Introduction to ElectroEncephaloGraphy (EEG) and MagnetoEncephaloGraphy (MEG).

Transcription

1 NEUR 570 Human Brain Imaging BIC Seminar 2011 Introduction to ElectroEncephaloGraphy (EEG) and MagnetoEncephaloGraphy (MEG). Christophe Grova Ph.D Biomedical Engineering Dpt Neurology and Neurosurgery Dpt Montreal Neurological Institute, McGill, University Centre de Recherches Mathématiques

2 Outline Origin of EEG and MEG signals EEG and MEG data acquisition Source localization

3 Outline Origin of EEG and MEG signals EEG and MEG data acquisition Source localization

4 A bit of history 1st EEG human recording: Dr. Hans Berger in st MEG human recording: Dr. David Cohen in 1972

5 Several brain imaging techniques Spatial scale (in mm) Epileptic spike Reference Intra-cerebral EEG recordings EEG 64 electrodes MEG 275 sensors Scalp topographies Simultaneous EEG/fMRI Hemodynamic brain response associated to EEG activity SPECT Brain perfusion FDG PET Glucose metabolism Anatomical MRI 1 ms 1 s 40 s static Temporal scale

6 Electric vs magnetic fields Moving electric charges, an electric current, create a magnetic field B

7 Electric vs magnetic signals in the brain Magneto-EncephaloGraphy (MEG): measures changes in magnetic fields on the scalp: sensitive to neuronal currents Electro-EnphaloGraphy (EEG): measures differences of electric potentials on the scalp: sensitive to conduction (volume) currents If there was air between the brain and the skull, no EEG could be measured on this subject, but it would be possible to measure MEG

8 The electro-magnetic dipole model r J p (r ) Current dipole r Measurement point Electric potential (in free space) Magnetic field (in free space)

9 Main generators or electro-magnetic scalp activity: pyramidal cells

10 Generation of a signal one can detect from scalp measurements Synchronisation of potentiels post-synaptic potentials along the cortical surface

11 Organization of pyramidal cells along the cortical surface

12 Neuronal conduction vs volume conduction Baillet et al 2001 Neuronal conduction: (primary currents) Action potentials (1ms), Post synaptic potentials (10ms) Active conduction. Origin of MEG signals Volume conduction: (secondary currents) The brain is a conductive medium Instantaneous propagation of electric fields Passive conduction. Origin of EEG signals

13 Magneto-encephalography (MEG) Measures magnetic fields generated outside the head from neuronal currents Magnetic and electric fields are perpendicular There is no influence of the skull on the propagation of magnetic fields Radial sources do not contribute to MEG signals

14 Differences between EEG and MEG Although the same neurophysiological processes generate EEG and MEG: 1. Magnetic fields are not distorted by resistance from skull and scalp better spatial resolution in MEG? 2. Electrical and magnetic fields are oriented perpendicularly to each other Hamalainen et al 1993

15 Differences between EEG and MEG cont 3. Scalp EEG is sensitive to both tangential and radial components of a current source in a sphere, while neuromagnetometers detect only its tangential components - a radial dipole does not produce a magnetic field outside the sphere David Cohen NMH/MIT

16 Differences in EEG and MEG cont - Thus, MEG selectively measures activity in the sulci. - EEG measures activity from both the gyri and the sulci (dominated by radial sources) David Cohen NMH/MIT

17 Epileptic activity: complementarity btw MEG and EEG Merlet et al 1997

18 Outline Origin of EEG and MEG signals EEG and MEG data acquisition Source localization

19 Electro-encephalography (EEG) 10/20 System (19electrodes) Measurement of scalp Electric potentials Measures potentials generated by volume conduction currents (from 19 to 256 electrodes) Scalp potentials are attenuated and distorted by the skull (highly resistive)

