Image Processing Reference
In-Depth Information
of the inverse problem while still being robust to the error of the assumed cortical
surface: random deviation of the orientation in range leads to just a slight
increase of distortion, thus not significantly affecting the accuracy of the localization
procedure. Anatomical constraints improve the localization and contrast of beam-
forming imaging methods as well, but the use of anatomical constraints is found
to be advantageous only in the case of good MRI/E/MEG coregistration [109].
30
8.4.1.3
Forward Modeling of EEG and MEG
Volumetric structures derived from the tessellation procedure are used to create
a realistic geometry of the head, which is crucial for the forward modeling of
E/MEG fields. Previously, rough approximations based on best-fit single or mul-
tiple sphere models were developed to overcome the burden of creating realistic
head geometry, but they became less favorable as the increased availability of
powerful computational resources made more realistic modeling possible. Spatial
information is especially important for EEG forward modeling due to the fact
that it is more strongly affected by the conductivities of the skull and the scalp
than the MEG forward model. Such inhomogeneities might not affect the mag-
netic field at all in the case of a spherical head model, when only the inner skull
surface is of the main concern for the forward modeling.
There are four numerical methods available to solve the E/MEG modeling
problem, and the boundary elements method (BEM) [110] is the most commonly
used when isotropy (direction independence) of the matter is assumed, so that only
boundary meshes obtained by the tessellation process are required. It was shown,
however, that anisotropy of the skull [111] and white matter [112] can bias EEG
and MEG forward models. To solve the forward problem in the case of an aniso-
tropic medium, the head volume is presented by a large assembly of small homo-
geneous tetrahedrons, and a finite elements method (FEM) [113] is used to approx-
imate the solution. Another possible way is to use the finite difference method
(FDM) on a regular computational mesh [14].
Table 8.2
lists some publicly available
software that can help performs forward E/MEG modeling. Forward modeling of
E/MEG signals rely on the knowledge of matter conductivities. Common values
of conductivities for different tissues can be found in the literature [115], or can be
estimated on a per-subject basis using electrical impedance tomography (EIT) [116]
or diffusion tensor (DT) [117] MRI.
8.4.2
F
ORWARD
M
ODELING
OF
BOLD S
IGNAL
The successful analysis of the results of a multimodal experiment remains prob-
lematic. The main problem of multimodal analysis is the absence of a general
unifying account of the BOLD fMRI signal in terms of the characteristics of a
neuronal response. Various models have been suggested. On the one hand, they
include naive modeling of BOLD signal in the context of a linear time invariant
system (LTIS). On the other hand, there are general models of the BOLD signal
in terms of detailed biophysical processes (Balloon [118] or Vein and Capillary
[119] models). The naive models are not general enough to explain the variability
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