Investigation of the emotional network in depression after stroke: A study of multivariate Granger causality analysis of fMRI data

Shi, Yu; Liu, Wei; Liu, Ruifen; Wu, Lei; Huang, Shimin; Cai, Guiyuan; Yang, Jian‐Ming; Wu, Wen

doi:10.1016/j.jad.2019.02.020

Cited by 22 publications

(40 citation statements)

References 73 publications

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“…These studies are model-driven methods that require considerable prior knowledge. In this study, we used Granger causality analysis (GCA), which is a data-driven EC calculation method and can be directly applied to the resting-state data ( Liu et al, 2018 ; Ning et al, 2018 ; Shi et al, 2019 ). It has been widely used for time-directed prediction between BOLD fMRI time series to measure the causal effects among brain regions ( Jiao et al, 2011 ).…”

Section: Introductionmentioning

confidence: 99%

Altered Effective Connectivity of Children and Young Adults With Unilateral Amblyopia: A Resting-State Functional Magnetic Resonance Imaging Study

Dai

Zhou

et al. 2021

Front. Neurosci.

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The altered functional connectivity (FC) in amblyopia has been investigated by many studies, but the specific causality of brain connectivity needs to be explored further to understand the brain activity of amblyopia. We investigated whether the effective connectivity (EC) of children and young adults with amblyopia was altered. The subjects included 16 children and young adults with left eye amblyopia and 17 healthy controls (HCs). The abnormalities between the left/right primary visual cortex (PVC) and the other brain regions were investigated in a voxel-wise manner using the Granger causality analysis (GCA). According to the EC results in the HCs and the distribution of visual pathways, 12 regions of interest (ROIs) were selected to construct an EC network. The alteration of the EC network of the children and young adults with amblyopia was analyzed. In the voxel-wise manner analysis, amblyopia showed significantly decreased EC between the left/right of the PVC and the left middle frontal gyrus/left inferior frontal gyrus compared with the HCs. In the EC network analysis, compared with the HCs, amblyopia showed significantly decreased EC from the left calcarine fissure, posterior cingulate gyrus, left lingual gyrus, right lingual gyrus, and right fusiform gyrus to the right calcarine fissure. Amblyopia also showed significantly decreased EC from the right inferior frontal gyrus and right lingual gyrus to the left superior temporal gyrus compared with the HCs in the EC network analysis. The results may indicate that amblyopia altered the visual feedforward and feedback pathway, and amblyopia may have a greater relevance with the feedback pathway than the feedforward pathway. Amblyopia may also correlate with the feedforward of the third visual pathway.

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Section: Introductionmentioning

confidence: 99%

Altered Effective Connectivity of Children and Young Adults With Unilateral Amblyopia: A Resting-State Functional Magnetic Resonance Imaging Study

Dai

Zhou

et al. 2021

Front. Neurosci.

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“…As DCM and GCA could capture the directional connectivity information that cannot be identified by FC analysis (43,44,100,101), combining these three techniques could better predict the subject-specific responsiveness to rTMS treatment. Since GCA is advantageous to handle a model that constitutes large numbers of sources for regions, we used GCA to narrow down the model space for DCM (65).…”

Section: Functional Network Between Psychopathologically Mdd-relatedmentioning

confidence: 99%

Converging Resting State Networks Unravels Potential Remote Effects of Transcranial Magnetic Stimulation for Major Depression

et al. 2020

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Despite being a commonly used protocol to treat major depressive disorder (MDD), the underlying mechanism of repetitive transcranial magnetic stimulation (rTMS) on dorsolateral prefrontal cortex (DLPFC) remains unclear. In the current study, we investigated the resting-state fMRI data of 100 healthy subjects by exploring three overlapping functional networks associated with the psychopathologically MDD-related areas (the nucleus accumbens, amygdala, and ventromedial prefrontal cortex). Our results showed that these networks converged at the bilateral DLPFC, which suggested that rTMS over DLPFC might improve MDD by remotely modulating the MDD-related areas synergistically. Additionally, they functionally converged at the DMPFC and bilateral insula which are known to be associated with MDD. These two areas could also be potential targets for rTMS treatment. Dynamic causal modelling (DCM) and Granger causality analysis (GCA) revealed that all pairwise connections among bilateral DLPFC, DMPFC, bilateral insula, and three psychopathologically MDD-related areas contained significant causality. The DCM results also suggested that most of the functional interactions between MDD-related areas and bilateral DLPFC, DMPFC, and bilateral insula can predominantly be explained by the effective connectivity from the psychopathologically MDD-related areas to the rTMS stimulation sites. Finally, we found the conventional functional connectivity to be a more representative measure to obtain connectivity parameters compared to GCA and DCM analysis. Our research helped inspecting the convergence of the functional networks related to a psychiatry disorder. The results identified potential targets for brain stimulation treatment and contributed to the optimization of patient-specific brain stimulation protocols.