20 Electro-encephalography (EEG) Background Signal EEG system 19 electrodes 256 electrodes

21 EEG during wakefulness From Dr. Gotman BMDE501 lecture

22 EEG during sleep From Dr. Gotman BMDE501 lecture

23 EEG during an epileptic seizure From Dr. Gotman BMDE501 lecture

24 MEG measures magnetic fields related to brain activity from femtotesla (10-15 T) to picotesla (10-12 T) Earth s magnetic field: 4,710-5 T. small magnetic field measurements lead to artifacts MEG brain signals = hearing the noise of a pin falling on a sofa in a dance club! (M. Hamalainen)

25 Noise sources

26 Challenges in MEG data acquisition Brain magnetic fields are tiny (pt,10-12 T): MRI: 1,000,000,000,000,000 (=1T) Earth magnetic field: 100,000,000,000 Magneto-CardioGram (MCG): 100,000 MEG signals: 1,000 Sensitivity of magnetometer: 10 Highly sensitive sensors: SQUID (Superconducting QUantum Interference Device) in liquid helium (-269ºC) Noise reduction: Magnetically shielded room First order gradiometer to eliminate remote interference and record only local field Reference sensors MEG is sensitive to head motion: head localization is required MEG setup costs approximately $3M

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28 MEG Systems CTF 275 sensors 4-D 148 or 248 sensors, NeuroMag 306 sensors KIT - Kanazawa Other (Los Alamos)

29 An highly sensitive sensor of magnetic field (ft) SQUID: Superconducting Quantum Interference Devices invented in 1965 by James Edward Zimmerman and Arnold Silver at Ford Research Lab. All the system requires superconducting state: Liquide Helium + Cryogenic Dewar

30 Environmental Noise Reduction Shielded Room Gradiometers Reference sensors used to pick up noise (3rd order gradient of env. noise)

31 Opening a MEG device Reference sensors

32 Passive noise reduction: Moving magnetic dipoles Reference system s Magnetically shielded room (mu metal) Power lines or other current lines Magnetic sensors are subjected not only to the measured MEG signal S, but also to unwanted signals (environmental noise, signals from parts of brain not being measured and other body parts).

33 Active noise cancellation: coils + external magnetometer to compensate external noise Noise cancellation coils MEG system shielded room (a) (b) Active noise cancellation. (a) coil system in an unshielded environment; (b) coil system combined with a shielded room.

34 Synthetic noise cancellation: flux transformers estimating local gradient of magnetic field 10 µt 1 µt (b) Magnetometers Power line Noise (B or G (3) d 1 d 2 d 3 ) (rms/ Hz) 100 nt 10 nt 1 nt 100 pt 10 pt 1 pt 100 ft 3rd-order gradiometer Unshielded, B Shielded, B Vibrational noise 10 ft 5 ft rms/ Hz 1 ft Frequency (Hz) Noise (G (1) d or G (3) d 1 d 2 d 3 ) (rms/ Hz) 10 µt 1 µt 100 nt 10 nt 1 nt 100 pt 10 pt 1 pt 100 ft (a) 1st-order gradiometers Shielded, G 3rd-order grad. Unshielded, G Vibrational noise Power line 10 ft 5 ft rms/ Hz 1 ft Frequency (Hz)

35 MEG is sensitive to head motions during data acquisition

36 Continuous head localization system Three emitting coils used for continuous head localization: Nasion and Peri-auricular points

37 3D localization of the three localization coils, the head shape and EEG electrodes on the subject s head Use of a magnetic device (Polhemus) for 3D localization

38 Co-registration with the subject sanatomy (skin surface segmented from anatomical MRI) MEG sensors (red) + EEG electrodes (blue) Co-registered on skin surface EEG electrodes (blue) + Digitized Head Shape (red) Co-registered on skin surface

39 Few EEG/MEG examples

40 Epileptic activity : interictal spikes Interictal spikes are spontaneous activity generated by the brain without any clinical sign Multimodal exploration is feasible Intra-cerebral EEG recordings showed that interictal spike generators are rarely focal (Merlet I. et al. Clin. Neurophys. 1999) A minimum brain activated area of 6 cm 2 is needed to generate a spike on the scalp (Ebersole J. Clin. Neurophys. 1997), spike generators may also be quite more extended than 6 cm 2 A minimum brain activated area of 3 cm 2 is needed to generate a spike on MEG EEG interictal spike

41 Epileptic spikes in EEG

42 Epileptic spikes in MEG

43 Generation of evoked activity: averaging will increase the Signal-to-Noise ratio Average of the 20 trials Trial 1 Trial 2 Trial 3.

