Characterisation of haemodynamic activity in resting state networks by fractal analysis

Research output: Contribution to journalArticlepeer-review

Standard

Characterisation of haemodynamic activity in resting state networks by fractal analysis. / Porcaro, Camillo; Mayhew, Stephen; Marino, Marco ; Mantini, Dante ; Bagshaw, Andrew.

In: International Journal of Neural Systems, Vol. 30, No. 12, 2050061, 09.10.2020.

Research output: Contribution to journalArticlepeer-review

Harvard

APA

Vancouver

Author

Bibtex

@article{e5a7164408c049d89a62f63316cf46b4,
title = "Characterisation of haemodynamic activity in resting state networks by fractal analysis",
abstract = "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 spatial and 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.",
keywords = "Group ICA Of fMRI Toolbox (GIFT), fractal analysis (FA), fractal dimension (FD), functional magnetic resonance imaging (fMRI), independent component analysis (ICA), resting state networks (RSNs)",
author = "Camillo Porcaro and Stephen Mayhew and Marco Marino and Dante Mantini and Andrew Bagshaw",
year = "2020",
month = oct,
day = "9",
doi = "10.1142/S0129065720500616",
language = "English",
volume = "30",
journal = "International Journal of Neural Systems",
issn = "0129-0657",
publisher = "World Scientific",
number = "12",

}

RIS

TY - JOUR

T1 - Characterisation of haemodynamic activity in resting state networks by fractal analysis

AU - Porcaro, Camillo

AU - Mayhew, Stephen

AU - Marino, Marco

AU - Mantini, Dante

AU - Bagshaw, Andrew

PY - 2020/10/9

Y1 - 2020/10/9

N2 - 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 spatial and 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.

AB - 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 spatial and 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.

KW - Group ICA Of fMRI Toolbox (GIFT)

KW - fractal analysis (FA)

KW - fractal dimension (FD)

KW - functional magnetic resonance imaging (fMRI)

KW - independent component analysis (ICA)

KW - resting state networks (RSNs)

UR - http://www.scopus.com/inward/record.url?scp=85091578869&partnerID=8YFLogxK

U2 - 10.1142/S0129065720500616

DO - 10.1142/S0129065720500616

M3 - Article

VL - 30

JO - International Journal of Neural Systems

JF - International Journal of Neural Systems

SN - 0129-0657

IS - 12

M1 - 2050061

ER -