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

TL;DR

This study uses complex network analysis on in silico cerebrovascular signals to reveal how atrial fibrillation disrupts microcirculatory flow patterns, potentially linking AF to cognitive decline.

Contribution

It introduces a novel network-based method to analyze cerebral hemodynamics during AF, highlighting significant microcirculatory alterations not seen in normal rhythm.

Findings

AF causes a shift from elongated to circular network topology in microcirculation.

Microvascular signals during AF exhibit more random-like, irregular features.

Large artery signals retain pseudo-periodic features in both AF and NSR.

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.