Measuring the neural network :
DCM and its applications
Chun-Chuan Chen1,2,*
Graduate Institute of Biomedical Engineering, National Central University, Taiwan
Wellcome Trust Centre for Neuroimaging, Institute of Neurology, University
College London, United Kingdom
This talk will present a dynamic causal model for induced responses (DCM_IR) as
measured with the magnetoencephalogram (MEG) or the electroencephalogram (EEG)
and its applications in neuroscience. DCM_IR models the time-varying power, over a
range of frequencies, as the response of a distributed system of coupled
electromagnetic sources to a spectral perturbation. The model parameters encode the
frequency response to exogenous input and coupling among sources and different
frequencies. The Bayesian inversion of this model, given data enables inferences
about the parameters of a particular model and allows us to compare different models,
or hypotheses. One key aspect of the model is that it differentiates between linear and
non-linear coupling; which correspond to within and between frequency coupling
respectively. To establish the face validity of our approach, we generate synthetic data
and test the identifiability of various parameters to ensure they can be estimated
accurately, under different levels of noise. We then apply our model to MEG data
from a motor grip experiment, to ask whether there is evidence for additional
nonlinear (between-frequency) coupling among neuronal sources and, in particular,
whether nonlinearities were found predominantly in connections within areas
(intrinsic), between areas (extrinsic) or both.