Biomedical Engineering Reference
In-Depth Information
for
its
potential
application
in
imaging
neural
activities
in
the human brain.
Our studies have shown that the sensitivity of the LEI
technique can be substantially improved by applying succes-
sive cycles of oscillating gradients and using optimized parame-
ters. Moreover, this sensitivity can be further increased by using
a higher field strength (since not only the SNR but also the
magnitude of the Lorentz effect increases with field strength)
and/or stronger oscillating gradients, as such advances in hard-
ware become increasingly more available on modern MRI scan-
ners. This is a clear advantage relative to methods that rely on the
intrinsic magnetic field changes induced by the current, which are
independent of the main magnetic field strength.
As shown here in the human median nerve, the LEI tech-
nique can potentially be applied to white matter tracts in the cen-
tral nervous system to study the functional connectivity between
various brain areas and to assess white matter integrity in diseases
such as multiple sclerosis. Further, since our technique does not
require the electrical current to be unidirectional, it can poten-
tially be extended to image focal dendritic neuroelectric activ-
ity in gray matter, which, if successful, could have a tremendous
impact on our ability to noninvasively study neuronal informa-
tion processing in the brain. However, unlike for applications in
the peripheral nervous system, the temporal delays between the
stimuli and the neural activation in various cortical areas are often
not known, making it difficult to synchronize the pulse sequence
with the neuroelectric activity. Nevertheless, scalp ERP recordings
could be used to help determine the proper delays, which could
then be incorporated into the pulse sequence to allow time-locked
detection of neural activation.
Despite the promising results presented here, direct nonin-
vasive neuroimaging in the human brain using the LEI tech-
nique remains experimentally challenging. First, synchronized
confounding factors, such as functional signals reflecting BOLD,
CBV, and CBF changes, as well as physiological noise, can domi-
nate the detected signal. Therefore, a careful design of the exper-
imental paradigm is required to separate these slow effects from
the rapid effects due to neuroelectric activity. In addition, as men-
tioned above, the criticality of the close timing synchrony between
the neuroelectric activity and the oscillating gradients will require
considerable further development for this technique to work
successfully in gray matter. Moreover, even if all the timing
information is known (e.g., using ERP or other neuroelectrical
data), the need for extremely accurate synchronization between
the stimulation and image acquisition will be highly demand-
ing on present hardware capabilities. Nevertheless, such efforts
to overcome these challenges and implement our technique
