eprintid: 1458480 rev_number: 34 eprint_status: archive userid: 608 dir: disk0/01/45/84/80 datestamp: 2014-12-17 04:53:25 lastmod: 2021-09-19 23:58:50 status_changed: 2014-12-17 04:53:25 type: article metadata_visibility: show item_issues_count: 0 creators_name: Rosa, MJ creators_name: Portugal, L creators_name: Hahn, T creators_name: Fallgatter, AJ creators_name: Garrido, MI creators_name: Shawe-Taylor, J creators_name: Mourao-Miranda, J title: Sparse network-based models for patient classification using fMRI. ispublished: pub divisions: UCL divisions: B04 divisions: C05 divisions: F48 keywords: Classification, Functional connectivity, Gaussian graphical models, Graphical LASSO, L1-norm SVM, Major depressive disorder, Reproducibility/stability, Sparse models, fMRI note: © 2014 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/3.0/). 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. date: 2015-01-15 official_url: http://dx.doi.org/10.1016/j.neuroimage.2014.11.021 vfaculties: VENG oa_status: green full_text_type: pub language: eng primo: open primo_central: open_green article_type_text: Journal Article, Research Support, Non-U.S. Gov't verified: verified_manual elements_source: PubMed elements_id: 999364 doi: 10.1016/j.neuroimage.2014.11.021 pii: S1053-8119(14)00938-0 lyricists_name: Duarte Rosa, Maria lyricists_name: Mourao-Miranda, Janaina lyricists_name: Shawe-Taylor, John lyricists_id: MJDRO79 lyricists_id: JMOUR63 lyricists_id: JSHAW87 full_text_status: public publication: Neuroimage volume: 105 pagerange: 493 - 506 event_location: United States citation: Rosa, MJ; Portugal, L; Hahn, T; Fallgatter, AJ; Garrido, MI; Shawe-Taylor, J; Mourao-Miranda, J; (2015) Sparse network-based models for patient classification using fMRI. Neuroimage , 105 493 - 506. 10.1016/j.neuroimage.2014.11.021 <https://doi.org/10.1016/j.neuroimage.2014.11.021>. Green open access document_url: https://discovery.ucl.ac.uk/id/eprint/1458480/1/1-s2.0-S1053811914009380-main.pdf