Name
Title | ||
|---|---|---|
An ant colony optimization algorithm for learning brain effective connectivity network from fMRI data |
Abstract | ||
|---|---|---|
Identifying brain effective connectivity networks from functional magnetic resonance imaging (fMRI) data is an important advanced subject in neuroinformatics in recent years, where the learning method based on bayesian networks (BN) has become a new hot topic in the field. This paper proposes a new method to learn the brain effective connectivity network structure by combining ant colony optimization (ACO) with BN method, named as ACOEC. In the proposed algorithm, a brain effective connectivity network is first mapped onto an ant, and then the ant colony optimization by simulating real ants looking for food is employed to construct network structures and finally an ant with the highest score is obtained as the optimal solution. The experimental results on simulated and real fMRI data sets show that the new method can not only accurately identify the connections and directions of the brain networks, but also quantitatively describe the connection strength of the brain networks, which has a good clinical application prospects. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/BIBM.2016.7822546 | 2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) |
Keywords | Field | DocType |
fMRI,Brain effective connectivity network,Bayesian network,Ant colony optimization,Connection strength | Ant colony optimization algorithms,Neuroinformatics,Data set,Functional magnetic resonance imaging,Computer science,Algorithm,Bayesian network,Artificial intelligence,Machine learning,Network structure | Conference |
ISSN | ISBN | Citations |
2156-1125 | 978-1-5090-1612-9 | 1 |
PageRank | References | Authors |
0.36 | 13 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jinduo Liu | 1 | 11 | 2.52 |
| Junzhong Ji | 2 | 222 | 29.30 |
| Aidong Zhang | 3 | 2970 | 405.63 |
| Peipeng Liang | 4 | 74 | 10.40 |