Name
Title | ||
|---|---|---|
Classifying early and late mild cognitive impairment stages of Alzheimer's disease by fusing default mode networks extracted with multiple seeds. |
Abstract | ||
|---|---|---|
In this study, we applied fusion analysis to the DMNs extracted by using different seeds for exploiting the complementary information hidden among the separately extracted DMNs, and the results supported our expectation that using the complementary information can improve classification accuracy. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1186/s12859-018-2528-0 | BMC Bioinformatics |
Keywords | Field | DocType |
Alzheimer’s disease,Classification,Default mode network,Joint independent component analysis,Seeding-based analysis | Default mode network,Pattern recognition,Biology,Resting state fMRI,Posterior parietal cortex,Artificial intelligence,Bioinformatics,Posterior cingulate,Support vector machine classification,Cognitive impairment | Journal |
Volume | Issue | ISSN |
19 | Suppl 19 | 1471-2105 |
Citations | PageRank | References |
0 | 0.34 | 25 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shengbing Pei | 1 | 0 | 0.34 |
| Jihong Guan | 2 | 657 | 81.13 |
| Shuigeng Zhou | 3 | 2089 | 207.00 |