Cargando…
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) +...
Autores principales: | , , |
---|---|
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 |
_version_ | 1783293814124838912 |
---|---|
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. |
format | Online Article Text |
id | pubmed-5774799 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT chenzikuan brainfunctionalboldperturbationmodellingforforwardfmriandinversemapping AT robinsonjennifer brainfunctionalboldperturbationmodellingforforwardfmriandinversemapping AT calhounvince brainfunctionalboldperturbationmodellingforforwardfmriandinversemapping |