Acupuncture Induces Reduction in Limbic-Cortical Feedback of a Neuralgia Rat Model: A Dynamic Causal Modeling Study

Background. Neuropathic pain after brachial plexus avulsion remained prevalent and intractable currently. However, the neuroimaging study about neural mechanisms or etiology was limited and blurred. Objective. This study is aimed at investigating the effect of electroacupuncture on effective connectivity and neural response in corticolimbic circuitries during implicit processing of nociceptive stimulus in rats with brachial plexus pain. Methods. An fMRI scan was performed in a total of 16 rats with brachial plexus pain, which was equally distributed into the model group and the electroacupuncture group. The analysis of task-dependent data determined pain-related activation in each group. Based on those results, several regions including AMY, S1, and h were recruited as ROI in dynamic causal modeling (DCM) analysis comparing evidence for different neuronal hypotheses describing the propagation of noxious stimuli in regions of interest and horizontal comparison of effective connections between the model and electroacupuncture groups. Results. In both groups, DCM revealed that noxious stimuli were most likely driven by the somatosensory cortex, with bidirectional propagation with the hypothalamus and amygdala and the interactions in them. Also, the 3-month intervention of acupuncture reduced effective connections of h-S1 and AMY-S1. Conclusions. We showed an evidence that a full connection model within the brain network of brachial plexus pain and electroacupuncture intervention reduces effective connectivity from h and AMY to S1. Our study for the first time explored the relationship of involved brain regions with dynamic causal modeling. It provided novel evidence for the feature of the organization of the cortical-limbic network and the alteration caused by acupuncture.

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Abnormal cortico-limbic connectivity during emotional processing correlates with symptom severity in schizophrenia

European Psychiatry ◽

10.1016/j.eurpsy.2015.01.002 ◽

2015 ◽

Vol 30 (5) ◽

pp. 590-597 ◽

Cited By ~ 27

Author(s):

B. Vai ◽

G. Sferrazza Papa ◽

S. Poletti ◽

D. Radaelli ◽

E. Donnici ◽

...

Keyword(s):

Prefrontal Cortex ◽

Emotional Processing ◽

Structural Model ◽

Structural Integrity ◽

Effective Connectivity ◽

Brain Regions ◽

Brain Network ◽

Causal Modeling ◽

Implicit Processing ◽

Affective Stimuli

AbstractBackgroundImpaired emotional processing is a core feature of schizophrenia (SZ). Consistent findings suggested that abnormal emotional processing in SZ could be paralleled by a disrupted functional and structural integrity within the fronto-limbic circuitry. The effective connectivity of emotional circuitry in SZ has never been explored in terms of causal relationship between brain regions. We used functional magnetic resonance imaging and Dynamic Causal Modeling (DCM) to characterize effective connectivity during implicit processing of affective stimuli in SZ.MethodsWe performed DCM to model connectivity between amygdala (Amy), dorsolateral prefrontal cortex (DLPFC), ventral prefrontal cortex (VPFC), fusiform gyrus (FG) and visual cortex (VC) in 25 patients with SZ and 29 HC. Bayesian Model Selection and average were performed to determine the optimal structural model and its parameters.ResultsAnalyses revealed that patients with SZ are characterized by a significant reduced top-down endogenous connectivity from DLPFC to Amy, an increased connectivity from Amy to VPFC and a decreased driving input to Amy of affective stimuli compared to HC. Furthermore, DLPFC to Amy connection in patients significantly influenced the severity of psychopathology as rated on Positive and Negative Syndrome Scale.ConclusionsResults suggest a functional disconnection in brain network that contributes to the symptomatic outcome of the disorder. Our findings support the study of effective connectivity within cortico-limbic structures as a marker of severity and treatment efficacy in SZ.

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Tinnitus Abnormal Brain Region Detection Based on Dynamic Causal Modeling and Exponential Ranking

BioMed Research International ◽

10.1155/2018/8656975 ◽

2018 ◽

Vol 2018 ◽

pp. 1-10 ◽

Cited By ~ 2

Author(s):

Ming-Chuan Tsai ◽

Yue-Xin Cai ◽

Chang-Dong Wang ◽

Yi-Qing Zheng ◽

Jia-Ling Ou ◽

...

