Exploring brain connectivity changes in major depressive disorder using <scp>functional‐structural</scp> 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; Lam, Raymond W.; Milev, Roumen; Müller, Daniel J.; Kennedy, Sidney H.; Rotzinger, Susan; Frey, Benício N.; Minuzzi, Luciano; Hall, Geoffrey B.; Team, Can-Bind Investigator

doi:10.1002/hbm.25590

Cited by 8 publications

(13 citation statements)

References 89 publications

(154 reference statements)

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“…Specifically, VAN is involved in top-down controlled attentional selection and bottom-up attentional processing through dynamic interaction with DAN (Geng and Vossel, 2013;Vossel et al, 2014), thus mediating externally oriented attention, cognitive and emotional regulation (Kaiser et al, 2015;Mao et al, 2020;Stumme et al, 2020). Previous studies had proved that abnormal function of VAN was implicated in several psychiatric disorders such as major depressive disorder and adolescent depression (Liu et al, 2019;Ayyash et al, 2021). The activation of VAN was triggered in response to violation of expectation, thereby reorienting to environmental stimuli, driving learning, reward, or affective mechanisms (Corbetta et al, 2008).…”

Section: Discussionmentioning

confidence: 99%

Altered Neural Network Connectivity Predicts Depression in de novo Parkinson’s Disease

Chen

Wang

et al. 2022

Front. Neurosci.

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BackgroundDepression, one of the most frequent non-motor symptoms in Parkinson’s disease (PD), was proposed to be related to neural network dysfunction in advanced PD patients. However, the underlying mechanisms in the early stage remain unclear. The study was aimed to explore the alterations of large-scale neural networks in de novo PD patients with depression.MethodsWe performed independent component analysis (ICA) on the data of resting-state functional magnetic resonance imaging from 21 de novo PD patients with depression (dPD), 34 de novo PD patients without depression (ndPD), and 43 healthy controls (HCs) to extract functional networks. Intranetwork and internetwork connectivity was calculated for comparison between groups, correlation analysis, and predicting the occurrence of depression in PD.ResultsWe observed an ordered decrease of connectivity among groups within the ventral attention network (VAN) (dPD < ndPD < HCs), mainly located in the left middle temporal cortex. Besides, dPD patients exhibited hypoconnectivity between the auditory network (AUD) and default mode network (DMN) or VAN compared to ndPD patients or healthy controls. Correlation analysis revealed that depression severity was negatively correlated with connectivity value within VAN and positively correlated with the connectivity value of AUD-VAN in dPD patients, respectively. Further analysis showed that the area under the curve (AUC) for dPD prediction was 0.863 when combining the intranetwork connectivity in VAN and internetwork connectivity in AUD-DMN and AUD-VAN.ConclusionOur results demonstrated that early dPD may be associated with abnormality of attention bias and especially auditory attention processing. Altered neural network connectivity is expected to be a potential neuroimaging biomarker to predict depression in PD.

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Section: Discussionmentioning

confidence: 99%

Altered Neural Network Connectivity Predicts Depression in de novo Parkinson’s Disease

Chen

Wang

et al. 2022

Front. Neurosci.

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“…The goal was to evaluate whether network-level differences can be detected (1) between REM and NREM groups at baseline, (2) between REM and NREM groups at week-8, and (3) within the REM group between baseline and week-8. We applied a FATCAT-awFC pipeline, developed in our previous work ( Ayyash et al, 2021 ), that involves the combination of the Functional and Tractographic Connectivity Analysis Toolbox ( FATCAT ) ( Taylor and Saad, 2013 ) with a computationally intense method, known as the Anatomically-Weighted Functional Connectivity ( awFC ) method ( Bowman et al, 2012 ). Our previous work ( Ayyash et al, 2021 ), identified that combining metrics from different modalities in a multiplicative manner, provides robust findings whereby the cost associated with the weaker modality is reduced and significant patterns are highlighted.…”

Section: Introductionmentioning

confidence: 99%

Assessing remission in major depressive disorder using a functional-structural data fusion pipeline: A CAN-BIND-1 study

Ayyash,

Davis,

Alders

et al. 2024

IBRO Neuroscience Reports

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“…Given that the macroscopic connectome is a blend of structural and functional connectivity, combining structural and functional connection in a data fusion approach may provide a better representation of the complex human connectome. While the FATCAT-awFC pipeline was first introduced in Ayyash et al (2021) , here we explore the utility in applying a data fusion approach to study brain changes associated with development. This article extends our previous work by applying the method to study brain connectivity within RSNs in a sample of children aged 7–9 years old.…”

Section: Introductionmentioning

confidence: 99%

Examining resting-state network connectivity in children exposed to perinatal maternal adversity using anatomically weighted functional connectivity (awFC) analyses; A preliminary report

Ayyash

Sunderji²,

Gallant³

et al. 2023

Front. Neurosci.

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IntroductionEnvironmental perturbations during critical periods can have pervasive, organizational effects on neurodevelopment. To date, the literature examining the long-term impact of early life adversity has largely investigated structural and functional imaging data outcomes independently. However, emerging research points to a relationship between functional connectivity and the brain’s underlying structural architecture. For instance, functional connectivity can be mediated by the presence of direct or indirect anatomical pathways. Such evidence warrants the use of structural and functional imaging in tandem to study network maturation. Accordingly, this study examines the impact of poor maternal mental health and socioeconomic context during the perinatal period on network connectivity in middle childhood using an anatomically weighted functional connectivity (awFC) approach. awFC is a statistical model that identifies neural networks by incorporating information from both structural and functional imaging data.MethodsResting-state fMRI and DTI scans were acquired from children aged 7–9 years old.ResultsOur results indicate that maternal adversity during the perinatal period can affect offspring’s resting-state network connectivity during middle childhood. Specifically, in comparison to controls, children of mothers who had poor perinatal maternal mental health and/or low socioeconomic status exhibited greater awFC in the ventral attention network.DiscussionThese group differences were discussed in terms of the role this network plays in attention processing and maturational changes that may accompany the consolidation of a more adult-like functional cortical organization. Furthermore, our results suggest that there is value in using an awFC approach as it may be more sensitive in highlighting connectivity differences in developmental networks associated with higher-order cognitive and emotional processing, as compared to stand-alone FC or SC analyses.

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

Cited by 8 publications

References 89 publications

Altered Neural Network Connectivity Predicts Depression in de novo Parkinson’s Disease

Altered Neural Network Connectivity Predicts Depression in de novo Parkinson’s Disease

Assessing remission in major depressive disorder using a functional-structural data fusion pipeline: A CAN-BIND-1 study

Examining resting-state network connectivity in children exposed to perinatal maternal adversity using anatomically weighted functional connectivity (awFC) analyses; A preliminary report

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