MEG & EEG
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Measurements of the magnetoencephalogram (MEG - the magnetic fields produced
by electrical brain activity) and the electroencephalogram (EEG - the
associated scalp potentials) provide unique insights into the dynamic
behavior of the human brain as they are able to follow changes in neural
activity on a millisecond time-scale. In comparison, the other functional
imaging modalities (positron tomography (PET) and functional magnetic
resonance imaging (fMRI)) are limited in temporal resolution to time scales
on the order of, at best, one second by physiological and signal-to-noise
considerations. The goal of this project is to develop and evaluate computational
techniques for estimating the location, extent and dynamic behavior of
the current sources that produce the observed MEG and EEG. Under this
NIMH supported project, we have so far developed a suite of methods and
software for head modeling, source localization and imaging. We have also
built and tested a skull based phantom and developed computational tools
for comparing and quantifying performance of different models and inverse
methods. We plan to build on this work in the future by concentrating
on using and extending the methods we have developed to date to address
several fundamental questions of relevance to both EEG/MEG researchers
and the brain imaging community as a whole:
- How reliably can E/MEG find the locations of multiple current sources
in the brain?
- To what extent can E/MEG determine the spatial extent of distributed
current sources?
- How accurately can we find the time series or activation sequence
of these sources?
- How do we best process data from cognitive studies involving differences
between conditions?
- How is E/MEG data best combined with functional MR or PET activation
data?
Figure: MEG instrumentation and typical signals. Typical scalp magnetic
fields are on the order of a 10 billionth of the earth’s magnetic
field. MEG fields are measured inside a magnetically shielded room for
protection against higher-frequency electromagnetic perturbations (left).
MEG sensors use low-temperature electronics cooled by liquid helium (upper
right) stored in a Dewar (left and upper right). Scalp magnetic fields
are then recorded typically every millisecond. The resulting data can
be visualized as time-evolving scalp magnetic field topographies (lower
right).
Modeling
Magnetoencelphlography (MEG) and Electroencephalography (EEG) is used
to image electrical activity in the brain. Clusters of thousands of synchronously
activated pyramidal cortical neurons are believed to be the main generators
of MEG and EEG signals.
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USC Phantom
A set of phantom data is available for other researchers to use in evaluating and comparing different forward and inverse methods for EEG and MEG. This directory contains all the EEG and MEG data files related to our human skull phantom studies.
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Volumetric rendering of the CT data collection
using our 32 dipole skull based MEG/EEG phantom that is used for
validating and comparing different forward and inverse methods.
(Click image to view hi-res animation. Warning modem users: 2862KB
GIF file)
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Statistical Analysis
The MEG and EEG literature encompasses a wide variety of reconstruction and localization methodologies. It is important to evaluate the relative performance of these methods under different experimental settings such as the number, location and time series of neural sources.
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Phantom Warping
In practice, EEG studies are often performed without accompanying anatomical scans. To overcome this problem we propose a stereotactic atlas-based procedure in which surface landmarks are used to warp an atlas to the subject’s scalp morphology.
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BrainStorm
BrainStorm, a MATLAB-based software library, was developed as part of
our NIMH supported project. This work is a collaboration between Richard
Leahy and Manbir Singh at USC; John Mosher and colleagues in the Biophysics
Group at the Los Alamos National Laboratory; and Sylvain Baillet in the
Cognitive Neuroscience & Brain Imaging Lab, Hopital de la Salpetriere,
CNRS, Paris, and is supported by the National Institute of Mental Health
(R01 MH53213).
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