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Sparse PLS hyper-parameters optimisation for investigating brain-behaviour relationships

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

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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.

Type: Proceedings paper
Title: Sparse PLS hyper-parameters optimisation for investigating brain-behaviour relationships
Event: 2018 International Workshop on Pattern Recognition in Neuroimaging (PRNI), 12-14 June 2018, Singapore
ISBN-13: 9781538668597
Open access status: An open access version is available from UCL Discovery
DOI: 10.1109/PRNI.2018.8423947
Publisher version: https://doi.org/10.1109/PRNI.2018.8423947
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Tuning parameters; High-dimensionality; Brainbehaviour; Regularisation; Sparse PLS
UCL classification: UCL
UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology > Imaging Neuroscience
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/10058961
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