Poster No:
1557
Submission Type:
Abstract Submission
Authors:
Dongmyeong Lee1, Yelim Lee2, Hae-Jeong Park1
Institutions:
1Yonsei University College of Medicine, Seoul, Korea, Republic of, 2Yonsei University College of Medicine, Seoul,Korea, Republic of
First Author:
Dongmyeong Lee
Yonsei University College of Medicine
Seoul, Korea, Republic of
Co-Author(s):
Yelim Lee
Yonsei University College of Medicine
Seoul,Korea, Republic of
Hae-Jeong Park
Yonsei University College of Medicine
Seoul, Korea, Republic of
Introduction:
It is widely recognized that the human brain reveals a myriad of cognitive functions through the segregation and integration of brain areas over time, a phenomenon illuminated by the fluctuations of correlation between these areas (Kucyi 2017). Despite extensive research on the relationship between functional connectivity, derived from average correlations, and underlying structural connectivity (Suárez 2020), there is a significant lack of studies exploring how the characteristics of structural connectivity topology shape the manifestation of fluctuations in brain dynamics. Functional correlations research is predominantly conducted based on data acquired from fMRI. However, data obtained from fMRI primarily represents the average signals of neuronal populations. As a result, there is a limited understanding of how characteristics of network topology at the single-neuronal connectivity influence macroscopic brain dynamics and correlations. In this study, we simulated the neuron signals from various structural networks to investigate the relationship between the characteristics of topology and the fluctuations of brain dynamics. Moreover, by analyzing calcium imaging data from zebrafish and comparing it with computationally simulated brain dynamics at the single-neuron level, we revealed a striking similarity between the computational simulation and the information processing occurring in the real brain.
Methods:
The anatomical connections in the brain can exhibit various network topology characteristics, such as small-world or scale-free features (He 2010). In this study, we simulated neuronal dynamics based on various structural connectivity to investigate how the characteristics of topology shape functional networks and influence brain dynamic fluctuations. To efficiently simulate the activity of thousands of neurons, we developed parallel GPU-based code, utilizing the Izhikevich neuron model for large-scale spiking neural network simulations. We used public calcium imaging data (Chen 2018). Zebrafish, known for easy genetic manipulation and real-time tracking of individual neuron activity, offer the advantage of providing activity at the single-neuron level for thousands of neurons.
Results:
We created various modules and connected them, each with different topology characteristics, such as a random network, a scale-free network, or a small-world network. We observed how spiking patterns were segregated and integrated under various topologies. To measure segregation, coherence of spikes within each module was measured, while for integration, entropy between modules was measured. The results revealed that in small-world and random networks, coherence within modules was low, and entropy values were not particularly high. However, in the scale-free network, both coherence and entropy values maintained a high level across coupling constants. The results were consistently confirmed through mathematical stability analysis. We showed that functional networks in actual zebrafish data exhibited scale-free network topology characteristics, and brain dynamic fluctuations of zebrafish are highly close to that, which is simulated scale-free network properties. Furthermore, when simulating the brain dynamics based on zebrafish structural connection data with scale-free network properties, it showed the highest similarity between empirical and simulated functional networks.
Conclusions:
Conclusively, through this study, we showed that connectivity properties at the individual neuron level, exhibiting scale-free topology characteristics, could lead to high brain dynamic fluctuations, which are related to brain information processing. Additionally, we believe that this research could contribute to the development of new neural chips by presenting a novel topology.
This work was supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIT) (NO. 2023R1A2C200621711)
Modeling and Analysis Methods:
Connectivity (eg. functional, effective, structural) 1
Other Methods 2
Keywords:
Computational Neuroscience
Modeling
Other - scale-free network
1|2Indicates the priority used for review
Provide references using author date format
Chen, X. (2018). Brain-wide organization of neuronal activity and convergent sensorimotor transformations in larval zebrafish. Neuron, 100(4), 876-890
He, B. J. (2010). The temporal structures and functional significance of scale-free brain activity. Neuron, 66(3), 353-369
Kucyi, A. (2017). Dynamic brain network correlates of spontaneous fluctuations in attention. Cerebral cortex, 27(3), 1831-1840
Suárez, L. E. (2020). Linking structure and function in macroscale brain networks. Trends in cognitive sciences, 24(4), 302-315.