Schrouff, J;
Monteiro, JM;
Portugal, L;
Rosa, MJ;
Phillips, C;
Mourão-Miranda, J;
(2018)
Embedding Anatomical or Functional Knowledge in Whole-Brain Multiple Kernel Learning Models.
Neuroinformatics
, 16
(1)
pp. 117-143.
10.1007/s12021-017-9347-8.
Preview |
Text
10.1007_s12021-017-9347-8.pdf - Published Version Download (3MB) | Preview |
Abstract
Pattern recognition models have been increasingly applied to neuroimaging data over the last two decades. These applications have ranged from cognitive neuroscience to clinical problems. A common limitation of these approaches is that they do not incorporate previous knowledge about the brain structure and function into the models. Previous knowledge can be embedded into pattern recognition models by imposing a grouping structure based on anatomically or functionally defined brain regions. In this work, we present a novel approach that uses group sparsity to model the whole brain multivariate pattern as a combination of regional patterns. More specifically, we use a sparse version of Multiple Kernel Learning (MKL) to simultaneously learn the contribution of each brain region, previously defined by an atlas, to the decision function. Our application of MKL provides two beneficial features: (1) it can lead to improved overall generalisation performance when the grouping structure imposed by the atlas is consistent with the data; (2) it can identify a subset of relevant brain regions for the predictive model. In order to investigate the effect of the grouping in the proposed MKL approach we compared the results of three different atlases using three different datasets. The method has been implemented in the new version of the open-source Pattern Recognition for Neuroimaging Toolbox (PRoNTo).
| Type: | Article |
|---|---|
| Title: | Embedding Anatomical or Functional Knowledge in Whole-Brain Multiple Kernel Learning Models |
| Location: | United States |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1007/s12021-017-9347-8 |
| Publisher version: | http://doi.org/10.1007/s12021-017-9347-8 |
| Language: | English |
| Additional information: | © The Author(s) 2018. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http:// creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. |
| Keywords: | Anatomically defined regions, MATLAB software, Machine learning, Model interpretation, Multiple Kernel Learning, Neuroimaging |
| 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/10041391 |
Archive Staff Only
 |
View Item |
