| Title |
Reconstructing the brain: from image stacks to neuron synthesis
|
|---|---|
| Published in |
Brain Informatics, February 2016
|
| DOI | 10.1007/s40708-016-0041-7 |
| Pubmed ID | |
| Authors |
Julian C. Shillcock, Michael Hawrylycz, Sean Hill, Hanchuan Peng |
| Abstract |
Large-scale brain initiatives such as the US BRAIN initiative and the European Human Brain Project aim to marshall a vast amount of data and tools for the purpose of furthering our understanding of brains. Fundamental to this goal is that neuronal morphologies must be seamlessly reconstructed and aggregated on scales up to the whole rodent brain. The experimental labor needed to manually produce this number of digital morphologies is prohibitively large. The BigNeuron initiative is assembling community-generated, open-source, automated reconstruction algorithms into an open platform, and is beginning to generate an increasing flow of high-quality reconstructed neurons. We propose a novel extension of this workflow to use this data stream to generate an unlimited number of statistically equivalent, yet distinct, digital morphologies. This will bring automated processing of reconstructed cells into digital neurons to the wider neuroscience community, and enable a range of morphologically accurate computational models. |
X Demographics
The data shown below were collected from the profiles of 4 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 1 | 25% |
| Unknown | 3 | 75% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 4 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 24 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 24 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 6 | 25% |
| Student > Ph. D. Student | 5 | 21% |
| Professor > Associate Professor | 3 | 13% |
| Professor | 2 | 8% |
| Student > Master | 2 | 8% |
| Other | 4 | 17% |
| Unknown | 2 | 8% |
| Readers by discipline | Count | As % |
|---|---|---|
| Neuroscience | 5 | 21% |
| Agricultural and Biological Sciences | 4 | 17% |
| Computer Science | 2 | 8% |
| Psychology | 2 | 8% |
| Engineering | 2 | 8% |
| Other | 4 | 17% |
| Unknown | 5 | 21% |
Attention Score in Context
This research output has an Altmetric Attention Score of 3. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 04 March 2016.
All research outputs
#13,109,157
of 22,851,489 outputs
Outputs from Brain Informatics
#47
of 103 outputs
Outputs of similar age
#137,305
of 298,866 outputs
Outputs of similar age from Brain Informatics
#9
of 14 outputs
Altmetric has tracked 22,851,489 research outputs across all sources so far. This one is in the 42nd percentile – i.e., 42% of other outputs scored the same or lower than it.
So far Altmetric has tracked 103 research outputs from this source. They receive a mean Attention Score of 4.3. This one has gotten more attention than average, scoring higher than 54% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 298,866 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 53% of its contemporaries.
We're also able to compare this research output to 14 others from the same source and published within six weeks on either side of this one. This one is in the 35th percentile – i.e., 35% of its contemporaries scored the same or lower than it.