| Title |
Multimodal neuroimaging computing: the workflows, methods, and platforms
|
|---|---|
| Published in |
Brain Informatics, September 2015
|
| DOI | 10.1007/s40708-015-0020-4 |
| Pubmed ID | |
| Authors |
Sidong Liu, Weidong Cai, Siqi Liu, Fan Zhang, Michael Fulham, Dagan Feng, Sonia Pujol, Ron Kikinis |
| Abstract |
The last two decades have witnessed the explosive growth in the development and use of noninvasive neuroimaging technologies that advance the research on human brain under normal and pathological conditions. Multimodal neuroimaging has become a major driver of current neuroimaging research due to the recognition of the clinical benefits of multimodal data, and the better access to hybrid devices. Multimodal neuroimaging computing is very challenging, and requires sophisticated computing to address the variations in spatiotemporal resolution and merge the biophysical/biochemical information. We review the current workflows and methods for multimodal neuroimaging computing, and also demonstrate how to conduct research using the established neuroimaging computing packages and platforms. |
X Demographics
The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Iran, Islamic Republic of | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 80 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United Kingdom | 1 | 1% |
| China | 1 | 1% |
| Australia | 1 | 1% |
| Canada | 1 | 1% |
| Unknown | 76 | 95% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 16 | 20% |
| Researcher | 14 | 18% |
| Student > Bachelor | 10 | 13% |
| Professor | 7 | 9% |
| Student > Master | 7 | 9% |
| Other | 16 | 20% |
| Unknown | 10 | 13% |
| Readers by discipline | Count | As % |
|---|---|---|
| Engineering | 19 | 24% |
| Neuroscience | 14 | 18% |
| Computer Science | 10 | 13% |
| Medicine and Dentistry | 8 | 10% |
| Psychology | 5 | 6% |
| Other | 7 | 9% |
| Unknown | 17 | 21% |
Attention Score in Context
This research output has an Altmetric Attention Score of 1. 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 03 September 2015.
All research outputs
#18,425,370
of 22,826,360 outputs
Outputs from Brain Informatics
#83
of 103 outputs
Outputs of similar age
#192,525
of 267,016 outputs
Outputs of similar age from Brain Informatics
#4
of 4 outputs
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