Utility of Partial Correlation for Characterising Brain Dynamics: MVPA-based Assessment of Regularisation and Network Selection

Correlation and partial correlation are often used to provide a characterisation of the network properties of the human brain, based on functional brain imaging data. However, for partial correlation, the choice of network nodes (brain regions) and regularisation parameters is crucial and not yet we...

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Bibliographic Details
Published in2013 International Workshop on Pattern Recognition in Neuroimaging pp. 58 - 61
Main Authors Duff, Eugene, Makin, Tamar, Madugula, Sasidhar, Smith, Stephen M., Woolrich, Mark W.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.06.2013
Subjects
Accuracy
Brain
Correlation
Covariance matrices
Imaging
Magnetic resonance
partial
prediction
resting-state
Visualization
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Summary:Correlation and partial correlation are often used to provide a characterisation of the network properties of the human brain, based on functional brain imaging data. However, for partial correlation, the choice of network nodes (brain regions) and regularisation parameters is crucial and not yet well explored. Here we assess a number of approaches by calculating how each approach performs when used to discriminate different ongoing states of brain activity. We find evidence that partial correlation matrices, when estimated with appropriate regularisation, can provide a useful characterisation of brain functional connectivity.
DOI:10.1109/PRNI.2013.24