Microsoft PowerPoint - 20071219_MEG-lfchen.ppt• Hämäläinen, M. et al. Magneto-encephalo-graphy — theory, instrumentation, and applications to non-invasive studies of the working human brain. Review of Modern Physics. 1993; 65(2):413-497.
• Baillet, S. et al. Electromagnetic Brain Mapping. IEEE Signal Processing Magazine. 2001; November: 14-30.
• Vigario et al. Independent Component Approach to the Analysis of EEG and MEG Recordings. IEEE Transactions on Biomedical Engineering. 2000; 47(5):589-593.
• Tallon-Baudry and Bertrand. Oscillatory gamma activity in humans and its role in object representation. Trends in Cognitive Sciences. 1999; 3(4): 151-162.
• Lauri Parkkonen, MEG/EEG training course in 12th Human Brain Mapping Conference, June, 2006.
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electromagnetic brain signals • EEG/MEG Instrument and data
acquisition • Signal processing methods for
EEG/MEG signals • Source localization for EEG/MEG
signals
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phenomenon into an electrical signal
Signal analysis to extract information
Event-Related Potential (ERP)
• Functional connectivity • Cognitive neuroscience
– Clinical research • Disease-oriented model
Functional Imaging of Brain Activity
• ElectroEncephaloGraphy, EEG – The first human EEG:
Hans Berger, a German neuropsychiatrist, 1929 • MagnetoEncephaloGraphy, MEG
– The first human brain MEG: D. Cohen, MIT, 1968 – SQUID (Superconducting QUantum Interference Device):
Zimmerman, 1969. • Transcranial Magnetic Stimulation, TMS
– The first device, Anthony Barker, University of Sheffield,
1985 • Functional Magnetic Resonance Imaging, fMRI
– BOLD-contrast: Ogawa et. al., 1990
• Positron Emission Tomography, PET
• Near-InfraRed Spectroscopy, NIRS
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• Spatial-temporal resolution illustration of different functional mapping modalities
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Outline
electromagnetic brain signals • MEG Instrument and data acquisition • Signal processing methods for MEG
signals • Source localization for MEG signals
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Physiological Origins of Electromagnetic Brain Signals
Signal reception: specific ions rush through the membrane and give rise to a post-synaptic potential of about 10mV with a duration of 10ms, what EEG and MEG measured (synaptic current flow).
Signal emission: Action potential is occurred when the neuron is propagating a signal along its axon and the frequency (not amplitude) encodes the neuronal information. (I.e. high frequency 1ms,)
The activity in human brain is an electrophysiological action of small currents.
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bioelectrical signals – triggered by depolarization of the membrane
beyond a threshold (40mV for a typical neuron) – caused by the flow of sodium (Na+), potassium
(K+), chloride (Cl-), and other ions across the cell membrane
– conveys information over distances – all-or-none phenomenon – frequency and temporal pattern constitute the
code
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AP Frequency vs. Level of Depolarization
• Maximum firing frequency is about 1kHz due to 1msec of absolute refractory period.
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Action Potential Conduction
• Propagation of action potential is similar to the propagation of the flame along the fuse.
• Action potential propagates in one direction because of the refractory period.
• Typically, action potential conduction velocity is 10 m/sec.
Chemical Synapse and Major Neurotransmitters
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• Mechanisms – Synthesizing and replenishing
neurotransmitters in the synaptic vesicles – Causing vesicles to spill into the synaptic
cleft in response to presynaptic action potential
– Producing an electrical or biochemical response to neurotransmitter in the postsynaptic neuron
– Removing neurotransmitter from the synaptic cleft
MEEG 2007 Spring 15
Excitatory Postsynaptic Potential (EPSP)
computations.
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• Neural origin of the brain electromagnetic fields – 1010, at least, in the outermost cortical layer – 1012 neurons in the central nervous system – 1015 synaptic connections between these neurons – 1018 chemical neuro-transmitters/second
• Sources in MEG/EEG recordings – Pyramidal cell
• only signals from open field can be detected – Post-synaptic potential
• Signal decreases with distance as 1/r2
– Concurrent activity • Macroscopic physical models of brain activity
– 105 pyramidal cells per mm2 of cortex – The measured magnetic-field strengths outside the
head are on the order of 10 nAm.
