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Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms
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Graph classification using signal-subgraphs: applications in statistical connectomics.

Joshua T Vogelstein1, William Gray Roncal, R Jacob Vogelstein

  • 1Department of Mathematics and Statistics, Duke University, PO Box 90251, 214 Old Chemistry, Research Drive, Durham, NC 27708-0251, USA. jovo@stat.duke.edu

IEEE Transactions on Pattern Analysis and Machine Intelligence
|May 18, 2013
PubMed
Summary
This summary is machine-generated.

This study introduces a statistical model for graph classification, identifying key "signal-subgraphs" to predict graph classes. The model accurately classifies brain connectomes by sex, outperforming benchmarks.

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Area of Science:

  • Graph theory
  • Statistical modeling
  • Machine learning

Background:

  • Graph classification is crucial for analyzing complex network data.
  • Identifying class-conditional signals within graphs is a key challenge.
  • Existing methods may not fully capture signal-subgraph properties.

Purpose of the Study:

  • To develop a statistical model for graph classification.
  • To identify and estimate the
  • signal-subgraph
  • ,

Main Methods:

  • Proposed a statistical model for graph/class pairs.
  • Developed estimators for class-conditional signals (signal-subgraphs).
  • Evaluated estimators based on signal-subgraph coherency and sample size via simulation.

Main Results:

  • Classifiers derived from estimators are asymptotically optimal and efficient.
  • Performance depends on signal-subgraph coherency and training sample size.
  • Successfully classified brain connectomes by sex, outperforming benchmarks.

Conclusions:

  • The proposed model and estimators offer a powerful approach to graph classification.
  • Signal-subgraph estimation requires careful consideration of sample size.
  • The method shows promise for neuroscience applications like sex-based connectome classification.