Stephan, KE;
Schlagenhauf, F;
Huys, QJ;
Raman, S;
Aponte, EA;
Brodersen, KH;
Rigoux, L;
... Heinz, A; + view all
(2017)
Computational neuroimaging strategies for single patient predictions.
NeuroImage
, 145
(Pt B)
pp. 180-199.
10.1016/j.neuroimage.2016.06.038.
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Abstract
Neuroimaging increasingly exploits machine learning techniques in an attempt to achieve clinically relevant single-subject predictions. An alternative to machine learning, which tries to establish predictive links between features of the observed data and clinical variables, is the deployment of computational models for inferring on the (patho)physiological and cognitive mechanisms that generate behavioural and neuroimaging responses. This paper discusses the rationale behind a computational approach to neuroimaging-based single-subject inference, focusing on its potential for characterising disease mechanisms in individual subjects and mapping these characterisations to clinical predictions. Following an overview of two main approaches - Bayesian model selection and generative embedding - which can link computational models to individual predictions, we review how these methods accommodate heterogeneity in psychiatric and neurological spectrum disorders, help avoid erroneous interpretations of neuroimaging data, and establish a link between a mechanistic, model-based approach and the statistical perspectives afforded by machine learning.
| Type: | Article |
|---|---|
| Title: | Computational neuroimaging strategies for single patient predictions |
| Location: | United States |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1016/j.neuroimage.2016.06.038 |
| Publisher version: | http://doi.org/10.1016/j.neuroimage.2016.06.038 |
| Language: | English |
| Additional information: | Copyright © 2017 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). |
| Keywords: | Bayesian, Classification, Clustering, Computational psychiatry, EEG, Generative embedding, Generative model, Model comparison, Model evidence, Model selection, Translational neuromodeling, fMRI |
| UCL classification: | UCL 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 > Division of Psychiatry 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 |
| URI: | https://discovery.ucl.ac.uk/id/eprint/1502683 |
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