Abnormal resting state effective connectivity within the default mode network in major depressive disorder: A spectral dynamic causal modeling study

Li, Liang; Li, Baojuan; Bai, Yuanhan; Liu, Wenlei; Wang, Huaning; Leung, Hoi‐Chung; Tian, Ping; Zhang, Linchuan; Guo, Fan; Cui, Long‐Biao; Yin, Hong; Lu, Hongbing; Tan, Qingrong

doi:10.1002/brb3.732

Cited by 40 publications

(33 citation statements)

References 40 publications

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“…the DMN was also reduced in FEDN, consistent with a recent rs-fMRI study that reported reduced EC from the left parietal cortex to other DMN regions in MDD patients who were antidepressant drug-free for at least 3 months (Li et al, 2017). In addition, we observed the EC between the cerebellum and other DMN regions substantially decreased in FEDN, in line with previous studies (Guo et al, 2013;Liu et al, 2012) …”

Section: Altered Ec Within the Dmnsupporting

confidence: 92%

“…The majority of DCM studies on MDD have used task-based fMRI yet there are still a handful of reports with rs-fMRI (Hyett, Breakspear, Friston, Guo, & Parker, 2015;Kandilarova, Stoyanov, Kostianev, & Specht, 2018;Li et al, 2017). Hyett et al (2015) used stochastic DCM to infer spontaneous interactions among five canonical resting-state networks (DMN, EXE, bilateral anterior insula, and left and right frontoparietal attention networks) from 16 participants with melancholia, 16 with nonmelancholic depression, and 16 healthy individuals.…”

Section: Introductionmentioning

confidence: 99%

“…Hyett et al (2015) used stochastic DCM to infer spontaneous interactions among five canonical resting-state networks (DMN, EXE, bilateral anterior insula, and left and right frontoparietal attention networks) from 16 participants with melancholia, 16 with nonmelancholic depression, and 16 healthy individuals. Li et al (2017) examined resting-state EC among four regions of the DMN using spectral DCM with 27 MDD patients and 27 healthy controls. Li et al (2017) examined resting-state EC among four regions of the DMN using spectral DCM with 27 MDD patients and 27 healthy controls.…”

Section: Introductionmentioning

confidence: 99%

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Large‐scale dynamic causal modeling of major depressive disorder based on resting‐state functional magnetic resonance imaging

Liu

Zheng

et al. 2019

Human Brain Mapping

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Major depressive disorder (MDD) is a serious mental illness characterized by dysfunctional connectivity among distributed brain regions. Previous connectome studies based on functional magnetic resonance imaging (fMRI) have focused primarily on undirected functional connectivity and existing directed effective connectivity (EC) studies concerned mostly task-based fMRI and incorporated only a few brain regions. To overcome these limitations and understand whether MDD is mediated by within-network or between-network connectivities, we applied spectral dynamic causal modeling to estimate EC of a large-scale network with 27 regions of interests from four distributed functional brain networks (default mode, executive control, salience, and limbic networks), based on large sample-size resting-state fMRI consisting of 100 healthy subjects and 100 individuals with first-episode drug-naive MDD. We applied a newly developed parametric empirical Bayes (PEB) framework to test specific hypotheses. We showed that MDD altered EC both within and between high-order functional networks. Specifically, MDD is associated with reduced excitatory connectivity mainly within the default mode network (DMN), and between the default mode and salience networks. In addition, the network-averaged inhibitory EC within the DMN was found to be significantly elevated in the MDD. The coexistence of the reduced excitatory but increased inhibitory causal connections within the DMNs may underlie disrupted self-recognition and emotional control in MDD.Guoshi Li and Yujie Liu contributed equally to this study. Hum Brain Mapp. 2020;41:865-881. wileyonlinelibrary.com/journal/hbm 865 SUPPORTING INFORMATION Additional supporting information may be found online in the Supporting Information section at the end of this article. How to cite this article: Li G, Liu Y, Zheng Y, et al. Largescale dynamic causal modeling of major depressive disorder based on resting-state functional magnetic resonance imaging.

