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Connectome topology of mammalian brains and its relationship to taxonomy and phylogeny

Network models of anatomical connections allow for the extraction of quantitative features describing brain organization, and their comparison across brains from different species. Such comparisons can inform our understanding of between-species differences in brain architecture and can be compared...

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Autores principales: Faskowitz, Joshua, Puxeddu, Maria Grazia, van den Heuvel, Martijn P., Mišić, Bratislav, Yovel, Yossi, Assaf, Yaniv, Betzel, Richard F., Sporns, Olaf
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9874302/
https://www.ncbi.nlm.nih.gov/pubmed/36711139
http://dx.doi.org/10.3389/fnins.2022.1044372
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author Faskowitz, Joshua
Puxeddu, Maria Grazia
van den Heuvel, Martijn P.
Mišić, Bratislav
Yovel, Yossi
Assaf, Yaniv
Betzel, Richard F.
Sporns, Olaf
author_facet Faskowitz, Joshua
Puxeddu, Maria Grazia
van den Heuvel, Martijn P.
Mišić, Bratislav
Yovel, Yossi
Assaf, Yaniv
Betzel, Richard F.
Sporns, Olaf
author_sort Faskowitz, Joshua
collection PubMed
description Network models of anatomical connections allow for the extraction of quantitative features describing brain organization, and their comparison across brains from different species. Such comparisons can inform our understanding of between-species differences in brain architecture and can be compared to existing taxonomies and phylogenies. Here we performed a quantitative comparative analysis using the MaMI database (Tel Aviv University), a collection of brain networks reconstructed from ex vivo diffusion MRI spanning 125 species and 12 taxonomic orders or superorders. We used a broad range of metrics to measure between-mammal distances and compare these estimates to the separation of species as derived from taxonomy and phylogeny. We found that within-taxonomy order network distances are significantly closer than between-taxonomy network distances, and this relation holds for several measures of network distance. Furthermore, to estimate the evolutionary divergence between species, we obtained phylogenetic distances across 10,000 plausible phylogenetic trees. The anatomical network distances were rank-correlated with phylogenetic distances 10,000 times, creating a distribution of coefficients that demonstrate significantly positive correlations between network and phylogenetic distances. Collectively, these analyses demonstrate species-level organization across scales and informational sources: we relate brain networks distances, derived from MRI, with evolutionary distances, derived from genotyping data.
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spelling pubmed-98743022023-01-26 Connectome topology of mammalian brains and its relationship to taxonomy and phylogeny Faskowitz, Joshua Puxeddu, Maria Grazia van den Heuvel, Martijn P. Mišić, Bratislav Yovel, Yossi Assaf, Yaniv Betzel, Richard F. Sporns, Olaf Front Neurosci Neuroscience Network models of anatomical connections allow for the extraction of quantitative features describing brain organization, and their comparison across brains from different species. Such comparisons can inform our understanding of between-species differences in brain architecture and can be compared to existing taxonomies and phylogenies. Here we performed a quantitative comparative analysis using the MaMI database (Tel Aviv University), a collection of brain networks reconstructed from ex vivo diffusion MRI spanning 125 species and 12 taxonomic orders or superorders. We used a broad range of metrics to measure between-mammal distances and compare these estimates to the separation of species as derived from taxonomy and phylogeny. We found that within-taxonomy order network distances are significantly closer than between-taxonomy network distances, and this relation holds for several measures of network distance. Furthermore, to estimate the evolutionary divergence between species, we obtained phylogenetic distances across 10,000 plausible phylogenetic trees. The anatomical network distances were rank-correlated with phylogenetic distances 10,000 times, creating a distribution of coefficients that demonstrate significantly positive correlations between network and phylogenetic distances. Collectively, these analyses demonstrate species-level organization across scales and informational sources: we relate brain networks distances, derived from MRI, with evolutionary distances, derived from genotyping data. Frontiers Media S.A. 2023-01-11 /pmc/articles/PMC9874302/ /pubmed/36711139 http://dx.doi.org/10.3389/fnins.2022.1044372 Text en Copyright © 2023 Faskowitz, Puxeddu, van den Heuvel, Mišić, Yovel, Assaf, Betzel and Sporns. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Neuroscience
Faskowitz, Joshua
Puxeddu, Maria Grazia
van den Heuvel, Martijn P.
Mišić, Bratislav
Yovel, Yossi
Assaf, Yaniv
Betzel, Richard F.
Sporns, Olaf
Connectome topology of mammalian brains and its relationship to taxonomy and phylogeny
title Connectome topology of mammalian brains and its relationship to taxonomy and phylogeny
title_full Connectome topology of mammalian brains and its relationship to taxonomy and phylogeny
title_fullStr Connectome topology of mammalian brains and its relationship to taxonomy and phylogeny
title_full_unstemmed Connectome topology of mammalian brains and its relationship to taxonomy and phylogeny
title_short Connectome topology of mammalian brains and its relationship to taxonomy and phylogeny
title_sort connectome topology of mammalian brains and its relationship to taxonomy and phylogeny
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9874302/
https://www.ncbi.nlm.nih.gov/pubmed/36711139
http://dx.doi.org/10.3389/fnins.2022.1044372
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