首页|NEATmap:a high-efficiency deep learning approach for whole mouse brain neuronal activity trace mapping

NEATmap:a high-efficiency deep learning approach for whole mouse brain neuronal activity trace mapping

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Quantitative analysis of activated neurons in mouse brains by a specific stimulation is usually a primary step to locate the responsive neurons throughout the brain.However,it is challenging to comprehensively and consistently analyze the neuronal activity trace in whole brains of a large cohort of mice from many terabytes of volumetric imaging data.Here,we introduce NEATmap,a deep learning-based high-efficiency,high-precision and user-friendly software for whole-brain neuronal activity trace mapping by automated segmentation and quantitative analysis of immunofluorescence labeled c-Fos+neurons.We applied NEATmap to study the brain-wide differentiated neuronal activation in response to physical and psychological stressors in cohorts of mice.

whole-brain imagingneuronal activity trace mappingdeep learningquantitative analysis

Weijie Zheng、Huawei Mu、Zhiyi Chen、Jiajun Liu、Debin Xia、Yuxiao Cheng、Qi Jing、Pak-Ming Lau、Jin Tang、Guo-Qiang Bi、Feng Wu、Hao Wang

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AHU-IAI AI Joint Laboratory,Anhui University,Hefei 230039,China

Anhui Province Key Laboratory of Biomedical Imaging and Intelligent Processing,Institute of Artificial Intelligence,Hefei Comprehensive National Science Center,Hefei 230088,China

National Engineering Laboratory for Brain-inspired Intelligence Technology and Application,School of Information Science and Technology,University of Science and Technology of China,Hefei 230026,China

Division of Life Sciences and Medicine,University of Science and Technology of China,Hefei 230026,China

Interdisciplinary Center for Brain Information,Brain Cognition and Brain Disease Institute,Shenzhen-Hong Kong Institute of Brain Science-Shenzhen Fundamental Research Institutions,Shenzhen Institute of Advanced Technology,Chinese

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国家自然科学基金Institute of Artificial Intelligence of Hefei Comprehensive National Science Center中国科学院战略规划重点项目

32100896Y10200902XDB32030200

2024

10.1093/nsr/nwae109

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ISSN：
年,卷(期)：2024.11(5)
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