@CONFERENCE\{IMM2012-06453,
    author       = "K. W. Andersen and M. M{\o}rup and H. Siebner and K. H. Madsen and L. K. Hansen",
    title        = "Identifying Modular Relations In Complex Brain Networks",
    year         = "2012",
    keywords     = "Infinite Relational Model, Complex Networks, fMRI",
    pages        = "1-6",
    booktitle    = "Machine Learning for Signal Processing (MLSP), 2012 {IEEE} International Workshop on",
    volume       = "",
    series       = "",
    editor       = "",
    publisher    = "",
    organization = "",
    address      = "",
    url          = "http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=6349739&contentType=Conference+Publications&searchField%3DSearch_All%26queryText%3Dindentifying+modular+relations+in+brain+networks",
    abstract     = "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.",
    isbn_issn    = "1551-2541"
}