| Title |
Automatic Classification of Early Parkinson's Disease with Multi-Modal MR Imaging
|
|---|---|
| Published in |
PLOS ONE, November 2012
|
| DOI | 10.1371/journal.pone.0047714 |
| Pubmed ID | |
| Authors |
Dan Long, Jinwei Wang, Min Xuan, Quanquan Gu, Xiaojun Xu, Dexing Kong, Minming Zhang |
| Abstract |
In recent years, neuroimaging has been increasingly used as an objective method for the diagnosis of Parkinson's disease (PD). Most previous studies were based on invasive imaging modalities or on a single modality which was not an ideal diagnostic tool. In this study, we developed a non-invasive technology intended for use in the diagnosis of early PD by integrating the advantages of various modals. |
X Demographics
The data shown below were collected from the profiles of 5 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Peru | 1 | 20% |
| United Kingdom | 1 | 20% |
| Egypt | 1 | 20% |
| Unknown | 2 | 40% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Science communicators (journalists, bloggers, editors) | 2 | 40% |
| Members of the public | 2 | 40% |
| Practitioners (doctors, other healthcare professionals) | 1 | 20% |
Mendeley readers
The data shown below were compiled from readership statistics for 177 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 5 | 3% |
| United Kingdom | 1 | <1% |
| Germany | 1 | <1% |
| Unknown | 170 | 96% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 31 | 18% |
| Student > Master | 27 | 15% |
| Student > Ph. D. Student | 25 | 14% |
| Student > Bachelor | 15 | 8% |
| Student > Doctoral Student | 11 | 6% |
| Other | 27 | 15% |
| Unknown | 41 | 23% |
| Readers by discipline | Count | As % |
|---|---|---|
| Neuroscience | 25 | 14% |
| Medicine and Dentistry | 24 | 14% |
| Psychology | 23 | 13% |
| Computer Science | 21 | 12% |
| Engineering | 20 | 11% |
| Other | 18 | 10% |
| Unknown | 46 | 26% |