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Fear memory-associated synaptic and mitochondrial changes revealed by deep learning-based processing of electron microscopy data

文献类型：期刊论文

作者Liu, Jing5,6; Qi, Junqian2,3,4; Chen, Xi6; Li, Zhenchen5,6; Hong, Bei6; Ma, Hongtu6; Li, Guoqing6; Shen, Lijun6; Liu, Danqian3; Kong, Yu3
刊名CELL REPORTS
出版日期2022-08-02
卷号40期号:5页码:28
ISSN号2211-1247
DOI10.1016/j.celrep.2022.111151
通讯作者Xie, Qiwei(qiwei.xie@bjut.edu.cn) ; Han, Hua(hua.han@ia.ac.cn) ; Yang, Yang(yangyang2@shanghaitech.edu.cn)
英文摘要Serial section electron microscopy (ssEM) can provide comprehensive 3D ultrastructural information of the brain with exceptional computational cost. Targeted reconstruction of subcellular structures from ssEM da-tasets is less computationally demanding but still highly informative. We thus developed a region-CNN -based deep learning method to identify, segment, and reconstruct synapses and mitochondria to explore the structural plasticity of synapses and mitochondria in the auditory cortex of mice subjected to fear con-ditioning. Upon reconstructing over 135,000 mitochondria and 160,000 synapses, we find that fear condition-ing significantly increases the number of mitochondria but decreases their size and promotes formation of multi-contact synapses, comprising a single axonal bouton and multiple postsynaptic sites from different dendrites. Modeling indicates that such multi-contact configuration increases the information storage ca-pacity of new synapses by over 50%. With high accuracy and speed in reconstruction, our method yields structural and functional insight into cellular plasticity associated with fear learning.
WOS关键词STRUCTURAL DETERMINANTS ; MULTIPLE SYNAPSES ; CEREBRAL CORTEX ; MOSSY FIBER ; LAYER 4 ; PLASTICITY ; EXPERIENCE ; MORPHOGENESIS ; SEGMENTATION ; TRANSMISSION
资助项目National Science and Technology Innovation 2030 Major Program[2021ZD0204503] ; National Science and Technology Innovation 2030 Major Program[2021ZD0 204500] ; Strategic Priority Research Program of Chinese Academy of Sciences[XDB32030208] ; Strategic Priority Research Program of Chinese Academy of Sciences[XDA16021104] ; National Natural Science Foundation of China[32171461] ; National Natural Science Foundation of China[31970960] ; National Natural Science Foundation of China[61673381] ; Program of Beijing Municipal Science & Technology Commission[Z201100008420004] ; ShanghaiTech University
WOS研究方向Cell Biology
语种英语
WOS记录号WOS:000843351100001
出版者CELL PRESS
资助机构National Science and Technology Innovation 2030 Major Program ; Strategic Priority Research Program of Chinese Academy of Sciences ; National Natural Science Foundation of China ; Program of Beijing Municipal Science & Technology Commission ; ShanghaiTech University
源URL[http://ir.ia.ac.cn/handle/173211/49870]  
专题类脑智能研究中心_微观重建与智能分析
通讯作者Xie, Qiwei; Han, Hua; Yang, Yang
作者单位1.Beijing Univ Technol, Res Base Beijing Modern Mfg Dev, Beijing 100124, Peoples R China
2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
3.Chinese Acad Sci, Inst Neurosci, Ctr Excellence Brain Sci & Intelligence Technol, State Key Lab Neurosci,Key Lab Primate Neurobiol, Shanghai 200031, Peoples R China
4.ShanghaiTech Univ, Sch Life Sci & Technol, Shanghai, Peoples R China
5.Univ Chinese Acad Sci, Sch Artificial Intelligence, Sch Future Technol, Beijing 101408, Peoples R China
6.Chinese Acad Sci, Inst Automat, Res Ctr Brain inspired Intelligence, Lab Pattern Recognit, Beijing 100190, Peoples R China
推荐引用方式
GB/T 7714		 Liu, Jing,Qi, Junqian,Chen, Xi,et al. Fear memory-associated synaptic and mitochondrial changes revealed by deep learning-based processing of electron microscopy data[J]. CELL REPORTS,2022,40(5):28.
APA Liu, Jing.,Qi, Junqian.,Chen, Xi.,Li, Zhenchen.,Hong, Bei.,...&Yang, Yang.(2022).Fear memory-associated synaptic and mitochondrial changes revealed by deep learning-based processing of electron microscopy data.CELL REPORTS,40(5),28.
MLA Liu, Jing,et al."Fear memory-associated synaptic and mitochondrial changes revealed by deep learning-based processing of electron microscopy data".CELL REPORTS 40.5(2022):28.

入库方式： OAI收割

来源：自动化研究所

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