Functional connectivity between salience, default mode and frontoparietal networks in post-stroke depression

Балаев, В.В.; Orlov, Ivan; Petrushevsky, A. G.; Martynova, Olga

doi:10.1016/j.jad.2017.11.044

Cited by 30 publications

(19 citation statements)

References 71 publications

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“…For example, the FC within the DMN was found to be significantly increased in the depressive subjects Ho et al, 2015). Also, it has been shown that the FC between anterior DMN and SAL positively correlated with depression severity in poststroke depression (Balaev, Orlov, Petrushevsky, & Martynova, 2017) Deceased FC between the SAL (insula) and frontolimbic networks have also been reported .…”

Section: Relevance To Other Network Measuresmentioning

confidence: 92%

“…Our major conclusion that MDD alters EC both within and between multiple functional networks is consistent with existing FC literature. Also, it has been shown that the FC between anterior DMN and SAL positively correlated with depression severity in poststroke depression (Balaev, Orlov, Petrushevsky, & Martynova, 2017) Deceased FC between the SAL (insula) and frontolimbic networks have also been reported . Also, it has been shown that the FC between anterior DMN and SAL positively correlated with depression severity in poststroke depression (Balaev, Orlov, Petrushevsky, & Martynova, 2017) Deceased FC between the SAL (insula) and frontolimbic networks have also been reported .…”

Section: Relevance To Other Network Measuresmentioning

confidence: 93%

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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: Relevance To Other Network Measuresmentioning

confidence: 92%

Section: Relevance To Other Network Measuresmentioning

confidence: 93%

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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“…Despite suggestions that resting-state methods may be a better approach to examine changes post stroke [71, 72], they have not been widely employed in PSD research. Most studies that have used functional connectivity have focused on specific regions of interest to investigate connectivity changes, e.g., from a default mode network and anterior cingulate [40–43, 73]. These studies showed inconsistent results, potentially due to differing times post stroke, methods used, and possible inclusion of artefact, a common issue with correlation-based methods [58] if not adequately controlled for.…”

Section: Discussionmentioning

confidence: 99%

“…Vicentini et al [41] found an association with the posterior cingulate cortex and depression score at approximately 1-month poststroke, while Liu et al [42] failed to find any regional correlations of the posterior cingulate with a depression score in a cohort of chronic stroke survivors. More recently, Balaev et al [43] explored changes in the default mode network and found changes post treatment. Only one study, by Egorova et al [44], used voxelwise spectral analysis of fALFF and found mean differences between depressed and nondepressed stroke survivors in the frontal and insular regions.…”

Section: Introductionmentioning

confidence: 99%

Correlated Resting-State Functional MRI Activity of Frontostriatal, Thalamic, Temporal, and Cerebellar Brain Regions Differentiates Stroke Survivors with High Compared to Low Depressive Symptom Scores

et al. 2019

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Background. One in three survivors of stroke experience poststroke depression (PSD). PSD has been linked with poorer recovery of function and cognition, yet our understanding of potential mechanisms is currently limited. Alterations in resting-state functional MRI have been investigated to a limited extent. Fluctuations in low frequency signal are reported, but it is unknown if interactions are present between the level of depressive symptom score and intrinsic brain activity in varying brain regions. Objective. To investigate potential interaction effects between whole-brain resting-state activity and depressive symptoms in stroke survivors with low and high levels of depressive symptoms. Methods. A cross-sectional analysis of 63 stroke survivors who were assessed at 3 months poststroke for depression, using the Montgomery–Åsberg Depression Rating Scale (MÅDRS-SIGMA), and for brain activity using fMRI. A MÅDRS-SIGMA score of >8 was classified as high depressive symptoms. Fractional amplitude of frequency fluctuations (fALFF) data across three frequency bands (broadband, i.e., ~0.01–0.08; subbands, i.e., slow-5: ~0.01–0.027 Hz, slow-4: 0.027–0.07) was examined. Results. Of the 63 stroke survivors, 38 were classified as “low-depressive symptoms” and 25 as “high depressive symptoms.” Six had a past history of depression. We found interaction effects across frequency bands in several brain regions that differentiated the two groups. The broadband analysis revealed interaction effects in the left insula and the left superior temporal lobe. The subband analysis showed contrasting fALFF response between the two groups in the left thalamus, right caudate, and left cerebellum. Across the three frequency bands, we found contrasting fALFF response in areas within the fronto-limbic-thalamic network and cerebellum. Conclusions. We provide evidence that fALFF is sensitive to changes in poststroke depressive symptom severity and implicates frontostriatal and cerebellar regions, consistent with previous studies. The use of multiband analysis could be an effective method to examine neural correlates of depression after stroke. The START-PrePARE trial is registered with the Australian New Zealand Clinical Trial Registry, number ACTRN12610000987066.

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“…Manoliu et al (2014) found that MDD patients showed increased FC between DMN and SAL, consistent with greater resting-state FC between insula and anterior DMN (Avery et al, 2014). It has also been observed that FC between anterior DMN and SAL was positively correlated with depression severity in post-stroke depression (Balaev et al, 2017). Using dynamic causal modeling of resting-state fMRI (rs-fMRI), we recently demonstrated that MDD was mainly associated with reduced effective connectivity (EC) within the DMN, and between the DMN and SAL networks (Li et al, 2020).…”

Section: Introductionmentioning

confidence: 93%

Multiscale Neural Modeling of Resting-state fMRI Reveals Executive-Limbic Malfunction as a Core Mechanism in Major Depressive Disorder

Liu

Zheng

et al. 2020

Preprint

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Computational neuroimaging has played a central role in characterizing functional abnormalities in major depressive disorder (MDD). However, most of existing non-invasive analysis tools based on functional magnetic resonance imaging (fMRI) are largely descriptive and superficial, thus cannot offer a deep mechanistic understanding of neural circuit dysfunction in MDD. To overcome this limitation, we significantly improved a previously developed Multiscale Neural Model Inversion (MNMI) framework that can link mesoscopic neural interaction with macroscale network dynamics and enable the estimation of both intra-regional and inter-regional effective connectivity. We applied the improved MNMI approach to test two well-established competing hypotheses of MDD pathophysiology (default mode-salience network disruption vs. executive-limbic network malfunction) based on resting-state fMRI with a relatively large sample size. Results indicate that MDD is primarily characterized by aberrant circuit interactions within the executive-limbic network, rather than the default mode-salience network. Specifically, we observed reduced frontoparietal effective connectivity that potentially contributes to hypoactivity in the dorsolateral prefrontal cortex (dlPFC), and decreased intrinsic inhibition combined with increased excitation from the superior parietal cortex (SPC) that potentially leads to amygdala hyperactivity, together resulting in connectivity imbalance in the PFC-amygdala circuit that pervades in MDD. Moreover, the model unravels reduced PFC-to-hippocampus excitation but decreased SPC-to-thalamus inhibition in MDD population that potentially leads to hypoactivity in hippocampus and hyperactivity in thalamus, consistent with previous experimental data. Overall, our findings provide strong support for the long-standing limbic-cortical dysregulation model in major depression but also offer novel insights into the multi-scale pathophysiology of this deliberating disease.

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Functional connectivity between salience, default mode and frontoparietal networks in post-stroke depression

Cited by 30 publications

References 71 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

Correlated Resting-State Functional MRI Activity of Frontostriatal, Thalamic, Temporal, and Cerebellar Brain Regions Differentiates Stroke Survivors with High Compared to Low Depressive Symptom Scores

Multiscale Neural Modeling of Resting-state fMRI Reveals Executive-Limbic Malfunction as a Core Mechanism in Major Depressive Disorder

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