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