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Frequency-phase analysis of resting-state functional MRI
We describe an analysis method that characterizes the correlation between coupled time-series functions by their frequencies and phases. It provides a unified framework for simultaneous assessment of frequency and latency of a coupled time-series. The analysis is demonstrated on resting-state functi...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5341062/ https://www.ncbi.nlm.nih.gov/pubmed/28272522 http://dx.doi.org/10.1038/srep43743 |
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author | Goelman, Gadi Dan, Rotem Růžička, Filip Bezdicek, Ondrej Růžička, Evžen Roth, Jan Vymazal, Josef Jech, Robert |
author_facet | Goelman, Gadi Dan, Rotem Růžička, Filip Bezdicek, Ondrej Růžička, Evžen Roth, Jan Vymazal, Josef Jech, Robert |
author_sort | Goelman, Gadi |
collection | PubMed |
description | We describe an analysis method that characterizes the correlation between coupled time-series functions by their frequencies and phases. It provides a unified framework for simultaneous assessment of frequency and latency of a coupled time-series. The analysis is demonstrated on resting-state functional MRI data of 34 healthy subjects. Interactions between fMRI time-series are represented by cross-correlation (with time-lag) functions. A general linear model is used on the cross-correlation functions to obtain the frequencies and phase-differences of the original time-series. We define symmetric, antisymmetric and asymmetric cross-correlation functions that correspond respectively to in-phase, 90° out-of-phase and any phase difference between a pair of time-series, where the last two were never introduced before. Seed maps of the motor system were calculated to demonstrate the strength and capabilities of the analysis. Unique types of functional connections, their dominant frequencies and phase-differences have been identified. The relation between phase-differences and time-delays is shown. The phase-differences are speculated to inform transfer-time and/or to reflect a difference in the hemodynamic response between regions that are modulated by neurotransmitters concentration. The analysis can be used with any coupled functions in many disciplines including electrophysiology, EEG or MEG in neuroscience. |
format | Online Article Text |
id | pubmed-5341062 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | Nature Publishing Group |
record_format | MEDLINE/PubMed |
spelling | pubmed-53410622017-03-10 Frequency-phase analysis of resting-state functional MRI Goelman, Gadi Dan, Rotem Růžička, Filip Bezdicek, Ondrej Růžička, Evžen Roth, Jan Vymazal, Josef Jech, Robert Sci Rep Article We describe an analysis method that characterizes the correlation between coupled time-series functions by their frequencies and phases. It provides a unified framework for simultaneous assessment of frequency and latency of a coupled time-series. The analysis is demonstrated on resting-state functional MRI data of 34 healthy subjects. Interactions between fMRI time-series are represented by cross-correlation (with time-lag) functions. A general linear model is used on the cross-correlation functions to obtain the frequencies and phase-differences of the original time-series. We define symmetric, antisymmetric and asymmetric cross-correlation functions that correspond respectively to in-phase, 90° out-of-phase and any phase difference between a pair of time-series, where the last two were never introduced before. Seed maps of the motor system were calculated to demonstrate the strength and capabilities of the analysis. Unique types of functional connections, their dominant frequencies and phase-differences have been identified. The relation between phase-differences and time-delays is shown. The phase-differences are speculated to inform transfer-time and/or to reflect a difference in the hemodynamic response between regions that are modulated by neurotransmitters concentration. The analysis can be used with any coupled functions in many disciplines including electrophysiology, EEG or MEG in neuroscience. Nature Publishing Group 2017-03-08 /pmc/articles/PMC5341062/ /pubmed/28272522 http://dx.doi.org/10.1038/srep43743 Text en Copyright © 2017, The Author(s) http://creativecommons.org/licenses/by/4.0/ This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ |
spellingShingle | Article Goelman, Gadi Dan, Rotem Růžička, Filip Bezdicek, Ondrej Růžička, Evžen Roth, Jan Vymazal, Josef Jech, Robert Frequency-phase analysis of resting-state functional MRI |
title | Frequency-phase analysis of resting-state functional MRI |
title_full | Frequency-phase analysis of resting-state functional MRI |
title_fullStr | Frequency-phase analysis of resting-state functional MRI |
title_full_unstemmed | Frequency-phase analysis of resting-state functional MRI |
title_short | Frequency-phase analysis of resting-state functional MRI |
title_sort | frequency-phase analysis of resting-state functional mri |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5341062/ https://www.ncbi.nlm.nih.gov/pubmed/28272522 http://dx.doi.org/10.1038/srep43743 |
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