Assessment of cerebral autoregulation using time-domain cross-correlation analysis

Area of Science:

• Neuroscience
• Physiology
• Biomedical Engineering

Background:

• Cerebral autoregulation (CA) is crucial for maintaining stable brain blood flow.
• Assessing CA dynamics is vital for understanding neurological health and disease.
• Non-invasive methods are needed to evaluate CA in real-time.

Purpose of the Study:

• To evaluate the utility of time-domain cross-correlation analysis for assessing cerebral autoregulation (CA).
• To investigate the impact of different frequency bands on the assessment of CA dynamics.
• To determine if cross-correlation functions (CCFs) of blood pressure and flow velocity can detect autoregulatory disturbances.

Main Methods:

• Beat-to-beat time series of mean arterial blood pressure (MABP) and mean cerebral blood flow velocity (MCBFV) were recorded from 13 healthy volunteers.
• MABP and MCBFV signals were bandpass filtered into very low (VLF), low (LF), and high (HF) frequency ranges.
• Cross-correlation functions (CCFs) were calculated using a moving window to analyze the time lags between MABP and MCBFV.

Main Results:

• Significant increases in time lags of peak MABP-MCBFV CCFs were observed between the LF and HF frequency ranges (p < 0.001).
• A left-shift (negative lag) in CCF peaks, indicating a phase-lead property, was noted.
• Increasing time lags correlated with evidence of autoregulatory disturbance.

Conclusions:

• Time-domain cross-correlation analysis of pre-filtered MABP and MCBFV is a potentially valuable tool for estimating CA dynamic response.
• Frequency-specific analysis of CCFs can reveal disturbances in cerebral autoregulation.
• This method offers a promising approach for non-invasively assessing CA in clinical and research settings.