Identifying modular relations in complex brain networks

Publication: Research - peer-reviewArticle in proceedings – Annual report year: 2012

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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.
Original languageEnglish
Title of host publication2012 IEEE International Workshop on Machine Learning for Signal Processing (MLSP)
Number of pages6
Place of publication978-1-4673-1025-3
PublisherIEEE
Publication date2012
ISBN (print)978-1-4673-1024-6
DOIs
StatePublished

Conference

Conference2012 IEEE International Workshop on Machine Learning for Signal Processing (MLSP)
CountrySpain
CitySantander
Period23/10/1226/10/12
Internet addresshttp://mlsp2012.conwiz.dk/
NameMachine Learning for Signal Processing
ISSN (Print)1551-2541
CitationsWeb of Science® Times Cited: No match on DOI

Keywords

  • Infinite Relational Model, Complex Networks, fMRI
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