fNIRS method
fNIRS uses several optodes attached mostly to the frontal head with light wavelength range between 750 and 850 nm (Davies et al., 2015) to measure kinetics of oxygenated (O2Hb) and deoxygenated hemoglobin (HHb) in order to asses NVC and cerebral autoregulation (Oldag et al., 2016). The deepest 5% of the fNIRS emitted light was calculated to reach gray cerebral tissue 23mm inside the head, 17,4% reaches 20,3mm meaning that most of the emitted light contains information about extracerebral skin, bone and muscle oxygenation (Haeussinger et al., 2011). To compensate for these extracerebral factors the short distance optode method or the identification of optodes measuring preferentially extracerebral factors was proposed (Haeussinger et al., 2014). The interpretation of fNIRS signals with respect to NVC under different neuronal stimulation conditions is limited therefore by anatomical and physiological interferences (Agbangla et al., 2017; Scholkmann et al., 2022). The simultaneous measurement of as many as possible anatomical and physiological parameters during the fNIRS registration and their identification in the fNIRS signal is a prerequisite for a complete NVC understanding (see for review (Scholkmann et al., 2022) ).
fNIRS analysis
Like in other organs, cerebral blood flow and herewith oxygen supply depends on physiological parameters like HR, blood volume changes (Pleth), SaO2, ANA and neural cell activity. We measured these physiological parameters together with four bipolar EEG recordings (see Figure 1) simultaneously with fNIRS. EEG was analyzed by a Fourier power analysis resulting finally in 24 parameters for analyzing fNIRS signal with respect to cerebral and extracerebral oxygenation factors. The importance of each parameter for the fNIRS signal was assessed by an artificial neural network (ANN), as accepted for nonlinear regression analysis of big data in medicine (B. Richards, D. Tsao, 2022). Furthermore O2Hb-HHb relation plots were established to analyze brain oxygen supply regulation. We applied fNIRS to 5 healthy control patients and to 5 patients with brain disorders (BD) like Alzheimer disease known to have a disturbed NVC (Kisler et al., 2017) due to imbalanced neuron astrocyte lactate shuttle (Zulfiqar et al., 2019). In future, we aim to introduce fNIRS with ANN analysis and O2Hb-HHb relation plots as ambulant tools to diagnose and document first signs of BD or to follow up therapeutic success in treating patient’s cognition deficiencies in general medicine practices.
Device set up
Figure 1 shows a head cap combining the 8-channel fNIRS OctaMon device (Artinis Medical Systems B.V, Elst, Netherlands) and the Bluetooth EEG TMSiMobi 6 channel amplifier (TMSi, Oldenzaal, Netherlands). Tx1-Tx8 transmitter diodes of the OctaMon emit light of ~751nm and ~843nm, which is mainly absorbed by hemoglobin and less by water or lipids (Davies et al., 2015). fNIRS light is recorded by two receivers, Rx1 and Rx2, after passing brain tissue for determining O2Hb and HHb blood level using the Modified Lambert-Beer Law. Simultaneously 4 bipolar EEG recordings (F3-F4, C3-C4, Fz-Oz, P3-P4), galvanic skin response (GSR) for measuring ANA, HR and SaO2 by means of photo plethysmography (Pleth) as well blood volume changes on the fingertip were recorded at a rate of 256 Hz. The fNIRS sampling rate was 50 Hz. fNIRS data were upsampled to 256 Hz to match the sampling rate of the other devices. The program OxySoft (Artinis Medical Systems B.V, Elst, Netherlands) was used to record the data, visualize and compute the recorded data. For further analysis data were stored as Excel files (Microsoft).
Method
Patients
10 patients consulting regularly a practice for general medicine linked to FONOG (see for more information: www.fonog.de) were offered a control of brain oxygen supply by fNIRS. 5 patients (mean age 45±10y) without BD served as control whereas 5 patients (mean age 62±22y) including 3 with Alzheimer disease (ICD10 G30.9, mean age 74±8y), 1 suffering from a stroke episode (ICD10 I69.4, age 69y), 1 with autism (ICD10 F84.1, age 24y) served as BD example. Both groups are statistically not different (p>0,05) regarding age. The patients gave written consent using the Patient Consent Form Template (https://www.medizininformatik-initiative.de/) for making use of their data in this publication. The institutional ethics committee of the Ärztekammer Westfalen Lippe (Münster/Germany) was informed about the use of the patient data for this study (2023-199-f-S).
EEG Time frequency analysis (ETFA)
EEG time series were detrended by subtracting the least-squares fit of a straight line to the data. The time frequency representation was then obtained by applying short time Fourier transform with a Hamming window of 128 points and 50% overlap. The amplitude of the resulting spectrum was converted to dB and the time frequency representation was finally interpolated to 256 Hz. Lastly, the mean amplitude for the EEG bandwidths delta (1-4 Hz), theta (4-8 Hz), alpha (8-13 Hz), beta (14-30 Hz) and gamma (30-100 Hz) was computed for bipolar EEG recordings.
NVC Stimulation
Patients were asked to rest for 10 minutes on a comfortable chair to get adapted to the fNIRS-EEG head cap. Three different tasks were used to test NVC.
1. Changes of blood gas levels for 20 seconds by 100% O2 ventilation improving hemoglobin oxygenation, breath hold inducing hypoxia and hypercapnia or hyperventilation followed by hypocapnia known to change brain blood flow(Oldag et al., 2016; Solis-Barquero et al., 2021)
2. Stimulation of the sensory network by smelling on a standardized sniffing stick and tasting a standardized taste test strip (Burghart Messtechnik GmbH, Wedel/BRD) or touching the neck with a soft brush each for 20 seconds.
3. Cognition challenges for 20 seconds by solving a simple arithmetic task without verbalizing the result, by presenting an aesthetic erotic picture of a Pfizer Viagra calendar or by playing popular music like songs by The Beatles.
Statistics
Analysis of the data started 10s before the 20s task onset with normalization of the fNIRS data and was finished about 30-40s after offset of the task. Differences of recordings between control patients (n=5) and brain disorder patients (n=5) regarding the impact of 24 parameters on brain O2Hb level or the time course per second of brain O2Hb level were compared with two-factor ANOVA (Excel, Microsoft, Munich, Germany). The level of statistical significance was set to the alpha error level of p<0.05. Graphs were generated by Excel and Power Point (Microsoft, Munich, Germany).
Relative importance - comparable to the standardized beta-coefficient of linear regression - of the 24 parameters for recalculating O2Hb and HHb reactions to the different NVC stimulation was assessed with nonlinear regression by ANN. ANN (see Figure 4) is composed of 1- neural layer containing up to 50 units called hidden layer, 1 input layer containing the 24 explanatory parameters and 1 output layer containing the fNIRS measured oxygenation variables (Multilayer Perceptron Network, IBM SPSS Statistics, Armonk, NY software version 27). About 70% of the measured parameter values were used for training of ANN. The subsequent testing by ANN used 30% of the measured parameter values. The time for training and testing was about 3 seconds respectively and the mean relative error of recalculation amounted to 0,065±0,017.