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“…The development of brain functional imaging technology has greatly improved research in psychology and neuroscience (Shi et al, 2019 ). Brain imaging technology is now widely used to study placebo analgesia and nocebo hyperalgesia (Atlas et al, 2012 ; Amanzio et al, 2013 ; Freeman et al, 2015 ; Wagner et al, 2020 ).…”

Section: Introductionmentioning

confidence: 99%

“…In-depth study of brain mechanisms requires exploration of direct connectivity (Hamilton et al, 2011 ; Shi et al, 2019 ). Granger causality analysis (GCA; Zhao et al, 2016 ), a resourceful approach for abstracting connectivity from time series data, has been widely used to assess dependencies and interrelationships between networks.…”

Section: Introductionmentioning

confidence: 99%

“…Granger causality analysis (GCA; Zhao et al, 2016 ), a resourceful approach for abstracting connectivity from time series data, has been widely used to assess dependencies and interrelationships between networks. This method leverages on estimated linear regression modeling and assumes that if past information on X predicts the future on Y, and is more accurate than considering past information on Y alone, then X is a “Granger cause” of Y (Granger, 1969 ), GCA provides the possibility to study directional associations between diverse brain regions, and can dynamically observe brain network changes (Hamilton et al, 2011 ; Liao et al, 2011 ; Zhao et al, 2016 ; Shi et al, 2019 ). Deshpande et al ( 2009 ) used multivariable GCA (mGCA) to analyze time-series fMRI data, and assess the dynamics of functional neural networks.…”

Section: Introductionmentioning

confidence: 99%

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Brain Network to Placebo and Nocebo Responses in Acute Experimental Lower Back Pain: A Multivariate Granger Causality Analysis of fMRI Data

Shi

Cui

Huang

et al. 2021

Front. Behav. Neurosci.

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Background and Objective: Placebo and nocebo responses are widely observed. Herein, we investigated the nocebo hyperalgesia and placebo analgesia responses in brain network in acute lower back pain (ALBP) model using multivariate Granger causality analysis (GCA). This approach analyses functional magnetic resonance imaging (fMRI) data for lagged-temporal correlation between different brain areas.Method: After completing the ALBP model, 20 healthy subjects were given two interventions, once during a placebo intervention and once during a nocebo intervention, pseudo-randomly ordered. fMRI scans were performed synchronously during each intervention, and visual analog scale (VAS) scores were collected at the end of each intervention. The fMRI data were then analyzed using multivariate GCA.Results: Our results found statistically significant differences in VAS scores from baseline (pain status) for both placebo and nocebo interventions, as well as between placebo and nocebo interventions. In placebo network, we found a negative lagged-temporal correlation between multiple brain areas, including the dorsolateral prefrontal cortex (DLPFC), secondary somatosensory cortex area, anterior cingulate cortex (ACC), and insular cortex (IC); and a positive lagged-temporal correlation between multiple brain areas, including IC, thalamus, ACC, as well as the supplementary motor area (SMA). In the nocebo network, we also found a positive lagged-temporal correlation between multiple brain areas, including the primary somatosensory cortex area, caudate, DLPFC and SMA.Conclusion: The results of this study suggest that both pain-related network and reward system are involved in placebo and nocebo responses. The placebo response mainly works by activating the reward system and inhibiting pain-related network, while the nocebo response is the opposite. Placebo network also involves the activation of opioid-mediated analgesia system (OMAS) and emotion pathway, while nocebo network involves the deactivation of emotional control. At the same time, through the construction of the GC network, we verified our hypothesis that nocebo and placebo networks share part of the same brain regions, but the two networks also have their own unique structural features.

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Investigation of the emotional network in depression after stroke: A study of multivariate Granger causality analysis of fMRI data

Cited by 22 publications

References 73 publications

Altered Effective Connectivity of Children and Young Adults With Unilateral Amblyopia: A Resting-State Functional Magnetic Resonance Imaging Study

Altered Effective Connectivity of Children and Young Adults With Unilateral Amblyopia: A Resting-State Functional Magnetic Resonance Imaging Study

Converging Resting State Networks Unravels Potential Remote Effects of Transcranial Magnetic Stimulation for Major Depression

Brain Network to Placebo and Nocebo Responses in Acute Experimental Lower Back Pain: A Multivariate Granger Causality Analysis of fMRI Data

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