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X Demographics
Mendeley readers
| Title |
Q&A: Why use synchrotron x-ray tomography for multi-scale connectome mapping?
|
|---|---|
| Published in |
BMC Biology, December 2017
|
| DOI | 10.1186/s12915-017-0461-8 |
| Pubmed ID | |
| Authors |
Yeukuang Hwu, Giorgio Margaritondo, Ann-Shyn Chiang |
| Abstract |
To understand how information flows and is used in the human brain, we must map neural structures at all levels, providing visualizations similar to those of Google Earth for continents, countries, cities, and streets. Unfortunately, the imaging and processing techniques currently used in connectomics projects cannot achieve complete mapping for the brains of large animals within the timespan of a typical research career. However, feasible improvements in x-ray imaging would change this situation. This Q&A discusses synchrotron x-ray tomography, an exciting new approach for in situ mapping of whole-brain wiring diagrams at multiple levels of spatial resolution. |
X Demographics
The data shown below were collected from the profiles of 6 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| France | 1 | 17% |
| United Kingdom | 1 | 17% |
| Ireland | 1 | 17% |
| Austria | 1 | 17% |
| Unknown | 2 | 33% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 4 | 67% |
| Scientists | 1 | 17% |
| Science communicators (journalists, bloggers, editors) | 1 | 17% |
Mendeley readers
The data shown below were compiled from readership statistics for 33 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 33 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Master | 6 | 18% |
| Student > Ph. D. Student | 5 | 15% |
| Researcher | 4 | 12% |
| Professor | 3 | 9% |
| Other | 2 | 6% |
| Other | 4 | 12% |
| Unknown | 9 | 27% |
| Readers by discipline | Count | As % |
|---|---|---|
| Neuroscience | 6 | 18% |
| Engineering | 4 | 12% |
| Agricultural and Biological Sciences | 3 | 9% |
| Materials Science | 3 | 9% |
| Physics and Astronomy | 2 | 6% |
| Other | 6 | 18% |
| Unknown | 9 | 27% |