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'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 thepersistence homological scaffoldof 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 anad hocthreshold 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.
authors.affiliation: Hedonia Research Group, Department of Psychiatry, University of Oxford Oxford, UK.
authors.forename: Louis-David
authors.initials: LD
authors.lastname: Lord
authors.affiliation: Department of Neuroimaging, Institute of Psychiatry, King's College London London, UK.
authors.forename: Paul
authors.initials: P
authors.lastname: Expert
authors.affiliation: Hedonia Research Group, Department of Psychiatry, University of OxfordOxford, UK; Center for Music in the Brain, Aarhus UniversityAarhus, Denmark.
authors.forename: Henrique M
authors.initials: HM
authors.lastname: Fernandes
authors.affiliation: Institute for Scientific Interchange (ISI Foundation) Torino, Italy.
authors.forename: Giovanni
authors.initials: G
authors.lastname: Petri
authors.affiliation: Hedonia Research Group, Department of Psychiatry, University of OxfordOxford, UK; Center for Music in the Brain, Aarhus UniversityAarhus, Denmark.
authors.forename: Tim J
authors.initials: TJ
authors.lastname: Van Hartevelt
authors.affiliation: Institute for Scientific Interchange (ISI Foundation)Torino, Italy; Department of Mathematical Sciences, Politecnico di TorinoTorino, Italy.
authors.forename: Francesco
authors.initials: F
authors.lastname: Vaccarino
authors.affiliation: Center for Brain and Cognition, Universitat Pompeu FabraBarcelona, Spain; Instituci Catalana de la Recerca i Estudis Avanats, Universitat Pompeu FabraBarcelona, Spain.
authors.forename: Gustavo
authors.initials: G
authors.lastname: Deco
authors.affiliation: Department of Neuroimaging, Institute of Psychiatry, King's College London London, UK.
authors.forename: Federico
authors.initials: F
authors.lastname: Turkheimer
authors.affiliation: Hedonia Research Group, Department of Psychiatry, University of OxfordOxford, UK; Center for Music in the Brain, Aarhus UniversityAarhus, Denmark.
authors.forename: Morten L
authors.initials: ML
authors.lastname: Kringelbach
keywords: fMRI; functional connectivity; homological scaffold; integration and segregation; persistent homology