Decoding cortical folding patterns in marmosets using machine learning and large language model
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...
Saved in:
| Main Authors: | , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-03-01
|
| Series: | NeuroImage |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1053811925000333 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832542479687090176 |
|---|---|
| author | Yue Wu Xuesong Gao Zhengliang Liu Pengcheng Wang Zihao Wu Yiwei Li Tuo Zhang Tianming Liu Tao Liu Xiao Li |
| author_facet | Yue Wu Xuesong Gao Zhengliang Liu Pengcheng Wang Zihao Wu Yiwei Li Tuo Zhang Tianming Liu Tao Liu Xiao Li |
| author_sort | Yue Wu |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-ec09d77b07254fdcaafa6291d23d019e |
| institution | Kabale University |
| issn | 1095-9572 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | NeuroImage |
| spelling | doaj-art-ec09d77b07254fdcaafa6291d23d019e2025-02-04T04:10:19ZengElsevierNeuroImage1095-95722025-03-01308121031Decoding cortical folding patterns in marmosets using machine learning and large language modelYue Wu0Xuesong Gao1Zhengliang Liu2Pengcheng Wang3Zihao Wu4Yiwei Li5Tuo Zhang6Tianming Liu7Tao Liu8Xiao Li9College of Science, North China University of Technology, Tangshan, ChinaCollege of Science, North China University of Technology, Tangshan, ChinaCortical Architecture Imaging and Discovery Lab, Department of Computer Science and Bioimaging Research Center, University of Georgia, Athens, United StatesDepartment of Electrical & Computer Engineering, Faculty of Applied Science & Engineering, University of Toronto, Toronto, Canada.Cortical Architecture Imaging and Discovery Lab, Department of Computer Science and Bioimaging Research Center, University of Georgia, Athens, United StatesCortical Architecture Imaging and Discovery Lab, Department of Computer Science and Bioimaging Research Center, University of Georgia, Athens, United StatesSchool of Automation, Northwestern Polytechnical University, Xi'an, ChinaCortical Architecture Imaging and Discovery Lab, Department of Computer Science and Bioimaging Research Center, University of Georgia, Athens, United StatesCollege of Science, North China University of Technology, Tangshan, China; Corresponding author at: College of Science, North China University of Technology, 063210, Tangshan, China.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.http://www.sciencedirect.com/science/article/pii/S1053811925000333Cortical foldingMarmosetISHMachine learningLLM |
| spellingShingle | Yue Wu Xuesong Gao Zhengliang Liu Pengcheng Wang Zihao Wu Yiwei Li Tuo Zhang Tianming Liu Tao Liu Xiao Li Decoding cortical folding patterns in marmosets using machine learning and large language model NeuroImage Cortical folding Marmoset ISH Machine learning LLM |
| title | Decoding cortical folding patterns in marmosets using machine learning and large language model |
| title_full | Decoding cortical folding patterns in marmosets using machine learning and large language model |
| title_fullStr | Decoding cortical folding patterns in marmosets using machine learning and large language model |
| title_full_unstemmed | Decoding cortical folding patterns in marmosets using machine learning and large language model |
| title_short | Decoding cortical folding patterns in marmosets using machine learning and large language model |
| title_sort | decoding cortical folding patterns in marmosets using machine learning and large language model |
| topic | Cortical folding Marmoset ISH Machine learning LLM |
| url | http://www.sciencedirect.com/science/article/pii/S1053811925000333 |
| work_keys_str_mv | AT yuewu decodingcorticalfoldingpatternsinmarmosetsusingmachinelearningandlargelanguagemodel AT xuesonggao decodingcorticalfoldingpatternsinmarmosetsusingmachinelearningandlargelanguagemodel AT zhengliangliu decodingcorticalfoldingpatternsinmarmosetsusingmachinelearningandlargelanguagemodel AT pengchengwang decodingcorticalfoldingpatternsinmarmosetsusingmachinelearningandlargelanguagemodel AT zihaowu decodingcorticalfoldingpatternsinmarmosetsusingmachinelearningandlargelanguagemodel AT yiweili decodingcorticalfoldingpatternsinmarmosetsusingmachinelearningandlargelanguagemodel AT tuozhang decodingcorticalfoldingpatternsinmarmosetsusingmachinelearningandlargelanguagemodel AT tianmingliu decodingcorticalfoldingpatternsinmarmosetsusingmachinelearningandlargelanguagemodel AT taoliu decodingcorticalfoldingpatternsinmarmosetsusingmachinelearningandlargelanguagemodel AT xiaoli decodingcorticalfoldingpatternsinmarmosetsusingmachinelearningandlargelanguagemodel |