Setting location priors using beamforming improves model comparison in MEG-DCM
Abstract
Modelling neuronal interactions using a directed network can be used to provide insight into
the activity of the brain during experimental tasks. Magnetoencephalography (MEG) allows
for the observation of the fast neuronal dynamics necessary to characterize the activity of
sources and their interactions. A network representation of these sources and their con-
nections can be formed by mapping them to nodes and their connection strengths to edge
weights. Dynamic Causal Modelling (DCM) presents a Bayesian framework to estimate the
parameters of these networks, as well as the ability to test hypotheses on the structure of
the network itself using Bayesian model comparison. DCM uses a neurologically-informed
representation of the active neural sources, which leads to an underdetermined system and
increased complexity in estimating the network parameters. This work shows that inform-
ing the MEG DCM source location with prior distributions defined using a MEG source
localization algorithm improves model selection accuracy. DCM inversion of a group of can-
didate models shows an enhanced ability to identify a ground-truth network structure when
source-localized prior means are used.
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- Masters Theses [19687]