自动排列光极的算法。然后,将所得的组合放置在由低成本和便携式导航系统引导的每个参与者的头部。我们将我们的方法与广泛使用的 10/20 系统辅助光极排列程序进行了比较,使用手指敲击和工作记忆任务作为低水平和高水平认知系统的示例。评估了性能,包括光极蒙片设计、每个参与者头皮上的位置、大脑激活以及从个体 MRI 数据得出的真实索引。结果为我们的方法提供更准确、一致和高效的 fNIRS 组成像光极排列的能力提供了一致的支持,优于 10/20 方法。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7b49/7978141/19132e0a4b41/HBM-42-1657-g001.jpg

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