P174 Functional brain regions analysis using single-neuron morphology-driven reservoir network
Yuze Liu, Linus Manubens-Gil*, Hanchuan Peng*
Institute for Brain and Intelligence, Southeast University, Nanjing, China
* Email: linus.ma.gi@gmail.com
* Email: h@braintell.org Introduction
The brain operates through network topology across brain regions and morphological diversity of neurons. Reservoir computing (RC), with its recurrent nonlinearly mapping, can accomplish temporal tasks [1], enabling network functional analysis. Previous work constructed reservoir using diffusion magnetic resonance imaging (dMRI)-derived connectivity matrices and proved randomness in weight signs improves network’s memory capacity (MC) [2]. However, limitations persist due to the macroscopic scale of connectome, leaving microscale neuronal contributions underexplored. Thus, we established a reservoir using mouse brain’s single-neuron full morphology tracings, analyzing its validity in exploring the variance of functional regions by MC task.
Methods We used structural connectivity (SC) from [3]. The connectome data is 1,774 fully reconstructed mouse neurons registered to Allen Mouse Brain Common Coordinate Framework (CCFv3) [4]. We used hyperbolic tangent as nonlinear mapping. Input signal is sampled randomly, target signal is the delayed input. We fitted output to target signal via ridge regression and quantified performance by squared Pearson correlation coefficient. We constructed small-world networks with connection density approximating SC. We selected functional brain regions, e.g., LGd (Dorsal part of the lateral geniculate complex) and visual cortex regions as input/output nodes. We adjusted spectral radius to optimize connectivity weights for enhanced memory retention. Results We found that 1) based on uniform random connectivity weights, biologically wired networks with input-output nodes defined by functional regions slightly outperformed the Watts-Strogatz small-world network with random input-output node in MC task, confirming that single-neuron-derived network topology is relevant for the establishment of memories in RC; 2) we observed statistically significant differences in MC task performance for different thalamocortical integration of sensory modalities across diverse spectral radii ρ (76% of tested ρ values, 19/25, 0.1 ≤ ρ ≤ 5.0, Δρ = 0.2); independent t-test and Mann-Whitney U test, p<0.05;), suggesting morphological specificity of neuronal connections may underlie functional specialization. Discussion This study establishes a microscale framework linking single-neuron connectome to network functionality. Future work integrating generative models for scaling up the network, spiking neuronal dynamics, and modality-specific tasks could further dissect latent determinants of regional brain function.
Acknowledgements This work was supported by the National Natural Science Foundation of China (NSFC) under Grant No. 32350410413 awarded to LMG. References 1. https://doi.org/10.3389/fams.2024.1221051 2. https://doi.org/10.1109/IJCNN60899.2024.10650803 3. https://doi.org/10.1016/j.celrep.2024.113871 4. https://doi.org/10.1038/s41586-021-03941-1
