Sparse network-based models for patient classification using fMRI

Rosa, Maria J., Portugal, Liana, Hahn, Tim, Fallgatter, Andreas J., Garrido, Marta I., Shawe-Taylor, John and Mourao-Miranda, Janaina (2015) Sparse network-based models for patient classification using fMRI. NeuroImage, 105 493-506. doi:10.1016/j.neuroimage.2014.11.021

Author Rosa, Maria J.
Portugal, Liana
Hahn, Tim
Fallgatter, Andreas J.
Garrido, Marta I.
Shawe-Taylor, John
Mourao-Miranda, Janaina
Title Sparse network-based models for patient classification using fMRI
Journal name NeuroImage   Check publisher's open access policy
ISSN 1095-9572
Publication date 2015-01-05
Year available 2014
Sub-type Article (original research)
DOI 10.1016/j.neuroimage.2014.11.021
Open Access Status DOI
Volume 105
Start page 493
End page 506
Total pages 14
Place of publication Amsterdam, Netherlands
Publisher Academic Press [Elsevier]
Collection year 2015
Language eng
Abstract Pattern recognition applied to whole-brain neuroimaging data, such as functional Magnetic Resonance Imaging (fMRI), has proved successful at discriminating psychiatric patients from healthy participants. However, predictive patterns obtained from whole-brain voxel-based features are difficult to interpret in terms of the underlying neurobiology. Many psychiatric disorders, such as depression and schizophrenia, are thought to be brain connectivity disorders. Therefore, pattern recognition based on network models might provide deeper insights and potentially more powerful predictions than whole-brain voxel-based approaches. Here, we build a novel sparse network-based discriminative modeling framework, based on Gaussian graphical models and L1-norm regularized linear Support Vector Machines (SVM). In addition, the proposed framework is optimized in terms of both predictive power and reproducibility/stability of the patterns. Our approach aims to provide better pattern interpretation than voxel-based whole-brain approaches by yielding stable brain connectivity patterns that underlie discriminative changes in brain function between the groups. We illustrate our technique by classifying patients with major depressive disorder (MDD) and healthy participants, in two (event- and block-related) fMRI datasets acquired while participants performed a gender discrimination and emotional task, respectively, during the visualization of emotional valent faces.
Keyword Classification
Functional connectivity
Gaussian graphical models
Graphical LASSO
L1-norm SVM
Major depressive disorder
Sparse models
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: Queensland Brain Institute Publications
Official 2015 Collection
Centre for Advanced Imaging Publications
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Citation counts: TR Web of Science Citation Count  Cited 6 times in Thomson Reuters Web of Science Article | Citations
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