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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

Correlation between graphs with an application to brain network analysis

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Fujita, Andre ; Takahashi, Daniel Yasumasa ; Balardin, Joana Bisol ; Vidal, Maciel Calebe ; Sato, Joao Ricardo
Total Authors: 5
Document type: Journal article
Web of Science Citations: 0

The global functional brain network (graph) is more suitable for characterizing brain states than local analysis of the connectivity of brain regions. Therefore, graph-theoretic approaches are natural methods to use for studying the brain. However, conventional graph theoretical analyses are limited due to the lack of formal statistical methods of estimation and inference. For example, the concept of correlation between two vectors of graphs has not yet been defined. Thus, the introduction of a notion of correlation between graphs becomes necessary to better understand how brain sub-networks interact. To develop a framework to infer correlation between graphs, one may assume that they are generated by models and that the parameters of the models are the random variables. Then, it is possible to define that two graphs are independent when the random variables representing their parameters are independent. In the real world, however, the model is rarely known, and consequently, the parameters cannot be estimated. By analyzing the graph spectrum, it is shown that the spectral radius is highly associated with the parameters of the graph model. Based on this, a framework for correlation inference between graphs is constructed and the approach illustrated on functional magnetic resonance imaging data on 814 subjects comprising 529 controls and 285 individuals diagnosed with autism spectrum disorder (ASD). Results show that correlations between the default-mode and control, default-mode and somatomotor, and default-mode and visual sub-networks are higher in individuals with ASD than in the controls. (C) 2016 Elsevier B.V. All rights reserved. (AU)

FAPESP's process: 13/10498-6 - Machine learning in neuroimaging: development of methods and clinical applications in psychiatric disorders
Grantee:João Ricardo Sato
Support type: Regular Research Grants
FAPESP's process: 16/13422-9 - Statistical methods in graphs with applications to life sciences
Grantee:André Fujita
Support type: Regular Research Grants
FAPESP's process: 13/00506-1 - Time series, wavelets and functional data analysis
Grantee:Pedro Alberto Morettin
Support type: Research Projects - Thematic Grants
FAPESP's process: 15/01587-0 - Storage, modeling and analysis of dynamical systems for e-Science applications
Grantee:João Eduardo Ferreira
Support type: Research Grants - eScience and Data Science Program - Thematic Grants