44 Visual evoked field / potential

45 Visual evoked field / potential (left stimulation) MEG topography EEG topography Source localization

46 Somatosensory vs motor evoked field (potential) 90ms after left thumb pneumatic stimulation 20ms after left thumb tapping

47 Somatosensory vs motor evoked field (potential) 90ms after left thumb pneumatic stimulation 20ms after left thumb tapping

48 Outline Origin of EEG and MEG signals EEG and MEG data acquisition Source localization

49 Is it possible to localize source of brain activity from scalp measurements?

50 Source localization Inverse problem: estimation of sources of brain activity from EEG/MEG scalp recordings? Forward problem = modelling : knowing where are the sources, computation of the EEG/MEG signals generated by these sources?

51 Forward problem Knowing brain sources and a model of the head, one can compute corresponding electric potentials or magnetic fields generated on the scalp?

52 Spherical model: analytical solution

53 Realistic models of the head a : spherical model (analytical solution) b : realistic surface model (BEM) BrainStorm c : realistic volume model (FEM)

54 The Direct Problem is Relatively Simple We know the geometry and the electrical and magnetic properties of the brain, CSF, skull and scalp The geometry is complex: we need a mathematically manageable representation of the head. The simplest is a sphere; the more complex is a realistic head model. The electrical properties are complex: electrical conductivities cannot be measured in vivo. Bone conductivity is quite variable and is the most important factor. Magnetic properties are more homogeneous across tissues (less influence of the bone)

55 The Inverse Problem Given a distribution of electric potentials or magnetic fields at the surface of a volume conductor, where are the sources inside the volume giving rise to this distribution, and what are their characteristics?

56 Inverse problem

57 The Inverse Problem is Hopeless There is an infinite number of distribution of sources inside a volume conductor that can give rise to the same potential distribution at the surface of the conductor (Helmholtz, 1853). There is an infinite number of possible arrangement of sources inside the brain giving rise to a particular EEG or MEG signal The inverse problem is hopeless in theory. It can only be solved with simplifying assumptions = constraints

58 Selection of the more appropriate model One need to add assumptions in the model to be able to find a unique solution Are they realistic?

59 Models of the sources of brain activity Equivalent current dipole non-linear nb of sources? what is an ECD? Distributed sources anatomical constraint linear p= 10 3 sources n= 10 2 electrodes ill-conditioned pb needs regularization

60 Summary: any source localization method relies on some a priori assumptions Number of generators well-known ECD approaches Few decorrelated sources, number unknown Dipole scanning approaches Distributed network and/or extended sources Distributed sources approaches

61 Model of signal generation J Lead field (forward pb) M signal Noise

62 Model of signal generation J sources Lead field (forward pb) M signal Noise

63 Model of signal generation J sources Lead field (forward pb) M signal Noise

64 Model of signal generation J sources Lead field (forward pb) M signal Noise

65 Model of signal generation J M sources Lead field (forward pb) signal Estimate of J using and Equivalent Current Dipole Noise

66 Model of signal generation J M sources Lead field (forward pb) signal Estimate Estimate of J of constrained J using and on The Equivalent cortical surface Current Dipole Noise

67 The Equivalent Current Dipole If one assumes that the EEG or MEG signals are generated by one or a small number of dipole sources, then it is possible to solve the inverse problem. A dipole is a point source, defined by its location (3 parameters), its orientation (2 parameters) and its moment (1 parameter). Is the dipole model reasonable?

