Decoding cortical folding patterns in marmosets using machine learning and large language model
Yue Wu,
Xuesong Gao,
Zhengliang Liu,
Pengcheng Wang,
Zihao Wu,
Yiwei Li,
Tuo Zhang,
Tianming Liu,
Tao Liu,
Xiao Li
Affiliations
Yue Wu
College of Science, North China University of Technology, Tangshan, China
Xuesong Gao
College of Science, North China University of Technology, Tangshan, China
Zhengliang Liu
Cortical Architecture Imaging and Discovery Lab, Department of Computer Science and Bioimaging Research Center, University of Georgia, Athens, United States
Pengcheng Wang
Department of Electrical & Computer Engineering, Faculty of Applied Science & Engineering, University of Toronto, Toronto, Canada.
Zihao Wu
Cortical Architecture Imaging and Discovery Lab, Department of Computer Science and Bioimaging Research Center, University of Georgia, Athens, United States
Yiwei Li
Cortical Architecture Imaging and Discovery Lab, Department of Computer Science and Bioimaging Research Center, University of Georgia, Athens, United States
Tuo Zhang
School of Automation, Northwestern Polytechnical University, Xi'an, China
Tianming Liu
Cortical Architecture Imaging and Discovery Lab, Department of Computer Science and Bioimaging Research Center, University of Georgia, Athens, United States
Tao Liu
College of Science, North China University of Technology, Tangshan, China; Corresponding author at: College of Science, North China University of Technology, 063210, Tangshan, China.
Xiao Li
School of information science and technology, Northwest University, Xi'an, China; Corresponding author at: School of information science and technology, Northwest University, 710127, Xi'an, China.
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.
