Depth-compensated diffuse optical tomography enhanced by general linear model analysis and an anatomical atlas of human head.
Depth-compensated diffuse optical tomography enhanced by general linear model analysis and an anatomical atlas of human head.
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DOI：
10.1016/j.neuroimage.2013.07.016
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发表时间：
2014-01-15
期刊：
影响因子：
5.7
通讯作者：
Liu H
中科院分区：
文献类型：
--
作者：
Tian F;Liu H
功能性扩散光学断层扫描（DOT）的主要挑战之一是准确地恢复大脑激活的深度，这在区分真实的大脑信号与头皮中的任务诱发伪影时更为重要。最近，我们开发了一种深度补偿算法（DCA），以最大限度地减少DOT中的深度定位误差。然而，DCA中使用的半无限模型与现实的人体头部解剖结构存在显著偏差。在目前的工作中，我们将深度补偿DOT（DC-DOT）与人体头部的标准解剖图谱。计算机模拟和人体测量的感觉运动激活进行检查和证明的深度特异性和定量准确性的大脑图谱为基础的DC-DOT。此外，基于一般线性模型（GLM）的逐节点统计分析也在本研究中实现和执行，显示了DC-DOT的鲁棒性，即使与浅表伪影共存，也可以准确识别功能性脑成像正确深度处的脑激活。
One of the main challenges in functional diffuse optical tomography (DOT) is to accurately recover the depth of brain activation, which is even more essential when differentiating true brain signals from task-evoked artifacts in the scalp. Recently, we developed a depth-compensated algorithm (DCA) to minimize the depth localization error in DOT. However, the semi-infinite model that was used in DCA deviated significantly from the realistic human head anatomy. In the present work, we incorporated depth-compensated DOT (DC-DOT) with a standard anatomical atlas of human head. Computer simulations and human measurements of sensorimotor activation were conducted to examine and prove the depth specificity and quantification accuracy of brain atlas-based DC-DOT. In addition, node-wise statistical analysis based on the general linear model (GLM) was also implemented and performed in this study, showing the robustness of DC-DOT that can accurately identify brain activation at the correct depth for functional brain imaging, even when co-existing with superficial artifacts.