Pyramidal cell
– Current dipole: can be viewed as a battery
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Outline
electromagnetic brain signals • MEG instrument and data acquisition • Signal processing methods for MEG
signals • Source localization for MEG signals
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and inhibitory post-synaptic potentials of pyramidal neurons.
(Lauri Parkkonen, HBM2006) 22
• Data acquisition – Electrode/SQUID coil – Amplifier – Digitization
• Stimulus presentation – Stimulus delivery
MEG Instrument
–Picks up and “squeezes” extracranial magnetic flux into the SQUID
–Requires superconductivity
low temperatures; immersion in liquid Helium (269 Celsius)
• The external magnetic flux threads the superconducting loop of the SQUID, changing the impedance across the loop.
• This changing can be detected by feeding a current and measuring the voltage.
• Outputs a magnetic flux dependent voltage
• Differences between EEG and MEG – The lead fields are different. In the spherical model, MEG is
sensitive only to the tangential component of the primary current, whereas EEG senses all primary current components.
– The lead field of EEG is affected by the conductivities of the skull and the scalp much more than the lead field of MEG. In the spherical model, concentric inhomogeneities do not affect the magnetic field at all
– MEG measurements can be accomplished more quickly, since no electrode contact to the scalp needs to be established. On the other hand, the subject has to be immobile during the MEG measurements, whereas telemetric and long term EEG recording are possible.
– The instrumentation necessary for MEG is more sophisticated and, therefore, more expensive than that for EEG.
EEG vs. MEG
– Stimuli preparation – Coordinate system
preauricular (RPA), nasion (NAS)
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MEG/MRI Co-registration •MEG source locations usually superimposed on anatomical MR images •Head coordinate frame is the link between the MEG and MRI device coordinate frames.
CMEGCMRI
CD
MRITMEG
3 anatomical landmarks: left preauricular point LPA, right preauricular point RPA, nasion NAS
4 coils (Head Position
3D Positioning - Digitization • 3D digitizer is used for localizing
landmarks as well as the HPI coils in the head coordinate frame prior to the MEG measurement (= digitization)
(Polhemus FASTRAK)
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Determining head position in MEG • 3 to 5 small coils (head position indicator) are
employed for localizing subject's head in the MEG device coordinate system, i.e., with respect to the sensor array.
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Co-registration with anatomical MRI/CT
Extra points are used to verify  the accuracy of co registration
RPA
NAS
LPA
Outline
electromagnetic brain signals • MEG instrument and data acquisition • Signal processing methods for MEG
signals • Source localization for MEG signals
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• Signal spectrum
• Noise spectrum with (upper curve) and without (lower curve) a subject
• Peak amplitude (arrows) and spectral densities of fields due to typical biomagnetic and noise sources
(Hamalainen et al. 1993) 35
Artefacts and Noise
• Solutions to remove different kinds of noise sources: – Bioelectric signals
• Cardiac signal (ECG): bandpass filtering, component analysis • Eye movement/blinking: EOG rejection , component analysis • Myoid (EMG): averaging, rejection • Non-task related signals: averaging
– External noise • Electricity power: bandpass filtering • Environmental noise: averaging, signal space projection
– Hardware-relevant signals • DC drift: baseline correction, detrend
Signal Processing Methods for MEG/EEG Data
• Categories – Signal characteristics
• Evoked response vs. induced response • Averaging data vs. trial-by-trial analysis
– Analysis domain • Time domain
• Frequency domain – Spectral analysis – Coherence
• Time-frequency
• Evoked response – Phase-locking to the stimulus onset – Detected by averaging single-trial responses
• Induced response – Non-phase-locking to the stimulus onset – Time-varying spectral analysis of single trials
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(Hamalainen et al. 1993)
Peak latency 1. When (onset and offset) 2. How long (duration)
Peak amplitude 1. How large
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Both vibrotactile and concomitant auditory stimuli (Vigario et al. 2000)
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• Delta rhythm: below 4 Hz.
