Reduced Default Mode Network Functional Connectivity in Patients with Recurrent Major Depressive Disorder

Chen, Yan; Chen, Xiao; Li, Le; Castellanos, F. Xavier; Bai, Tongjian; Bo, Qijing; Chen, Guanmao; Chen, Ning-Xuan; Chen, Wei; Cheng, Chang; Cheng, Yuqi; Cui, Xilong; Duan, Jia; Fang, Yiru; Gong, Qiyong; Guo, Wenbin; Hou, Zhenghua; Hu, Lan; Kuang, Li; Feng, Li; Li, Kaiming; Li, Tao; Liu, Zhening; Long, Yicheng; Luo, Qiang; Meng, Huaqing; Peng, Daihui; Qiu, Hu; Qiu, Jiang; Shen, Yongjia; Shi, Yushu; Wang, Chuanyue; Wang, Fei; Wang, Kai; Wang, Li; Wang, Xiang; Wang, Ying; Wu, Xiaoping; Wu, Xinran; Xie, Chunming; Xie, Gary; Xie, Hui; Xie, Peng; Xu, Xiufeng; Yang, Hong; Yang, Jian; Yao, Jiashu; Yao, Shuqiao; Yin, Yingying; Yuan, Yonggui; Zhang, Aixia; Zhang, Hong; Zhang, Kerang; Zhang, Lei; Zhang, Zhijun; Zhou, Rubai; Zhou, Yongzhao; Zhu, Jun‐Juan; Zou, Chao‐Jie; Si, Tianmei; Zuo, Xi‐Nian; Zhao, Jingping; Zang, Yu‐Feng

doi:10.1101/321745

Cited by 23 publications

(35 citation statements)

References 41 publications

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“…A majority of previous studies have reported increased FC in the DMN related to MDD pathophysiology. In contrast, a recently published large-scale multicenter rs-fMRI study (the RESTmeta-MDD project) found that the FC was decreased within the DMN in recurrent MDD, but not in FEDN (Yan et al, 2019). In contrast, a recently published large-scale multicenter rs-fMRI study (the RESTmeta-MDD project) found that the FC was decreased within the DMN in recurrent MDD, but not in FEDN (Yan et al, 2019).…”

Section: Altered Ec Within the Dmnmentioning

confidence: 82%

“…Converging neuroimaging studies based on blood-oxygen-level-dependent functional magnetic resonance imaging (fMRI) have revealed disrupted functional connectivity (FC) in the resting-state brain networks including the default mode network (DMN), executive control network (EXE), and salience network (SAL; Brakowski et al, 2017;Dutta, McKie, & Deakin, 2014;Menon, 2011;Mulders, van Eijndhoven, Schene, Beckmann, & Tendolkar, 2015;Zheng et al, 2015). It is now generally accepted that MDD can be characterized as a disorder with dysfunctional connections among various brain regions and networks (Drysdale et al, 2017;Mulders et al, 2015;Yan et al, 2019). In addition to these "core" triple networks, altered interactions have also been observed in other FC links such as those in the frontolimbic networks Pezawas et al, 2005;Zhong, Pu, & Yao, 2016).…”

Section: Introductionmentioning

confidence: 99%

“…, and the FC within the EXE (dorsolateral prefrontal cortex) and between the LIM and DMN (amygdala-posterior cingulate cortex) increased in MDD adolescents(Peters, Burkhouse, Feldhaus, Langenecker, & Jacobs, 2016).The main difference is that we found reduced (excitatory) EC within the DMN and between the DMN and other networks, while most FC studies reported increased connectivity in these networks, as discussed above. For instance, a recent largescale multicenter rs-fMRI study revealed decreased FC within the DMN in recurrent MDD(Yan et al, 2019) Connolly et al (2017). For instance, a recent largescale multicenter rs-fMRI study revealed decreased FC within the DMN in recurrent MDD(Yan et al, 2019) Connolly et al (2017).…”

mentioning

confidence: 99%

“…In addition to these "core" triple networks, altered interactions have also been observed in other FC links such as those in the frontolimbic networks Pezawas et al, 2005;Zhong, Pu, & Yao, 2016). It is now generally accepted that MDD can be characterized as a disorder with dysfunctional connections among various brain regions and networks (Drysdale et al, 2017;Mulders et al, 2015;Yan et al, 2019).…”

