Name
Abstract | ||
|---|---|---|
The aim of the present study was to understand whether modeling brain function in terms of network structure makes it possible to find markers of prediction of motor learning performance in a sensory motor learning task. By applying graph theory indexes of brain segregation – such as modularity and transitivity – to functional connectivity data derived from electroencephalographic (EEG) rhythms, we further studied pre- (baseline) versus post-task brain network architecture to understand whether motor learning induces changes in functional brain connectivity. The results showed that, after the training session with measurable learning, transitivity increased in the alpha1 EEG frequency band and modularity increased in the theta band and decreased in the gamma band, suggesting that brain segregation is modulated by the cognitive task. Furthermore, it was observed that theta modularity at the baseline negatively correlated with the performance improvement; namely, the lower this connectivity index at the baseline pre-task period, the higher the improvement of performance with training. The present results show that brain segregation is modulated by the cognitive task and that it is possible to predict performance by the study of pre-task EEG rhythm connectivity parameters. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1016/j.neunet.2018.07.005 | Neural Networks |
Keywords | Field | DocType |
EEG,Graph theory,Learning,Connectivity,Brain segregation,Precision medicine | Brain mapping,Neuroscience,Motor learning,Nerve net,Artificial intelligence,Gamma Rhythm,Cognition,Sensory system,Machine learning,Mathematics,Modularity,Electroencephalography | Journal |
Volume | Issue | ISSN |
106 | 1 | 0893-6080 |
Citations | PageRank | References |
1 | 0.38 | 8 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Miraglia, F | 1 | 3 | 2.48 |
| Fabrizio Vecchio | 2 | 13 | 4.06 |
| Paolo Maria Rossini | 3 | 60 | 12.06 |