eprintid: 10058961
rev_number: 17
eprint_status: archive
userid: 608
dir: disk0/10/05/89/61
datestamp: 2018-10-25 14:58:54
lastmod: 2021-09-28 22:27:17
status_changed: 2018-10-25 14:58:54
type: proceedings_section
metadata_visibility: show
creators_name: Ferreira, FS
creators_name: Rosa, MJ
creators_name: Moutoussis, M
creators_name: Dolan, R
creators_name: Shawe-Taylor, J
creators_name: Ashburner, J
creators_name: Mourao-Miranda, J
title: Sparse PLS hyper-parameters optimisation for investigating brain-behaviour relationships
ispublished: pub
divisions: UCL
divisions: B02
divisions: C07
divisions: D07
divisions: F83
divisions: B04
divisions: C05
divisions: F48
keywords: Tuning parameters; High-dimensionality; Brainbehaviour;
Regularisation; Sparse PLS
note: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
abstract: Unsupervised learning approaches, such as Partial Least Squares, can be used to investigate relationships between multiple sources of data, such as neuroimaging and behavioural data. In cases of high-dimensional datasets with limited number of examples (e.g. neuroimaging data) there is a need for regularisation to enable the solution of the ill-posed problem and prevent overfitting. Different approaches have been proposed to optimise the regularisation parameters in unsupervised models, however, so far, there has been no comparison between the different approaches using the same data. In this work, two optimisation frameworks (i.e. a permutation and a train/test framework) were compared using sparse PLS to investigate associations between brain connectivity and behaviour data. Both frameworks were able to identify at least one brain-behaviour associative effect. A second brain-behaviour effect was only found using the train/test framework. More importantly, the results show that the multivariate associative effects found with the train/test framework generalise better to new data, suggesting that results based on the permutation framework should be carefully interpreted.
date: 2018
date_type: published
publisher: IEEE
official_url: https://doi.org/10.1109/PRNI.2018.8423947
oa_status: green
full_text_type: other
language: eng
primo: open
primo_central: open_green
verified: verified_manual
elements_id: 1576491
doi: 10.1109/PRNI.2018.8423947
isbn_13: 9781538668597
lyricists_name: Ashburner, John
lyricists_name: Dolan, Raymond
lyricists_name: Mourao-Miranda, Janaina
lyricists_name: Moutoussis, Michael
lyricists_name: Santos Ferreira, Fabio
lyricists_name: Shawe-Taylor, John
lyricists_id: JTASH57
lyricists_id: RJDOL46
lyricists_id: JMOUR63
lyricists_id: MMOUT81
lyricists_id: FDSFE64
lyricists_id: JSHAW87
actors_name: Santos Ferreira, Fabio
actors_id: FDSFE64
actors_role: owner
full_text_status: public
publication: 2018 International Workshop on Pattern Recognition in Neuroimaging, PRNI 2018
event_title: 2018 International Workshop on Pattern Recognition in Neuroimaging (PRNI), 12-14 June 2018, Singapore
book_title: 2018 International Workshop on Pattern Recognition in Neuroimaging (PRNI)
citation:        Ferreira, FS;    Rosa, MJ;    Moutoussis, M;    Dolan, R;    Shawe-Taylor, J;    Ashburner, J;    Mourao-Miranda, J;      (2018)    Sparse PLS hyper-parameters optimisation for investigating brain-behaviour relationships.                     In:  2018 International Workshop on Pattern Recognition in Neuroimaging (PRNI).    IEEE       Green open access   
 
document_url: https://discovery.ucl.ac.uk/id/eprint/10058961/1/FerreiraFS_PRNI2018.pdf