Conference paper
Identifying modular relations in complex brain networks
We evaluate the infinite relational model (IRM) against two simpler alternative nonparametric Bayesian models for identifying structures in multi subject brain networks. The models are evaluated for their ability to predict new data and infer reproducible structures. Prediction and reproducibility are measured within the data driven NPAIRS split-half framework.
Using synthetic data drawn from each of the generative models we show that the IRM model outperforms the two competing models when data contain relational structure. For data drawn from the other two simpler models the IRM does not overfit and obtains comparable reproducibility and predictability. For resting state functional magnetic resonance imaging data from 30 healthy controls the IRM model is also superior to the two simpler alternatives, suggesting that brain networks indeed exhibit universal complex relational structure in the population.
| Language: | English |
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| Publisher: | IEEE |
| Year: | 2012 |
| Pages: | 1-6 |
| Proceedings: | 2012 IEEE International Workshop on Machine Learning for Signal Processing |
| Series: | Machine Learning for Signal Processing |
| ISBN: | 1467310247 , 1467310255 , 1467310263 , 9781467310246 , 9781467310253 and 9781467310260 |
| ISSN: | 21610363 and 15512541 |
| Types: | Conference paper |
| DOI: | 10.1109/MLSP.2012.6349739 |
| ORCIDs: | Mørup, Morten and Hansen, Lars Kai |
Bayes methods Bayesian methods Brain modeling Communities Data models IRM model Mathematical model Predictive models Sociology alternative nonparametric Bayesian models brain complex brain networks complex relational structure data driven NPAIRS split-half framework infinite relational model magnetic resonance imaging medical image processing modular relations identification multi subject brain networks neural nets neurophysiology reproducible structures state functional magnetic resonance imaging data synthetic data
