54 research outputs found
Correlation between amygdala BOLD activity and frontal EEG asymmetry during real-time fMRI neurofeedback training in patients with depression
Real-time fMRI neurofeedback (rtfMRI-nf) is an emerging approach for studies
and novel treatments of major depressive disorder (MDD). EEG performed
simultaneously with an rtfMRI-nf procedure allows an independent evaluation of
rtfMRI-nf brain modulation effects. Frontal EEG asymmetry in the alpha band is
a widely used measure of emotion and motivation that shows profound changes in
depression. However, it has never been directly related to simultaneously
acquired fMRI data. We report the first study investigating
electrophysiological correlates of the rtfMRI-nf procedure, by combining
rtfMRI-nf with simultaneous and passive EEG recordings. In this pilot study,
MDD patients in the experimental group (n=13) learned to upregulate BOLD
activity of the left amygdala using an rtfMRI-nf during a happy emotion
induction task. MDD patients in the control group (n=11) were provided with a
sham rtfMRI-nf. Correlations between frontal EEG asymmetry in the upper alpha
band and BOLD activity across the brain were examined. Average individual
changes in frontal EEG asymmetry during the rtfMRI-nf task for the experimental
group showed a significant positive correlation with the MDD patients'
depression severity ratings, consistent with an inverse correlation between the
depression severity and frontal EEG asymmetry at rest. Temporal correlations
between frontal EEG asymmetry and BOLD activity were significantly enhanced,
during the rtfMRI-nf task, for the amygdala and many regions associated with
emotion regulation. Our findings demonstrate an important link between amygdala
BOLD activity and frontal EEG asymmetry. Our EEG asymmetry results suggest that
the rtfMRI-nf training targeting the amygdala is beneficial to MDD patients,
and that alpha-asymmetry EEG-nf would be compatible with the amygdala
rtfMRI-nf. Combination of the two could enhance emotion regulation training and
benefit MDD patients.Comment: 28 pages, 16 figures, to appear in NeuroImage: Clinica
Microtesla MRI of the human brain combined with MEG
One of the challenges in functional brain imaging is integration of
complementary imaging modalities, such as magnetoencephalography (MEG) and
functional magnetic resonance imaging (fMRI). MEG, which uses highly sensitive
superconducting quantum interference devices (SQUIDs) to directly measure
magnetic fields of neuronal currents, cannot be combined with conventional
high-field MRI in a single instrument. Indirect matching of MEG and MRI data
leads to significant co-registration errors. A recently proposed imaging method
- SQUID-based microtesla MRI - can be naturally combined with MEG in the same
system to directly provide structural maps for MEG-localized sources. It
enables easy and accurate integration of MEG and MRI/fMRI, because microtesla
MR images can be precisely matched to structural images provided by high-field
MRI and other techniques. Here we report the first images of the human brain by
microtesla MRI, together with auditory MEG (functional) data, recorded using
the same seven-channel SQUID system during the same imaging session. The images
were acquired at 46 microtesla measurement field with pre-polarization at 30
mT. We also estimated transverse relaxation times for different tissues at
microtesla fields. Our results demonstrate feasibility and potential of human
brain imaging by microtesla MRI. They also show that two new types of imaging
equipment - low-cost systems for anatomical MRI of the human brain at
microtesla fields, and more advanced instruments for combined functional (MEG)
and structural (microtesla MRI) brain imaging - are practical.Comment: 8 pages, 5 figures - accepted by JM
Real-time fMRI neurofeedback training of the amygdala activity with simultaneous EEG in veterans with combat-related PTSD
Posttraumatic stress disorder (PTSD) is a chronic and disabling
neuropsychiatric disorder characterized by insufficient top-down modulation of
the amygdala activity by the prefrontal cortex. Real-time fMRI neurofeedback
(rtfMRI-nf) is an emerging method with potential for modifying the
amygdala-prefrontal interactions. We report the first controlled emotion
self-regulation study in veterans with combat-related PTSD utilizing rtfMRI-nf
of the amygdala activity. PTSD patients in the experimental group (EG, n=20)
learned to upregulate BOLD activity of the left amygdala (LA) using rtfMRI-nf
during a happy emotion induction task. PTSD patients in the control group (CG,
n=11) were provided with a sham rtfMRI-nf. The study included three rtfMRI-nf
training sessions, and EEG recordings were performed simultaneously with fMRI.
PTSD severity was assessed using the Clinician-Administered PTSD Scale (CAPS).
The EG participants showed a significant reduction in total CAPS ratings,
including significant reductions in avoidance and hyperarousal symptoms.
Overall, 80% of the EG participants demonstrated clinically meaningful
reductions in CAPS ratings, compared to 38% in the CG. During the first
session, fMRI connectivity of the LA with the orbitofrontal cortex and the
dorsolateral prefrontal cortex (DLPFC) was progressively enhanced, and this
enhancement significantly and positively correlated with initial CAPS ratings.
