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Brain functional BOLD perturbation modelling for forward fMRI and inverse mapping

PURPOSE: To computationally separate dynamic brain functional BOLD responses from static background in a brain functional activity for forward fMRI signal analysis and inverse mapping. METHODS: A brain functional activity is represented in terms of magnetic source by a perturbation model: χ = χ(0) +...

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Detalles Bibliográficos
Autores principales: Chen, Zikuan, Robinson, Jennifer, Calhoun, Vince
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5774799/
https://www.ncbi.nlm.nih.gov/pubmed/29351339
http://dx.doi.org/10.1371/journal.pone.0191266
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author Chen, Zikuan
Robinson, Jennifer
Calhoun, Vince
author_facet Chen, Zikuan
Robinson, Jennifer
Calhoun, Vince
author_sort Chen, Zikuan
collection PubMed
description PURPOSE: To computationally separate dynamic brain functional BOLD responses from static background in a brain functional activity for forward fMRI signal analysis and inverse mapping. METHODS: A brain functional activity is represented in terms of magnetic source by a perturbation model: χ = χ(0) +δχ, with δχ for BOLD magnetic perturbations and χ(0) for background. A brain fMRI experiment produces a timeseries of complex-valued images (T2* images), whereby we extract the BOLD phase signals (denoted by δP) by a complex division. By solving an inverse problem, we reconstruct the BOLD δχ dataset from the δP dataset, and the brain χ distribution from a (unwrapped) T2* phase image. Given a 4D dataset of task BOLD fMRI, we implement brain functional mapping by temporal correlation analysis. RESULTS: Through a high-field (7T) and high-resolution (0.5mm in plane) task fMRI experiment, we demonstrated in detail the BOLD perturbation model for fMRI phase signal separation (P + δP) and reconstructing intrinsic brain magnetic source (χ and δχ). We also provided to a low-field (3T) and low-resolution (2mm) task fMRI experiment in support of single-subject fMRI study. Our experiments show that the δχ-depicted functional map reveals bidirectional BOLD χ perturbations during the task performance. CONCLUSIONS: The BOLD perturbation model allows us to separate fMRI phase signal (by complex division) and to perform inverse mapping for pure BOLD δχ reconstruction for intrinsic functional χ mapping. The full brain χ reconstruction (from unwrapped fMRI phase) provides a new brain tissue image that allows to scrutinize the brain tissue idiosyncrasy for the pure BOLD δχ response through an automatic function/structure co-localization.
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spelling pubmed-57747992018-02-05 Brain functional BOLD perturbation modelling for forward fMRI and inverse mapping Chen, Zikuan Robinson, Jennifer Calhoun, Vince PLoS One Research Article PURPOSE: To computationally separate dynamic brain functional BOLD responses from static background in a brain functional activity for forward fMRI signal analysis and inverse mapping. METHODS: A brain functional activity is represented in terms of magnetic source by a perturbation model: χ = χ(0) +δχ, with δχ for BOLD magnetic perturbations and χ(0) for background. A brain fMRI experiment produces a timeseries of complex-valued images (T2* images), whereby we extract the BOLD phase signals (denoted by δP) by a complex division. By solving an inverse problem, we reconstruct the BOLD δχ dataset from the δP dataset, and the brain χ distribution from a (unwrapped) T2* phase image. Given a 4D dataset of task BOLD fMRI, we implement brain functional mapping by temporal correlation analysis. RESULTS: Through a high-field (7T) and high-resolution (0.5mm in plane) task fMRI experiment, we demonstrated in detail the BOLD perturbation model for fMRI phase signal separation (P + δP) and reconstructing intrinsic brain magnetic source (χ and δχ). We also provided to a low-field (3T) and low-resolution (2mm) task fMRI experiment in support of single-subject fMRI study. Our experiments show that the δχ-depicted functional map reveals bidirectional BOLD χ perturbations during the task performance. CONCLUSIONS: The BOLD perturbation model allows us to separate fMRI phase signal (by complex division) and to perform inverse mapping for pure BOLD δχ reconstruction for intrinsic functional χ mapping. The full brain χ reconstruction (from unwrapped fMRI phase) provides a new brain tissue image that allows to scrutinize the brain tissue idiosyncrasy for the pure BOLD δχ response through an automatic function/structure co-localization. Public Library of Science 2018-01-19 /pmc/articles/PMC5774799/ /pubmed/29351339 http://dx.doi.org/10.1371/journal.pone.0191266 Text en © 2018 Chen et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://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
Chen, Zikuan
Robinson, Jennifer
Calhoun, Vince
Brain functional BOLD perturbation modelling for forward fMRI and inverse mapping
title Brain functional BOLD perturbation modelling for forward fMRI and inverse mapping
title_full Brain functional BOLD perturbation modelling for forward fMRI and inverse mapping
title_fullStr Brain functional BOLD perturbation modelling for forward fMRI and inverse mapping
title_full_unstemmed Brain functional BOLD perturbation modelling for forward fMRI and inverse mapping
title_short Brain functional BOLD perturbation modelling for forward fMRI and inverse mapping
title_sort brain functional bold perturbation modelling for forward fmri and inverse mapping
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5774799/
https://www.ncbi.nlm.nih.gov/pubmed/29351339
http://dx.doi.org/10.1371/journal.pone.0191266
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