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Regression models for partially localized fMRI connectivity analyses

BACKGROUND: Brain functional connectivity analysis of resting-state functional magnetic resonance imaging (fMRI) data is typically performed in a standardized template space assuming consistency of connections across subjects. Analysis methods can come in the form of one-edge-at-a-time analyses or d...

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Autores principales: Smith, Bonnie B., Zhao, Yi, Lindquist, Martin A., Caffo, Brian
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10679340/
https://www.ncbi.nlm.nih.gov/pubmed/38025311
http://dx.doi.org/10.3389/fnimg.2023.1178359
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author Smith, Bonnie B.
Zhao, Yi
Lindquist, Martin A.
Caffo, Brian
author_facet Smith, Bonnie B.
Zhao, Yi
Lindquist, Martin A.
Caffo, Brian
author_sort Smith, Bonnie B.
collection PubMed
description BACKGROUND: Brain functional connectivity analysis of resting-state functional magnetic resonance imaging (fMRI) data is typically performed in a standardized template space assuming consistency of connections across subjects. Analysis methods can come in the form of one-edge-at-a-time analyses or dimension reduction/decomposition methods. Common to these approaches is an assumption that brain regions are functionally aligned across subjects; however, it is known that this functional alignment assumption is often violated. METHODS: In this paper, we use subject-level regression models to explain intra-subject variability in connectivity. Covariates can include factors such as geographic distance between two pairs of brain regions, whether the two regions are symmetrically opposite (homotopic), and whether the two regions are members of the same functional network. Additionally, a covariate for each brain region can be included, to account for the possibility that some regions have consistently higher or lower connectivity. This style of analysis allows us to characterize the fraction of variation explained by each type of covariate. Additionally, comparisons across subjects can then be made using the fitted connectivity regression models, offering a more parsimonious alternative to edge-at-a-time approaches. RESULTS: We apply our approach to Human Connectome Project data on 268 regions of interest (ROIs), grouped into eight functional networks. We find that a high proportion of variation is explained by region covariates and network membership covariates, while geographic distance and homotopy have high relative importance after adjusting for the number of predictors. We also find that the degree of data repeatability using our connectivity regression model—which uses only partial location information about pairs of ROI's—is comparably as high as the repeatability obtained using full location information. DISCUSSION: While our analysis uses data that have been transformed into a common template-space, we also envision the method being useful in multi-atlas registration settings, where subject data remains in its own geometry and templates are warped instead. These results suggest the tantalizing possibility that fMRI connectivity analysis can be performed in subject-space, using less aggressive registration, such as simple affine transformations, multi-atlas subject-space registration, or perhaps even no registration whatsoever.
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spelling pubmed-106793402023-11-13 Regression models for partially localized fMRI connectivity analyses Smith, Bonnie B. Zhao, Yi Lindquist, Martin A. Caffo, Brian Front Neuroimaging Neuroimaging BACKGROUND: Brain functional connectivity analysis of resting-state functional magnetic resonance imaging (fMRI) data is typically performed in a standardized template space assuming consistency of connections across subjects. Analysis methods can come in the form of one-edge-at-a-time analyses or dimension reduction/decomposition methods. Common to these approaches is an assumption that brain regions are functionally aligned across subjects; however, it is known that this functional alignment assumption is often violated. METHODS: In this paper, we use subject-level regression models to explain intra-subject variability in connectivity. Covariates can include factors such as geographic distance between two pairs of brain regions, whether the two regions are symmetrically opposite (homotopic), and whether the two regions are members of the same functional network. Additionally, a covariate for each brain region can be included, to account for the possibility that some regions have consistently higher or lower connectivity. This style of analysis allows us to characterize the fraction of variation explained by each type of covariate. Additionally, comparisons across subjects can then be made using the fitted connectivity regression models, offering a more parsimonious alternative to edge-at-a-time approaches. RESULTS: We apply our approach to Human Connectome Project data on 268 regions of interest (ROIs), grouped into eight functional networks. We find that a high proportion of variation is explained by region covariates and network membership covariates, while geographic distance and homotopy have high relative importance after adjusting for the number of predictors. We also find that the degree of data repeatability using our connectivity regression model—which uses only partial location information about pairs of ROI's—is comparably as high as the repeatability obtained using full location information. DISCUSSION: While our analysis uses data that have been transformed into a common template-space, we also envision the method being useful in multi-atlas registration settings, where subject data remains in its own geometry and templates are warped instead. These results suggest the tantalizing possibility that fMRI connectivity analysis can be performed in subject-space, using less aggressive registration, such as simple affine transformations, multi-atlas subject-space registration, or perhaps even no registration whatsoever. Frontiers Media S.A. 2023-11-13 /pmc/articles/PMC10679340/ /pubmed/38025311 http://dx.doi.org/10.3389/fnimg.2023.1178359 Text en Copyright © 2023 Smith, Zhao, Lindquist and Caffo. 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 Neuroimaging
Smith, Bonnie B.
Zhao, Yi
Lindquist, Martin A.
Caffo, Brian
Regression models for partially localized fMRI connectivity analyses
title Regression models for partially localized fMRI connectivity analyses
title_full Regression models for partially localized fMRI connectivity analyses
title_fullStr Regression models for partially localized fMRI connectivity analyses
title_full_unstemmed Regression models for partially localized fMRI connectivity analyses
title_short Regression models for partially localized fMRI connectivity analyses
title_sort regression models for partially localized fmri connectivity analyses
topic Neuroimaging
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10679340/
https://www.ncbi.nlm.nih.gov/pubmed/38025311
http://dx.doi.org/10.3389/fnimg.2023.1178359
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