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Section: Altered Ec Within the Dmnsupporting

confidence: 92%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Large‐scale dynamic causal modeling of major depressive disorder based on resting‐state functional magnetic resonance imaging

Liu

Zheng

et al. 2019

Human Brain Mapping

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“…The PCC is considered one of the hubs of the DMN with a general role in attention modulation, and in episodic and working memory. Declined influences from left LP to PCC in MDD compared with HC group have been reported in a recent study (Li et al, 2017b ). This dysfunction of the connection may be related to the impaired signals propagate from one region to another in DMN.…”

Section: Discussionsupporting

confidence: 59%

Multivariate Classification of Major Depressive Disorder Using the Effective Connectivity and Functional Connectivity

Geng

Liu

et al. 2018

Front. Neurosci.

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Major depressive disorder (MDD) is a mental disorder characterized by at least 2 weeks of low mood, which is present across most situations. Diagnosis of MDD using rest-state functional magnetic resonance imaging (fMRI) data faces many challenges due to the high dimensionality, small samples, noisy and individual variability. To our best knowledge, no studies aim at classification with effective connectivity and functional connectivity measures between MDD patients and healthy controls. In this study, we performed a data-driving classification analysis using the whole brain connectivity measures which included the functional connectivity from two brain templates and effective connectivity measures created by the default mode network (DMN), dorsal attention network (DAN), frontal-parietal network (FPN), and silence network (SN). Effective connectivity measures were extracted using spectral Dynamic Causal Modeling (spDCM) and transformed into a vectorial feature space. Linear Support Vector Machine (linear SVM), non-linear SVM, k-Nearest Neighbor (KNN), and Logistic Regression (LR) were used as the classifiers to identify the differences between MDD patients and healthy controls. Our results showed that the highest accuracy achieved 91.67% (p < 0.0001) when using 19 effective connections and 89.36% when using 6,650 functional connections. The functional connections with high discriminative power were mainly located within or across the whole brain resting-state networks while the discriminative effective connections located in several specific regions, such as posterior cingulate cortex (PCC), ventromedial prefrontal cortex (vmPFC), dorsal cingulate cortex (dACC), and inferior parietal lobes (IPL). To further compare the discriminative power of functional connections and effective connections, a classification analysis only using the functional connections from those four networks was conducted and the highest accuracy achieved 78.33% (p < 0.0001). Our study demonstrated that the effective connectivity measures might play a more important role than functional connectivity in exploring the alterations between patients and health controls and afford a better mechanistic interpretability. Moreover, our results showed a diagnostic potential of the effective connectivity for the diagnosis of MDD patients with high accuracies allowing for earlier prevention or intervention.

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“…On the other hand, the majority of the effective connectivity studies in depressed patients have focused on task-related fMRI probing cognitive and emotional processing ( 21 – 23 ) with only a few reports on resting state fMRI ( 24 , 25 ). By means of spectral DCM, Li et al ( 24 ) investigated the DMN in a sample of healthy controls and depressed patients before and after treatment.…”

Section: Introductionmentioning

confidence: 99%

Altered Resting State Effective Connectivity of Anterior Insula in Depression

et al. 2018

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Depression has been associated with changes in both functional and effective connectivity of large scale brain networks, including the default mode network, executive network, and salience network. However, studies of effective connectivity by means of spectral dynamic causal modeling (spDCM) are still rare and the interaction between the different resting state networks has not been investigated in detail. Thus, we aimed at exploring differences in effective connectivity among eight right hemisphere brain areas—anterior insula, inferior frontal gyrus, middle frontal gyrus (MFG), frontal eye field, anterior cingulate cortex, superior parietal lobe, amygdala, and hippocampus, between a group of healthy controls (N = 20) and medicated depressed patients (N = 20). We found that patients not only had significantly reduced strength of the connection from the anterior insula to the MFG (i.e., dorsolateral prefrontal cortex) but also a significant connection between the amygdala and the anterior insula. Moreover, depression severity correlated with connectivity of the hippocampal node. In conclusion, the results from this resting state spDCM study support and enrich previous data on the role of the right anterior insula in the pathophysiology of depression. Furthermore, our findings add to the growing evidence of an association between depression severity and disturbances of the hippocampal function in terms of impaired connectivity with other brain regions.

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Abnormal resting state effective connectivity within the default mode network in major depressive disorder: A spectral dynamic causal modeling study

Cited by 40 publications

References 40 publications

Large‐scale dynamic causal modeling of major depressive disorder based on resting‐state functional magnetic resonance imaging

Large‐scale dynamic causal modeling of major depressive disorder based on resting‐state functional magnetic resonance imaging

Multivariate Classification of Major Depressive Disorder Using the Effective Connectivity and Functional Connectivity

Altered Resting State Effective Connectivity of Anterior Insula in Depression

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