Cargando…

A fast and intuitive method for calculating dynamic network reconfiguration and node flexibility

Dynamic interactions between brain regions, either during rest or performance of cognitive tasks, have been studied extensively using a wide variance of methods. Although some of these methods allow elegant mathematical interpretations of the data, they can easily become computationally expensive or...

Descripción completa

Detalles Bibliográficos
Autores principales: Chinichian, Narges, Kruschwitz, Johann D., Reinhardt, Pablo, Palm, Maximilian, Wellan, Sarah A., Erk, Susanne, Heinz, Andreas, Walter, Henrik, Veer, Ilya M.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9949291/
https://www.ncbi.nlm.nih.gov/pubmed/36845440
http://dx.doi.org/10.3389/fnins.2023.1025428
_version_ 1784892945909940224
author Chinichian, Narges
Kruschwitz, Johann D.
Reinhardt, Pablo
Palm, Maximilian
Wellan, Sarah A.
Erk, Susanne
Heinz, Andreas
Walter, Henrik
Veer, Ilya M.
author_facet Chinichian, Narges
Kruschwitz, Johann D.
Reinhardt, Pablo
Palm, Maximilian
Wellan, Sarah A.
Erk, Susanne
Heinz, Andreas
Walter, Henrik
Veer, Ilya M.
author_sort Chinichian, Narges
collection PubMed
description Dynamic interactions between brain regions, either during rest or performance of cognitive tasks, have been studied extensively using a wide variance of methods. Although some of these methods allow elegant mathematical interpretations of the data, they can easily become computationally expensive or difficult to interpret and compare between subjects or groups. Here, we propose an intuitive and computationally efficient method to measure dynamic reconfiguration of brain regions, also termed flexibility. Our flexibility measure is defined in relation to an a-priori set of biologically plausible brain modules (or networks) and does not rely on a stochastic data-driven module estimation, which, in turn, minimizes computational burden. The change of affiliation of brain regions over time with respect to these a-priori template modules is used as an indicator of brain network flexibility. We demonstrate that our proposed method yields highly similar patterns of whole-brain network reconfiguration (i.e., flexibility) during a working memory task as compared to a previous study that uses a data-driven, but computationally more expensive method. This result illustrates that the use of a fixed modular framework allows for valid, yet more efficient estimation of whole-brain flexibility, while the method additionally supports more fine-grained (e.g. node and group of nodes scale) flexibility analyses restricted to biologically plausible brain networks.
format Online
Article
Text
id pubmed-9949291
institution National Center for Biotechnology Information
language English
publishDate 2023
publisher Frontiers Media S.A.
record_format MEDLINE/PubMed
spelling pubmed-99492912023-02-24 A fast and intuitive method for calculating dynamic network reconfiguration and node flexibility Chinichian, Narges Kruschwitz, Johann D. Reinhardt, Pablo Palm, Maximilian Wellan, Sarah A. Erk, Susanne Heinz, Andreas Walter, Henrik Veer, Ilya M. Front Neurosci Neuroscience Dynamic interactions between brain regions, either during rest or performance of cognitive tasks, have been studied extensively using a wide variance of methods. Although some of these methods allow elegant mathematical interpretations of the data, they can easily become computationally expensive or difficult to interpret and compare between subjects or groups. Here, we propose an intuitive and computationally efficient method to measure dynamic reconfiguration of brain regions, also termed flexibility. Our flexibility measure is defined in relation to an a-priori set of biologically plausible brain modules (or networks) and does not rely on a stochastic data-driven module estimation, which, in turn, minimizes computational burden. The change of affiliation of brain regions over time with respect to these a-priori template modules is used as an indicator of brain network flexibility. We demonstrate that our proposed method yields highly similar patterns of whole-brain network reconfiguration (i.e., flexibility) during a working memory task as compared to a previous study that uses a data-driven, but computationally more expensive method. This result illustrates that the use of a fixed modular framework allows for valid, yet more efficient estimation of whole-brain flexibility, while the method additionally supports more fine-grained (e.g. node and group of nodes scale) flexibility analyses restricted to biologically plausible brain networks. Frontiers Media S.A. 2023-02-09 /pmc/articles/PMC9949291/ /pubmed/36845440 http://dx.doi.org/10.3389/fnins.2023.1025428 Text en Copyright © 2023 Chinichian, Kruschwitz, Reinhardt, Palm, Wellan, Erk, Heinz, Walter and Veer. 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
Chinichian, Narges
Kruschwitz, Johann D.
Reinhardt, Pablo
Palm, Maximilian
Wellan, Sarah A.
Erk, Susanne
Heinz, Andreas
Walter, Henrik
Veer, Ilya M.
A fast and intuitive method for calculating dynamic network reconfiguration and node flexibility
title A fast and intuitive method for calculating dynamic network reconfiguration and node flexibility
title_full A fast and intuitive method for calculating dynamic network reconfiguration and node flexibility
title_fullStr A fast and intuitive method for calculating dynamic network reconfiguration and node flexibility
title_full_unstemmed A fast and intuitive method for calculating dynamic network reconfiguration and node flexibility
title_short A fast and intuitive method for calculating dynamic network reconfiguration and node flexibility
title_sort fast and intuitive method for calculating dynamic network reconfiguration and node flexibility
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9949291/
https://www.ncbi.nlm.nih.gov/pubmed/36845440
http://dx.doi.org/10.3389/fnins.2023.1025428
work_keys_str_mv AT chinichiannarges afastandintuitivemethodforcalculatingdynamicnetworkreconfigurationandnodeflexibility
AT kruschwitzjohannd afastandintuitivemethodforcalculatingdynamicnetworkreconfigurationandnodeflexibility
AT reinhardtpablo afastandintuitivemethodforcalculatingdynamicnetworkreconfigurationandnodeflexibility
AT palmmaximilian afastandintuitivemethodforcalculatingdynamicnetworkreconfigurationandnodeflexibility
AT wellansaraha afastandintuitivemethodforcalculatingdynamicnetworkreconfigurationandnodeflexibility
AT erksusanne afastandintuitivemethodforcalculatingdynamicnetworkreconfigurationandnodeflexibility
AT heinzandreas afastandintuitivemethodforcalculatingdynamicnetworkreconfigurationandnodeflexibility
AT walterhenrik afastandintuitivemethodforcalculatingdynamicnetworkreconfigurationandnodeflexibility
AT veerilyam afastandintuitivemethodforcalculatingdynamicnetworkreconfigurationandnodeflexibility
AT chinichiannarges fastandintuitivemethodforcalculatingdynamicnetworkreconfigurationandnodeflexibility
AT kruschwitzjohannd fastandintuitivemethodforcalculatingdynamicnetworkreconfigurationandnodeflexibility
AT reinhardtpablo fastandintuitivemethodforcalculatingdynamicnetworkreconfigurationandnodeflexibility
AT palmmaximilian fastandintuitivemethodforcalculatingdynamicnetworkreconfigurationandnodeflexibility
AT wellansaraha fastandintuitivemethodforcalculatingdynamicnetworkreconfigurationandnodeflexibility
AT erksusanne fastandintuitivemethodforcalculatingdynamicnetworkreconfigurationandnodeflexibility
AT heinzandreas fastandintuitivemethodforcalculatingdynamicnetworkreconfigurationandnodeflexibility
AT walterhenrik fastandintuitivemethodforcalculatingdynamicnetworkreconfigurationandnodeflexibility
AT veerilyam fastandintuitivemethodforcalculatingdynamicnetworkreconfigurationandnodeflexibility