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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Bibliographic Details
Published in:Human brain mapping 2021-10, Vol.42 (15), p.4940-4957
Main Authors: 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.
Format: Article
Language:English
Subjects:
Automation
Biomarkers
Brain
Data acquisition
data fusion
Data integration
functional connectivity
Information sources
major depressive disorder
Mental depression
Neural networks
Neuroimaging
Pipelines
resting brain networks
Software
Statistical analysis
structural connectivity
Structure-function relationships
toolbox
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Summary: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 
ISSN:1065-9471
1097-0193
DOI:10.1002/hbm.25590