68 Estimation of the Equivalent Current Dipole G: Lead field matrix M = G(Φ).J + E Φ: dipoles location and orientation J: amplutide of the the dipole E: Noise Several types of dipole models: Moving dipole: Position?, Orientation?, Amplitude? Rotating dipole: fixed position, Orientation?, Amplitude? Fixed dipole: fixed position, fixed orientation, Amplitude? Number of dipoles: Estimation of the signal subspace (PCA) Iterative approaches Solving the inverse problem: Non-linear optimisation to find dipole locations Minimisation of residual variance:

69 Minisation of residual variance (RV) RV = M - G(Φ).J 2 / M 2 Variance non explained by the model Total variance RV is not a validation metric!!! A misleading solution can provide a very low RV

70 Is the Dipole Model Reasonable? In primary sensory evoked experiments, particularly somatosensory and auditory, the dipole model appears justified (it has been validated by comparing the results of modeling to what we know about sensory physiology). In other situations, particularly in epilepsy, the dipole model requires validation.

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72 Can the Dipole Model be Misleading? YES In most instances, it is possible to find a dipolar source that can model well a scalp distribution. This only indicates that the dipole is a possible source of the distribution. It does not prove that it is the source of the distribution. There are systematic errors caused by the fact that the source is likely to have a significant spatial extent.

73 Confidence intervals

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76 The Dipole Model: Conclusions Very powerful method to find intracerebral sources from a scalp recording. Only valid if its underlying assumptions are correct (that sources are dipolar). Appears to localize well sources of primary sensory activity. Localizes reasonably well the maximum of extended sources of epileptic activity, although secondary (small amplitude) sources are probably less reliable. No information on extent of actual source. Main limitation: number of dipoles

77 Solving the inverse problem using distributed sources M = G J + B Inverse pb: LINEAR, but ill-posed. Under-determined equation system: 10 2 measures 10 4 dipole sources (=unknown) Lead field G = ill-conditioned Regularization is needed to find a solution (requires a priori assumptions)

78 Classical assumptions Solution of minimum energy: Minimum Norm Hamalainen et al. Med Biol. Eng. Comput. 94 Solution maximum spatial smoothness: LORETA Pascual-Marqui et al. Int. J. Psychophys. 94

79 Minimum Norm Estimate

80 LORETA: maximum of spatial smoothing

81 Anatomical constraints: sources distributed on the cortical surface Dale A., Sereno M., J. Cogn. Neurosci. 5,

82 Extraction of a distributed sources model from an anatomical MRI 3D T1-weighted MRI acquisition: Matrix = 170x256x256, voxel = 1mm TR = 22 ms, TE = 9.2 ms Automatic segmentation of the cortical surface = White Matter/Grey Matter interface Brainvisa:

83 Regularization Inverse problem = ill posed problem (10,000 sources vs 100 sensors) No unique solution The problem needs regularization (assumptions) 1. Minimum norm (MN): minimum of energy (may be weighted W). Minimise M-GJ 2 + α WJ 2 2. LORETA : maximum of smoothness. Minimise J 2 under the constraint M = GJ where is a discrete spatial Laplacian operator 3. MEM : maximum entropy of the mean

84 Minimum Norm Estimate within the Bayesian Framework (1/5) Linear distributed model for source localization M: n x t signal on the n scalp sensors G: n x p forward model of Gain matrix J: p x t current density distribution on the sources along the cortical surface p >> n Bayes Law:

85 Minimum Norm Estimate within the Bayesian Framework (2/5) Bayes Law: A priori distribution of the sensor noise E: Gaussian distribution with null mean and Assumption of uncorrelated noise Data likelihood: A priori distribution of the source distribution J: Gaussian distribution with null mean and

86 Minimum Norm Estimate within the Bayesian Framework (3/5) Bayes Law: Maximum likelihood solution Data fit term Regularization hyperparameter Weighted Min. Energy constraint

87 Minimum Norm Estimate within the Bayesian Framework (4/5) Bayes Law: Maximum likelihood solution This solution depends on the regularization hyparameter α α can be estimated using the L-curve technique

88 Minimum Norm Estimate within the Bayesian Framework (5/5) Maximum likelihood solution Estimation of the regularization hyparameter α using the L-curve technique

89 Examples of source localisation of an epileptic spike using anatomical constraints fmri activation for a similar spike

