Exploring brain connectivity changes in major depressive disorder using functional‐structural data fusion: A CAN‐BIND‐1 study. Issue 15 (23rd July 2021)

• Record Type: Journal Article
• 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.
• Is Part Of: Human brain mapping. Volume 42:Issue 15(2021)
• Page Start: 4940
• Page End: 4957
• Publication Date: 2021-07-23
• Subjects: data fusion -- functional connectivity -- major depressive disorder -- neuroimaging -- resting brain networks -- structural connectivity -- toolbox; Brain mapping -- Periodicals; 611.81
• DOI: 10.1002/hbm.25590
• Languages: English
• ISSNs: 1065-9471

Abstract

Abstract: 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. Abstract : Here we developed a novel processing pipeline for a data fusion analysis of functional and structural imaging information sources. The novel FATCAT‐awFC pipeline was then utilized to identify anatomically weighted functional connectivity changes in patients with major depressive disorder compared to healthy comparison participants.