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Anatomically compliant modes of variations: New tools for brain connectivity

Anatomical complexity and data dimensionality present major issues when analysing brain connectivity data. The functional and anatomical aspects of the connections taking place in the brain are in fact equally relevant and strongly intertwined. However, due to theoretical challenges and computationa...

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Autores principales: Clementi, Letizia, Arnone, Eleonora, Santambrogio, Marco D., Franceschetti, Silvana, Panzica, Ferruccio, Sangalli, Laura M.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10629624/
https://www.ncbi.nlm.nih.gov/pubmed/37934760
http://dx.doi.org/10.1371/journal.pone.0292450
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author Clementi, Letizia
Arnone, Eleonora
Santambrogio, Marco D.
Franceschetti, Silvana
Panzica, Ferruccio
Sangalli, Laura M.
author_facet Clementi, Letizia
Arnone, Eleonora
Santambrogio, Marco D.
Franceschetti, Silvana
Panzica, Ferruccio
Sangalli, Laura M.
author_sort Clementi, Letizia
collection PubMed
description Anatomical complexity and data dimensionality present major issues when analysing brain connectivity data. The functional and anatomical aspects of the connections taking place in the brain are in fact equally relevant and strongly intertwined. However, due to theoretical challenges and computational issues, their relationship is often overlooked in neuroscience and clinical research. In this work, we propose to tackle this problem through Smooth Functional Principal Component Analysis, which enables to perform dimensional reduction and exploration of the variability in functional connectivity maps, complying with the formidably complicated anatomy of the grey matter volume. In particular, we analyse a population that includes controls and subjects affected by schizophrenia, starting from fMRI data acquired at rest and during a task-switching paradigm. For both sessions, we first identify the common modes of variation in the entire population. We hence explore whether the subjects’ expressions along these common modes of variation differ between controls and pathological subjects. In each session, we find principal components that are significantly differently expressed in the healthy vs pathological subjects (with p-values < 0.001), highlighting clearly interpretable differences in the connectivity in the two subpopulations. For instance, the second and third principal components for the rest session capture the imbalance between the Default Mode and Executive Networks characterizing schizophrenia patients.
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spelling pubmed-106296242023-11-08 Anatomically compliant modes of variations: New tools for brain connectivity Clementi, Letizia Arnone, Eleonora Santambrogio, Marco D. Franceschetti, Silvana Panzica, Ferruccio Sangalli, Laura M. PLoS One Research Article Anatomical complexity and data dimensionality present major issues when analysing brain connectivity data. The functional and anatomical aspects of the connections taking place in the brain are in fact equally relevant and strongly intertwined. However, due to theoretical challenges and computational issues, their relationship is often overlooked in neuroscience and clinical research. In this work, we propose to tackle this problem through Smooth Functional Principal Component Analysis, which enables to perform dimensional reduction and exploration of the variability in functional connectivity maps, complying with the formidably complicated anatomy of the grey matter volume. In particular, we analyse a population that includes controls and subjects affected by schizophrenia, starting from fMRI data acquired at rest and during a task-switching paradigm. For both sessions, we first identify the common modes of variation in the entire population. We hence explore whether the subjects’ expressions along these common modes of variation differ between controls and pathological subjects. In each session, we find principal components that are significantly differently expressed in the healthy vs pathological subjects (with p-values < 0.001), highlighting clearly interpretable differences in the connectivity in the two subpopulations. For instance, the second and third principal components for the rest session capture the imbalance between the Default Mode and Executive Networks characterizing schizophrenia patients. Public Library of Science 2023-11-07 /pmc/articles/PMC10629624/ /pubmed/37934760 http://dx.doi.org/10.1371/journal.pone.0292450 Text en © 2023 Clementi et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Clementi, Letizia
Arnone, Eleonora
Santambrogio, Marco D.
Franceschetti, Silvana
Panzica, Ferruccio
Sangalli, Laura M.
Anatomically compliant modes of variations: New tools for brain connectivity
title Anatomically compliant modes of variations: New tools for brain connectivity
title_full Anatomically compliant modes of variations: New tools for brain connectivity
title_fullStr Anatomically compliant modes of variations: New tools for brain connectivity
title_full_unstemmed Anatomically compliant modes of variations: New tools for brain connectivity
title_short Anatomically compliant modes of variations: New tools for brain connectivity
title_sort anatomically compliant modes of variations: new tools for brain connectivity
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10629624/
https://www.ncbi.nlm.nih.gov/pubmed/37934760
http://dx.doi.org/10.1371/journal.pone.0292450
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