Characterisation of Haemodynamic Activity in Resting State Networks by Fractal Analysis


Intrinsic brain activity is organized into large-scale networks displaying specific structural-functional architecture, known as resting-state networks (RSNs). RSNs reflect complex neurophysiological processes and interactions, and have a central role in distinct sensory and cognitive functions, making it crucial to understand and quantify their anatomical and functional properties. Fractal dimension (FD) provides a parsimonious way of summarizing self-similarity over different spatialand temporal scales but despite its suitability for functional magnetic resonance imaging (fMRI) signal analysis its ability to characterize and investigate RSNs is poorly understood. We used FD in a large sample of healthy participants to differentiate fMRI RSNs and examine how the FD property of RSNs is linked with their functional roles. We identified two clusters of RSNs, one mainly consisting of sensory networks (C1, including auditory, sensorimotor and visual networks) and the other more related to higher cognitive (HCN) functions (C2, including dorsal default mode network and fronto-parietal networks). These clusters were defined in a completely data-driven manner using hierarchical clustering, suggesting that quantification of Blood Oxygen Level Dependent (BOLD) signal complexity with FD is able to characterize meaningful physiological and functional variability. Understanding the mechanisms underlying functional RSNs, and developing tools to study their signal properties, is essential for assessing specific brain alterations and FD could potentially be used for the early detection and treatment of neurological disorders.

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Divisions: College of Health & Life Sciences > School of Psychology
College of Health & Life Sciences
Funding Information: The authors thank the following sources for funding this research. Engineering and Physical Science Research Council (EPSRC), APB: EP/F023057/1; The Royal Society International Joint Project — 2010/R1; SDM was funded by an EPSRC Fellowship (EP/I022325/1)
Additional Information: Copyright © 2020 The Author(s). This is an Open Access article published by World Scientific Publishing Company. It is distributed under the terms of theCreative Commons Attribution 4.0 (CC BY) License which permits use, distribution and reproduction in any medium,provided the original work is properly cited.
Uncontrolled Keywords: fractal analysis (FA),fractal dimension (FD),functional magnetic resonance imaging (fMRI),Group ICA Of fMRI Toolbox (GIFT),independent component analysis (ICA),resting state networks (RSNs),Computer Networks and Communications
Publication ISSN: 1793-6462
Last Modified: 08 Jul 2024 08:28
Date Deposited: 08 May 2024 15:06
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Related URLs: https://www.wor ... 129065720500616 (Publisher URL)
http://www.scop ... tnerID=8YFLogxK (Scopus URL)
PURE Output Type: Article
Published Date: 2020-12
Published Online Date: 2020-10-09
Accepted Date: 2020-07-29
Authors: Porcaro, Camillo
Mayhew, Stephen D. (ORCID Profile 0000-0003-1240-1488)
Marino, Marco
Mantini, Dante
Bagshaw, Andrew P.



Version: Published Version

License: Creative Commons Attribution

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