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Insights into brain architectures from the homological scaffolds of functional connectivity networks

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Insights into brain architectures from the homological scaffolds of functional connectivity networks. / Lord, Louis-David; Expert, Paul; Fernandes, Henrique; Petri, Giovanni; Van Hartevelt, Tim; Vaccarino, Francesco; Deco, Gustavo; Turkheimer, Federico; Kringelbach, Morten.

In: Frontiers in Systems Neuroscience, Vol. 10, No. 85, 10.2016.

Research output: Contribution to journal/Conference contribution in journal/Contribution to newspaperJournal articleResearchpeer-review

Harvard

Lord, L-D, Expert, P, Fernandes, H, Petri, G, Van Hartevelt, T, Vaccarino, F, Deco, G, Turkheimer, F & Kringelbach, M 2016, 'Insights into brain architectures from the homological scaffolds of functional connectivity networks', Frontiers in Systems Neuroscience, vol. 10, no. 85. https://doi.org/10.3389/fnsys.2016.00085

APA

Lord, L-D., Expert, P., Fernandes, H., Petri, G., Van Hartevelt, T., Vaccarino, F., Deco, G., Turkheimer, F., & Kringelbach, M. (2016). Insights into brain architectures from the homological scaffolds of functional connectivity networks. Frontiers in Systems Neuroscience, 10(85). https://doi.org/10.3389/fnsys.2016.00085

CBE

Lord L-D, Expert P, Fernandes H, Petri G, Van Hartevelt T, Vaccarino F, Deco G, Turkheimer F, Kringelbach M. 2016. Insights into brain architectures from the homological scaffolds of functional connectivity networks. Frontiers in Systems Neuroscience. 10(85). https://doi.org/10.3389/fnsys.2016.00085

MLA

Vancouver

Lord L-D, Expert P, Fernandes H, Petri G, Van Hartevelt T, Vaccarino F et al. Insights into brain architectures from the homological scaffolds of functional connectivity networks. Frontiers in Systems Neuroscience. 2016 Oct;10(85). https://doi.org/10.3389/fnsys.2016.00085

Author

Lord, Louis-David ; Expert, Paul ; Fernandes, Henrique ; Petri, Giovanni ; Van Hartevelt, Tim ; Vaccarino, Francesco ; Deco, Gustavo ; Turkheimer, Federico ; Kringelbach, Morten. / Insights into brain architectures from the homological scaffolds of functional connectivity networks. In: Frontiers in Systems Neuroscience. 2016 ; Vol. 10, No. 85.

Bibtex

@article{b982821a1ab94291952e67331f282888,
title = "Insights into brain architectures from the homological scaffolds of functional connectivity networks",
abstract = "In recent years, the application of network analysis to neuroimaging data has provided useful insights about the brain{\textquoteright}s functional and structural organization in both health and disease. This has proven a significant paradigm shift from the study of individual brain regions in isolation. Graph-based models of the brain consist of vertices, which represent distinct brain areas, and edges which encode the presence (or absence) of a structural or functional relationship between each pair of vertices. By definition, any graph metric will be defined upon this dyadic representation of the brain activity. It is however unclear to what extent these dyadic relationships can capture the brain{\textquoteright}s complex functional architecture and the encoding of information in distributed networks. Moreover, because network representations of global brain activity are derived from measures that have a continuous response (i.e. interregional BOLD signals), it is methodologically complex to characterize the architecture of functional networks using traditional graph-based approaches. In the present study, we investigate the relationship between standard network metrics computed from dyadic interactions in a functional network, and a metric defined on the persistence homological scaffold of the network, which is a summary of the persistent homology structure of resting-state fMRI data. The persistence homological scaffold is a summary network that differs in important ways from the standard network representations of functional neuroimaging data: i) it is constructed using the information from all edge weights comprised in the original network without applying an ad hoc threshold and ii) as a summary of persistent homology, it considers the contributions of simplicial structures to the network organization rather than dyadic edge-vertices interactions. We investigated the information domain captured by the persistence homological scaffold by computing the strength of each node in the scaffold and comparing it to local graph metrics traditionally employed in neuroimaging studies. We conclude that the persistence scaffold enables the identification of network elements that may support the functional integration of information across distributed brain networks.",
author = "Louis-David Lord and Paul Expert and Henrique Fernandes and Giovanni Petri and {Van Hartevelt}, Tim and Francesco Vaccarino and Gustavo Deco and Federico Turkheimer and Morten Kringelbach",
year = "2016",
month = oct,
doi = "10.3389/fnsys.2016.00085",
language = "English",
volume = "10",
journal = "Frontiers in Systems Neuroscience",
issn = "1662-5137",
publisher = "Frontiers Research Foundation",
number = "85",

