Evaluation and Systematization of the Transfer Function Method for Cerebral Autoregulation Assessment

Cerebral Autoregulation (CA) is a intrinsic mechanism of the brain responsible for maintaining constant cerebral blood flow (CBF) despite the variation in arterial blood pressure (ABP). Transfer function analysis (TFA) is one of the most used signal processing techniques proposed to quantify this ABP-CBF relationship, by using spontaneous ABP oscillations to assess CA. The TFA is based on the concept that CA minimizes the effect of dynamic ABP fluctuations on CBF, which is reflected by the reduction in the CBF magnitude in the low frequency band and the phase of CBF related to ABP. Previous studies have assessed how different parameter settings in the TFA analysis have distorted its outcome, in order to understand the influence of each parameter variation. Currently, researchers at the Cerebral Autoregulation Network (CARNet) have concentrated efforts to standardize the TFA parameters used to quantify CA. In this context, this study aims to analyze the differences in the TFA indexes (gain, phase and coherence) varying its parameters in the Cerebral Autoregulation Assessment Open Source Platforman—CAAos (cross-platform toolbox for processing and analysing CA) such as window length, type and overlap in order to study the statistical similarities of its combinations. After all, the results are compared with the CARNet parameters setting in order to validate the CAAos platform as a valid tool for processing CA. The ANOVA test (Bonferroni’s post hoc test) applied over the TFA indexes for 5 healthy patients showed significant variations in the TFA indexes with the window length (particularly for 25 and 50 s) and type (Rectangular and Tukey), whereas overlap percentage variations resulted in no significant differences. The correct assessment of CA mechanism will improve our current understanding of the physiopathology of neurological diseases, as well as helping identifying patients with high-risk of cerebral hemodynamic disturbances.