Journal
NEUROIMAGE
Volume 39, Issue 1, Pages 269-278Publisher
ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.neuroimage.2007.08.019
Keywords
functional magnetic resonance imaging; dynamic causal modelling; neural mass model
Funding
- Wellcome Trust Funding Source: Medline
Ask authors/readers for more resources
Dynamical causal modelling (DCM) for functional magnetic resonance imaging (fMRI) is a technique to infer directed connectivity among brain regions. These models distinguish between a neuronal level, which models neuronal interactions among regions, and an observation level, which models the hemodynamic responses each region. The original DCM formulation considered only one neuronal state per region. In this work, we adopt a more plausible and less constrained neuronal model, using two neuronal states (populations) per region. Critically, this gives us an explicit model of intrinsic (between-population) connectivity within a region. In addition, by using positivity constraints, the model conforms to the organization of real cortical hierarchies, whose extrinsic connections are excitatory (glutamatergic). By incorporating two populations within each region we can model selective changes in both extrinsic and intrinsic connectivity. Using synthetic data, we show that the two-state model is internal consistent and identifiable. We then apply the model to real data, explicitly modelling intrinsic connections. Using model comparison, we found that the two-state model is better than the single-state model. Furthermore, using the two-state model we find that it is possible to disambiguate between subtle changes in coupling; we were able to show that attentional gain, in the context of visual motion processing, is accounted for sufficiently by an increased sensitivity of excitatory populations of neurons in V5, to forward afferents from earlier visual areas. (C) 2007 Elsevier Inc. All rights reserved.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available