Single-neuron Reconstruction of the Macaque Primary Motor Cortex
<div> <div> <div> <div> <div> <div> <div> <div> <div> <div> <div> <div> <div> <div> <div> <div> <div> <div>Here are 26 reconstructed neuron SWC files of the primary motor cortex from a one-year-old cynomolgus monkey.</div> </div> </div> </div> </div> </div> </div> </div> </div> </div> </div> </div> </div> </div> </div> </div> </div> </div> <div> <div> <div> <div> <div> <div> </div> </div> </div> </div> </div> <div> <div> <div> <div> <div> <div> <div> <div> <div> <div> <div> <div> <div> <div> <div> <div> <div> <div> <div> <div>In this study, we achieved visualization of neurons through sparse labeling and volumetric imaging with synchronized on-the-fly-scan and readout (VISoR) technique. Following whole-brain 3D reconstruction (<a href="https://github.com/SMART-pipeline/Volume-reconstruction" target="_blank" rel="noopener noreferrer">GitHub - SMART-pipeline/Volume-reconstruction</a>), we employed Lychnis for single-neuron reconstruction (<a href="https://github.com/SMART-pipeline/Lychnis-tracing" target="_blank" rel="noopener noreferrer">GitHub - SMART-pipeline/Lychnis-tracing</a>). Within Lychnis, the visualization toolkit (VTK) and virtual finger are utilized for 3D rendering and interactive labeling purposes.</div> </div> </div> </div> </div> </div> </div> </div> </div> </div> </div> </div> </div> </div> </div> </div> </div> </div> </div> </div> </div> <div> <div> <div> <div> <div> <div> <div> <div> <div> <div> <div> <div> </div> </div> </div> </div> </div> <div> <div> <div> <div> <div> <div> <div> <div> <div> <div> <div>The complete image datasets (raw and processed) of macaque brains exceed 1 petabyte, rendering it impractical to upload the entirety to a public data repository. This part of datasets generated and/or analyzed during the current study is available from the corresponding authors upon reasonable request.</div> </div> </div> </div> </div> </div> </div> </div> </div> </div> </div> </div> </div> </div> </div> </div> </div> </div>
ShareScore
28/100
Overall dataset sharing score
Score breakdown
These five areas show where the dataset supports — or may limit — practical reuse.
- Stewardship
- 4
- Harmonization
- 4
- Access
- 16
- Reuse readiness
- 4
- Engagement
- 0