Poster No:
1486
Submission Type:
Late-Breaking Abstract Submission
Authors:
Songjun Peng1, Jiawen Chang1, Xinran Wu2, Changsong Zhou1, Jie Zhang2
Institutions:
1Hong Kong Baptist University, Hong Kong, Hong Kong, 2Fudan University, Shanghai, Shanghai
First Author:
Songjun Peng
Hong Kong Baptist University
Hong Kong, Hong Kong
Co-Author(s):
Jiawen Chang
Hong Kong Baptist University
Hong Kong, Hong Kong
Xinran Wu
Fudan University
Shanghai, Shanghai
Jie Zhang
Fudan University
Shanghai, Shanghai
Late Breaking Reviewer(s):
Jaehee Kim
Duksung Women's University
Seoul, 서울특별시
Introduction:
Diverse brain functional patterns emerge from the same underlying brain structure. Under the constraints of the structural connectivity, how dynamical features such as excitation-inhibition balance alter the emergent functional characteristics, and under what dynamical scenarios resting-state functional connectivity (FC) emerges, remain unanswered questions.
Methods:
To answer these questions, we built brain network models based on group-averaged structural connectivity(Kong et al., 2021) and dynamic mean field models(Deco et al., 2014) to simulate resting-state brain functional activities. Under varying excitation-inhibition ratios, we derived and calculated the Jacobian matrix of the model as well as simulated static and dynamic FC. We analyzed the eigenvalues and eigenvectors of the Jacobian matrix and performed k-means clustering on the eigenvectors corresponding to the maximum eigenvalues. The similarity between simulated static and dynamic FC and empirical data were assessed. Finally, we proposed the autocorrelation function (ACF) of BOLD signals as an indicator of intrinsic dynamical states and analyzed their associations with cognitive functions. All the data was from the Human Connectome Project (HCP) S1200 release(Van Essen et al., 2013)
Results:
With increasing excitation-inhibition ratio, the patterns of leading eigenvectors of brain network dynamics (corresponding to the maximum eigenvalues in the model's Jacobian matrix) evolved and could be categorized into 8 classes through k-means clustering. Two classes of eigenvectors exhibited bifurcation from monostable to bistable states, reaching criticality at specific excitation-inhibition ratios, characterized by increased variance and timescales of neural activity as well as increased ACFs of simulated BOLD signals. When the eigenvector representing the parietal networks (including superior parietal lobule, inferior parietal lobule, precuneus, precentral gyrus, and left postcentral gyrus) reach criticality, the model generated realistic static and dynamic resting-state FC. Association analysis based on HCP data revealed that the ACF of BOLD signals in the inferior parietal lobule significantly positively correlated with fluid intelligence across 1048 subjects (p<0.001).
Conclusions:
This study demonstrated the alteration of the leading patterns in brain network dynamics driven by excitation-inhibition ratio and proposed critical dynamics in the parietal network as a potential mechanism underlying the emergent resting-state functional patterns. It suggests that brain function during rest may focus on perceptual information integration and rely on the sensitivity to external inputs of the parietal network at criticality. The correlation between ACFs in the inferior parietal lobule and fluid intelligence further emphasizes the potential significance of this sensitivity for flexible cognitive functions. These findings enriched our understanding of the dynamics of brain resting-state activity and its functional implications, and provided guidance for constructing whole-brain computational models under appropriate dynamical states.
Modeling and Analysis Methods:
Connectivity (eg. functional, effective, structural) 2
fMRI Connectivity and Network Modeling 1
Task-Independent and Resting-State Analysis
Keywords:
Computational Neuroscience
FUNCTIONAL MRI
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.
Resting state
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?
NOTE: Any animal studies without IACUC approval will be automatically rejected.
Not applicable
Please indicate which methods were used in your research:
Functional MRI
Structural MRI
Diffusion MRI
Behavior
Computational modeling
For human MRI, what field strength scanner do you use?
3.0T
Provide references using APA citation style.
Deco, G., Ponce-Alvarez, A., Hagmann, P., Romani, G. L., Mantini, D., & Corbetta, M. (2014). How local excitation–inhibition ratio impacts the whole brain dynamics. Journal of Neuroscience, 34(23), 7886-7898.
Kong, X., Kong, R., Orban, C., Wang, P., Zhang, S., Anderson, K., ... & Yeo, B. T. (2021). Sensory-motor cortices shape functional connectivity dynamics in the human brain. Nature communications, 12(1), 6373.
Van Essen, D. C., Smith, S. M., Barch, D. M., Behrens, T. E., Yacoub, E., Ugurbil, K., & Wu-Minn HCP Consortium. (2013). The WU-Minn human connectome project: an overview. Neuroimage, 80, 62-79.
No