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Multiple hold-outs with stability: improving the generalizability of machine learning analyses of brain-behaviour relationships

Mihalik, A; Ferreira, F; Moutoussis, M; Ziegler, G; Adams, RA; Rosa, MJ; Prabhu, G; ... Mourao-Miranda, J; + view all (2020) Multiple hold-outs with stability: improving the generalizability of machine learning analyses of brain-behaviour relationships. Biological Psychiatry , 87 (4) pp. 368-376. 10.1016/j.biopsych.2019.12.001. Green open access

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Abstract

Background: In 2009, the National Institute of Mental Health launched the Research Domain Criteria (RDoC), a paradigm shift in psychiatry highlighting the need to move beyond the currently used diagnostic categories, and ultimately promoting precision psychiatry. RDoC is a research framework integrating different levels of measures (e.g. brain imaging and behaviour) with the aim of understanding the basic dimensions of functioning from normal to abnormal. / Methods: Here, we propose an innovative machine learning framework combined with sparse partial least squares (SPLS) to identify hidden dimensions of brain-behaviour associations, therefore a potential analytic tool to subserve the RDoC ideal. To illustrate the approach, we investigate structural brain-behaviour associations in an extensively phenotyped developmental sample of 345 participants (312 healthy, 33 clinically depressed). The brain data consisted of whole brain grey matter volumes, the behavioural data included item-level self-report questionnaires, IQ and demographic measures. / Results: SPLS captured two hidden dimensions of brain-behaviour relationships: one related to age and drinking and the other one to depression. The applied machine learning framework indicates that these results are stable and generalize well to new data. Indeed, the identified brain-behaviour associations are in agreement with previous findings in the literature concerning age, alcohol use and depression-related changes in brain volume. / Conclusion: SPLS embedded in our novel framework is a promising tool to link behaviour/symptoms to neurobiology, thus it has a great potential to contribute to a biologically grounded definition of psychiatric diseases.

Type: Article
Title: Multiple hold-outs with stability: improving the generalizability of machine learning analyses of brain-behaviour relationships
Open access status: An open access version is available from UCL Discovery
DOI: 10.1016/j.biopsych.2019.12.001
Publisher version: https://doi.org/10.1016/j.biopsych.2019.12.001
Language: English
Additional information: This is an Open Access article published under a Creative Commons Attribution 4.0 International (CC BY 4.0) licence (https://creativecommons.org/licenses/by/4.0/).
Keywords: RDoC, Brain-behaviour relationship, SPLS, Framework, Depression, Adolescence
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 > Div of Psychology and Lang Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > Div of Psychology and Lang Sciences > Clinical, Edu and Hlth Psychology
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/10087653
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