Optimisation of functional near-infrared spectroscopy: differentiation of cortical and extracerebral signals

功能性近红外光谱（fNIRS）能够测量皮层血流量，作为大脑活动的指标——如功能性磁共振成像（fMRI）。与功能磁共振成像和其他神经成像方法相比，fNIRS具有重要的优势，因为它具有很高的生态有效性。这种情况主要出现在精神障碍患者或儿童的调查中。尽管该方法具有较高的有效性和可靠性，但仍有必要进一步优化。由于fNIRS不仅测量靠近大脑区域的血流，而且还测量头部皮肤和肌肉的血流，因此确定这种肱外信号在总信号中的比例及其原因（例如肌肉和心血管活动）非常重要。此外，关于先验避免和后验滤波噪声的信息是必要的。总的来说，关于这一主题（识别、作用和处理脑外信号）的证据有限。本提案的目的是开发和验证特定的fNIRS分析策略（识别和过滤脑外噪声的算法），并进一步建立fNIRS作为神经成像的有效方法。

Functional near-infrared spectroscopy (fNIRS) is capable to measure cortical blood flow as indicator for brain activity - such as functional magentic resonance imaging (fMRI). fNIRS has crucial advantages in contrast to fMRI and other neuroimaging methods due to its high ecological validity. This is mainly the case in the investigation of patients with mental disorders or children. Further optimisation of this method is necessary despite high validity and reliablity. As fNIRS measures not only blood flow in calotte-near brain areas but also in skin and muscles of the head it is of high importance to identify the proportion of this extracerabral signal in the total signal and its reasons (e.g., muscular and cardiovascular activity). In addition, information with respect to a priori avoiding and a posteriori filtering of this noise is necessary. In general, there is limited evidence with respect to this subjects (identification, effect, and handling of extracerebral signals). Aim of the present proposal is the development and validation of specific fNIRS analysis strategies (algorithms for identification and filtering of extracerebral noise) and the further establishment of fNIRS as valide method of neuroimaging.