Keyword(s):

Brain Region ◽

Effective Connectivity ◽

Underlying Mechanism ◽

Brain Regions ◽

Generative Model ◽

Causal Modeling ◽

Dynamic Causal Modeling ◽

Region Detection ◽

Linear Relationships ◽

Abnormal Brain

Dynamic Causal Modeling (DCM) has been extended for the analysis of electroencephalography (EEG) based on a specific biophysical and neurobiological generative model for EEG. Comparing to methods that summarize neural activities with linear relationships, the generative model enables DCM to better describe how signals are generated and better reveal the underlying mechanism of the activities occurring in human brains. Since DCM provides us with an approach to the effective connectivity between brain areas, with exponential ranking, the abnormality of the observed signals can be further located to a specific brain region. In this paper, a combination of DCM and exponential ranking is proposed as a new method aiming at searching for the abnormal brain regions which are associated with chronic tinnitus.

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Evaluating Effective Connectivity of Trust in Human–Automation Interaction: A Dynamic Causal Modeling (DCM) Study

Human Factors The Journal of the Human Factors and Ergonomics Society ◽

10.1177/0018720820987443 ◽

2021 ◽

pp. 001872082098744

Author(s):

Jiali Huang ◽

Sanghyun Choo ◽

Zachary H. Pugh ◽

Chang S. Nam

Keyword(s):

Interface Design ◽

Bayesian Model Averaging ◽

Effective Connectivity ◽

Real Life ◽

Model Averaging ◽

Brain Regions ◽

Causal Modeling ◽

Computer Algorithms ◽

Dynamic Causal Modeling ◽

Lateral Connection

Objective Using dynamic causal modeling (DCM), we examined how credibility and reliability affected the way brain regions exert causal influence over each other—effective connectivity (EC)—in the context of trust in automation. Background Multiple brain regions of the central executive network (CEN) and default mode network (DMN) have been implicated in trust judgment. However, the neural correlates of trust judgment are still relatively unexplored in terms of the directed information flow between brain regions. Method Sixteen participants observed the performance of four computer algorithms, which differed in credibility and reliability, of the system monitoring subtask of the Air Force Multi-Attribute Task Battery (AF-MATB). Using six brain regions of the CEN and DMN commonly identified to be activated in human trust, a total of 30 (forward, backward, and lateral) connection models were developed. Bayesian model averaging (BMA) was used to quantify the connectivity strength among the brain regions. Results Relative to the high trust condition, low trust showed unique presence of specific connections, greater connectivity strengths from the prefrontal cortex, and greater network complexity. High trust condition showed no backward connections. Conclusion Results indicated that trust and distrust can be two distinctive neural processes in human–automation interaction—distrust being a more complex network than trust, possibly due to the increased cognitive load. Application The causal architecture of distributed brain regions inferred using DCM can help not only in the design of a balanced human–automation interface design but also in the proper use of automation in real-life situations.

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Dynamic Causal Modeling Self-Connectivity Findings in the Functional Magnetic Resonance Imaging Neuropsychiatric Literature

Frontiers in Neuroscience ◽

10.3389/fnins.2021.636273 ◽

2021 ◽

Vol 15 ◽

Author(s):

Andrew D. Snyder ◽

Liangsuo Ma ◽

Joel L. Steinberg ◽

Kyle Woisard ◽

Frederick G. Moeller

Keyword(s):

Magnetic Resonance Imaging ◽

Magnetic Resonance ◽

Functional Magnetic Resonance Imaging ◽

Functional Neuroimaging ◽

Effective Connectivity ◽

Brain Regions ◽

Causal Modeling ◽

Functional Magnetic Resonance ◽

Resonance Imaging ◽

Dynamic Causal Modeling

Dynamic causal modeling (DCM) is a method for analyzing functional magnetic resonance imaging (fMRI) and other functional neuroimaging data that provides information about directionality of connectivity between brain regions. A review of the neuropsychiatric fMRI DCM literature suggests that there may be a historical trend to under-report self-connectivity (within brain regions) compared to between brain region connectivity findings. These findings are an integral part of the neurologic model represented by DCM and serve an important neurobiological function in regulating excitatory and inhibitory activity between regions. We reviewed the literature on the topic as well as the past 13 years of available neuropsychiatric DCM literature to find an increasing (but still, perhaps, and inadequate) trend in reporting these results. The focus of this review is fMRI as the majority of published DCM studies utilized fMRI and the interpretation of the self-connectivity findings may vary across imaging methodologies. About 25% of articles published between 2007 and 2019 made any mention of self-connectivity findings. We recommend increased attention toward the inclusion and interpretation of self-connectivity findings in DCM analyses in the neuropsychiatric literature, particularly in forthcoming effective connectivity studies of substance use disorders.

