Discrimination of the hierarchical structure of cortical layers in 2-photon microscopy data by combined unsupervised and supervised machine learning

doi:10.1038/s41598-019-43432-y

	Discrimination of the hierarchical structure of cortical layers in 2-photon microscopy data by combined unsupervised and supervised machine learning
	Li, Dong 1; Zavaglia, Melissa 1,5; Wang, Guangyu 2,3; Xie, Hong 4; Hu, Yi 2,3; Werner, Rene 1; Guan, Ji-Song2,3 ; Hilgetag, Claus C.1,6
	2019-05-15
发表期刊	SCIENTIFIC REPORTS (IF:3.8[JCR-2023],4.3[5-Year])
ISSN	2045-2322
卷号	9
发表状态	已发表
DOI	10.1038/s41598-019-43432-y
摘要	The laminar organization of the cerebral cortex is a fundamental characteristic of the brain, with essential implications for cortical function. Due to the rapidly growing amount of high-resolution brain imaging data, a great demand arises for automated and flexible methods for discriminating the laminar texture of the cortex. Here, we propose a combined approach of unsupervised and supervised machine learning to discriminate the hierarchical cortical laminar organization in high-resolution 2-photon microscopic neural image data of mouse brain without observer bias, that is, without the prerequisite of manually labeled training data. For local cortical foci, we modify an unsupervised clustering approach to identify and represent the laminar cortical structure. Subsequently, supervised machine learning is applied to transfer the resulting layer labels across different locations and image data, to ensure the existence of a consistent layer label system. By using neurobiologically meaningful features, the discrimination results are shown to be consistent with the layer classification of the classical Brodmann scheme, and provide additional insight into the structure of the cerebral cortex and its hierarchical organization. Thus, our work paves a new way for studying the anatomical organization of the cerebral cortex, and potentially its functional organization.
收录类别	SCI ; SCIE
语种	英语
资助项目	NSFC[31671104]
WOS研究方向	Science & Technology - Other Topics
WOS类目	Multidisciplinary Sciences
WOS记录号	WOS:000468025300020
出版者	NATURE PUBLISHING GROUP
WOS关键词	LAMINAR ORGANIZATION ; PYRAMIDAL NEURONS ; REPRESENTATION ; CIRCUITS ; REVEALS ; NETWORK ; COLUMN ; BRAIN ; ATLAS ; MRI
原始文献类型	Article
引用统计	正在获取...
文献类型	期刊论文
条目标识符	https://kms.shanghaitech.edu.cn/handle/2MSLDSTB/40843
专题	生命科学与技术学院_PI研究组_管吉松组
通讯作者	Hilgetag, Claus C.
作者单位	1.Univ Med Ctr Hamburg Eppendorf, Inst Computat Neurosci, D-20246 Hamburg, Germany 2.ShanghaiTech Univ, Sch Life Sci & Technol, Shanghai 201210, Peoples R China 3.Tsinghua Univ, Sch Life Sci, Beijing 100086, Peoples R China 4.Shanghai Res Ctr Brain Sci & Brain Inspired Intel, Zhangjiang Lab, Inst Brain Intelligence Technol, Shanghai 200031, Peoples R China 5.Jacobs Univ Bremen, Focus Area Hlth, D-28759 Bremen, Germany 6.Boston Univ, Dept Hlth Sci, Boston, MA 02215 USA
推荐引用方式 GB/T 7714	Li, Dong,Zavaglia, Melissa,Wang, Guangyu,et al. Discrimination of the hierarchical structure of cortical layers in 2-photon microscopy data by combined unsupervised and supervised machine learning[J]. SCIENTIFIC REPORTS,2019,9.
APA	Li, Dong.,Zavaglia, Melissa.,Wang, Guangyu.,Xie, Hong.,Hu, Yi.,...&Hilgetag, Claus C..(2019).Discrimination of the hierarchical structure of cortical layers in 2-photon microscopy data by combined unsupervised and supervised machine learning.SCIENTIFIC REPORTS,9.
MLA	Li, Dong,et al."Discrimination of the hierarchical structure of cortical layers in 2-photon microscopy data by combined unsupervised and supervised machine learning".SCIENTIFIC REPORTS 9(2019).