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

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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.