}

RIS

TY - JOUR

T1 - Insights into brain architectures from the homological scaffolds of functional connectivity networks

AU - Lord, Louis-David

AU - Expert, Paul

AU - Fernandes, Henrique

AU - Petri, Giovanni

AU - Van Hartevelt, Tim

AU - Vaccarino, Francesco

AU - Deco, Gustavo

AU - Turkheimer, Federico

AU - Kringelbach, Morten

PY - 2016/10

Y1 - 2016/10

N2 - In recent years, the application of network analysis to neuroimaging data has provided useful insights about the brain’s functional and structural organization in both health and disease. This has proven a significant paradigm shift from the study of individual brain regions in isolation. Graph-based models of the brain consist of vertices, which represent distinct brain areas, and edges which encode the presence (or absence) of a structural or functional relationship between each pair of vertices. By definition, any graph metric will be defined upon this dyadic representation of the brain activity. It is however unclear to what extent these dyadic relationships can capture the brain’s complex functional architecture and the encoding of information in distributed networks. Moreover, because network representations of global brain activity are derived from measures that have a continuous response (i.e. interregional BOLD signals), it is methodologically complex to characterize the architecture of functional networks using traditional graph-based approaches. In the present study, we investigate the relationship between standard network metrics computed from dyadic interactions in a functional network, and a metric defined on the persistence homological scaffold of the network, which is a summary of the persistent homology structure of resting-state fMRI data. The persistence homological scaffold is a summary network that differs in important ways from the standard network representations of functional neuroimaging data: i) it is constructed using the information from all edge weights comprised in the original network without applying an ad hoc threshold and ii) as a summary of persistent homology, it considers the contributions of simplicial structures to the network organization rather than dyadic edge-vertices interactions. We investigated the information domain captured by the persistence homological scaffold by computing the strength of each node in the scaffold and comparing it to local graph metrics traditionally employed in neuroimaging studies. We conclude that the persistence scaffold enables the identification of network elements that may support the functional integration of information across distributed brain networks.

AB - In recent years, the application of network analysis to neuroimaging data has provided useful insights about the brain’s functional and structural organization in both health and disease. This has proven a significant paradigm shift from the study of individual brain regions in isolation. Graph-based models of the brain consist of vertices, which represent distinct brain areas, and edges which encode the presence (or absence) of a structural or functional relationship between each pair of vertices. By definition, any graph metric will be defined upon this dyadic representation of the brain activity. It is however unclear to what extent these dyadic relationships can capture the brain’s complex functional architecture and the encoding of information in distributed networks. Moreover, because network representations of global brain activity are derived from measures that have a continuous response (i.e. interregional BOLD signals), it is methodologically complex to characterize the architecture of functional networks using traditional graph-based approaches. In the present study, we investigate the relationship between standard network metrics computed from dyadic interactions in a functional network, and a metric defined on the persistence homological scaffold of the network, which is a summary of the persistent homology structure of resting-state fMRI data. The persistence homological scaffold is a summary network that differs in important ways from the standard network representations of functional neuroimaging data: i) it is constructed using the information from all edge weights comprised in the original network without applying an ad hoc threshold and ii) as a summary of persistent homology, it considers the contributions of simplicial structures to the network organization rather than dyadic edge-vertices interactions. We investigated the information domain captured by the persistence homological scaffold by computing the strength of each node in the scaffold and comparing it to local graph metrics traditionally employed in neuroimaging studies. We conclude that the persistence scaffold enables the identification of network elements that may support the functional integration of information across distributed brain networks.

U2 - 10.3389/fnsys.2016.00085

DO - 10.3389/fnsys.2016.00085

M3 - Journal article

C2 - 27877115

VL - 10

JO - Frontiers in Systems Neuroscience

JF - Frontiers in Systems Neuroscience

SN - 1662-5137

IS - 85

ER -