Dynamic causal modeling on the identification of interacting networks in the brain: a systematic review

Dynamic causal modeling (DCM) was implemented on datasets obtained from an externally-triggered finger tapping functional MRI experiment performed by 5 male and female subjects. The objective was to model the effective connectivity between two significantly activated primary motor regions (M1). The left and right hemisphere M1s are found to be effectively and bidirectionally connected to each other. Both connections are modulated by the stimulus-free contextual input. These connectivities are however not gated (influenced) by any of the two M1s, ruling out the possibility of the non-linear behavior of connections between both M1s. A dynamic causal model was finally suggested.

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Reliability of Dynamic Causal Modeling using the Statistical Parametric Mapping Toolbox

International Journal of System Dynamics Applications ◽

10.4018/ijsda.2014040101 ◽

2014 ◽

Vol 3 (2) ◽

pp. 1-16

Author(s):

Pegah T. Hosseini ◽

Shouyan Wang ◽

Julie Brinton ◽

Steven Bell ◽

David M. Simpson

Keyword(s):

Operating Systems ◽

Brain Connectivity ◽

Effective Connectivity ◽

Statistical Parametric Mapping ◽

Causal Modeling ◽

Current Work ◽

Dynamic Causal Modeling ◽

Somatosensory Stimulation ◽

Parametric Mapping ◽

The Brain

Dynamic causal modeling (DCM) is a recently developed approach for effective connectivity measurement in the brain. It has attracted considerable attention in recent years and quite widespread used to investigate brain connectivity in response to different tasks as well as auditory, visual, and somatosensory stimulation. This method uses complex algorithms, and currently the only implementation available is the Statistical Parametric Mapping (SPM8) toolbox with functionality for use on EEG and fMRI. The objective of the current work is to test the robustness of the toolbox when applied to EEG, by comparing results obtained from various versions of the software and operating systems when using identical datasets. Contrary to expectations, it was found that estimated connectivities were not consistent between different operating systems, the version of SPM8, or the version of MATLAB being used. The exact cause of this problem is not clear, but may relate to the high number of parameters in the model. Caution is thus recommended when interpreting the results of DCM estimated with the SPM8 software.

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Dynamic Causal Modeling of the Response to Frequency Deviants

Journal of Neurophysiology ◽

10.1152/jn.90291.2008 ◽

2009 ◽

Vol 101 (5) ◽

pp. 2620-2631 ◽

Cited By ~ 105

Author(s):

Marta I. Garrido ◽

James M. Kilner ◽

Stefan J. Kiebel ◽

Karl J. Friston

Keyword(s):

Mismatch Negativity ◽

Sensory Input ◽

Model Comparison ◽

Memory Trace ◽

Effective Connectivity ◽

Predictive Coding ◽

Causal Modeling ◽

Evoked Responses ◽

Dynamic Causal Modeling ◽

The Brain

This article describes the use of dynamic causal modeling to test hypotheses about the genesis of evoked responses. Specifically, we consider the mismatch negativity (MMN), a well-characterized response to deviant sounds and one of the most widely studied evoked responses. There have been several mechanistic accounts of how the MMN might arise. It has been suggested that the MMN results from a comparison between sensory input and a memory trace of previous input, although others have argued that local adaptation, due to stimulus repetition, is sufficient to explain the MMN. Thus the precise mechanisms underlying the generation of the MMN remain unclear. This study tests some biologically plausible spatiotemporal dipole models that rest on changes in extrinsic top-down connections (that enable comparison) and intrinsic changes (that model adaptation). Dynamic causal modeling suggested that responses to deviants are best explained by changes in effective connectivity both within and between cortical sources in a hierarchical network of distributed sources. Our model comparison suggests that both adaptation and memory comparison operate in concert to produce the early (N1 enhancement) and late (MMN) parts of the response to frequency deviants. We consider these mechanisms in the light of predictive coding and hierarchical inference in the brain.

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