Loading...

Effective connectivity inference in the whole-brain network by using rDCM method for investigating the distinction between emotional states in fMRI data

Farahani, N ; Sharif University of Technology | 2022

34 Viewed
  1. Type of Document: Article
  2. DOI: 10.1080/21681163.2022.2077235
  3. Publisher: Taylor and Francis Ltd , 2022
  4. Abstract:
  5. In recent years, the regression dynamic causal modelling (rDCM) method was introduced as a new version of dynamic causal modelling (DCM) to derive effective connectivity in whole-brain networks for functional magnetic resonance imaging (fMRI) data. In this research, we used data obtained while applying the stimulation of audio movie comprised different emotional states. We applied this method to two networks consisting of ten auditory and forty-four regions, respectively. This method was used to study effective connections between emotional states and represent the distinction between emotions. Finally, significant effective connections were found in emotional processing and auditory regions, and between visual and memory-related regions. We also observed the distinctive connections between the pair of emotions in both models. The greatest number of significant distinctions in the coupling between regions was represented in happiness-anger and happiness-fear for the whole-brain model and happiness-sadness, sadness-love, and anger-love for the auditory model. © 2022 Informa UK Limited, trading as Taylor & Francis Group
  6. Keywords:
  7. Functional neuroimaging ; Brain networks ; Dynamic causal modeling ; Effective connectivities ; Emotion ; Emotional state ; Functional magnetic resonance imaging ; Model method ; Regression dynamic causal modeling ; Resonance imaging data ; Whole brains ; Magnetic resonance imaging
  8. Source: Computer Methods in Biomechanics and Biomedical Engineering: Imaging and Visualization ; 2022 ; 21681163 (ISSN)
  9. URL: https://www.tandfonline.com/doi/full/10.1080/21681163.2022.2077235