[C03] Neurobiology and Brain & Physiology and Biorhythms

[C03-01] A data-driven approach to understanding the wiring principles of the brain connectome

*Jigen Koike^1,2, Naoki Honda^1,2 (1. Hiroshima University (Japan), 2. Nagoya University (Japan))

Keywords:

Chemoaffinity theory,Connectome,Neural wiring,Machine Learning

Neural circuits are formed through the extension of axons to appropriate target locations, where they establish synaptic connections with other neurons. The projection sites of these axons are believed to be determined by the concentration gradients of guidance molecules and their receptors, a hypothesis widely recognized as the “chemoaffinity theory.” For instance, in the retinotectal system, Eph receptors and their ligands, ephrins, form concentration gradients in the retina and its target, the tectum, respectively. These molecular gradients function as positional information for axonal projections, enabling the establishment of topographic connectivity patterns. Various other molecules involved in axonal guidance have also been identified, further reinforcing the chemoaffinity theory as a fundamental mechanism of neural wiring. However, previous studies have mainly focused on relatively simple neural structures, such as the retinotectal circuit, while the wiring mechanisms of more complex circuits, such as those in the cerebral cortex, remain poorly understood.To address this gap, we developed a novel data-driven analytical approach based on the chemoaffinity theory to investigate the wiring mechanisms of complex neural circuits. Our method employs canonical correlation analysis (CCA), a machine learning technique, to analyze vector pairs of gene expression levels at the source and target regions of neural projections. This approach allows us to compute gradient pairs that best explain actual neural wiring patterns within the framework of the chemoaffinity theory. Furthermore, we applied this method to the publicly available mouse brain dataset from the Allen Brain Atlas to examine the relationship between whole-brain connectivity patterns and the chemoaffinity theory. Our analysis revealed that the gradient information governing the wiring structure of the mouse brain connectome exhibits a two-layered organization: (1) global connectivity patterns that define projection frequencies between regions, and (2) local connectivity patterns that reflect the spatial relationships between specific brain areas. These findings provide novel insights into the principles underlying the formation of complex neural circuits and offer a data-driven framework for understanding brain connectivity.