腦電磁波於腦功能造影之應用. Outline. Introduction. Introduction. Functional Imaging of Brain Activity. Major References

Transcription

1 Major References 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): 腦電磁波於腦功能造影之應用 Baillet, S. et al. Electromagnetic Brain Mapping. IEEE Signal Processing Magazine. 2001; November: Vigario et al. Independent Component Approach to the Analysis of EEG and MEG Recordings. IEEE Transactions on Biomedical Engineering. 2000; 47(5): Tallon-Baudry and Bertrand. Oscillatory gamma activity in humans and its role in object representation. Trends in Cognitive Sciences. 1999; 3(4): 陳麗芬 Lauri Parkkonen, MEG/EEG training course in 12th Human Brain Mapping Conference, June, Outline Introduction Physiological origins of electromagnetic brain signals EEG/MEG Instrument and data acquisition Signal processing methods for EEG/MEG signals Source localization for EEG/MEG signals 3 Introduction Sensors and instrumentation to transduce the phenomenon into an electrical signal Event-Related Magnetic Field (ERF) Event-Related Potential (ERP) Signal analysis to extract information Introduction Functional Imaging of Brain Activity Three major research topics Basic research Functional connectivity Cognitive neuroscience Clinical research Disease-oriented model Technical development Signal processing Source localization ElectroEncephaloGraphy, EEG ( 腦電波 ) The first human EEG: Hans Berger, a German neuropsychiatrist, 1929 MagnetoEncephaloGraphy, MEG ( 腦磁波 ) The first human brain MEG: D. Cohen, MIT, 1968 SQUID (Superconducting QUantum Interference Device): Zimmerman, Transcranial Magnetic Stimulation, TMS( 穿頭顱磁刺激 ) The first device, Anthony Barker, University of Sheffield, 1985 Functional Magnetic Resonance Imaging, fmri ( 功能性磁振造影 ) BOLD-contrast: Ogawa et. al., 1990 Positron Emission Tomography, PET ( 正子斷層攝影 ) Near-InfraRed Spectroscopy, NIRS( 近紅外光造影 ) 6

2 Functional Imaging of Brain Activity Spatial-temporal resolution illustration of different functional mapping modalities Outline Introduction to brain research Physiological origins of electromagnetic brain signals MEG Instrument and data acquisition Signal processing methods for MEG signals Source localization for MEG signals 7 8 Physiological Origins of Electromagnetic Brain Signals Action Potential The activity in human brain is an electrophysiological action of small currents. 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,) 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). 9 Action potential the basic component of all 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 AP Frequency vs. Level of Depolarization 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. Maximum firing frequency is about 1kHz due to 1msec of absolute refractory period

3 Chemical Synapse and Major Neurotransmitters Basic Requirements of Chemical Synaptic Transmission 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 14 Excitatory Postsynaptic Potential (EPSP) Inhibitory Postsynaptic Potential (IPSP) (Ach, Glu) (GABA) E Cl MEEG 2007 Spring EPSP Integration IPSP and Shunting Inhibition Neurons in CNS perform computations. 0.2 mv spatial summation temporal summation 17 MEEG 2007 Spring 18

4 Macroscopic Views of Electromagnetic Sources in Brain Source Model of MEG Signals Neural origin of the brain electromagnetic fields 10 10, at least, in the outermost cortical layer neurons in the central nervous system synaptic connections between these neurons 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/r 2 Concurrent activity Macroscopic physical models of brain activity 10 5 pyramidal cells per mm 2 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 Outline Introduction to brain research Physiological origins of electromagnetic brain signals MEG instrument and data acquisition Signal processing methods for MEG signals Source localization for MEG signals Neuronal Electromagnetic Signals Cortical potentials are generated due to excitatory and inhibitory post-synaptic potentials of pyramidal neurons. 21 (Lauri Parkkonen, HBM2006) 22 Data acquisition Electrode/SQUID coil Amplifier Digitization Stimulus presentation Stimulus delivery Data analysis software Signal processing Source localization Major Components of an ERP Recording System Instrument Magnetic Shielding Room SQUID sensor MEG Instrument Dewar 23 24

5 MEG Sensors Flux transformer SQUID (Superconduction Picks up and squeezes Quantum Interference Device) extracranial magnetic flux Requires superconductivity and thus into the SQUID low temperatures; immersion in Requires superconductivity 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 25 EEG vs. MEG 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. (Hamalainen et al. 1993) 26 Experiment (Lauri Parkkonen, HBM2006) 27 Data Acquisition Preparation Paradigm design Trigger sequence Inter-stimuli interval Recording duration Amount of trials Stimuli preparation Coordinate system Head Position Indicator Left preauricular (LPA), right preauricular (RPA), nasion (NAS) Biomagnetic noise EOG ECG EMG 28 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. C MRI MRI T MEG C MEG 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) MRI T D D T MEG (KIT-MIT MEG system) 3 anatomical landmarks: left preauricular point LPA, right preauricular point RPA, nasion NAS 4 coils (Head Position Indicator) (Polhemus FASTRAK) (VGHTPE MEG) C D 29 30

6 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. Co-registration with anatomical MRI/CT Manual determination of anatomical landmarks RPA NAS Extra points are used to verify the accuracy of coregistration LPA Outline Introduction to brain research Physiological origins of electromagnetic brain signals MEG instrument and data acquisition Signal processing methods for MEG signals Source localization for MEG signals (Baillet et al. 2001) MEG Recordings Temporal profile 33 Topographic map Butterfly drawing 34 Components in MEG Signals Artefacts and Noise Signal spectrum Peak amplitude (arrows) and spectral densities of fields due to typical biomagnetic and noise sources Noise spectrum with (upper curve) and without (lower curve) a subject (Hamalainen et al. 1993) (Lauri Parkkonen, HBM2006)

