A hierarchical model for integrating unsupervised generative embedding and empirical Bayes.

Journal: Journal of neuroscience methods
Published Date: Apr 30, 2016

Abstract

BACKGROUND: Generative models of neuroimaging data, such as dynamic causal models (DCMs), are commonly used for inferring effective connectivity from individual subject data. Recently introduced "generative embedding" approaches have used DCM-based connectivity parameters for supervised classification of individual patients or to find unknown subgroups in heterogeneous groups using unsupervised clustering methods.

Authors

  • Sudhir Raman
    Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich and ETH Zurich, Switzerland. Electronic address: ssudhir@ethz.ch.
  • Lorenz Deserno
    Department of Psychiatry and Psychotherapy, Charité Universitätsmedizin Berlin, Berlin, Germany; Max Planck Fellow Group "Cognitive and Affective Control of Behavioral Adaptation", Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany; Department of Neurology, Otto-von-Guericke University, Magdeburg, Germany.
  • Florian Schlagenhauf
    Department of Psychiatry and Psychotherapy, Charité Universitätsmedizin Berlin, Berlin, Germany; Max Planck Fellow Group "Cognitive and Affective Control of Behavioral Adaptation", Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany.
  • Klaas Enno Stephan
    Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich and ETH Zurich, Switzerland; Wellcome Trust Centre for Neuroimaging, University College London, UK; Max Planck Institute for Metabolism Research, Cologne, Germany.

Keywords

External Resources

Popular Topics
Recent Journals