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A network-based approach is presented to investigate the cerebrovascular flowpatterns during atrial fibrillation (AF) with respect to normal sinus rhythm(NSR). AF, the most common cardiac arrhythmia with faster and irregularbeating, has been recently and independently associated with the increased riskof dementia. However, the underlying hemodynamic mechanisms relating the twopathologies remain mainly undetermined so far; thus the contribution ofmodeling and refined statistical tools is valuable. Pressure and flow ratetemporal series in NSR and AF are here evaluated along representative cerebralsites (from carotid arteries to capillary brain circulation), exploitingreliable artificially built signals recently obtained from an in silicoapproach. The complex network analysis evidences, in a synthetic and originalway, a dramatic signal variation towards the distal/capillary cerebral regionsduring AF, which has no counterpart in NSR conditions. At the large arterylevel, networks obtained from both AF and NSR hemodynamic signals exhibitelongated and chained features, which are typical of pseudo-periodic series.These aspects are almost completely lost towards the microcirculation duringAF, where the networks are topologically more circular and present random-likecharacteristics. As a consequence, all the physiological phenomena atmicrocerebral level ruled by periodicity - such as regular perfusion, meanpressure per beat, and average nutrient supply at cellular level - can bestrongly compromised, since the AF hemodynamic signals assume irregularbehaviour and random-like features. Through a powerful approach which iscomplementary to the classical statistical tools, the present findings furtherstrengthen the potential link between AF hemodynamic and cognitive decline.

From time-series to complex networks: Application to the cerebrovascular flow patterns in atrial fibrillation