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

Abstract

Abstract Quantitative analysis of activated neurons in mice brains by a specific stimulation is usually a primary step to locate the responsive neurons throughout the brain. However, it’s challenging to comprehensively and consistently analyze the neuronal activity trace in whole brains of 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.