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Related Experiment Video

Updated: Jun 18, 2026

Dynamic Inter-subject Functional Connectivity Reveals Moment-to-Moment Brain Network Configurations Driven by Continuous or Communication Paradigms
08:36

Dynamic Inter-subject Functional Connectivity Reveals Moment-to-Moment Brain Network Configurations Driven by Continuous or Communication Paradigms

Published on: March 21, 2019

[Study of effective connectivity based on dynamic causal modeling in subtraction calculation task].

Yan Zhang1, Chunxiao Chen, Guangming Lu

  • 1Department of Medical Imaging, Nanjing General Hospital of Nanjing Military Command, Nanjing 210002, China.

Sheng Wu Yi Xue Gong Cheng Xue Za Zhi = Journal of Biomedical Engineering = Shengwu Yixue Gongchengxue Zazhi
|December 2, 2009
PubMed
Summary

Dynamic causal modeling (DCM) reveals effective connectivity in the brain using fMRI data. This study used DCM and Bayesian estimation to identify key brain regions involved in mental calculation tasks.

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Related Experiment Videos

Last Updated: Jun 18, 2026

Dynamic Inter-subject Functional Connectivity Reveals Moment-to-Moment Brain Network Configurations Driven by Continuous or Communication Paradigms
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Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms
08:51

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Published on: November 1, 2019

Area of Science:

  • Neuroscience
  • Cognitive Neuroscience
  • Computational Neuroscience

Context:

  • Functional magnetic resonance imaging (fMRI) measures brain activity.
  • Effective connectivity analysis is crucial for understanding brain function.
  • Dynamic Causal Modeling (DCM) offers a framework for inferring directed interactions between brain regions.

Purpose:

  • To apply Dynamic Causal Modeling (DCM) to fMRI data for analyzing effective connectivity.
  • To investigate the causal interactions between brain regions during a mental calculation task.
  • To utilize Bayesian estimation and Bayes factors for model selection and validation.

Summary:

  • fMRI time series data from activated regions were analyzed using DCM.
  • Trial-bound inputs served as perturbations to a spatio-temporal network model.
  • Bayesian estimation evaluated intrinsic connectivity, with Bayes factors selecting the optimal neuro-physiological model.
  • The study identified the left superior parietal lobule (SPL), left inferior parietal lobule (IPL), and left middle frontal gyrus (MFG) as key regions.

Impact:

  • Successfully obtained a physiologically significant connected network model.
  • Demonstrates the utility of DCM in elucidating brain network dynamics.
  • Provides insights into the neural underpinnings of mental calculation.