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Shedding light on human brain activity - Biomedical signal processing of changes in tissue oxygenation and hemodynamics measured non-invasively using functional near-infrared spectroscopy: new methods and applications

机译:揭示人脑活动-使用功能性近红外光谱非侵入性测量的组织氧合和血液动力学变化的生物医学信号处理:新方法和应用

摘要

Light can be used to measure the activity of the human brain non-invasively. This is realized by shining near-infrared light into brain tissue, measuring the diffuse reflected light at different wavelengths, and determining the concentration changes of oxy- and deoxyhemoglobin ([O2Hb], [HHb]) which are related to changes in tissue hemodynamics and oxygenation – and thus to brain activity. This method, termed ‘functional near-infrared spectroscopy’ (fNIRS), is increasingly employed for basic brain research, and its routine usage for medical applications in a clinical setting is imminent.\udThe thesis set out to address three aims: develop and apply new approaches in order to (i) improve the fNIRS signal quality; (ii) realize advanced multivariate signal-analysis in the time-frequency domain using fNIRS signals; and (iii) investigate the systemic confounders in fNIRS studies by studying the effect of changes in partial pressure of arterial CO2 (PaCO2) on fNIRS-derived changes in brain hemodynamics and oxygenation.\udThe first aim was tackled by developing two novel signal processing methods that detect and remove movement artifacts (MAs) from fNIRS signals, either by using only the signal characteristic of the fNIRS input signal by itself (the ‘movement artifact removal algorithm’, MARA) or by adding signals from an accelerometer (the ‘acceleration-based movement artifact reduction algorithm’, AMARA).\udBoth algorithms were successfully validated. Another study investigated how different methods for determining [O2Hb] and [HHb] are affected by MAs. A systematic analysis was performed showing that multi-distance based fNIRS methods are superior to single-distance ones with regard to their robustness to MAs. In another work, a general equation was derived (based on measured data) for modeling the light-path length in human brain tissue (i.e. the differential pathlength factor, DPF) depending on wavelength and age of the subject. The equation can be used in all fNIRS applications where the light transport through tissue is modeled based on the modified Beer-Lambert law. This will improve the signal quality by minimizing the crosstalk in the determination of [O2Hb] and [HHb]. In two subsequent projects, it was shown how to extract the blood volume pulse (BVP) from fNIRS signals; and a novel approach was developed (the ‘automatic multiscale-based peak detection’, AMPD) for a precise and reliable detection of the BVP peaks.\udThe second aim was addressed using frameworks based on the continuous wavelet transform and Stockwell transform to quantify the relationship between fNIRS signals measured simultaneously on two human brains (the ‘hyperscanning’ approach), and between fNIRS and skin conductance signals. The value of both methods in quantifying signal correlations in the time-frequency domain was successfully demonstrated.\udFinally, the third aim was realized by performing and analyzing measurements of changes in cerebral hemodynamics/oxygenation and PaCO2 dynamics in parallel during different types of speech tasks. It was demonstrated that even small changes in PaCO2 during periods of task-evoked brain activity are reflected in characteristic\udchanges in the fNIRS-derived signals – an observation that is of relevance for all future fNIRS studies involving speech tasks.\udIn conclusion, within the scope of the thesis, new fNIRS signal processing methods were developed and applied, enabling new insights into the physiological proii cesses influencing fNIRS-derived signals. The work presented in this thesis was published in ten peer-reviewed journal papers. Three additional papers have been submitted and are under review as of the submission date of this thesis. If the results presented in this thesis stimulate further research in this area and help other researchers in performing future fNIRS studies, this thesis has fulfilled its purpose.\udI am convinced that spectroscopic methods with light will play an important role in future brain research and medical applications.
机译:光可以用于非侵入性地测量人脑的活动。这是通过将近红外光照射到脑组织中,测量不同波长的漫反射光并确定与组织血流动力学变化有关的氧和脱氧血红蛋白([O2Hb],[HHb])的浓度变化来实现的。氧合作用-从而影响大脑活动。这种被称为“功能近红外光谱法”(fNIRS)的方法正越来越多地用于基础脑研究,并且迫切需要在临床环境中将其常规用于医疗应用。\ ud本文旨在解决三个目标:开发和应用为了(i)改善fNIRS信号质量的新方法; (ii)使用fNIRS信号在时频域中实现高级多元信号分析; (iii)通过研究动脉二氧化碳分压(PaCO2)变化对fNIRS衍生的脑血流动力学和氧合变化的影响,研究fNIRS研究中的系统混杂因素。\ ud第一个目标是通过开发两种新颖的信号处理方法来解决的通过仅使用fNIRS输入信号的信号特征本身(“运动伪影去除算法”,MARA)或通过添加来自加速度计的信号(“加速度- \ ud两种算法均已成功验证。另一项研究调查了MAs如何影响确定[O2Hb]和[HHb]的不同方法。进行了系统分析,结果表明基于多距离的fNIRS方法对MA的鲁棒性优于单距离的方法。在另一项工作中,推导了一个通用方程(基于测量数据),用于根据受试者的波长和年龄对人脑组织中的光程长度进行建模(即差分光程系数DPF)。该方程式可用于所有fNIRS应用中,其中基于修改的Beer-Lambert定律对通过组织的光传输进行建模。在确定[O2Hb]和[HHb]时,通过最小化串扰可以改善信号质量。在随后的两个项目中,展示了如何从fNIRS信号中提取血容量脉冲(BVP)。并且开发了一种新颖的方法(“基于多尺度的自动峰检测”,AMPD),用于精确可靠地检测BVP峰。\ ud使用基于连续小波变换和Stockwell变换的框架对量化的在两个人脑上同时测量的fNIRS信号之间的关系(“超扫描”方法),以及fNIRS和皮肤电导信号之间的关系。成功地证明了这两种方法在时频域中量化信号相关性的价值。\ ud最后,通过并行执行和分析不同类型语音任务中脑血流动力学/充氧和PaCO2动态变化的测量,实现了第三个目标。结果表明,在任务诱发的大脑活动期间,即使PaCO2的微小变化也反映在fNIRS衍生信号的特征\变化中-这一观察结果与所有未来涉及语音任务的fNIRS研究都具有相关性。在本文的范围内,开发并应用了新的fNIRS信号处理方法,从而使人们对影响fNIRS衍生信号的生理过程有了新的认识。本论文介绍的工作发表在十篇同行评审的期刊论文中。截至本论文提交之日,已经提交了三篇论文,并且正在接受审查。如果本论文中提出的结果能刺激该领域的进一步研究并帮助其他研究人员进行未来的fNIRS研究,那么本论文已实现了其目的。\ ud我相信带光的光谱方法将在未来的脑研究和医学中起重要作用。应用程序。

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    Scholkmann, Felix;

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  • 年度 2014
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  • 正文语种 eng
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