Magnitude of structure-function coupling is related to the intensities of receptor density
Poster No:
1530
Submission Type:
Abstract Submission
Authors:
Yeongjun Park1, Bo-yong Park2, Hyunjin Park1
Institutions:
1Sungkyunkwan University, Suwon-si, Gyeonggi-do, 2Korea University, Seoul, Seoul
First Author:
Co-Author(s):
Introduction:
Distinct regions of the brain possess unique characteristics and are functionally specialized. Recent studies have developed biophysical models that account for the heterogeneity between these regions (Kong et al., 2021; Zhang et al., 2024). However, not all aspects of biological heterogeneity across brain regions have been systematically explored in existing research. These studies cannot handle structure-function coupling strength differences between regions (Deco et al., 2014; Kong et al., 2021; Zhang et al., 2024). In this study, we propose a model that reflects regional differences in structure-function coupling strength and aim to analyze the regional differences that emerge as a result of this heterogeneity. Furthermore, we also investigate the biological implications of regional variations by analyzing neurotransmitter receptor density.
Methods:
In this study, we used data from 973 subjects provided by the Human Connectome Project (HCP) (Van Essen et al., 2013). Each subject had diffusion tensor imaging (DTI) and resting-state functional magnetic resonance imaging (rs-fMRI) data, which were preprocessed using the HCP minimal pipelines (Glasser et al., 2013).
The Feedback Inhibition Control (FIC) model represents local inhibitory feedback (Deco et al., 2014; Zhang et al., 2024). We adopted the equations proposed in previous studies to model the neural activity in the j-th region. In this model, E and I represent excitatory and inhibitory neuron populations. S,r,and I correspond to the degree of synaptic gating activation, population firing rate, and population current, respectively. C_jk is the element of the structural connectivity (SC) matrix at the j-th row and k-th column, and v denotes Gaussian noise.
To reflect regional differences in structure-function coupling strength, we modified the equations as below:
IjE = WE * I0 + WEE * JNMDA * SjE + JNMDA * ∑k dej Cjk SkE - WIE * SjI.
We also used the parameter learning method from previous studies (Kong et al., 2021; Zhang et al., 2024), with the addition of training for structure-function coupling strength.
The Feedback Inhibition Control (FIC) model represents local inhibitory feedback (Deco et al., 2014; Zhang et al., 2024). We adopted the equations proposed in previous studies to model the neural activity in the j-th region. In this model, E and I represent excitatory and inhibitory neuron populations. S,r,and I correspond to the degree of synaptic gating activation, population firing rate, and population current, respectively. C_jk is the element of the structural connectivity (SC) matrix at the j-th row and k-th column, and v denotes Gaussian noise.
To reflect regional differences in structure-function coupling strength, we modified the equations as below:
IjE = WE * I0 + WEE * JNMDA * SjE + JNMDA * ∑k dej Cjk SkE - WIE * SjI.
We also used the parameter learning method from previous studies (Kong et al., 2021; Zhang et al., 2024), with the addition of training for structure-function coupling strength.
Results:
For qualitative evaluation, the results were visualized (Fig. 1). We visualized the structure-function coupling magnitude across the intrinsic functional communities using Yeo-7 network and the correlation between structure-function coupling magnitude and receptor densities (Markello et al., 2022; Thomas Yeo et al., 2011) (Fig. 2). The correlation between the magnitude and the dopamine D1 receptor was -0.30, and the glutamate receptor showed -0.37.
Conclusions:
In this study, we simulated rs-fMRI in more biologically plausible way by introducing heterogeneity through region-specific differences in structure-function coupling magnitude. The coupling strength was found to be higher in sensory regions and lower in association regions, consistent with previous studies (Fotiadis et al., 2024). Furthermore, significant correlations were observed when the coupling strength was compared with the density of dopamine and glutamate receptors. These findings provide a foundation for future studies exploring how structure-function coupling heterogeneity may be influenced by factors such as disease or aging.
This study was supported by National Research Foundation (RS-2024-00408040), Institute for Basic Science (IBS-R015-D1), AI Graduate School Support Program (Sungkyunkwan University) (RS-2019-II190421), ICT Creative Consilience program (RS-2020-II201821), the Artificial Intelligence Innovation Hub program (RS-2021-II212068), and the Institute for Information and Com- munications Technology Planning and Evaluation (IITP) funded by the Korea Government (MSIT) (No. 2022-0-00448/RS-2022-II220448, Deep Total Recall: Continual Learning for Human-Like Recall of Artificial Neural Networks)
This study was supported by National Research Foundation (RS-2024-00408040), Institute for Basic Science (IBS-R015-D1), AI Graduate School Support Program (Sungkyunkwan University) (RS-2019-II190421), ICT Creative Consilience program (RS-2020-II201821), the Artificial Intelligence Innovation Hub program (RS-2021-II212068), and the Institute for Information and Com- munications Technology Planning and Evaluation (IITP) funded by the Korea Government (MSIT) (No. 2022-0-00448/RS-2022-II220448, Deep Total Recall: Continual Learning for Human-Like Recall of Artificial Neural Networks)
Modeling and Analysis Methods:
Activation (eg. BOLD task-fMRI)
fMRI Connectivity and Network Modeling 2
Methods Development 1
Keywords:
Computational Neuroscience
FUNCTIONAL MRI
1|2Indicates the priority used for review
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Fotiadis, P., Parkes, L., Davis, K. A., Satterthwaite, T. D., Shinohara, R. T., & Bassett, D. S. (2024). Structure–function coupling in macroscale human brain networks. In Nature Reviews Neuroscience. Springer Nature.
Glasser, M. F., Sotiropoulos, S. N., Wilson, J. A., Coalson, T. S., Fischl, B., Andersson, J. L., Xu, J., Jbabdi, S., Webster, M., Polimeni, J. R., Van Essen, D. C., & Jenkinson, M. (2013). The minimal preprocessing pipelines for the Human Connectome Project. NeuroImage, 80, 105–124.
Kong, X., Kong, R., Orban, C., Wang, P., Zhang, S., Anderson, K., Holmes, A., Murray, J. D., Deco, G., van den Heuvel, M., & Yeo, B. T. T. (2021). Sensory-motor cortices shape functional connectivity dynamics in the human brain. Nature Communications, 12(1).
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Thomas Yeo, B. T., Krienen, F. M., Sepulcre, J., Sabuncu, M. R., Lashkari, D., Hollinshead, M., Roffman, J. L., Smoller, J. W., Zöllei, L., Polimeni, J. R., Fisch, B., Liu, H., & Buckner, R. L. (2011). The organization of the human cerebral cortex estimated by intrinsic functional connectivity. Journal of Neurophysiology, 106(3), 1125–1165.
Van Essen, D. C., Smith, S. M., Barch, D. M., Behrens, T. E. J., Yacoub, E., & Ugurbil, K. (2013). The WU-Minn Human Connectome Project: An overview. NeuroImage, 80, 62–79.
Zhang, S., Larsen, B., Sydnor, V. J., Zeng, T., An, L., Yan, X., Kong, R., Kong, X., Gur, R. C., Gur, R. E., Moore, T. M., Wolf, D. H., Holmes, A. J., Xie, Y., Zhou, J. H., Fortier, M. V., Tan, A. P., Gluckman, P., Chong, Y. S., … Thomas Yeo, B. T. (2024). In vivo whole-cortex marker of excitation-inhibition ratio indexes cortical maturation and cognitive ability in youth. Proceedings of the National Academy of Sciences of the United States of America, 121(23).