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 the microcerebral level ruled by periodicity—such as regular perfusion, mean pressure per beat, and average nutrient supply at the 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.
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September 2017
Research Article|
September 25 2017
From time-series to complex networks: Application to the cerebrovascular flow patterns in atrial fibrillation
Stefania Scarsoglio;
Stefania Scarsoglio
a)
1
Department of Mechanical and Aerospace Engineering
, Politecnico di Torino, Torino, Italy
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Fabio Cazzato;
Fabio Cazzato
2
Medacta International SA
, Castel San Pietro, Switzerland
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Luca Ridolfi
Luca Ridolfi
3
Department of Environmental, Land and Infrastructure Engineering
, Politecnico di Torino, Torino, Italy
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a)
Electronic mail: stefania.scarsoglio@polito.it
Chaos 27, 093107 (2017)
Article history
Received:
March 25 2017
Accepted:
September 07 2017
Citation
Stefania Scarsoglio, Fabio Cazzato, Luca Ridolfi; From time-series to complex networks: Application to the cerebrovascular flow patterns in atrial fibrillation. Chaos 1 September 2017; 27 (9): 093107. https://doi.org/10.1063/1.5003791
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