UCL Discovery
UCL home » Library Services » Electronic resources » UCL Discovery

Sparse network-based models for patient classification using fMRI.

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. Green open access

[thumbnail of 1-s2.0-S1053811914009380-main.pdf]
Preview
Text
1-s2.0-S1053811914009380-main.pdf

Download (1MB) | Preview

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.

Type: Article
Title: Sparse network-based models for patient classification using fMRI.
Location: United States
Open access status: An open access version is available from UCL Discovery
DOI: 10.1016/j.neuroimage.2014.11.021
Publisher version: http://dx.doi.org/10.1016/j.neuroimage.2014.11.021
Language: English
Additional information: © 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/).
Keywords: Classification, Functional connectivity, Gaussian graphical models, Graphical LASSO, L1-norm SVM, Major depressive disorder, Reproducibility/stability, Sparse models, fMRI
UCL classification: UCL
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
URI: https://discovery.ucl.ac.uk/id/eprint/1458480
Downloads since deposit
151Downloads
Download activity - last month
Download activity - last 12 months
Downloads by country - last 12 months

Archive Staff Only

View Item View Item