Sparse Network-Based Models for Patient Classification Using fMRI

Pattern recognition applied to whole-brain neuroimaging data, such as functional Magnetic Resonance Imaging (fMRI), has been successful at discriminating psychiatric patients from healthy subjects. However, predictive patterns obtained from whole-brain voxel-based features are difficult to interpret...

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Bibliographic Details
Published in2013 International Workshop on Pattern Recognition in Neuroimaging pp. 66 - 69
Main Authors Rosa, Maria J., Portugal, Liana, Shawe-Taylor, John, Mourao-Miranda, Janaina
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.06.2013
Subjects
Accuracy
Brain models
Correlation
Covariance matrices
fMRI
functional brain connectivity
graphical LASSO
Graphical models
L1-norm SVM
major depression disorder
sparse models
Support vector machines
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Summary:Pattern recognition applied to whole-brain neuroimaging data, such as functional Magnetic Resonance Imaging (fMRI), has been successful at discriminating psychiatric patients from healthy subjects. However, predictive patterns obtained from whole-brain voxel-based features are difficult to interpret in terms of the underlying neurobiology. As is generally accepted, many psychiatric disorders, such as depression and schizophrenia, are brain connectivity disorders. Therefore, pattern recognition based on network models should provide more scientific insight and potentially more powerful predictions than voxel-based approaches. Here, we build a sparse network-based discriminative modelling framework, based on Gaussian graphical models and L1-norm regularised linear Support Vector Machines (SVM). The proposed framework provides easier pattern interpretation in terms of underlying network changes between groups, and we illustrate our technique by classifying patients with depression and controls, using fMRI data from a sad facial processing task.
DOI:10.1109/PRNI.2013.26