Dynamic causal modelling revisited

This paper revisits the dynamic causal modelling of fMRI timeseries by replacing the usual (Taylor) approximation to neuronal dynamics with a neural mass model of the canonical microcircuit. This provides a generative or dynamic causal model of laminar specific responses that can generate haemodynam...

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Vydáno v:NeuroImage (Orlando, Fla.) Ročník 199; s. 730 - 744
Hlavní autoři: Friston, K.J., Preller, Katrin H., Mathys, Chris, Cagnan, Hayriye, Heinzle, Jakob, Razi, Adeel, Zeidman, Peter
Médium: Journal Article
Jazyk:angličtina
Vydáno: United States Elsevier Inc 01.10.2019
Elsevier Limited
Academic Press
Témata:
Adult
Attention
Bayesian
Bayesian analysis
Blood
Brain - diagnostic imaging
Brain - physiology
Cortex
Dynamic causal modelling
Effective connectivity
Electroencephalography
Functional magnetic resonance imaging
Functional Neuroimaging - methods
Haemodynamic models
Hemodynamics
Hemodynamics - physiology
Humans
Interneurons
Magnetic Resonance Imaging
Mathematical models
Models, Biological
Motion detection
Motion Perception - physiology
Nerve Net - diagnostic imaging
Nerve Net - physiology
Neural mass models
Neurovascular Coupling - physiology
Population
Pyramidal cells
Sensory neurons
Time series
Visual perception
Haemodynamic models
Neural mass models
Bayesian
Dynamic causal modelling
Effective connectivity
ISSN:1053-8119, 1095-9572, 1095-9572
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Abstract This paper revisits the dynamic causal modelling of fMRI timeseries by replacing the usual (Taylor) approximation to neuronal dynamics with a neural mass model of the canonical microcircuit. This provides a generative or dynamic causal model of laminar specific responses that can generate haemodynamic and electrophysiological measurements. In principle, this allows the fusion of haemodynamic and (event related or induced) electrophysiological responses. Furthermore, it enables Bayesian model comparison of competing hypotheses about physiologically plausible synaptic effects; for example, does attentional modulation act on superficial or deep pyramidal cells – or both? In this technical note, we describe the resulting dynamic causal model and provide an illustrative application to the attention to visual motion dataset used in previous papers. Our focus here is on how to answer long-standing questions in fMRI; for example, do haemodynamic responses reflect extrinsic (afferent) input from distant cortical regions, or do they reflect intrinsic (recurrent) neuronal activity? To what extent do inhibitory interneurons contribute to neurovascular coupling? What is the relationship between haemodynamic responses and the frequency of induced neuronal activity? This paper does not pretend to answer these questions; rather it shows how they can be addressed using neural mass models of fMRI timeseries. •This paper describes a DCM for fMRI based on neural mass models and canonical microcircuits.•This enables the (Bayesian) fusion of EEG and fMRI data.•That encompasses the formal modelling of neurovascular coupling.•Offers a surprising insight into the relationship between haemodynamic and electrophysiological responses.
AbstractList This paper revisits the dynamic causal modelling of fMRI timeseries by replacing the usual (Taylor) approximation to neuronal dynamics with a neural mass model of the canonical microcircuit. This provides a generative or dynamic causal model of laminar specific responses that can generate haemodynamic and electrophysiological measurements. In principle, this allows the fusion of haemodynamic and (event related or induced) electrophysiological responses. Furthermore, it enables Bayesian model comparison of competing hypotheses about physiologically plausible synaptic effects; for example, does attentional modulation act on superficial or deep pyramidal cells – or both? In this technical note, we describe the resulting dynamic causal model and provide an illustrative application to the attention to visual motion dataset used in previous papers. Our focus here is on how to answer long-standing questions in fMRI; for example, do haemodynamic responses reflect extrinsic (afferent) input from distant cortical regions, or do they reflect intrinsic (recurrent) neuronal activity? To what extent do inhibitory interneurons contribute to neurovascular coupling? What is the relationship between haemodynamic responses and the frequency of induced neuronal activity? This paper does not pretend to answer these questions; rather it shows how they can be addressed using neural mass models of fMRI timeseries. • This paper describes a DCM for fMRI based on neural mass models and canonical microcircuits. • This enables the (Bayesian) fusion of EEG and fMRI data. • That encompasses the formal modelling of neurovascular coupling. • Offers a surprising insight into the relationship between haemodynamic and electrophysiological responses.
