P173 Large-scale Neural Network Model of the Human Cerebral Cortex Based on High-spatial-resolution Imaging Data
Chang Liu1, Dahui Wang*1, Yuxiu Shao*1
1School of Systems Science, Beijing Normal University, Beijing, China *Email: wangdh@bnu.edu.cn (DW); shaoyx@bnu.edu.cn (YS) Introduction
Large-scale brain models have received interest for their ability to probe complex dynamical phenomena. However, large-scale models guided by high-spatial-resolution imaging data remain largely underexplored. We develop a comprehensive large-scale model of the human cerebral cortex, utilizing the recently released data on receptor density[1] and white-matter connectivity[2]. Furthermore, we refine undirected white-matter connectivity into directed connectivity using tracer data[3]. Our model replicates the characteristic spatio-temporal patterns of whole-brain activities observed experimentally during the resting state, enabling a deeper exploration of the interplay between anatomical structure, dynamics, and potential functional roles. Methods Our network comprises about 60k vertices, each modeled as a microcircuit of coupled excitatory and inhibitory populations connected via AMPA, NMDA, and GABA synapses, exhibiting Wilson-Cowan type dynamics[4]. Intra-vertex connection strengths are proportional to receptor density[1]. Inter-vertex connections are derived from anatomical fiber data, obtained via dMRI tractography at vertex resolution[2], and averaged across 255 unrelated healthy individuals (Fig. 1A). Since this anatomical data is undirected, we redistribute fiber bundles between vertices using directed macaque neocortex tracer data[3]. Results The simulation results demonstrate that the averaged firing rate (FR) across all vertices is around 3Hz[5]. Interconnected vertices show reduced correlation between FR and the excitatory-inhibitory receptor density ratio compared to independent vertices (Fig. 1B). Beta-band peak frequency exhibits a posterior-anterior gradient, which is disrupted by shuffling the spatial distribution of the AMPA-NMDA receptor ratio (Fig. 1C). The projection of power spectral density and FR onto the first principal component positively correlate with T1w/T2w (Fig. 1D, 1E). These findings align with experimental observations[6–8]. Moreover, asymmetric connectivity induces traveling waves, with sinks exhibiting higher FR than surrounding vertices (Fig. 1F). Discussion Our large-scale brain model with high-spatial-resolution not only introduces a novel approach to understanding the computational mechanisms of the brain but also offers critical insights into the neural dynamic mechanisms underlying cognitive dysfunction and mental disorders. However, our model still has some limitations: we directly assume synaptic strength is proportional to receptor density; estimate the directed-weighted connections based on coarse matching of macaque-human brain areas; and omit signal propagation delays. Future work will focus on simulating the information transmission across the cortex, exploring how this model can enhance our understanding of brain function and support the development of therapeutic strategies.
Figure 1. Fig 1: (A) Schematic. (B) Relationship between mean FR and E:I ratio. Blue: independent vertices. Red: interconnected vertices. (C) Dependency between the vertex’s location along the posterior-anterior axis. Blue: original. Pink: shuffled density ratio. (D-E) Correlation of model PSD PC1 maps (D) with T1w/T2w, and model FR PC1 maps (E). (F) Sinks displaying higher FR than surrounding vertices. Acknowledgements This work was supported by NSFC (No.32171094 to D.W., No.32400936 to Y.S.) and National Key R&D Program of China (2019YFA0709503 to D.W.) and International Brain Research Organization Early Career Award (to Y.S.). References 1. https://doi.org/10.1038/s41593-022-01186-3 2. https://doi.org/10.1016/j.neuroimage.2020.117695 3. https://doi.org/10.1093/cercor/bhs270 4. https://doi.org/10.1523/JNEUROSCI.3733-05.2006 5. https://doi.org/10.1023/A:1011204814320 6. https://doi.org/10.7554/eLife.53715 7. https://doi.org/10.1016/j.neuron.2019.01.017 8. https://doi.org/10.1073/pnas.1608282113
