Computational neuroimaging strategies for single patient predictions

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 com...

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Published inNeuroImage (Orlando, Fla.) Vol. 145; no. Pt B; pp. 180 - 199
Main Authors Stephan, K.E., Schlagenhauf, F., Huys, Q.J.M., Raman, S., Aponte, E.A., Brodersen, K.H., Rigoux, L., Moran, R.J., Daunizeau, J., Dolan, R.J., Friston, K.J., Heinz, A.
Format Journal Article
LanguageEnglish
Published United States Elsevier Inc 15.01.2017
Elsevier Limited
Subjects
Attention deficit hyperactivity disorder
Bayesian
Brain Diseases - diagnostic imaging
Classification
Clustering
Computational psychiatry
Decision making
Disease
EEG
fMRI
Generative embedding
Generative model
Humans
Medical imaging
Mental Disorders - diagnostic imaging
Model comparison
Model evidence
Model selection
Models, Theoretical
Neuroimaging - methods
Patients
Physiology
Schizophrenia
Translational neuromodeling
fMRI
Computational psychiatry
Generative embedding
Model selection
EEG
Bayesian
Classification
Translational neuromodeling
Model comparison
Clustering
Generative model
Model evidence
Online AccessGet full text
ISSN1053-8119
1095-9572
1095-9572
DOI10.1016/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. •Reviews computational neuroimaging strategies for single patient predictions.•Generative models for inferring individual disease mechanisms in psychiatry and neurology.•Mapping inferred mechanisms to clinical predictions by Bayesian model selection and•generative embedding.•Links a mechanistic model-based approach to statistical perspectives by machine learning.
AbstractList 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.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.
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. •Reviews computational neuroimaging strategies for single patient predictions.•Generative models for inferring individual disease mechanisms in psychiatry and neurology.•Mapping inferred mechanisms to clinical predictions by Bayesian model selection and•generative embedding.•Links a mechanistic model-based approach to statistical perspectives by machine learning.
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.
Author Dolan, R.J.
Brodersen, K.H.
Schlagenhauf, F.
Huys, Q.J.M.
Rigoux, L.
Stephan, K.E.
Raman, S.
Aponte, E.A.
Moran, R.J.
Daunizeau, J.
Heinz, A.
Friston, K.J.
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BackLink https://www.ncbi.nlm.nih.gov/pubmed/27346545$$D View this record in MEDLINE/PubMed
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ContentType Journal Article
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Fri Feb 23 02:25:04 EST 2024
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Issue Pt B
Keywords fMRI
Computational psychiatry
Generative embedding
Model selection
EEG
Bayesian
Classification
Translational neuromodeling
Model comparison
Clustering
Generative model
Model evidence
Language English
License This is an open access article under the CC BY license.
Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved.
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Snippet Neuroimaging increasingly exploits machine learning techniques in an attempt to achieve clinically relevant single-subject predictions. An alternative to...
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SubjectTerms Attention deficit hyperactivity disorder
Bayesian
Brain Diseases - diagnostic imaging
Classification
Clustering
Computational psychiatry
Decision making
Disease
EEG
fMRI
Generative embedding
Generative model
Humans
Medical imaging
Mental Disorders - diagnostic imaging
Model comparison
Model evidence
Model selection
Models, Theoretical
Neuroimaging - methods
Patients
Physiology
Schizophrenia
Translational neuromodeling
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Title Computational neuroimaging strategies for single patient predictions
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