Artificial Neural Network Analysis of Prefrontal fnirs Blood Oxygenation Recordings

Author(s): Wilhelm Ehleben, Jörn M. Horschig, Helmut Acker


Noninvasive functional near infra-red spectroscopy (fNIRS) measuring brain oxygenated (O2Hb) and deoxygenated hemoglobin (HHb) is a promising technique for studying dementia diseases. fNIRS signals are determined by cerebral and extracerebral factors. The simultaneous measurement of as many as possible anatomical and physiological factors during fNIRS of the brain is a prerequisite to interpret fNIRS signals with respect to the degree of brain tissue oxygenation and blood flow microcirculation.


We measured brain O2Hb-HHb relation by fNIRS and four bipolar EEG recordings simultaneously with HR, blood volume changes, SaO2 and galvanic skin resistance as autonomic nervous system marker. We analyzed the EEG recordings by a Fourier power analysis. The importance of each parameter for the fNIRS signal was assessed by nonlinear regression using an artificial neural network (ANN) analysis as a new tool of fNIRS signal interpretation.


We applied fNIRS to 5 healthy control patients and to 5 patients with brain disorders (BD). The fNIRS recordings of brain O2Hb and HHb of control patients responding to different task challenges like breath holding, odor presentation, skin touching or listening to music is mainly influenced by SaO2 and HR changes. The fNIRS recordings of brain O2Hb and HHb changes of BD patients responding to the different task challenges, however, is mainly influenced by high gamma and low theta EEG power frequencies as expression of high neurovascular coupling activity.


Brain O2Hb-HHb relation in response to brain task challenges is significantly reduced in BD patients hinting to a disturbed brain blood microcirculation.

© 2016-2024, Copyrights Fortune Journals. All Rights Reserved