• Theta rhythm: – 4-7 Hz, parieto-temporal region, disappeared after 12 years old.
• Alpha rhythm: – 8-13Hz, parieto-occipital area, eye-closed and relaxed adults,
dampened by opening the eyes. • Mu rhythm:
– 10-20 Hz, rolandic region (primary somatosensory cortex for the hand), dampened by limb movement.
• Tau rhythm: – supra-temporal auditory cortex, dampened by sounds.
• Beta rhythm: – 14-22Hz, motor area, nervous or drugged adults.
• Gamma rhythm: beyond 22Hz. • Sinusoidal rhythm: any rhythm with sin waveform.
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(Hamalainen et al. 1993) 48
Time-Frequency Analysis
(Tallon-Baudry, 1999)
Time-Frequency Analysis
(Tallon-Baudry, 1999)
duration) while watching a silent video movie. – Active listening: binaurally auditory stimulus (1000Hz, 50ms
duration) while detecting 1050Hz rare tones.
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Outline
electromagnetic brain signals • MEG instrument and data acquisition • Signal processing methods for MEG
signals • Source localization for MEG signals
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Functional Imaging of Electromagnetic Brain Signals
• Functional source imaging of brain activity
• Networks of cortical neural cell assemblies are the main generators of MEG/EEG signals. (Baillet et al. 2001)
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• Forward problem: – Input: the positions, the amplitude and the orientations
of the source current dipoles – Output: to estimate the measured data from MEG
sensors
• Inverse problem: – Input: a set of measured data – Output: to estimate the parameters representing the
source current dipoles, including positions, the amplitude and the orientations
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• The brain is not a passive medium, but is subject to an electrical activity reflected by a primary current density, Jp
Here, is the conductivity and where is the current dipole appeared at a single point and is a Dirac delta function.
Usually, we consider as a line element of current I pumped from a sink at to a source at ; then (current x length, nAm)
)()()()()()()( rrrJrrrJrJ VE pp ∇−=+= σσ
so-called current dipole
– neglect all time derivatives in the Maxwell equations
• The primary current distribution is precisely what EEG and MEG are trying to estimate, so-called the inverse EEG/MEG problem.
– The forward problem in EEG/MEG: • Given Jp and σ compute V(r) / B(r)
– The inverse problem in EEG/MEG: • Given measures of V(ri) / B(ri) at point r1, …, rn and σ estimate Jp and σ
r rr
rrr rr
4 1)(
4 )( 00
Measured MEG signals at each sensor position r Primary current of
brain activity Potential field induced by
the primary current
Algorithms
ECD fitting BeamformingMNE/MCE/ MUSIC
(Baillet et al. 2001)
Minimum Least-Square Estimation
• Parameters: – Location (G): nonlinear – Orientation (*strength) (J): linear
• Optimization: Nelder-Mead simplex method
Dipole Modeling
• Limitation of dipole modeling approach – Requires a priori knowledge of number of sources – Limited to small number of sources – Requires high S/N (signal averaging)
(Lauri Parkkonen, HBM2006)
Minimum Norm/Current Estimation (MNE/MCE)
• Linear model: b = GJ + n without consideration of magnetic forward model
• Objective function: min || J || , subject to GJ = b
• Calculate J for each time point, separately. • Fixed source position and orientation, based
on constructed brain mesh; only tangential direction to the sphere is considered.
• Difference between MNE and MCE – The former uses l2-norm while the latter uses l1-norm
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Comparison
SAM/Beamforming
– Regularization parameter ( ) • Tradeoff between specificity and noise
sensitivity
α
TT α
Group Analysis
• Sensor-based analysis – Easy to do, but need some justification – Choose appropriate “channel-of-interest”
• Verification of the relative neuroanatomy – Make sure the signal-to-noise ratio are
comparable between subjects • Verification of the distance between the MEG device
and subject’s head
complicated
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Prior information
Cortical-based Brain Group Analysis
Spatiotemporal Imaging of Brain Activation
MRI fMRI
Brain-Machine Interface
(http://www.ece.ubc.ca/~garyb/BCI.htm)