Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Videos

Dynamic causal modelling for fMRI: a two-state model.

A C Marreiros1, S J Kiebel, K J Friston

  • 1Wellcome Trust Centre for Neuroimaging, Institute of Neurology, UCL, 12 Queen Square, London WC1N 3BG, UK. amarreiros@fil.ion.ucl.ac.uk

Neuroimage
|October 16, 2007
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Topographic Variation in Human Neurotransmitter Receptor Densities Explains Differences in Intracranial EEG Spectra.

Human brain mapping·2025
Same author

Renal nerve afferents drive preferential renal sympathoexcitation in response to acute renal ischemia/reperfusion in rats.

Autonomic neuroscience : basic & clinical·2025
Same author

Canalization and plasticity in psychopathology.

Neuropharmacology·2022
Same author

REBUS and the Anarchic Brain: Toward a Unified Model of the Brain Action of Psychedelics.

Pharmacological reviews·2019
Same author

A validation of dynamic causal modelling for 7T fMRI.

Journal of neuroscience methods·2018
Same author

Neurophysiologically-informed markers of individual variability and pharmacological manipulation of human cortical gamma.

NeuroImage·2017
Same journal

Segmentation of the parasagittal dura mater on multi-center 3D-FLAIR MRI.

NeuroImage·2026
Same journal

Spatial frequency channels implement a mental ruler in spatial vision.

NeuroImage·2026
Same journal

Exploring the Link Between Intravoxel Incoherent Motion Measured Brain Diffusivity During Wakefulness and Sleep Macrostructure in the Elderly.

NeuroImage·2026
Same journal

Closed-loop adaptation of transcranial magnetic stimulation intensity with electroencephalography feedback.

NeuroImage·2026
Same journal

Volumetric postmortem MRI of the medial temporal lobe in Alzheimer's disease and related disorders: methodological advances and implications for in vivo biomarker development.

NeuroImage·2026
Same journal

Neural responses to equity and inequity when receiving vicarious rewards for self and charity during adolescence.

NeuroImage·2026
See all related articles

This study introduces a new two-state neuronal model for Dynamical Causal Modelling (DCM) in functional magnetic resonance imaging (fMRI). The enhanced model better explains brain connectivity and neuronal interactions, improving upon previous single-state models.

Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Systems Neuroscience

Background:

  • Dynamical Causal Modelling (DCM) infers directed brain connectivity from fMRI data.
  • Traditional DCM uses a single neuronal state per brain region.
  • This limits the explicit modeling of intrinsic neuronal dynamics within regions.

Purpose of the Study:

  • To develop and validate a more biologically plausible DCM with two neuronal states per region.
  • To explicitly model intrinsic (within-region) and extrinsic (between-region) connectivity.
  • To investigate how this enhanced model captures cortical hierarchy and excitatory connections.

Main Methods:

  • Implemented a two-state neuronal model within the DCM framework.
  • Incorporated positivity constraints reflecting excitatory cortical connections.

Related Experiment Videos

  • Validated the model using synthetic data for consistency and identifiability.
  • Applied the model to real fMRI data and compared it against the single-state model using Bayesian model comparison.

    • Main Results:

    • The two-state DCM is internally consistent and identifiable.
    • Model comparison favored the two-state model over the single-state model for real fMRI data.
    • The two-state model successfully disambiguated subtle changes in neuronal coupling.
    • Attentional gain in visual motion processing was explained by increased sensitivity in excitatory populations in V5.

    Conclusions:

    • The two-state DCM provides a more refined and accurate representation of neuronal interactions and connectivity.
    • Explicitly modeling intrinsic connectivity enhances the understanding of brain network dynamics.
    • This approach offers improved insights into mechanisms like attentional modulation in specific visual processing pathways.