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; Cao, Jian; 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; Li, Feng; Li, Kaiming; Li, Tao; Liu, Yansong; 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:10.1073/pnas.1900390116

Cited by 568 publications

(459 citation statements)

References 40 publications

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“…All the images were preprocessed via a MATLAB toolbox, Data Processing Assistant for RS-fMRI (DPARSF) (Yan and Zang, 2010) which was based on Statistical Parametric Mapping (SPM8) 2 and RS-fMRI Data Processing Toolkit (REST) (Song et al, 2011). The first 10 time points (20 s) were discarded because the subject took time to adapt to the scanning noise and additionally for the scanner to calibrate (Zang et al, 2004;Wang et al, 2017;Yan et al, 2019). Given that there were only 170 time points in NYU data, we retained 170 time points for PKU1, PKU2, and PKU3.…”

Section: Data Preprocessingmentioning

confidence: 99%

Higher Sensitivity and Reproducibility of Wavelet-Based Amplitude of Resting-State fMRI

et al. 2020

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Section: Data Preprocessingmentioning

confidence: 99%

Higher Sensitivity and Reproducibility of Wavelet-Based Amplitude of Resting-State fMRI

et al. 2020

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“…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, 2015;Mulders, van Eijndhoven, Schene, Beckmann, & Tendolkar, 2015;Yan et al, 2019).…”

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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“…Whereas these previous studies provided informative regional evidence, our results provided an overall network-wise interaction pattern. Critically, our primary finding adds to the recent challenge to the prevailing notion that rumination causes the abnormally enhanced within DMN-FCs in MDD patients (39), indicating rumination's DMN neural underpinnings are more nuanced than previous hypothesis have predicted.…”

Section: Discussionmentioning

confidence: 53%

The Subsystem Mechanism of Default Mode Network Underlying Rumination: a Reproducible Neuroimaging Study

Chen

Shen

et al. 2019

Preprint

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Rumination is a specific form of self-generated thoughts and posited to be the psychological expression of abnormalities in the default mode network (DMN) in patients with major depressive disorder (MDD). Although converging lines of evidence link the neural basis of MDD and the DMN, the mechanisms through which DMN regions cooperatively underlie rumination remain largely unclear. Here, with a modified continuous state-like paradigm, we induce healthy participants to ruminate or imagine objective scenarios (as a distraction control condition) on 3 different MRI scanners. We compared functional connectivities (FC) and inter-subject correlations (ISC) of the DMN and its 3 functionally-defined subsystems between rumination and distraction states. Results yielded a highly reproducible and dissociated pattern. During rumination, within-DMN FC was generally decreased compared to the distraction state. At the subsystem level, we found increased FC between the core and medial temporal lobe (MTL) subsystem and decreased FC between the core and dorsal medial prefrontal cortex (DMPFC) subsystem and within the MTL subsystem. Furthermore, we found decreased ISC within the MTL subsystem. These results suggest that DMN regions interacted with each other differently and are activated less synchronously across subjects during rumination and shed new light on the association between rumination and MDD patients' DMN abnormalities. KEYWORDS: functional MRI, major depressive disorder, self-generated thought 3 SIGNIFICANCE STATEMENTRumination is widely accepted as the psychological expression of default mode network (DMN) alterations in patients with major depressive disorder (MDD), but our understanding of the DMN's mechanistic involvement remains rudimentary. Here, with a repeated measure continuous paradigm, we systematically examined how DMN subsystems underlie rumination. We found distinct patterns of functional connectivity within subject and less synchronized neural activities across subjects in DMN during rumination. These findings can help illuminate the association between rumination and DMN alterations in MDD. Confidence in our hypothesis-driven results is enhanced by their high reproducibility across three different scanners.

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Reduced default mode network functional connectivity in patients with recurrent major depressive disorder

Cited by 568 publications

References 40 publications

Higher Sensitivity and Reproducibility of Wavelet-Based Amplitude of Resting-State fMRI

Higher Sensitivity and Reproducibility of Wavelet-Based Amplitude of Resting-State fMRI

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

The Subsystem Mechanism of Default Mode Network Underlying Rumination: a Reproducible Neuroimaging Study

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