Skip to main content
SpringerLink
Log in

Modelling Effective Connectivity with Dynamic Causal Models

  • Chapter
  • First Online:
MRI in Psychiatry

  • 2074 Accesses

Abstract

Analyses of effective connectivity provide neuropsychiatric studies with new endophenotypes that are expressed in psychiatric disorders. Dynamic causal modelling (DCM) is a framework for the identification of neural networks in the brain that treats the networks as nonlinear input-state-output systems. In setting up a DCM, one can estimate (1) parameters that mediate the driving influence of exogenous or experimental inputs on brain states, (2) parameters that mediate endogenous coupling among neuronal states and (3) parameters that allow the inputs to modulate that coupling. Issues concerning selection among alternative models naturally arise in DCM analyses. Bayesian model selection (BMS) is a statistical procedure for computing how probable one model is in relation to another. This chapter presents the motivation and procedures for DCM of evoked brain responses – as well as the theoretical and operational details on which BMS rests. We describe procedures for parameter-, model- and family-level inference in the context of analysis of data from a group of subjects, and close with examples of how these procedures have been used in psychiatric neuroimaging.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 79.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 99.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Abbreviations

ANOVA:

Analysis of variance

BMA:

Bayesian model averaging

BMS:

Bayesian model selection

BOLD:

Blood oxygenation level dependent

BPA:

Bayesian parameter averaging

DCM:

Dynamic causal modelling

FFX:

Fixed effect analysis

GBF:

Group Bayes factor

MAP:

Maximum a posteriori

OMPFC:

Orbitomedial prefrontal cortex

RFX:

Random effect analysis

VL:

Variational Laplace

References

  • Almeida JR et al (2009) Abnormal amygdala-prefrontal effective connectivity to happy faces differentiates bipolar from major depression. Biol Psychiatry 66(5):451–459

    Article  PubMed Central  PubMed  Google Scholar 

  • Almeida JR et al (2011) Abnormal left-sided orbitomedial prefrontal cortical amygdala connectivity during happy and fear face processing: a potential neural mechanism of female MDD. Front Psychiatry 2:69

    Article  PubMed Central  PubMed  Google Scholar 

  • Anderson DR (2008) Model based inference in the life sciences: a primer on evidence. Springer, New York

    Book  Google Scholar 

  • Banyai M et al (2011) Model-based dynamical analysis of functional disconnection in schizophrenia. Neuroimage 58(3):870–877

    Article  PubMed Central  PubMed  Google Scholar 

  • Buxton RB et al (1998) Dynamics of blood flow and oxygenation changes during brain activation: the balloon model. Magn Reson Med 39(6):855–864

    Article  CAS  PubMed  Google Scholar 

  • Buxton RB et al (2004) Modeling the hemodynamic response to brain activation. Neuroimage 23(Suppl 1):S220–S233

    Article  PubMed  Google Scholar 

  • Chen CC et al (2009) Forward and backward connections in the brain: a DCM study of functional asymmetries. Neuroimage 45(2):453–462

    Article  CAS  PubMed  Google Scholar 

  • den Ouden HE et al (2010) Striatal prediction error modulates cortical coupling. J Neurosci 30(9):3210–3219

    Article  Google Scholar 

  • Deserno L et al (2012) Reduced prefrontal-parietal effective connectivity and working memory deficits in schizophrenia. J Neurosci 32(1):12–20

    Article  CAS  PubMed  Google Scholar 

  • Desseilles M et al (2011) Depression alters “top-down” visual attention: a dynamic causal modeling comparison between depressed and healthy subjects. Neuroimage 54(2):1662–1668

    Article  PubMed  Google Scholar 

  • Diwadkar VA et al (2012) Disordered corticolimbic interactions during affective processing in children and adolescents at risk for schizophrenia revealed by functional magnetic resonance imaging and dynamic causal modeling. Arch Gen Psychiatry 69(3):231–242

    Article  PubMed  Google Scholar 

  • Friston KJ et al (2000) Nonlinear responses in fMRI: the Balloon model, Volterra kernels, and other hemodynamics. Neuroimage 12(4):466–477

    Article  CAS  PubMed  Google Scholar 

  • Friston KJ et al (2003) Dynamic causal modelling. Neuroimage 19(4):1273–1302

    Article  CAS  PubMed  Google Scholar 

  • Friston K et al (2007) Variational free energy and the Laplace approximation. Neuroimage 34(1):220–234

    Article  PubMed  Google Scholar 

  • Friston K et al (2008) Multiple sparse priors for the M/EEG inverse problem. Neuroimage 39(3):1104–1120

    Article  PubMed  Google Scholar 

  • Gillihan SJ, Parens E (2011) Should we expect “neural signatures” for DSM diagnoses? J Clin Psychiatry 72(10):1383–1389

    Article  PubMed  Google Scholar 

  • Grubb RLJ et al (1974) The effects of changes in PaCO2 on cerebral blood volume, blood flow, and vascular mean transit time. Stroke 5(5):630–639

    Article  PubMed  Google Scholar 

  • Kasess CH et al (2010) Multi-subject analyses with dynamic causal modeling. Neuroimage 49(4):3065–3074

    Article  PubMed Central  PubMed  Google Scholar 

  • Li B et al (2011a) Generalised filtering and stochastic DCM for fMRI. Neuroimage 58(2):442–457

    Article  PubMed  Google Scholar 

  • Li X et al (2011b) Using interleaved transcranial magnetic stimulation/functional magnetic resonance imaging (fMRI) and dynamic causal modeling to understand the discrete circuit specific changes of medications: lamotrigine and valproic acid changes in motor or prefrontal effective connectivity. Psychiatry Res 194(2):141–148

