UCL logo

UCL Discovery

UCL home » Library Services » Electronic resources » UCL Discovery

Computational neuroimaging strategies for single patient predictions

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. Green open access

[img]
Preview
Text
1-s2.0-S1053811916302877-main.pdf - ["content_typename_Published version" not defined]

Download (3MB) | Preview

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 > 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 > 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: http://discovery.ucl.ac.uk/id/eprint/1502683
Downloads since deposit
249Downloads
Download activity - last month
Download activity - last 12 months
Downloads by country - last 12 months

Archive Staff Only

View Item View Item