90 Regularizing the ill-posed linear model: M = GJ +E 2. Maximum Entropy on the Mean (MEM) µ J (J) Reference distribution = a priori information Relative entropy MEM solution is the one with maximum µ entropy, i.e., the one that makes the least assumption regarding missing information Prior information on J: Parcelling of the cortical surface in K parcels: MEM solution p * J (J) p J (J) Data fit: set of all distributions p J (J) explaining the data on average

91 Validation Results: 4th order spatial extent (14 cm 2 ) Gold Standard: Temporo-Radial Source Gold Standard: Temporo-Tangential Source MN : AUC = 0.78 MEM : AUC = 0.93 LORETA : AUC = 0.99 MN : AUC = 0.79 MEM : AUC = 0.93 LORETA : AUC = 0.96 Grova C, Daunizeau J, Lina JM, Benar CG, Benali H, Gotman J. Neuroimage Feb 1;29(3):

92 Validation Results: MEG source localization (1/2) Chowdhury R. et al, Proc. of HBM 2010 conference

93 Application of model evidence for model comparison: Henson et al. Neuroimage 2009

94 Validation: comparison between MEM source localization and intracranial EEG recordings Lina et al, IEEE TBME Sumitted

95 Summary: any source localization method relies on some a priori assumptions Number of generators well-known ECD approaches Few decorrelated sources, number unknown Dipole scanning approaches Distributed network and/or extended sources Distributed sources approaches Requires a good knowledge of the signal to be localized Requires statistics (SPM-like, non parametric) Requires the comparaison of several methods

96 Take home messages Complementarity between EEG and MEG: MEG signals are not distorted by the skull (better spatial accuracy) MEG can see only tangential sources whereas EEG can see tangential and radial sources MEG data acquisition is challenging: tiny magnetic fields in a noisy environment EEG/MEG source localization: Any source localization requires an a priori model of the underlying sources Main models: Equivalent current dipole Dipole scanning approaches Distributed sources (along the cortical surface) Model comparison or model selection is required (hypothesis test) Multimodal data fusion: There is no one to one correspondance between EEG source, MEG source and BOLD response: COMPLEMENTARITY EEG/MEG sources can be associated either with BOLD activation or deactivation EEG/MEG signals require synchronization of neuronal activity, the BOLD signal does not

97 Suggested Bibliography Niedermeyer s ElectroEncephalography: Basic Principles, Clinical Applications and Related Fields. 6 th Edition, Ed. D.L. Schomer and F.H. Lopes da Silva. Wolters Kuwer, Lippincott Williams and Wilkins MEG: an introduction to methods. Eds. P.C. Hansen, M.L. Kringelbach and R.Salmelin Oxford University Press Review Papers Baillet S., Mosher J., Leahy R., Electromagnetic brain mapping. IEEE Signal Process. Mag., Hamalainen, M, Hari, R, Ilmoniemi, R, Knuutila, J, Lounasmaa, O (1993). Magnetoencephalography--theory, instrumentation, and applications to noninvasive studies of the working human brain. Rev Mod Phys, 65: Michel C., Murray M., Lantz G., Gonzales S., Spinelli L., Grave de Peralta R., EEG source imaging. Clin. Neurophysiol. 115,

98 MEG The Neuro 275 magnetometers (MEG sensors) 64 simultaneous EEG all channels up to 12kHz multimodal stimulus presentation (video, audio, somesthetic,...) audio, video subject monitoring operates in upright or supine positions located in the Neuro s new extension

99 The Neuro: more information

100 NEW COURSE offered in January 2012: BMDE 610 Functional Neuroimaging fusion Registration is now open on Minerva for Winter 2012 Space is limited, register ASAP!!!

101 Acknowledgements The Multimodal Functional Imaging Lab. (Multi-FunkIm) Christophe Grova R. Chowdhury, Y. Potiez, A. Machado, T. Hedrich, A. Blanc Montreal Neurological Inst. Eliane Kobayashi M.L. Jones, M. Aiguabella M. Sangani, D. Rosenberg, M. Porras-Betancourt Ecole de Technologie Sup. Jean Marc Lina A.S. Dubarry E. Lemay S. Deslauriers Collaborators: -MNI EEG/fMRI studies: J. Gotman, F. Dubeau, an their team!