title: Sparse network-based models for patient classification using fMRI.
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.
authors.affiliation: Department of Computer Science, Centre for Computational Statistics and Machine Learning, University College London, London, UK; Centre for Neuroimaging Sciences, Department of Neuroimaging, Institute of Psychiatry, King's College London, London, UK. Electronic address: m.rosa@ucl.ac.uk.
authors.forename: Maria J
authors.initials: MJ
authors.lastname: Rosa
authors.affiliation: Department of Computer Science, Centre for Computational Statistics and Machine Learning, University College London, London, UK; LABNEC, Universidade Federal Fluminense, Rio de Janeiro, Brazil.
authors.forename: Liana
authors.initials: L
authors.lastname: Portugal
authors.affiliation: Department of Cognitive Psychology II, Johann Wolfgang Goethe University Frankfurt am Main, Germany.
authors.forename: Tim
authors.initials: T
authors.lastname: Hahn
authors.affiliation: University of Tuebingen, Department of Psychiatry and Psychotherapy, Tuebingen, Germany.
authors.forename: Andreas J
authors.initials: AJ
authors.lastname: Fallgatter
authors.affiliation: Queensland Brain Institute, The University of Queensland, Brisbane, Australia; Centre for Advanced Imaging, The University of Queensland, Brisbane, Australia; Australian Research Council Centre of Excellence for Integrative Brain Function, Australia.
authors.forename: Marta I
authors.initials: MI
authors.lastname: Garrido
authors.affiliation: Department of Computer Science, Centre for Computational Statistics and Machine Learning, University College London, London, UK.
authors.forename: John
authors.initials: J
authors.lastname: Shawe-Taylor
authors.affiliation: Department of Computer Science, Centre for Computational Statistics and Machine Learning, University College London, London, UK.
authors.forename: Janaina
authors.initials: J
authors.lastname: Mourao-Miranda
mesh_terms: D000328:Adult; D001921:Brain / Q000503:physiopathology*; D001931:Brain Mapping / Q000379:methods*; D003865:Major Depressive Disorder / Q000175:diagnosis*; D005260:Female; D006801:Humans; D008279:Magnetic Resonance Imaging / Q000379:methods; D008297:Male; D008875:Middle Aged; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated / Q000379:methods*
keywords: Classification; Functional connectivity; Gaussian graphical models; Graphical LASSO; L1-norm SVM; Major depressive disorder; Reproducibility/stability; Sparse models; fMRI