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ZENODO
Dataset . 2023
License: CC BY
Data sources: ZENODO
ZENODO
Dataset . 2023
License: CC BY
Data sources: Datacite
ZENODO
Dataset . 2023
License: CC BY
Data sources: Datacite
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NEATmap: a high-efficiency deep learning approach for whole mouse brain neuronal activity trace mapping

Authors: Zheng, Weijie;

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

Abstract

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.

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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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