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Exploring brain connectivity changes in major depressive disorder using functional‐structural data fusion: A CAN‐BIND‐1 study

There is a growing interest in examining the wealth of data generated by fusing functional and structural imaging information sources. These approaches may have clinical utility in identifying disruptions in the brain networks that underlie major depressive disorder (MDD). We combined an existing so...

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Autores principales: Ayyash, Sondos, Davis, Andrew D., Alders, Gésine L., MacQueen, Glenda, Strother, Stephen C., Hassel, Stefanie, Zamyadi, Mojdeh, Arnott, Stephen R., Harris, Jacqueline K., Lam, Raymond W., Milev, Roumen, Müller, Daniel J., Kennedy, Sidney H., Rotzinger, Susan, Frey, Benicio N., Minuzzi, Luciano, Hall, Geoffrey B.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley & Sons, Inc. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8449113/
https://www.ncbi.nlm.nih.gov/pubmed/34296501
http://dx.doi.org/10.1002/hbm.25590
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author Ayyash, Sondos
Davis, Andrew D.
Alders, Gésine L.
MacQueen, Glenda
Strother, Stephen C.
Hassel, Stefanie
Zamyadi, Mojdeh
Arnott, Stephen R.
Harris, Jacqueline K.
Lam, Raymond W.
Milev, Roumen
Müller, Daniel J.
Kennedy, Sidney H.
Rotzinger, Susan
Frey, Benicio N.
Minuzzi, Luciano
Hall, Geoffrey B.
author_facet Ayyash, Sondos
Davis, Andrew D.
Alders, Gésine L.
MacQueen, Glenda
Strother, Stephen C.
Hassel, Stefanie
Zamyadi, Mojdeh
Arnott, Stephen R.
Harris, Jacqueline K.
Lam, Raymond W.
Milev, Roumen
Müller, Daniel J.
Kennedy, Sidney H.
Rotzinger, Susan
Frey, Benicio N.
Minuzzi, Luciano
Hall, Geoffrey B.
author_sort Ayyash, Sondos
collection PubMed
description There is a growing interest in examining the wealth of data generated by fusing functional and structural imaging information sources. These approaches may have clinical utility in identifying disruptions in the brain networks that underlie major depressive disorder (MDD). We combined an existing software toolbox with a mathematically dense statistical method to produce a novel processing pipeline for the fast and easy implementation of data fusion analysis (FATCAT‐awFC). The novel FATCAT‐awFC pipeline was then utilized to identify connectivity (conventional functional, conventional structural and anatomically weighted functional connectivy) changes in MDD patients compared to healthy comparison participants (HC). Data were acquired from the Canadian Biomarker Integration Network for Depression (CAN‐BIND‐1) study. Large‐scale resting‐state networks were assessed. We found statistically significant anatomically‐weighted functional connectivity (awFC) group differences in the default mode network and the ventral attention network, with a modest effect size (d < 0.4). Functional and structural connectivity seemed to overlap in significance between one region‐pair within the default mode network. By combining structural and functional data, awFC served to heighten or reduce the magnitude of connectivity differences in various regions distinguishing MDD from HC. This method can help us more fully understand the interconnected nature of structural and functional connectivity as it relates to depression.
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spelling pubmed-84491132021-09-24 Exploring brain connectivity changes in major depressive disorder using functional‐structural data fusion: A CAN‐BIND‐1 study Ayyash, Sondos Davis, Andrew D. Alders, Gésine L. MacQueen, Glenda Strother, Stephen C. Hassel, Stefanie Zamyadi, Mojdeh Arnott, Stephen R. Harris, Jacqueline K. Lam, Raymond W. Milev, Roumen Müller, Daniel J. Kennedy, Sidney H. Rotzinger, Susan Frey, Benicio N. Minuzzi, Luciano Hall, Geoffrey B. Hum Brain Mapp Research Articles There is a growing interest in examining the wealth of data generated by fusing functional and structural imaging information sources. These approaches may have clinical utility in identifying disruptions in the brain networks that underlie major depressive disorder (MDD). We combined an existing software toolbox with a mathematically dense statistical method to produce a novel processing pipeline for the fast and easy implementation of data fusion analysis (FATCAT‐awFC). The novel FATCAT‐awFC pipeline was then utilized to identify connectivity (conventional functional, conventional structural and anatomically weighted functional connectivy) changes in MDD patients compared to healthy comparison participants (HC). Data were acquired from the Canadian Biomarker Integration Network for Depression (CAN‐BIND‐1) study. Large‐scale resting‐state networks were assessed. We found statistically significant anatomically‐weighted functional connectivity (awFC) group differences in the default mode network and the ventral attention network, with a modest effect size (d < 0.4). Functional and structural connectivity seemed to overlap in significance between one region‐pair within the default mode network. By combining structural and functional data, awFC served to heighten or reduce the magnitude of connectivity differences in various regions distinguishing MDD from HC. This method can help us more fully understand the interconnected nature of structural and functional connectivity as it relates to depression. John Wiley & Sons, Inc. 2021-07-23 /pmc/articles/PMC8449113/ /pubmed/34296501 http://dx.doi.org/10.1002/hbm.25590 Text en © 2021 The Authors. Human Brain Mapping published by Wiley Periodicals LLC. https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc-nd/4.0/ (https://creativecommons.org/licenses/by-nc-nd/4.0/) License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made.
spellingShingle Research Articles
Ayyash, Sondos
Davis, Andrew D.
Alders, Gésine L.
MacQueen, Glenda
Strother, Stephen C.
Hassel, Stefanie
Zamyadi, Mojdeh
Arnott, Stephen R.
Harris, Jacqueline K.
Lam, Raymond W.
Milev, Roumen
Müller, Daniel J.
Kennedy, Sidney H.
Rotzinger, Susan
Frey, Benicio N.
Minuzzi, Luciano
Hall, Geoffrey B.
Exploring brain connectivity changes in major depressive disorder using functional‐structural data fusion: A CAN‐BIND‐1 study
title Exploring brain connectivity changes in major depressive disorder using functional‐structural data fusion: A CAN‐BIND‐1 study
title_full Exploring brain connectivity changes in major depressive disorder using functional‐structural data fusion: A CAN‐BIND‐1 study
title_fullStr Exploring brain connectivity changes in major depressive disorder using functional‐structural data fusion: A CAN‐BIND‐1 study
title_full_unstemmed Exploring brain connectivity changes in major depressive disorder using functional‐structural data fusion: A CAN‐BIND‐1 study
title_short Exploring brain connectivity changes in major depressive disorder using functional‐structural data fusion: A CAN‐BIND‐1 study
title_sort exploring brain connectivity changes in major depressive disorder using functional‐structural data fusion: a can‐bind‐1 study
topic Research Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8449113/
https://www.ncbi.nlm.nih.gov/pubmed/34296501
http://dx.doi.org/10.1002/hbm.25590
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