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

Here are some demo datasets for validating the NEATmap pipeline for high-efficiency whole brain c-Fos+ cell automated segmentation and quantitative analysis, including:

BrainImage_group.zip.001-007: Validation of NEATmap for automated segmentation and quantitative analysis of mouse whole-brain c-Fos activity images (in Forced Swimming Test).

Segmentation_result.zip: Figure 1a, Supplementary Videos 1 and 2 show dual-channel brain slices and segmentation results. They can be merged using Imaris to validate the segmentation results of NEATmap.

RawImage_example.zip: High-resolution 3D images of mouse brain slices showing c-Fos+ cells in Figure 1e.

Due to the total size of the mouse whole-brain image datasets (both raw and processed) included in all the tests exceeding 10 Terabytes, uploading it to a public data repository is impractical. In this work, we provide a dataset of dual-channel (c-Fos+ channel and autofluorescence channel in forced swimming test experimental group) whole-brain images of mouse for the validation of NEATmap automated segmentation method.