Open AccessExploring brain connectivity changes in major depressive disorder using functional‐structural data fusion: A CAN‐BIND‐1 study
Author(s) -
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.
Publication year - 2021
Publication title -
human brain mapping
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.005
H-Index - 191
eISSN - 1097-0193
pISSN - 1065-9471
DOI - 10.1002/hbm.25590
Subject(s) - default mode network , major depressive disorder , functional connectivity , resting state fmri , computer science , neuroscience , pipeline (software) , psychology , artificial intelligence , pattern recognition (psychology) , cognition , programming language
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.