Poster No:
1344
Submission Type:
Abstract Submission
Authors:
Euisun Kim1, Jiyoung Park2, Jinseok Eo3, Woo Yong Lee4, Bohyun Lee5, Dawon Park5, Jiyoung Kang6, Hae-Jeong Park7
Institutions:
1Graduate School of Medical Science, Brain Korea 21 Project, Yonsei University College of Medicine, Seoul, Republic of Korea, 2Department Cognitive Science, Yonsei University, Seoul, Republic of Korea, 3Center for Systems and Translational Brain Sciences, Yonsei University, Seoul, Republic of Korea, 4Graduate School of Medical Science, Brain Korea 21 Project, Yonsei University College of Medicine, R, Seoul, Republic of Korea, 5Department of Psychology, Yonsei University, Seoul, Republic of Korea, 6Department of Scientific Computing, Pukyong National University, Busan, Republic of Korea, 7Department of Nuclear Medicine, Severance Hospital, Yonsei University College of Medicine, Seoul, Republic of Korea
First Author:
Euisun Kim
Graduate School of Medical Science, Brain Korea 21 Project, Yonsei University College of Medicine
Seoul, Republic of Korea
Co-Author(s):
Jiyoung Park
Department Cognitive Science, Yonsei University
Seoul, Republic of Korea
Jinseok Eo
Center for Systems and Translational Brain Sciences, Yonsei University
Seoul, Republic of Korea
Woo Yong Lee
Graduate School of Medical Science, Brain Korea 21 Project, Yonsei University College of Medicine, R
Seoul, Republic of Korea
Bohyun Lee
Department of Psychology, Yonsei University
Seoul, Republic of Korea
Dawon Park
Department of Psychology, Yonsei University
Seoul, Republic of Korea
Jiyoung Kang
Department of Scientific Computing, Pukyong National University
Busan, Republic of Korea
Hae-Jeong Park
Department of Nuclear Medicine, Severance Hospital, Yonsei University College of Medicine
Seoul, Republic of Korea
Introduction:
To explore the brain processes related to anxiety, we investigated how effective connectivity in the brain fluctuates over time, as anxiety level increases due to the temporal anticipation of negative events. While previous research has primarily examined brain connectivity during anxiety-inducing situations as static constructs, our study adopts a dynamic perspective to elucidate the underlying mechanism of anxiety and analyzes the patterns of time-varying effective connectivity using spectral dynamic causal modelling (spDCM) of EEG to assess dynamic effective connectivity.
Methods:
Eighteen participants watched videos of inflating balloons, which served as anxiety-inducing stimuli. A total of 20 balloons were shown, with an equal number bursting and not bursting, and each balloon video lasted for 15 seconds. After each trial, participants rated their anxiety on a scale from 1 to 9. EEG data were recorded while participants watched these stimuli. Our regions of interest included the bilateral amygdala, bilateral anterior insula, and right vmPFC. To examine dynamic effective connectivity in these brain regions, EEG data were divided into 5-second time windows with a 2.5-second overlap for spDCM analysis. Hierarchical Parametric Empirical Bayesian (PEB) analysis with a temporal basis set was used to estimate dynamic components of effective connectivity in each window. For the group-level analysis, the balloon bursting outcome was modeled as a condition effect in the second level PEB.
Results:
Participants reported significantly higher anxiety levels in response to the 'bursting' condition, indicating that the balloon stimulus effectively elicited anxiety. DCM analysis revealed distinct patterns of effective connectivity between the two conditions over time. Compared to the non-bursting condition, the bursting condition exhibited a general reduction in forward connectivity and an increase in backward connectivity. This pattern indicates that as the anxiety-inducing event becomes imminent, top-down regulatory processes are increasingly engaged. Forward connectivity primarily exhibited strengthening between lower brain regions (e.g., the anterior insula and amygdala) over time. In contrast, connectivity between lower brain regions and higher-order regions (e.g., vmPFC) showed a negative trend. Backward connectivity demonstrated an overall increase between lower brain regions and higher-order regions.
Conclusions:
This study is the first to investigate the dynamic effective connectivity of the brain as the anxiety level increases over time. The findings suggest that anxiety-inducing situations disrupt forward connectivity patterns while enhancing backward connectivity, highlighting the complex interplay between brain regions involved in threat detection (amygdala, insula) and regulation (vmPFC). The use of spectral dynamic causal modeling with a sliding window approach allowed for a detailed examination of these temporal changes, offering insights beyond static connectivity models.
Modeling and Analysis Methods:
Connectivity (eg. functional, effective, structural) 2
EEG/MEG Modeling and Analysis 1
Keywords:
Anxiety
Computational Neuroscience
Emotions
Modeling
1|2Indicates the priority used for review
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Please indicate below if your study was a "resting state" or "task-activation” study.
Task-activation
Healthy subjects only or patients (note that patient studies may also involve healthy subjects):
Healthy subjects
Was this research conducted in the United States?
No
Were any human subjects research approved by the relevant Institutional Review Board or ethics panel?
NOTE: Any human subjects studies without IRB approval will be automatically rejected.
Yes
Were any animal research approved by the relevant IACUC or other animal research panel?
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Not applicable
Please indicate which methods were used in your research:
EEG/ERP
Computational modeling
Which processing packages did you use for your study?
SPM
Provide references using APA citation style.
Van de Steen, F., Almgren, H., Razi, A., Friston, K., & Marinazzo, D. (2019). Dynamic causal modelling of fluctuating connectivity in resting-state EEG. Neuroimage, 189, 476-484.
Park, H. J., Friston, K. J., Pae, C., Park, B., & Razi, A. (2018). Dynamic effective connectivity in resting state fMRI. NeuroImage, 180, 594-608.
No