    Article  PubMed  Google Scholar 

  • Linden DE (2012) The challenges and promise of neuroimaging in psychiatry. Neuron 73(1):8–22

    Article  CAS  PubMed  Google Scholar 

  • Linden D, Thome J (2011) Modern neuroimaging in psychiatry: towards the integration of functional and molecular information. World J Biol Psychiatry 12(Suppl 1):6–10

    PubMed  Google Scholar 

  • Litvak V et al (2011) EEG and MEG data analysis in SPM8. Comput Intell Neurosci 2011:852961

    Article  PubMed Central  PubMed  Google Scholar 

  • MacKay DJC (2003) Information theory, inference and learning algorithms. Cambridge University Press, Cambridge, UK

    Google Scholar 

  • Mandeville JB et al (1999) Evidence of a cerebrovascular postarteriole windkessel with delayed compliance. J Cereb Blood Flow Metab 19(6):679–689

    Article  CAS  PubMed  Google Scholar 

  • Masdeu JC (2011) Neuroimaging in psychiatric disorders. Neurotherapeutics 8(1):93–102

    Article  PubMed Central  PubMed  Google Scholar 

  • Mechelli A et al (2003) A dynamic causal modeling study on category effects: bottom-up or top-down mediation? J Cogn Neurosci 15(7):925–934

    Article  PubMed  Google Scholar 

  • Neufang S et al (2011) Disconnection of frontal and parietal areas contributes to impaired attention in very early Alzheimer’s disease. J Alzheimers Dis 25(2):309–321

    CAS  PubMed  Google Scholar 

  • O’Doherty JP et al (2007) Model-based fMRI and its application to reward and decision making. Ann N Y Acad Sci 1104:35–53

    Article  PubMed  Google Scholar 

  • Passamonti L et al (2012) Effects of acute tryptophan depletion on prefrontal-amygdala connectivity while viewing facial signals of aggression. Biol Psychiatry 71(1):36–43

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  • Penny WD (2012) Comparing dynamic causal models using AIC, BIC and free energy. Neuroimage 59(1):319–330

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  • Penny W et al (2003) Variational Bayesian inference for fMRI time series. Neuroimage 19(3):727–741

    Article  PubMed  Google Scholar 

  • Penny WD et al (2004) Comparing dynamic causal models. Neuroimage 22(3):1157–1172

    Article  CAS  PubMed  Google Scholar 

  • Penny WD et al (2010) Comparing families of dynamic causal models. PLoS Comput Biol 6(3):e1000709

    Article  PubMed Central  PubMed  Google Scholar 

  • Pitt MA, Myung IJ (2002) When a good fit can be bad. Trends Cogn Sci 6(10):421–425

    Article  PubMed  Google Scholar 

  • Raftery AE (1995) Bayesian model selection in social research. Sociol Methodol 25:111–164

    Article  Google Scholar 

  • Rowe JB (2010) Connectivity analysis is essential to understand neurological disorders. Front Syst Neurosci 4:144

    Article  PubMed Central  PubMed  Google Scholar 

  • Schlosser RG et al (2010) Fronto-cingulate effective connectivity in obsessive compulsive disorder: a study with fMRI and dynamic causal modeling. Hum Brain Mapp 31(12):1834–1850

    Article  PubMed  Google Scholar 

  • Stephan KE et al (2007a) Interhemispheric integration of visual processing during task-driven lateralization. J Neurosci 27(13):3512–3522

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  • Stephan KE et al (2007b) Comparing hemodynamic models with DCM. Neuroimage 38(3):387–401

    Article  PubMed Central  PubMed  Google Scholar 

  • Stephan KE et al (2008) Nonlinear dynamic causal models for fMRI. Neuroimage 42(2):649–662

    Article  PubMed Central  PubMed  Google Scholar 

  • Stephan KE et al (2009) Bayesian model selection for group studies. Neuroimage 46(4):1004–1017

    Article  PubMed Central  PubMed  Google Scholar 

  • Stephan KE et al (2009) Tractography-based priors for dynamic causal models. Neuroimage 47(4):1628–1638

    Google Scholar 

  • Stephan KE et al (2010) Ten simple rules for dynamic causal modeling. Neuroimage 49(4):3099–3109

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  • van Leeuwen TM et al (2011) Effective connectivity determines the nature of subjective experience in grapheme-color synesthesia. J Neurosci 31(27):987 9–9884

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Wellcome Trust Centre for Neuroimaging, University College London, London, UK

    Yen Yu, William Penny & Karl Friston FRS

Authors
  1. Yen Yu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. William Penny
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Karl Friston FRS
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yen Yu .

Editor information

Editors and Affiliations

  1. Psychiatry Neuroimaging Branch, UKE, Department of Psychiatry, Hamburg, Germany

    Christoph Mulert

  2. Department of Psychiatry and Radiology, Harvard Medical School, Boston, Massachusetts, USA

    Martha E. Shenton

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer Berlin Heidelberg

About this chapter

Cite this chapter

Yu, Y., Penny, W., Friston, K. (2014). Modelling Effective Connectivity with Dynamic Causal Models. In: Mulert, C., Shenton, M. (eds) MRI in Psychiatry. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-54542-9_3

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-54542-9_3

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-54541-2

  • Online ISBN: 978-3-642-54542-9

  • eBook Packages: MedicineMedicine (R0)

Publish with us

Policies and ethics

Search

Navigation