NEATmap: a high-efficiency deep learning approach for whole mouse brain neuronal activity trace mapping
Weijie Zheng, Huawei Mu, Zhiyi Chen, Jiajun Liu, Debin Xia, Yuxiao Cheng, Jing Qi, Pak-Ming Lau, Jin Tang, Guo‐Qiang Bi, Feng Wu, Hao Wang
Abstract
ABSTRACT 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.