Mild poikilocapnic hypoxia increases very low frequency haemoglobin oxygenation oscillations in prefrontal cortex

Abstract

Background The aim of the study was to investigate the effect of mild cerebral hypoxia on haemoglobin oxygenation (HbO2), cerebrospinal fluid dynamics and cardiovascular physiology. To achieve this goal, four signals were recorded simultaneously: blood pressure, heart rate / electrocardiogram, HbO2 from right hemisphere and changes of subarachnoid space (SAS) width from left hemisphere. Signals were registered from 30 healthy, young participants (2 females and 28 males, body mass index = 24.5 ± 2.3 kg/m2, age 30.8 ± 13.4 years). Results We analysed the recorded signals using wavelet transform and phase coherence. We demonstrated for the first time that in healthy subjects exposed to mild poikilokapnic hypoxia there were increases in very low frequency HbO2 oscillations (< 0.052 Hz) in prefrontal cortex. Additionally, SAS fluctuation diminished in the whole frequency range which could be explained by brain oedema. Conclusions Consequently the study provides insight into mechanisms governing brain response to a mild hypoxic challenge. Our study supports the notion that HbO2 and SAS width monitoring might be beneficial for patients with acute lung disease.

Wavelet transform analysis reveals differences between patients with impaired left ventricular systolic function and healthy individuals

“…The presence of 0.1 Hz oscillation in vessel radius has been widely documented in humans. The application of wavelet transform has allowed the detection of these oscillations in BP signals (Gruszecki et al 2018; Gruszecka et al 2021), as well as signals obtained from laser Doppler flowmetry measurements (Kvernmo et al 1998; Kvandal et al 2003; Stefanovska et al 2007). Despite extensive discussions in the literature, there is still no consensus regarding the origin of these oscillations.…”

History of Brain Injury Alters Cerebral Haemodynamic Oscillations with Cardiac Influence

“…A wavelet transformation was used for our analyses as a major strength of using wavelet transformation is that it allows for the analysis of biological signals as they change with time due to physiological perturbations [ 44 ]. Specifically, the wavelet transformation can quantify and delineate the investigated interactions in both frequency and time domains [ 38 , 44 ]. As such, the wavelet transformation can convert the NIRS signals from time domain to a time-frequency domain [ 45 , 46 ], thus providing information on the main components of the time series in frequency domain by detecting spontaneous fluctuations, with the amplitude describing the activity intensity of the cortex at the brain regions [ 47 ].…”

“…Furthermore, the mother Morlet wavelet was used for its strong localisation of events in time and frequency due to its Gaussian shape [ 44 ]. The methodology for the wavelet transformation analyses used for this study has been described extensively, including what each frequency interval represents [ 38 , 39 , 44 ].…”

“…Wavelet transformation is a time-frequency analysis methodology that provides optimal resolution for low and high frequencies and provides critical information about changes that occur in time without making prior assumptions of the data [ 37 ]. This makes wavelet analysis an ideal method to investigate biological systems in their natural resting state [ 38 , 39 ].…”

“…Based on the mounting research showing that the autonomic nervous system is affected in acute SRC, and that limited information is available on the long-term effects, the primary objective of this study was to examine changes in the cerebral autoregulatory response in retired contact sport athletes who had three or more concussions in their playing careers and compare them to retired non-contact sport athletes without a history of concussion. We used wavelet transformation analysis of the biological signals as this is a precise methodology to investigate biological oscillations over time [ 38 , 39 ]. We hypothesised that wavelet transformation of NIRS-based data could be used to discern physiological differences in the autoregulatory response in retired contact sport athletes with concussion when compared to control participants without concussion.…”