NEATmap: a high-efficiency deep learning approach for whole mouse brain neuronal activity trace mapping.
Weijie ZhengHuawei MuZhiyi ChenJiajun LiuDebin XiaYuxiao ChengQi JingPak-Ming LauJin TangGuo-Qiang BiFeng WuHao WangPublished in: National science review (2024)
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.
Keyphrases
- deep learning
- high efficiency
- cerebral ischemia
- high resolution
- resting state
- white matter
- spinal cord
- convolutional neural network
- artificial intelligence
- subarachnoid hemorrhage
- heavy metals
- functional connectivity
- machine learning
- blood brain barrier
- physical activity
- brain injury
- high fat diet induced
- mental health
- multiple sclerosis
- risk assessment
- mass spectrometry
- high throughput
- high density
- adipose tissue
- pet imaging
- depressive symptoms
- metabolic syndrome
- computed tomography
- drug delivery
- data analysis
- positron emission tomography