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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 Dmnmentioning

confidence: 82%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

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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“…A meta‐analysis of diffusion tensor imaging (DTI) studies including 641 patients and 581 healthy controls reported fractional anisotropy (FA) reductions in the genu of the corpus callosum and the anterior limb of the internal capsule (Chen et al, ), implicating interhemispheric and frontal–striatal–thalamic connections among the neuronal correlates of depression. Supporting the relevance of brain connectivity in mood disorders, resting‐state fMRI studies have reported aberrant connectivity within the default mode network (DMN) in patients with depression compared to healthy controls (Kaiser, Andrews‐Hanna, Wager, & Pizzagalli, ; Mulders, van Eijndhoven, Schene, Beckmann, & Tendolkar, ; Yan et al, ).…”

Section: Introductionmentioning

confidence: 99%

Multimodal fusion of structural and functional brain imaging in depression using linked independent component analysis

Maglanoc

Kaufmann

Jonassen

et al. 2019

Human Brain Mapping

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Previous structural and functional neuroimaging studies have implicated distributed brain regions and networks in depression. However, there are no robust imaging biomarkers that are specific to depression, which may be due to clinical heterogeneity and neurobiological complexity. A dimensional approach and fusion of imaging modalities may yield a more coherent view of the neuronal correlates of depression. We used linked independent component analysis to fuse cortical macrostructure (thickness, area, gray matter density), white matter diffusion properties and resting‐state functional magnetic resonance imaging default mode network amplitude in patients with a history of depression (n = 170) and controls (n = 71). We used univariate and machine learning approaches to assess the relationship between age, sex, case–control status, and symptom loads for depression and anxiety with the resulting brain components. Univariate analyses revealed strong associations between age and sex with mainly global but also regional specific brain components, with varying degrees of multimodal involvement. In contrast, there were no significant associations with case–control status, nor symptom loads for depression and anxiety with the brain components, nor any interaction effects with age and sex. Machine learning revealed low model performance for classifying patients from controls and predicting symptom loads for depression and anxiety, but high age prediction accuracy. Multimodal fusion of brain imaging data alone may not be sufficient for dissecting the clinical and neurobiological heterogeneity of depression. Precise clinical stratification and methods for brain phenotyping at the individual level based on large training samples may be needed to parse the neuroanatomy of depression.

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Decoupling of Gray and White Matter Functional Networks in Medication‐Naïve Patients With Major Depressive Disorder

Zhao

Zhang

et al. 2020

Magnetic Resonance Imaging

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Background Major depressive disorder (MDD) has been increasingly conceptualized as a disconnection syndrome. However, most studies have only focused on functional connectivity (FC) alterations in gray matter (GM), and the functional alterations in white matter (WM) remain largely unknown in MDD. Purpose To investigate WM functional alterations and the functional interaction between GM and WM networks in medication‐naïve MDD. Study Type Prospective. Subjects Sixty‐eight patients with MDD and 66 age‐ and sex‐matched healthy controls (HCs). Field Strength/Sequence Resting state‐functional MRI (fMRI) using a gradient‐echo imaging sequence and T1‐weighted images were acquired at 3.0T. Assessment Functional GM and WM networks, based on resting‐state blood oxygenation level‐dependent (BOLD) signals, were identified by the K‐means clustering algorithm, and FC matrices were obtained for each subject. Statistical Tests Two‐sample t‐tests, Pearson chi‐square test, and Pearson correlation analysis. Results Both the GM and WM of the visual network (GM1 and WM11) showed reduced FC with the sensorimotor network (WM5 and GM8), lateral temporal network (GM5 and WM6), cingulo‐opercular network (GM9), and dorsal attention network (GM7) in MDD patients compared to controls (P < 0.05, false discovery rate [FDR]‐corrected). Reduced FC between the anterior cingulum network (WM3) and the lateral temporal network (GM5 and WM6) and temporal pole network (GM13) and between GM13 and the medial temporal network (GM4) and medial prefrontal‐subcortical network (GM10) were also observed in MDD patients (P < 0.05, FDR‐corrected). In addition, the WM BOLD signal in the sensorimotor network was negatively correlated with illness duration (r = –0.286, P = 0.018). Data Conclusion Disconnectivity between the GM and WM networks in the perception‐motor system may be the foundation of extensively disrupted connections in MDD. Furthermore, the observed decoupling between subsystems of the default mode network may help explain previous findings of persistent negative rumination and theory of mind deficits in depression. Level of Evidence 3. Technical Efficacy Stage 3.

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Reduced Default Mode Network Functional Connectivity in Patients with Recurrent Major Depressive Disorder

Abstract: ABSTRACT

Cited by 23 publications

References 41 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

Multimodal fusion of structural and functional brain imaging in depression using linked independent component analysis

Decoupling of Gray and White Matter Functional Networks in Medication‐Naïve Patients With Major Depressive Disorder

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