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A rigorous approach for testing the constructionist hypotheses of brain function

Behavioral and Brain Sciences ◽

10.1017/s0140525x1100149x ◽

2012 ◽

Vol 35 (3) ◽

pp. 148-149 ◽

Cited By ~ 11

Author(s):

Gopikrishna Deshpande ◽

K. Sathian ◽

Xiaoping Hu ◽

Joseph A. Buckhalt

Keyword(s):

Granger Causality ◽

Strong Evidence ◽

Bayesian Modeling ◽

Brain Function ◽

Brain Regions ◽

Causal Modeling ◽

Dynamic Causal Modeling ◽

Rigorous Approach ◽

Target Article ◽

Co Activation

AbstractAlthough the target article provides strong evidence against the locationist view, evidence for the constructionist view is inconclusive, because co-activation of brain regions does not necessarily imply connectivity between them. We propose a rigorous approach wherein connectivity between co-activated regions is first modeled using exploratory Granger causality, and then confirmed using dynamic causal modeling or Bayesian modeling.

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Effective connectivity decreases in specific brain networks with postparalysis facial synkinesis: a dynamic causal modeling study

Brain Imaging and Behavior ◽

10.1007/s11682-021-00547-z ◽

2021 ◽

Author(s):

Zhen-Zhen Ma ◽

Ye-Chen Lu ◽

Jia-Jia Wu ◽

Xu-Yun Hua ◽

Si-Si Li ◽

...

Keyword(s):

Effective Connectivity ◽

Brain Networks ◽

Causal Modeling ◽

Dynamic Causal Modeling ◽

Facial Synkinesis ◽

Modeling Study

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Altered Prefrontal–Basal Ganglia Effective Connectivity in Patients With Poststroke Cognitive Impairment

Frontiers in Neurology ◽

10.3389/fneur.2020.577482 ◽

2020 ◽

Vol 11 ◽

Author(s):

Jing Zhang ◽

Zixiao Li ◽

Xingxing Cao ◽

Lijun Zuo ◽

Wei Wen ◽

...

Keyword(s):

Cognitive Impairment ◽

Basal Ganglia ◽

Cognitive Decline ◽

Effective Connectivity ◽

Network Connectivity ◽

Brain Regions ◽

Neuronal Model ◽

Causal Modeling ◽

Healthy Controls

We investigated the association between poststroke cognitive impairment and a specific effective network connectivity in the prefrontal–basal ganglia circuit. The resting-state effective connectivity of this circuit was modeled by employing spectral dynamic causal modeling in 11 poststroke patients with cognitive impairment (PSCI), 8 poststroke patients without cognitive impairment (non-PSCI) at baseline and 3-month follow-up, and 28 healthy controls. Our results showed that different neuronal models of effective connectivity in the prefrontal–basal ganglia circuit were observed among healthy controls, non-PSCI, and PSCI patients. Additional connected paths (extra paths) appeared in the neuronal models of stroke patients compared with healthy controls. Moreover, changes were detected in the extra paths of non-PSCI between baseline and 3-month follow-up poststroke, indicating reorganization in the ipsilesional hemisphere and suggesting potential compensatory changes in the contralesional hemisphere. Furthermore, the connectivity strengths of the extra paths from the contralesional ventral anterior nucleus of thalamus to caudate correlated significantly with cognitive scores in non-PSCI and PSCI patients. These suggest that the neuronal model of effective connectivity of the prefrontal–basal ganglia circuit may be sensitive to stroke-induced cognitive decline, and it could be a biomarker for poststroke cognitive impairment 3 months poststroke. Importantly, contralesional brain regions may play an important role in functional compensation of cognitive decline.

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