Decoding cortical folding patterns in marmosets using machine learning and large language model

Résumé fourni par la source

• Identification of genes with transcriptomic differences between concave and convex cortical patterns using machine learning and LLM. • Gene enrichment, neural migration, and axon guidance pathway analysis to explore molecular mechanisms. • Utilization of the Marmoset Gene Atlas for high-resolution genome-wide gene expression support. • Insights into the molecular basis of cortical folding and potential applications in neurodevelopmental research. Macroscale neuroimaging results have revealed significant differences in the structural and functional connectivity patterns of gyri and sulci in the primate cerebral cortex. Despite these findings, understanding these differences at the molecular level has remained challenging. This study leverages a comprehensive dataset of whole-brain in situ hybridization (ISH) data from marmosets, with updates continuing through 2024, to systematically analyze cortical folding patterns. Utilizing advanced machine learning algorithm and large language model (LLM), we identified genes with significant transcriptomic differences between concave (sulci) and convex (gyri) cortical patterns. Further, gene enrichment analysis, neural migration analysis, and axon guidance pathway analysis were employed to elucidate the molecular mechanisms underlying these structural and functional differences. Our findings provide new insights into the molecular basis of cortical folding, demonstrating the potential of LLM in enhancing our understanding of brain structural and functional connectivity.

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