This paper revisits the dynamic causal modelling of fMRI timeseries by replacing the usual (Taylor) approximation to neuronal dynamics with a neural mass model of the canonical microcircuit. This provides a generative or dynamic causal model of laminar specific responses that can generate haemodynamic and electrophysiological measurements. In principle, this allows the fusion of haemodynamic and (event related or induced) electrophysiological responses. Furthermore, it enables Bayesian model comparison of competing hypotheses about physiologically plausible synaptic effects; for example, does attentional modulation act on superficial or deep pyramidal cells - or both? In this technical note, we describe the resulting dynamic causal model and provide an illustrative application to the attention to visual motion dataset used in previous papers. Our focus here is on how to answer long-standing questions in fMRI; for example, do haemodynamic responses reflect extrinsic (afferent) input from distant cortical regions, or do they reflect intrinsic (recurrent) neuronal activity? To what extent do inhibitory interneurons contribute to neurovascular coupling? What is the relationship between haemodynamic responses and the frequency of induced neuronal activity? This paper does not pretend to answer these questions; rather it shows how they can be addressed using neural mass models of fMRI timeseries.
This paper revisits the dynamic causal modelling of fMRI timeseries by replacing the usual (Taylor) approximation to neuronal dynamics with a neural mass model of the canonical microcircuit. This provides a generative or dynamic causal model of laminar specific responses that can generate haemodynamic and electrophysiological measurements. In principle, this allows the fusion of haemodynamic and (event related or induced) electrophysiological responses. Furthermore, it enables Bayesian model comparison of competing hypotheses about physiologically plausible synaptic effects; for example, does attentional modulation act on superficial or deep pyramidal cells – or both? In this technical note, we describe the resulting dynamic causal model and provide an illustrative application to the attention to visual motion dataset used in previous papers. Our focus here is on how to answer long-standing questions in fMRI; for example, do haemodynamic responses reflect extrinsic (afferent) input from distant cortical regions, or do they reflect intrinsic (recurrent) neuronal activity? To what extent do inhibitory interneurons contribute to neurovascular coupling? What is the relationship between haemodynamic responses and the frequency of induced neuronal activity? This paper does not pretend to answer these questions; rather it shows how they can be addressed using neural mass models of fMRI timeseries. •This paper describes a DCM for fMRI based on neural mass models and canonical microcircuits.•This enables the (Bayesian) fusion of EEG and fMRI data.•That encompasses the formal modelling of neurovascular coupling.•Offers a surprising insight into the relationship between haemodynamic and electrophysiological responses.
This paper revisits the dynamic causal modelling of fMRI timeseries by replacing the usual (Taylor) approximation to neuronal dynamics with a neural mass model of the canonical microcircuit. This provides a generative or dynamic causal model of laminar specific responses that can generate haemodynamic and electrophysiological measurements. In principle, this allows the fusion of haemodynamic and (event related or induced) electrophysiological responses. Furthermore, it enables Bayesian model comparison of competing hypotheses about physiologically plausible synaptic effects; for example, does attentional modulation act on superficial or deep pyramidal cells - or both? In this technical note, we describe the resulting dynamic causal model and provide an illustrative application to the attention to visual motion dataset used in previous papers. Our focus here is on how to answer long-standing questions in fMRI; for example, do haemodynamic responses reflect extrinsic (afferent) input from distant cortical regions, or do they reflect intrinsic (recurrent) neuronal activity? To what extent do inhibitory interneurons contribute to neurovascular coupling? What is the relationship between haemodynamic responses and the frequency of induced neuronal activity? This paper does not pretend to answer these questions; rather it shows how they can be addressed using neural mass models of fMRI timeseries.This paper revisits the dynamic causal modelling of fMRI timeseries by replacing the usual (Taylor) approximation to neuronal dynamics with a neural mass model of the canonical microcircuit. This provides a generative or dynamic causal model of laminar specific responses that can generate haemodynamic and electrophysiological measurements. In principle, this allows the fusion of haemodynamic and (event related or induced) electrophysiological responses. Furthermore, it enables Bayesian model comparison of competing hypotheses about physiologically plausible synaptic effects; for example, does attentional modulation act on superficial or deep pyramidal cells - or both? In this technical note, we describe the resulting dynamic causal model and provide an illustrative application to the attention to visual motion dataset used in previous papers. Our focus here is on how to answer long-standing questions in fMRI; for example, do haemodynamic responses reflect extrinsic (afferent) input from distant cortical regions, or do they reflect intrinsic (recurrent) neuronal activity? To what extent do inhibitory interneurons contribute to neurovascular coupling? What is the relationship between haemodynamic responses and the frequency of induced neuronal activity? This paper does not pretend to answer these questions; rather it shows how they can be addressed using neural mass models of fMRI timeseries.
Author Zeidman, Peter
Heinzle, Jakob
Cagnan, Hayriye
Razi, Adeel
Mathys, Chris
Preller, Katrin H.
Friston, K.J.
