Name
Abstract | ||
|---|---|---|
Brain functional network has become an increasingly used approach in understanding brain functions and diseases. Many network construction methods have been developed, whereas the majority of the studies still used static pairwise Pearson's correlation-based functional connectivity. The goal of this work is to introduce a toolbox namely "Brain Network Construction and Classification" (BrainNetClass) to the field to promote more advanced brain network construction methods. It comprises various brain network construction methods, including some state-of-the-art methods that were recently developed to capture more complex interactions among brain regions along with connectome feature extraction, reduction, parameter optimization towards network-based individualized classification. BrainNetClass is a MATLAB-based, open-source, cross-platform toolbox with graphical user-friendly interfaces for cognitive and clinical neuroscientists to perform rigorous computer-aided diagnosis with interpretable result presentations even though they do not possess neuroimage computing and machine learning knowledge. We demonstrate the implementations of this toolbox on real resting-state functional MRI datasets. BrainNetClass (v1.0) can be downloaded from https://github.com/zzstefan/BrainNetClass. |
Year | Venue | DocType |
|---|---|---|
2019 | CoRR | Journal |
Volume | Citations | PageRank |
abs/1906.09908 | 0 | 0.34 |
References | Authors | |
0 | 8 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhen Zhou | 1 | 0 | 1.01 |
| Xiaobo Chen | 2 | 350 | 32.59 |
| Yu Zhang | 3 | 0 | 0.68 |
| Lishan Qiao | 4 | 552 | 19.44 |
| Renping Yu | 5 | 0 | 0.34 |
| Gang Pan | 6 | 0 | 1.35 |
| Han Zhang | 7 | 20 | 8.76 |
| Dinggang Shen | 8 | 7837 | 611.27 |