5,479 research outputs found
Comparative power spectral analysis of simultaneous elecroencephalographic and magnetoencephalographic recordings in humans suggests non-resistive extracellular media
The resistive or non-resistive nature of the extracellular space in the brain
is still debated, and is an important issue for correctly modeling
extracellular potentials. Here, we first show theoretically that if the medium
is resistive, the frequency scaling should be the same for electroencephalogram
(EEG) and magnetoencephalogram (MEG) signals at low frequencies (<10 Hz). To
test this prediction, we analyzed the spectrum of simultaneous EEG and MEG
measurements in four human subjects. The frequency scaling of EEG displays
coherent variations across the brain, in general between 1/f and 1/f^2, and
tends to be smaller in parietal/temporal regions. In a given region, although
the variability of the frequency scaling exponent was higher for MEG compared
to EEG, both signals consistently scale with a different exponent. In some
cases, the scaling was similar, but only when the signal-to-noise ratio of the
MEG was low. Several methods of noise correction for environmental and
instrumental noise were tested, and they all increased the difference between
EEG and MEG scaling. In conclusion, there is a significant difference in
frequency scaling between EEG and MEG, which can be explained if the
extracellular medium (including other layers such as dura matter and skull) is
globally non-resistive.Comment: Submitted to Journal of Computational Neuroscienc
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
Causal evidence that intrinsic beta frequency is relevant for enhanced signal propagation in the motor system as shown through rhythmic TMS
Correlative evidence provides support for the idea that brain oscillations underpin neural computations. Recent work using rhythmic stimulation techniques in humans provide causal evidence but the interactions of these external signals with intrinsic rhythmicity remain unclear. Here, we show that sensorimotor cortex precisely follows externally applied rhythmic TMS (rTMS) stimulation in the beta-band but that the elicited responses are strongest at the intrinsic individual beta-peak-frequency. While these entrainment effects are of short duration, even subthreshold rTMS pulses propagate through the network and elicit significant cortico-spinal coupling, particularly when stimulated at the individual beta-frequency. Our results show that externally enforced rhythmicity interacts with intrinsic brain rhythms such that the individual peak frequency determines the effect of rTMS. The observed downstream spinal effect at the resonance frequency provides evidence for the causal role of brain rhythms for signal propagation
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