AuthorAffiliation f Department of Electronic Engineering, NED University of Engineering and Technology, Karachi, Pakistan
d MRC Brain Network Dynamics Unit (BNDU), Department of Pharmacology and Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
c Max Planck UCL Centre for Computational Psychiatry and Ageing Research, University College London, United Kingdom
b Neuropsychopharmacology and Brain Imaging, Department of Psychiatry, Psychotherapy and Psychosomatics, University Hospital for Psychiatry Zurich, 8032 Zurich, Switzerland
a The Wellcome Trust Centre for Neuroimaging, University College London, United Kingdom
e Translational Neuromodeling Unit, Institute for Biomedical Engineering, University of Zurich and ETH Zurich, 8032 Zürich, Switzerland
AuthorAffiliation_xml – name: f Department of Electronic Engineering, NED University of Engineering and Technology, Karachi, Pakistan
– name: a The Wellcome Trust Centre for Neuroimaging, University College London, United Kingdom
– name: c Max Planck UCL Centre for Computational Psychiatry and Ageing Research, University College London, United Kingdom
– name: b Neuropsychopharmacology and Brain Imaging, Department of Psychiatry, Psychotherapy and Psychosomatics, University Hospital for Psychiatry Zurich, 8032 Zurich, Switzerland
– name: d MRC Brain Network Dynamics Unit (BNDU), Department of Pharmacology and Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
– name: e Translational Neuromodeling Unit, Institute for Biomedical Engineering, University of Zurich and ETH Zurich, 8032 Zürich, Switzerland
Author_xml – sequence: 1
  givenname: K.J.
  surname: Friston
  fullname: Friston, K.J.
  email: k.friston@ucl.ac.uk
  organization: The Wellcome Trust Centre for Neuroimaging, University College London, United Kingdom
– sequence: 2
  givenname: Katrin H.
  surname: Preller
  fullname: Preller, Katrin H.
  email: preller@bli.uzh.ch
  organization: The Wellcome Trust Centre for Neuroimaging, University College London, United Kingdom
– sequence: 3
  givenname: Chris
  surname: Mathys
  fullname: Mathys, Chris
  email: c.mathys@ucl.ac.uk
  organization: The Wellcome Trust Centre for Neuroimaging, University College London, United Kingdom
– sequence: 4
  givenname: Hayriye
  surname: Cagnan
  fullname: Cagnan, Hayriye
  email: hayriye.cagnan@ndcn.ox.ac.uk
  organization: The Wellcome Trust Centre for Neuroimaging, University College London, United Kingdom
– sequence: 5
  givenname: Jakob
  surname: Heinzle
  fullname: Heinzle, Jakob
  email: heinzle@biomed.ee.ethz.ch
  organization: The Wellcome Trust Centre for Neuroimaging, University College London, United Kingdom
– sequence: 6
  givenname: Adeel
  surname: Razi
  fullname: Razi, Adeel
  email: a.razi@ucl.ac.uk
  organization: The Wellcome Trust Centre for Neuroimaging, University College London, United Kingdom
– sequence: 7
  givenname: Peter
  surname: Zeidman
  fullname: Zeidman, Peter
  email: peter.zeidman@ucl.ac.uk
  organization: The Wellcome Trust Centre for Neuroimaging, University College London, United Kingdom
BackLink https://www.ncbi.nlm.nih.gov/pubmed/28219774$$D View this record in MEDLINE/PubMed
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ContentType Journal Article
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Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.
2017. The Authors
2017 The Authors 2017
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Keywords Haemodynamic models
Neural mass models
Bayesian
Dynamic causal modelling
Effective connectivity
Language English
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Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.
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Snippet This paper revisits the dynamic causal modelling of fMRI timeseries by replacing the usual (Taylor) approximation to neuronal dynamics with a neural mass model...
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StartPage 730
SubjectTerms Adult
Attention
Bayesian
Bayesian analysis
Blood
Brain - diagnostic imaging
Brain - physiology
Cortex
Dynamic causal modelling
Effective connectivity
Electroencephalography
Functional magnetic resonance imaging
Functional Neuroimaging - methods
Haemodynamic models
Hemodynamics
Hemodynamics - physiology
Humans
Interneurons
Magnetic Resonance Imaging
Mathematical models
Models, Biological
Motion detection
Motion Perception - physiology
Nerve Net - diagnostic imaging
Nerve Net - physiology
Neural mass models
Neurovascular Coupling - physiology
Population
Pyramidal cells
Sensory neurons
Time series
Visual perception
Title Dynamic causal modelling revisited
URI https://www.clinicalkey.com/#!/content/1-s2.0-S1053811917301568
https://dx.doi.org/10.1016/j.neuroimage.2017.02.045
https://www.ncbi.nlm.nih.gov/pubmed/28219774
https://www.proquest.com/docview/2268548023
https://www.proquest.com/docview/1870648969
https://pubmed.ncbi.nlm.nih.gov/PMC6693530
Volume 199
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