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Mapping hybrid functional-structural connectivity traits in the human connectome

One of the crucial questions in neuroscience is how a rich functional repertoire of brain states relates to its underlying structural organization. How to study the associations between these structural and functional layers is an open problem that involves novel conceptual ways of tackling this que...

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Autores principales: Amico, Enrico, Goñi, Joaquín
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MIT Press 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6145853/
https://www.ncbi.nlm.nih.gov/pubmed/30259007
http://dx.doi.org/10.1162/netn_a_00049
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author Amico, Enrico
Goñi, Joaquín
author_facet Amico, Enrico
Goñi, Joaquín
author_sort Amico, Enrico
collection PubMed
description One of the crucial questions in neuroscience is how a rich functional repertoire of brain states relates to its underlying structural organization. How to study the associations between these structural and functional layers is an open problem that involves novel conceptual ways of tackling this question. We here propose an extension of the Connectivity Independent Component Analysis (connICA) framework to identify joint structural-functional connectivity traits. Here, we extend connICA to integrate structural and functional connectomes by merging them into common “hybrid” connectivity patterns that represent the connectivity fingerprint of a subject. We tested this extended approach on the 100 unrelated subjects from the Human Connectome Project. The method is able to extract main independent structural-functional connectivity patterns from the entire cohort that are sensitive to the realization of different tasks. The hybrid connICA extracts two main task-sensitive hybrid traits. The first trait encompasses the within and between connections of dorsal attentional and visual areas, as well as frontoparietal circuits. The second trait mainly encompasses the connectivity between visual, attentional, default mode network (DMN), and subcortical network. Overall, these findings confirm the potential of the hybrid connICA for the compression of structural/functional connectomes into integrated patterns from a set of individual brain networks.
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spelling pubmed-61458532018-09-24 Mapping hybrid functional-structural connectivity traits in the human connectome Amico, Enrico Goñi, Joaquín Netw Neurosci Research One of the crucial questions in neuroscience is how a rich functional repertoire of brain states relates to its underlying structural organization. How to study the associations between these structural and functional layers is an open problem that involves novel conceptual ways of tackling this question. We here propose an extension of the Connectivity Independent Component Analysis (connICA) framework to identify joint structural-functional connectivity traits. Here, we extend connICA to integrate structural and functional connectomes by merging them into common “hybrid” connectivity patterns that represent the connectivity fingerprint of a subject. We tested this extended approach on the 100 unrelated subjects from the Human Connectome Project. The method is able to extract main independent structural-functional connectivity patterns from the entire cohort that are sensitive to the realization of different tasks. The hybrid connICA extracts two main task-sensitive hybrid traits. The first trait encompasses the within and between connections of dorsal attentional and visual areas, as well as frontoparietal circuits. The second trait mainly encompasses the connectivity between visual, attentional, default mode network (DMN), and subcortical network. Overall, these findings confirm the potential of the hybrid connICA for the compression of structural/functional connectomes into integrated patterns from a set of individual brain networks. MIT Press 2018-09-01 /pmc/articles/PMC6145853/ /pubmed/30259007 http://dx.doi.org/10.1162/netn_a_00049 Text en © 2018 Massachusetts Institute of Technology http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research
Amico, Enrico
Goñi, Joaquín
Mapping hybrid functional-structural connectivity traits in the human connectome
title Mapping hybrid functional-structural connectivity traits in the human connectome
title_full Mapping hybrid functional-structural connectivity traits in the human connectome
title_fullStr Mapping hybrid functional-structural connectivity traits in the human connectome
title_full_unstemmed Mapping hybrid functional-structural connectivity traits in the human connectome
title_short Mapping hybrid functional-structural connectivity traits in the human connectome
title_sort mapping hybrid functional-structural connectivity traits in the human connectome
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6145853/
https://www.ncbi.nlm.nih.gov/pubmed/30259007
http://dx.doi.org/10.1162/netn_a_00049
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