2015/04/09 by Tiago Simas, Simas, Tiago, Mario Chávez +8
Biochemistry, Genetics and Molecular Biology · Computer Science · Neuroscience · Physics and Astronomy · #Adaptation and Self-Organizing Systems (nlin.AO) #Bioinformatics and Genomic Networks #FOS: Biological sciences #FOS: Physical sciences #Functional Brain Connectivity Studies #Neurons and Cognition (q-bio.NC) #Physics and Society (physics.soc-ph) #Topological and Geometric Data Analysis #nlin.AO #physics.soc-ph #q-bio.NC
paper · pdf · doi:10.48550/arxiv.1504.02265
openalex publication_date 2015/04/09 · arxiv created 2015/04/10 · arxiv updated 2015/04/13 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Understanding brain connectivity has become one of the most important issues in neuroscience. But connectivity data can reflect either the functional relationships of the brain activities or the anatomical properties between brain areas. Although one should expect a clear relationship between both representations it is not straightforward. Here we present a formalism that allows for the comparison of structural (DTI) and functional (fMRI) networks by embedding both in a common metric space. In this metric space one can then find for which regions the two networks are significantly different. Our methodology can be used not only to compare multimodal networks but also to extract statistically significant aggregated networks of a set of subjects. Actually, we use this procedure to aggregate a set of functional (fMRI) networks from different subjects in an aggregated network that is compared with the anatomical (DTI) connectivity. The comparison of the aggregated network reveals some features that are not observed when the comparison is done with the classical averaged network.