TY - JOUR A1 - Rosa, Maria J. A1 - Portugal, Liana A1 - Hahn, Tim A1 - Fallgatter, Andreas J. A1 - Garrido, Marta I. A1 - Shawe-Taylor, John A1 - MourĂ£o-Miranda, Janaina T1 - Sparse network-based models for patient classification using fMRI T2 - NeuroImage N2 - 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. KW - Classification KW - Sparse models KW - Gaussian graphical models KW - Graphical LASSO KW - L1-norm SVM KW - Reproducibility/stability KW - Functional connectivity KW - Major depressive disorder KW - fMRI Y1 - 2014 UR - http://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/77202 UR - https://nbn-resolving.org/urn:nbn:de:hebis:30:3-772021 SN - 1053-8119 VL - 105.2015 SP - 493 EP - 506 PB - Elsevier CY - Amsterdam ER -