Cargando…

Comparison of Resting-State Functional MRI Methods for Characterizing Brain Dynamics

Resting-state functional MRI (fMRI) exhibits time-varying patterns of functional connectivity. Several different analysis approaches have been developed for examining these resting-state dynamics including sliding window connectivity (SWC), phase synchrony (PS), co-activation pattern (CAP), and quas...

Descripción completa

Detalles Bibliográficos
Autores principales: Maltbie, Eric, Yousefi, Behnaz, Zhang, Xiaodi, Kashyap, Amrit, Keilholz, Shella
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9013751/
https://www.ncbi.nlm.nih.gov/pubmed/35444518
http://dx.doi.org/10.3389/fncir.2022.681544
_version_ 1784688061896982528
author Maltbie, Eric
Yousefi, Behnaz
Zhang, Xiaodi
Kashyap, Amrit
Keilholz, Shella
author_facet Maltbie, Eric
Yousefi, Behnaz
Zhang, Xiaodi
Kashyap, Amrit
Keilholz, Shella
author_sort Maltbie, Eric
collection PubMed
description Resting-state functional MRI (fMRI) exhibits time-varying patterns of functional connectivity. Several different analysis approaches have been developed for examining these resting-state dynamics including sliding window connectivity (SWC), phase synchrony (PS), co-activation pattern (CAP), and quasi-periodic patterns (QPP). Each of these approaches can be used to generate patterns of activity or inter-areal coordination which vary across time. The individual frames can then be clustered to produce temporal groupings commonly referred to as “brain states.” Several recent publications have investigated brain state alterations in clinical populations, typically using a single method for quantifying frame-wise functional connectivity. This study directly compares the results of k-means clustering in conjunction with three of these resting-state dynamics methods (SWC, CAP, and PS) and quantifies the brain state dynamics across several metrics using high resolution data from the human connectome project. Additionally, these three dynamics methods are compared by examining how the brain state characterizations vary during the repeated sequences of brain states identified by a fourth dynamic analysis method, QPP. The results indicate that the SWC, PS, and CAP methods differ in the clusters and trajectories they produce. A clear illustration of these differences is given by how each one results in a very different clustering profile for the 24s sequences explicitly identified by the QPP algorithm. PS clustering is sensitive to QPPs with the mid-point of most QPP sequences grouped into the same single cluster. CAPs are also highly sensitive to QPPs, separating each phase of the QPP sequences into different sets of clusters. SWC (60s window) is less sensitive to QPPs. While the QPPs are slightly more likely to occur during specific SWC clusters, the SWC clustering does not vary during the 24s QPP sequences, the goal of this work is to improve both the practical and theoretical understanding of different resting-state dynamics methods, thereby enabling investigators to better conceptualize and implement these tools for characterizing functional brain networks.
format Online
Article
Text
id pubmed-9013751
institution National Center for Biotechnology Information
language English
publishDate 2022
publisher Frontiers Media S.A.
record_format MEDLINE/PubMed
spelling pubmed-90137512022-04-19 Comparison of Resting-State Functional MRI Methods for Characterizing Brain Dynamics Maltbie, Eric Yousefi, Behnaz Zhang, Xiaodi Kashyap, Amrit Keilholz, Shella Front Neural Circuits Neural Circuits Resting-state functional MRI (fMRI) exhibits time-varying patterns of functional connectivity. Several different analysis approaches have been developed for examining these resting-state dynamics including sliding window connectivity (SWC), phase synchrony (PS), co-activation pattern (CAP), and quasi-periodic patterns (QPP). Each of these approaches can be used to generate patterns of activity or inter-areal coordination which vary across time. The individual frames can then be clustered to produce temporal groupings commonly referred to as “brain states.” Several recent publications have investigated brain state alterations in clinical populations, typically using a single method for quantifying frame-wise functional connectivity. This study directly compares the results of k-means clustering in conjunction with three of these resting-state dynamics methods (SWC, CAP, and PS) and quantifies the brain state dynamics across several metrics using high resolution data from the human connectome project. Additionally, these three dynamics methods are compared by examining how the brain state characterizations vary during the repeated sequences of brain states identified by a fourth dynamic analysis method, QPP. The results indicate that the SWC, PS, and CAP methods differ in the clusters and trajectories they produce. A clear illustration of these differences is given by how each one results in a very different clustering profile for the 24s sequences explicitly identified by the QPP algorithm. PS clustering is sensitive to QPPs with the mid-point of most QPP sequences grouped into the same single cluster. CAPs are also highly sensitive to QPPs, separating each phase of the QPP sequences into different sets of clusters. SWC (60s window) is less sensitive to QPPs. While the QPPs are slightly more likely to occur during specific SWC clusters, the SWC clustering does not vary during the 24s QPP sequences, the goal of this work is to improve both the practical and theoretical understanding of different resting-state dynamics methods, thereby enabling investigators to better conceptualize and implement these tools for characterizing functional brain networks. Frontiers Media S.A. 2022-04-04 /pmc/articles/PMC9013751/ /pubmed/35444518 http://dx.doi.org/10.3389/fncir.2022.681544 Text en Copyright © 2022 Maltbie, Yousefi, Zhang, Kashyap and Keilholz. 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 Neural Circuits
Maltbie, Eric
Yousefi, Behnaz
Zhang, Xiaodi
Kashyap, Amrit
Keilholz, Shella
Comparison of Resting-State Functional MRI Methods for Characterizing Brain Dynamics
title Comparison of Resting-State Functional MRI Methods for Characterizing Brain Dynamics
title_full Comparison of Resting-State Functional MRI Methods for Characterizing Brain Dynamics
title_fullStr Comparison of Resting-State Functional MRI Methods for Characterizing Brain Dynamics
title_full_unstemmed Comparison of Resting-State Functional MRI Methods for Characterizing Brain Dynamics
title_short Comparison of Resting-State Functional MRI Methods for Characterizing Brain Dynamics
title_sort comparison of resting-state functional mri methods for characterizing brain dynamics
topic Neural Circuits
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9013751/
https://www.ncbi.nlm.nih.gov/pubmed/35444518
http://dx.doi.org/10.3389/fncir.2022.681544
work_keys_str_mv AT maltbieeric comparisonofrestingstatefunctionalmrimethodsforcharacterizingbraindynamics
AT yousefibehnaz comparisonofrestingstatefunctionalmrimethodsforcharacterizingbraindynamics
AT zhangxiaodi comparisonofrestingstatefunctionalmrimethodsforcharacterizingbraindynamics
AT kashyapamrit comparisonofrestingstatefunctionalmrimethodsforcharacterizingbraindynamics
AT keilholzshella comparisonofrestingstatefunctionalmrimethodsforcharacterizingbraindynamics