A random-effects model for group-level analysis of diffuse optical brain imaging.
A random-effects model for group-level analysis of diffuse optical brain imaging.
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DOI：
10.1364/boe.2.000001
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发表时间：
2010-11-30
影响因子：
3.4
通讯作者：
Huppert T
中科院分区：
文献类型：
--
作者：
Abdelnour F;Huppert T
扩散光学成像是一种非侵入性的技术，用于测量大脑中血氧的变化。这种技术是基于时间和空间分辨的记录光的近红外范围内的组织中的光学吸收。光学成像可用于研究功能性大脑活动，类似于功能性MRI。然而，由于受试者之间的光学传感器的配准，从漫射光学数据中对脑活动进行组水平比较是困难的。此外，光学信号对颅骨解剖结构中的受试者间差异以及光学传感器相对于底层功能区域的特定布置敏感。这些因素可能导致部分容积误差和灵敏度损失，因此在合并多个受试者的数据时必须考虑这些因素。在这项工作中，我们描述了一种图像重建方法，使用参数贝叶斯模型，同时重建组级图像的大脑活动的背景下，随机效应分析。使用这个模型，我们证明了定位精度和统计效应的大小组级重建相比，个性化重建可以提高。在这个模型中，我们使用限制最大似然（ReML）方法来优化贝叶斯随机效应模型。
Diffuse optical imaging is a non-invasive technique for measuring changes in blood oxygenation in the brain. This technique is based on the temporally and spatially resolved recording of optical absorption in tissue within the near-infrared range of light. Optical imaging can be used to study functional brain activity similar to functional MRI. However, group level comparisons of brain activity from diffuse optical data are difficult due to registration of optical sensors between subjects. In addition, optical signals are sensitive to inter-subject differences in cranial anatomy and the specific arrangement of optical sensors relative to the underlying functional region. These factors can give rise to partial volume errors and loss of sensitivity and therefore must be accounted for in combining data from multiple subjects. In this work, we describe an image reconstruction approach using a parametric Bayesian model that simultaneously reconstructs group-level images of brain activity in the context of a random-effects analysis. Using this model, we demonstrate that localization accuracy and the statistical effects size of group-level reconstructions can be improved when compared to individualized reconstructions. In this model, we use the Restricted Maximum Likelihood (ReML) method to optimize a Bayesian random-effects model.