Left-lateralized enhancement in upper alpha EEG coherence also exhibited a
significant positive correlation with the initial CAPS. Reduction in PTSD
severity between the first and last rtfMRI-nf sessions significantly correlated
with enhancement in functional connectivity between the LA and the left DLPFC.
Our results demonstrate that the rtfMRI-nf of the amygdala activity has the
potential to correct the amygdala-prefrontal functional connectivity
deficiencies specific to PTSD.Comment: 26 pages, 16 figures, to appear in NeuroImage: Clinica
EEG-assisted retrospective motion correction for fMRI: E-REMCOR
We propose a method for retrospective motion correction of fMRI data in
simultaneous EEG-fMRI that employs the EEG array as a sensitive motion
detector. EEG motion artifacts are used to generate motion regressors
describing rotational head movements with millisecond temporal resolution.
These regressors are utilized for slice-specific motion correction of
unprocessed fMRI data. Performance of the method is demonstrated by correction
of fMRI data from five patients with major depressive disorder, who exhibited
head movements by 1-3 mm during a resting EEG-fMRI run. The fMRI datasets,
corrected using eight to ten EEG-based motion regressors, show significant
improvements in temporal SNR (TSNR) of fMRI time series, particularly in the
frontal brain regions and near the surface of the brain. The TSNR improvements
are as high as 50% for large brain areas in single-subject analysis and as high
as 25% when the results are averaged across the subjects. Simultaneous
application of the EEG-based motion correction and physiological noise
correction by means of RETROICOR leads to average TSNR enhancements as high as
35% for large brain regions. These TSNR improvements are largely preserved
after the subsequent fMRI volume registration and regression of fMRI motion
parameters. The proposed EEG-assisted method of retrospective fMRI motion
correction (referred to as E-REMCOR) can be used to improve quality of fMRI
data with severe motion artifacts and to reduce spurious correlations between
the EEG and fMRI data caused by head movements. It does not require any
specialized equipment beyond the standard EEG-fMRI instrumentation and can be
applied retrospectively to any existing EEG-fMRI data set.Comment: 19 pages, 10 figures, to appear in NeuroImag
Multi-Channel SQUID System for MEG and Ultra-Low-Field MRI
A seven-channel system capable of performing both magnetoencephalography
(MEG) and ultra-low-field magnetic resonance imaging (ULF MRI) is described.
The system consists of seven second-order SQUID gradiometers with 37 mm
diameter and 60 mm baseline, having magnetic field resolution of 1.2-2.8
fT/rtHz. It also includes four sets of coils for 2-D Fourier imaging with
pre-polarization. The system's MEG performance was demonstrated by measurements
of auditory evoked response. The system was also used to obtain a multi-channel
2-D image of a whole human hand at the measurement field of 46 microtesla with
3 by 3 mm resolution.Comment: To appear in Proceedings of 2006 Applied Superconductivity Conferenc
Integration of Simultaneous Resting-State EEG, fMRI, and Eye Tracker Methods to Determine and Verify EEG Vigilance Measure
Resting-state functional magnetic resonance imaging (rsfMRI) has been widely
used for studying the (presumably) awake and alert human brain. Although rsfMRI
scans are typically collected while individuals are instructed to focus their
eyes on a fixation cross, objective and verified experimental measures to
quantify degree of alertness (e.g., vigilance) are not readily available.
Concurrent electroencephalography and fMRI (EEG-fMRI) measurements are also
widely used to study human brain with high spatial/temporal resolution. EEG is
the modality extensively used for estimating vigilance during eyes-closed
resting state. On the other hand, pupil size measured using an eye-tracker
device could provide an indirect index of vigilance. In this study, we
investigated whether simultaneous multimodal EEG-fMRI combined with eye-tracker
measurements can be used to determine EEG signal feature associated with pupil
size changes (e.g., vigilance measure) in healthy human subjects (n=10) during
brain rest with eyes open. We found that EEG frontal and occipital beta power
(FOBP) correlates with pupil size changes, an indirect index for locus
coeruleus activity implicated in vigilance regulation (r=0.306, p<0.001).
Moreover, FOBP also correlated with heart rate (r=0.255, p<0.001), as well as
several brain regions in the anti-correlated network, including the bilateral
insula and inferior parietal lobule. These results support the conclusion that
FOBP is an objective measure of vigilance in healthy human subjects
Multi-sensor system for simultaneous ultra-low-field MRI and MEG
Magnetoencephalography (MEG) and magnetic resonance imaging at ultra-low
fields (ULF MRI) are two methods based on the ability of SQUID (superconducting
quantum interference device) sensors to detect femtotesla magnetic fields.
Combination of these methods will allow simultaneous functional (MEG) and
structural (ULF MRI) imaging of the human brain. In this paper, we report the
first implementation of a multi-sensor SQUID system designed for both MEG and
ULF MRI. We present a multi-channel image of a human hand obtained at 46
microtesla field, as well as results of auditory MEG measurements with the new
system.Comment: To appear in Proceedings of 15th International Conference on
Biomagnetis
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