Identifying modular relations in complex brain networks

Kasper Winther Andersen, Morten Mørup, Hartwig Siebner, Kristoffer H. Madsen, Lars Kai Hansen

    Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review


    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
    Publication date2012
    ISBN (Print)978-1-4673-1024-6
    Publication statusPublished - 2012
    Event2012 IEEE International Workshop on Machine Learning for Signal Processing - Santander, Spain
    Duration: 23 Oct 201226 Oct 2012
    Conference number: 22


    Conference2012 IEEE International Workshop on Machine Learning for Signal Processing
    Internet address
    SeriesMachine Learning for Signal Processing


    • Infinite Relational Model
    • Complex Networks
    • fMRI


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