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We hypothesized that DCM based on induced responses and the BOLD-signal would reveal a similar network architecture.
This DCM, based on pantograph geometry, was specifically modeled to respond to relevant engineering constraints and requirements.
An extended discrete cell model (DCM), based on minimization of energy dissipation rate, is applied to predict two-phase flow distribution in the two-dimensional trickle-bed reactors.
A discrete cell model (DCM), based on the minimization of the total rate of energy dissipation, is applied to compute the fluid velocity field in two-dimensional packed beds.
Dynamic causal modeling (DCM) based on functional magnetic resonance imaging (FMRI) and magneto-/electroencephalographic (MEEG) data has increasingly extended the understanding of intrinsic brain network dynamics in a variety of functional systems (Friston et al., Curr Opin Neurobiol, 2013).
Here, we report an analysis of the effects of cardiac-specific mIGF1 expression in a mouse model of DCM based on SRF gene disruption.
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She found that a DCM that included a hidden thalamic source performed better than DCMs based solely on observed cortical timeseries, and established a dissociation between the effects of (measured) cortical and (inferred) subcortical structures on levels of consciousness.
The Dirichlet compound multinomial (DCM) distribution based on the Polya Urn scheme, which can also be considered as a hierarchical Bayesian model, is a more appropriate generative model than the traditional multinomial distribution for text documents.
DCM was based on a modeling framework that included a core motor cortical network comprising the left M1, lPM, and SMA (Fig. 1 A).
DCM is based on the concept of free energy which incorporates the available degrees of freedom in each model (Penny et al., 2004).
We further assessed the efficacy and impact of reframing Ttn transcripts by AON-mediated skipping in the heart muscle and used the mouse DCM model based on the human TTN exon 326 A-band truncating mutation (Gramlich et al, 2009).
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