Journal
NEUROIMAGE
Volume 105, Issue -, Pages 493-506Publisher
ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.neuroimage.2014.11.021
Keywords
Classification; Sparse models; Gaussian graphical models; Graphical LASSO; L1-norm SVM; Reproducibility/stability; Functional connectivity; Major depressive disorder; fMRI
Funding
- Wellcome Trust (UK) [WT086565/Z/08/Z]
- National Council for Scientific and Technological Development (Brazil) [201935/2012-0]
- Australian Research Council (Discovery Early Career Researcher Award) [DE130101393]
- Australian Research Council [DE130101393] Funding Source: Australian Research Council
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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 acquiredwhile participants performed a gender discrimination and emotional task, respectively, during the visualization of emotional valent faces. (C) 2014 The Authors. Published by Elsevier Inc.
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