Kalman estimator- and general linear model-based on-line brain activation mapping by near-infrared spectroscopy.
Kalman estimator- and general linear model-based on-line brain activation mapping by near-infrared spectroscopy.
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DOI：
10.1186/1475-925x-9-82
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发表时间：
2010-12-08
影响因子：
3.9
通讯作者：
Jeong MY
中科院分区：
文献类型：
--
作者：
Hu XS;Hong KS;Ge SS;Jeong MY
近红外光谱（NIRS）是近年来发展起来的一种非侵入性的神经成像技术，用于测量与脑活动相关的脑血氧变化。迄今为止，对于功能性脑映射应用，没有标准的在线方法来分析NIRS数据。本文提出了一种新的在线NIRS数据分析框架，该框架结合了一般线性模型（GLM）和卡尔曼估计器的优点。卡尔曼估计器被用来更新GLM系数递归，和一个关键系数关于大脑活动，然后通过t-统计检验。t统计检验结果用于更新地形脑激活图。同时，一组高通滤波器插入到GLM，以防止非常低频的噪声，自回归（AR）模型被用来防止生理噪声造成的时间相关性的NIRS时间序列。一组数据记录在手指敲击实验研究使用所提出的框架。实验结果表明，该方法能有效地跟踪任务相关脑区的激活，并能有效地防止实验过程中的噪声失真。因此，所提出的方法的潜力，实时近红外光谱为基础的脑成像被证明。本文提出了一种新的在线方法分析近红外光谱数据的功能脑映射应用。这种方法展示了实时更新地形脑激活图的潜力。
Near-infrared spectroscopy (NIRS) is a non-invasive neuroimaging technique that recently has been developed to measure the changes of cerebral blood oxygenation associated with brain activities. To date, for functional brain mapping applications, there is no standard on-line method for analysing NIRS data. In this paper, a novel on-line NIRS data analysis framework taking advantages of both the general linear model (GLM) and the Kalman estimator is devised. The Kalman estimator is used to update the GLM coefficients recursively, and one critical coefficient regarding brain activities is then passed to a t-statistical test. The t-statistical test result is used to update a topographic brain activation map. Meanwhile, a set of high-pass filters is plugged into the GLM to prevent very low-frequency noises, and an autoregressive (AR) model is used to prevent the temporal correlation caused by physiological noises in NIRS time series. A set of data recorded in finger tapping experiments is studied using the proposed framework. The obtained results suggest that the method can effectively track the task related brain activation areas, and prevent the noise distortion in the estimation while the experiment is running. Thereby, the potential of the proposed method for real-time NIRS-based brain imaging was demonstrated. This paper presents a novel on-line approach for analysing NIRS data for functional brain mapping applications. This approach demonstrates the potential of a real-time-updating topographic brain activation map.