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. 2021 Oct 15;42(15):4940-4957.
doi: 10.1002/hbm.25590. Epub 2021 Jul 23.

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

Sondos Ayyash  1   2 , Andrew D Davis  3   4 , Gésine L Alders  5 , Glenda MacQueen  6   7 , Stephen C Strother  4   8   9 , Stefanie Hassel  6   7 , Mojdeh Zamyadi  4 , Stephen R Arnott  4 , Jacqueline K Harris  10 , Raymond W Lam  11 , Roumen Milev  12 , Daniel J Müller  13   14 , Sidney H Kennedy  8   14   15   16   17 , Susan Rotzinger  14   16   17 , Benicio N Frey  3   5   18 , Luciano Minuzzi  3   5   18 , Geoffrey B Hall  1   2   3   5 ; CAN-BIND Investigator Team
Affiliations

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

Sondos Ayyash et al. Hum Brain Mapp. .

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.

Keywords: data fusion; functional connectivity; major depressive disorder; neuroimaging; resting brain networks; structural connectivity; toolbox.

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Conflict of interest statement

Dr. Milev has received consulting and speaking honoraria from Allergan, Janssen, KYE, Lundbeck, Otsuka, Pfizer and Sunovion, and research grants from CAN‐BIND, CIHR, Janssen, Lallemand, Lundbeck, Nubiyota, OBI, OMHF and Pfizer. Dr. Frey has received a research grant from Pfizer. Dr. Strother receives funding from the OBI and CIHR (MOP137097) for neuroimaging analysis in CAN‐BIND and he is the Chief Scientific Officer of ADMdx, Inc., a neuroimaging consulting company. Dr. MacQueen has had consultant payments or honoraria from: Allergen, Pfizer, Lundbeck, Janssen, Johnson & Johnson. Dr. Kennedy has received research funding or honoraria from the following sources: Abbott, Alkermes, Allergan, BMS, Brain Canada, Canadian Institutes for Health Research (CIHR), Janssen, Lundbeck, Lundbeck Institute, Ontario Brain Institute, Ontario Research Fund (ORF), Otsuka, Pfizer, Servier, Sunovion and Xian‐Janssen. Dr. Kennedy holds stock in Field Trip Health. Dr. Lam has received honoraria for ad hoc speaking or advising/consulting, or received research funds, from: Allergan, Asia‐Pacific Economic Cooperation, BC Leading Edge Foundation, Canadian Institutes of Health Research, Canadian Network for Mood and Anxiety Treatments, Canadian Psychiatric Association, Hansoh, Healthy Minds Canada, Janssen, Lundbeck, Lundbeck Institute, MITACS, Movember Foundation, Ontario Brain Institute, Otsuka, Pfizer, St. Jude Medical, University Health Network Foundation, and VGH‐UBCH Foundation. All other authors report no biomedical financial interests or potential conflicts of interest.

Figures

FIGURE 1
FIGURE 1
The resting state networks and corresponding regions of interest (ROIs) derived through group independent component analyses of RS fMRI data. CANBIND‐1 resting‐state fMRI data was used to extract ROIs. Five resting‐state networks were identified and extracted from the components (DMN, default mode network; DAN, dorsal attention network; FPN, fronto‐parietal network; Limbic, limbic network; VAN, ventral attention network). Z‐score maps were thresholded and binarized using FATCATs 3dROIMaker to generate network masks (DMN, Z = 5.5; FPN, Z = 9; Limbic, Z =6; DAN, Z = 5.5; VAN, Z = 11). The colored regions depicted represent different ROIs within each network
FIGURE 2
FIGURE 2
The FATCAT‐awFC analysis pipeline. awFC, anatomically weighted functional connectivity; FATCAT, functional and tractographic connectivity analysis toolbox
FIGURE 3
FIGURE 3
ROIs within RSNs with significant brain connectivity group differences (a) VAN, orange = right DLPFC, blue = left temporal lobe, green = right temporal lobe. (b) DMN, red ROI = cerebellum/lateral occipital cortex, yellow ROI = posterior cingulate cortex (PCC). ROIs, regions of interest; RSNs, resting state networks; A, anterior; P, posterior; R, right; L, left; S, superior; I, inferior
FIGURE 4
FIGURE 4
Boxplots demonstrated lower anatomically weighted functional connectivity between ROI‐pairs for the major depressive disorder (MDD) and healthy comparison (HC) participants. Boxplots also quantified the strength of connectivity for MDD and HC groups (a) AwFC between the left temporal lobe and the right DLPFC within the VAN (b) AwFC between the right temporal lobe and the right DLPFC within the VAN (c) AwFC between the occiptial lobe/cerebellum and the PCC within the DMN. AwFC, anatomically weighted functional connectivity; DLPFC, dorsolateral prefrontal cortex; VAN, ventral attention network; DMN, default mode network; Occ., occipital; Cerr., cerebellum; PCC, posterior cingulate cortex. Asterisks identify significant between‐group differences following FDR correction (p < .05)
See this image and copyright information in PMC

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