7 Data Pre-processing: Artefacts Removal Data Pre-processing: Averaging 37 (Lauri Parkkonen, HBM2006) (Lauri Parkkonen, HBM2006) 38 Data Pre-processing: Filtering Review of Data Pre-processing Methods (Lauri Parkkonen, HBM2006) 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 Profile features: peak amplitude/latency Component extraction: PCA, ICA Frequency domain Spectral analysis Coherence Time-frequency Evoked vs. Induced Response 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 (Jensen, HBM2006) 41 42

8 Profile Features Component Extraction Auditory evoked responses to noise bursts Peak latency 1. When (onset and offset) 2. How long (duration) Peak amplitude 1. How large (Hamalainen et al. 1993) 43 Both vibrotactile and concomitant auditory stimuli (Vigario et al. 2000) 44 Frequency-domain Signal Processing Spectral analysis Periodic signals Frequency-domain Signal Processing Spectral analysis Windowing Fourier Transform X ( Ω) x( t) e jωt dt jθ e = cosθ + j sinθ jθ e + e cosθ = 2 jθ e e sinθ = 2 j jθ jθ Power Spectrum Density P x X ( Ω) ( Ω) = T Categories of Brain Rhythms Dynamics of Brain Rhythm 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: 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. 47 Alpha rhythm vs. mu rhythm detection using spectra analysis 48 (Hamalainen et al. 1993)

9 Time-Frequency Analysis Time-Frequency Analysis Task: Passive listening: binaurally auditory stimulus (1000Hz, 50ms duration) while watching a silent video movie. Active listening: binaurally auditory stimulus (1000Hz, 50ms duration) while detecting 1050Hz rare tones. Basis function of Morlet wavelet (Tallon-Baudry, 1999) 49 (Tallon-Baudry, 1999) 50 Outline Introduction to brain research Physiological origins of electromagnetic brain signals MEG instrument and data acquisition Signal processing methods for MEG signals Source localization for MEG signals 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) MEG/EEG Source Imaging 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 Forward problem: Input: the positions, the amplitude and the orientations of the source current dipoles Output: to estimate the measured data from MEG sensors 53 The brain is not a passive medium, but is subject to an electrical activity reflected by a primary current density, J p p p J( r) = J ( r) + σ ( r) E( r) = J ( r) σ ( r) V ( r) Here, σ (r) is the conductivity and J p ( r) = Qδ ( r rq ) where is the current dipole appeared at a single point r Q and δ ( r) is a Dirac delta function. Usually, we consider as a line element of current I pumped from a sink at r 1 to a source at r 2 ; then Q = I( r 2 r 1 ) (current x length, nam) What we can obtain from source analysis tools, so-called current dipole Q Forward Model Q p Q = J (r) dv 54

10 MEG Forward Model MEG Forward Model Maxwell equation Quasi-static approximation 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. μ μ B r = J r r σ r r r π d r r π V p 0 ( ) ( ) ( ) ( ) d 4 Ω 4 Ω r r Measured MEG signals at each sensor position r Primary current of brain activity Potential field induced by the primary current The forward problem in EEG/MEG: Given J p and σ compute V(r) / B(r) The inverse problem in EEG/MEG: Given measures of V(r i ) / B(r i ) at point r 1,, r n and σ estimate J p and σ55 56 Approaches to Solve Inverse Problem Dipole Fitting Algorithms (Baillet et al. 2001) Dipole fitting Imaging methods Scanning methods ECD fitting MNE/MCE/ MUSIC Beamforming 57 (Douglas Cheyne, Univ. of Toronto, 2002) 58 Minimum Least-Square Estimation Objective function: min W b ( B Bˆ ) F where Bˆ = GJ ˆ ˆ and ( 1,, 1 Wb = diag σ1 K σm ) is noise model. 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) Parameters: Location (G): nonlinear Orientation (*strength) (J): linear Optimization: Nelder-Mead simplex method Goodness of fit: T ( b bˆ ) ( b bˆ ) 1 peak- to - peak amp. 59 (Lauri Parkkonen, HBM2006) 60

11 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 Comparison (Baillet et al. 2001) Spatial Filtering Methods SAM/Beamforming Issues Source Significance p = w T Cactw p = w T Cctrw n F = p / p n Desired spatial resolution of source imaging Regularization parameter ( α ) Tradeoff between specificity and noise sensitivity T T min{ (θ )Cw(θ ) + αw (θ )w(θ )} s.t. w l w w(θ0 ) T = 1 (Douglas Cheyne, Univ. of Toronto, 2002) Computational Neurofunction 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 Cortical-based analysis Easy to interpret, but the procedure is complicated 65 66

12 Cortical-based Group Analysis Functional Brain Imaging Using Electroencephalography/Magnetoencephalography EEG MEG sources in digitizer-head coordinate Digitizer-based landmarks Landmark-based Alignment Transformation Matrix Individual MRI MEG sources in MRI-head coordinate Image-based Spatial Normalization Deformation field MRI Template MEG sources in template coordinate MEG Brain-Machine Interface Sequential single-trial data Adaptive online data acquisition/ signal processing Spatiotemporal Imaging of Brain Activation Prior information MRI fmri Cortical-based Brain Group Analysis 67 Brain Computer Interface Brain Computer Interface ( ( ( (BSP Lab, NCTU, Taiwan) Multi-modality Brain Source Imaging Hypothesis-driven/ Data-driven MEG fmri MRI Mutual Information Hypothesis-driven/ Data-driven EEG 71