Title: From time-series to complex networks: Application to the cerebrovascular flow patterns in atrial fibrillation,
Abstract: A network-based approach is presented to investigate the cerebrovascular flow
patterns during atrial fibrillation (AF) with respect to normal sinus rhythm
(NSR). AF, the most common cardiac arrhythmia with faster and irregular
beating, has been recently and independently associated with the increased risk
of dementia. However, the underlying hemodynamic mechanisms relating the two
pathologies remain mainly undetermined so far; thus the contribution of
modeling and refined statistical tools is valuable. Pressure and flow rate
temporal series in NSR and AF are here evaluated along representative cerebral
sites (from carotid arteries to capillary brain circulation), exploiting
reliable artificially built signals recently obtained from an in silico
approach. The complex network analysis evidences, in a synthetic and original
way, a dramatic signal variation towards the distal/capillary cerebral regions
during AF, which has no counterpart in NSR conditions. At the large artery
level, networks obtained from both AF and NSR hemodynamic signals exhibit
elongated and chained features, which are typical of pseudo-periodic series.
These aspects are almost completely lost towards the microcirculation during
AF, where the networks are topologically more circular and present random-like
characteristics. As a consequence, all the physiological phenomena at
microcerebral level ruled by periodicity - such as regular perfusion, mean
pressure per beat, and average nutrient supply at cellular level - can be
strongly compromised, since the AF hemodynamic signals assume irregular
behaviour and random-like features. Through a powerful approach which is
complementary to the classical statistical tools, the present findings further
strengthen the potential link between AF hemodynamic and cognitive decline.