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  Computational neuroimaging strategies for single patient predictions

Stephan, K. E., Schlagenhauf, F., Huys, Q. J. M., Raman, S., Aponte, E. A., Brodersen, K. H., et al. (2017). Computational neuroimaging strategies for single patient predictions. NeuroImage, 145(Part B), 180-199. doi:10.1016/j.neuroimage.2016.06.038.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-002B-8252-F Version Permalink: http://hdl.handle.net/21.11116/0000-0003-C543-3
Genre: Journal Article

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 Creators:
Stephan, K. E.1, 2, 3, Author
Schlagenhauf, Florian4, 5, Author              
Huys, Quentin J. M.1, 6, Author
Raman, Sudhir1, Author
Aponte, Eduardo A.1, Author
Brodersen, Kay H.1, Author
Rigoux, L.1, 3, Author
Moran, Rosalyn J.2, 7, Author
Daunizeau, J.1, 8, Author
Dolan, R. J.2, 9, Author
Friston, K. J.2, Author
Heinz, Andrea4, 10, Author
Affiliations:
1Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich, Switzerland, ou_persistent22              
2Wellcome Trust Centre for Neuroimaging, University College London, United Kingdom, ou_persistent22              
3Max Planck Institute for Metabolism Research, Cologne, Germany, ou_persistent22              
4Department of Psychiatry and Psychotherapy, Charité University Medicine Berlin, Germany, ou_persistent22              
5Department Neurology, MPI for Human Cognitive and Brain Sciences, Max Planck Society, ou_634549              
6Department of Psychiatry, Psychotherapy and Psychosomatics, University Hospital Zurich, Switzerland, ou_persistent22              
7Virginia Polytechnic Institute and State University, Blacksburg, VA, USA, ou_persistent22              
8Institut du Cerveau et de la Moelle épinière (ICM), Paris, France, ou_persistent22              
9Max Planck UCL Centre for Computational Psychiatry and Ageing Research, London, United Kingdom, ou_persistent22              
10Berlin School of Mind and Brain, Humboldt University Berlin, Germany, ou_persistent22              

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Free keywords: Generative model; fMRI; EEG; Bayesian; Model selection; Model comparison; Model evidence; Generative embedding; Classification; Clustering; Computational psychiatry; Translational neuromodeling
 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.

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Language(s): eng - English
 Dates: 2016-05-212015-11-272016-06-202016-06-222017-01-15
 Publication Status: Published in print
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Method: Peer
 Identifiers: DOI: 10.1016/j.neuroimage.2016.06.038
PMID: 27346545
Other: Epub 2016
 Degree: -

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Project name : -
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Funding program : -
Funding organization : René and Susanne Braginsky Foundation
Project name : Molecular Imaging
Grant ID : -
Funding program : Clinical Research Priority Program (CRPP)
Funding organization : University Zurich
Project name : Multiple Sclerosis
Grant ID : -
Funding program : Clinical Research Priority Program (CRPP)
Funding organization : University Zurich
Project name : Essverhalten: Homöostase und Belohnungssysteme / TRR 134
Grant ID : -
Funding program : -
Funding organization : Deutsche Forschungsgemeinschaft (DFG)
Project name : -
Grant ID : 320030L_153449/1
Funding program : -
Funding organization : Swiss National Science Foundation
Project name : -
Grant ID : -
Funding program : -
Funding organization : Wellcome Trust

Source 1

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Title: NeuroImage
Source Genre: Journal
 Creator(s):
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Publ. Info: Orlando, FL : Academic Press
Pages: - Volume / Issue: 145 (Part B) Sequence Number: - Start / End Page: 180 - 199 Identifier: ISSN: 1053-8119
CoNE: /journals/resource/954922650166