Source Estimation in EEG The forward and inverse problems Christophe Phillips, Ir, Dr Cyclotron Research Centre, University of Liège, Belgium Agenda • Introduction Why , what for, where, how... • Part I : the forward problem From sources to electrodes • Part II : the inverse problem From electrodes to sources EEG Recordings Electroencephalography (EEG) is ‘simply’ about recording electromagnetic signals produced by neuronal activity : EEG signal is spread in space and time. EEG signal: origin Neurone Cell body Dendrites Head anatomy: •gray matter, •white matter, •CSF, •bone, air, skin, mucle, etc. Axon Synaptic terminals EEG signal: origin • From a distance, postsynaptic potential (PSP) looks like a current dipole oriented along the dentrite. • Pyramidal cells have parallel dendrites, oriented perpendicular to the cortical surface. • Typical dipole strength : ~20 fA m (1fA = 10 A) • Current-dipole moments required to be measured outside the head : ~10 nA m (1nA = 10 A) -15 -9 About 106 synapses must be simultaneously and effectively active to produce an evoked response. About 40 to 200 mm2 of active cortex. Cell body Dendrites Synaptic terminals Axon EEG signal: origin What about action potentials ? • large but brief potential compared to PSP PSP AP 100 mV 10 mV 10 ms 1 ms • modelled by a current-quadrupole the field decreases with distance as 1/r3, compared to 1/r2 for the PSP dipole. EEG signals are produced in large part by synaptic current flow, which is approximately dipolar. EEG vs fMRI Recordings •fMRI •EEG Signal change Difference between haemodynamic and electromagnetic signals produced by neuronal activity as recorded by : Fitted haemodynamic response function Preconditions for signal detection EEG signal •Activation of a neural population must be synchronous in time. •Active neural population must be spatially organised (parallel fibers). •The sources must be in an ‘open-field’ configuration. fMRI signal •Neural activity needs not be synchronous in time. •Geometrical orientation of the sources is totally irrelevant. Signal Sensitivity EEG signal fMRI signal •Measured signal is sensitive in relative timing and amplitude of activity. •Measured signal amplitude influenced by both duration and amplitude of activity. •Critical neural activity needs not be extended in time. •Signal detected only if net haemodynamic changes Source Localisation in EEG Forward Problem Inverse Problem Forward Problem Head anatomy Neurone Cell body Axon Dendrites Synaptic terminals Head model : conductivity layout Source model : current dipole Solution by Maxwell’s equations Inverse Problem Solving v f (r , j ) v, potential at the electrodes, (Nel x 1) , source(s) location, r where [rx , ry , rz]’ j , source(s) orientation & amplitude [jx , jy , jz]’ f, solution of the forward problem , additive noise, (Nel x 1) is an “ill-posed” problem, i.e. the inverse problem does NOT have a unique solution, (von Helmholtz, 1853). Source Estimation in EEG Part I : The forward problem Agenda Part I : Solving the forward problem • Maxwell’s equations • Analytical solution • Numerical solution • Pseudo-sphere approach • Conclusion Maxwell’s equations (1873) Ohm’s law : E j E B 0 B Continuity equation : E t E j B j t t Quasistatic approximation Maxwell’s equations can be simplified because : • EEG frequencies are genrally below 100hz. • Cellular electrical phenomena contain mostly frequencies below 1kHz. No propagation phenomenon, i.e. changes in the field/sources are «instantaneous». Derivative terms can be discarded. Simplified Maxwell’s equation With • quasi-static approximation of Maxwell’s equations • and dipolar current sources j V j conductivity where V electric potential j current source i.e. a “simple” mathematical relationship linking current sources and electric potential, depending on the conductivity of the media. Solving the forward Problem From V j find V f ( j ,r ) where f(.) will depend on the conductivity of the media (and the boundary conditions). The head model is the conductivity layout adopted ! Solving the forward Problem From V j find V f ( j ,r ) f(.) can be estimated • analytically, i.e. an exact solution ‘formula’ exists, • numerically, i.e. numerical methods provide an approximation of the solution. Agenda Part I : Solving the forward problem • Maxwell’s equations • Analytical solution • Numerical solution • Pseudo-sphere approach • Conclusion Analytical solution From V j find V f ( j ,r ) f(.) can be estimated analytically only for particular cases: • higly symmetrical geometry, e.g. spheres, cubes, concentric spheres, etc. • homogeneous isotropic conductivity These are very restricted head models! Analytical solution, cont’d Human head is not spherical neither is its conductivity homogeneous and isotropic... Agenda Part I : Solving the forward problem • Maxwell’s equations • Analytical solution • Numerical solution • Pseudo-sphere approach • Conclusion Numerical solution, 1 From V j find V f ( j ,r ) f(.) can be estimated numerically for ANY conductivity layout! The most general method is the “Finite Element Method”, or FEM: • conductivity can be arbitrary, i.e. anisotropic and inhomogeneous, • potential is estimated throughout the volume. Numerical solution, FEM Principles of the FEM: • The (head) volume is tesselated into small volume elements on which Maxwell’s equation is solved locally. • The conductivity is defined for every volume elements individually. Drawbacks: • How to build up the model and define the conductivity at each element ? • Computation time is huge ! Numerical solution, 2 From V j find V f ( j ,r ) f(.) can be more easily estimated numerically with some assumptions: • volume divided into sub-volumes of homogeneous and isotropic conductivity, • potential is only estimated on the surfaces seperating those sub-volumes. This is the “Boundary Element Method”, or BEM. Numerical solution, BEM The surfaces are tessellated into flat triangles and the potential is approximated on each triangle as a constant or linear function. Example of BEM head model : Brain surface The sources, current dipoles, are placed in the brain volume. Skull surface Scalp surface BEM, Potential approximation On each triangle of the surfaces, the potential function can be approximated by : a linear function between the potential at the vertices (LPV) V ( s3 ) V V ( s1 ) s1 s3 V ( s2 ) s2 BEM application Homogeneous volumes definition BEM head model Scalp surface with electrode locations BEM limitations Superficial dipoles have sharper potential distribution. BEM fails when the size of surface elements is ‘large’ compared to the sharpness of potential distribution. Agenda Part I : Solving the forward problem • Maxwell’s equations • Analytical solution • Numerical solution • Pseudo-sphere approach • Conclusion Solutions: Analytical vs. numerical The head is NOT spherical: cannot use the exact analytical solution because of model/anatomical errors. Realistic model needs BEM solution: surfaces extraction computationnaly heavy errors for superficial sources Could we combine the advantages of both solutions ? Anatomically constrained spherical head models, or pseudo-spherical model. Pseudo-spherical model Scalp (or brain) surface Best fitting sphere: centre and radii (scalp, skull, brain) Spherical transformation of source locations Leadfield for the spherical model Applications, scalp surface Fitted sphere and scalp surface Applications, cortical surface Agenda Part I : Solving the forward problem • Maxwell’s equations • Analytical solution • Numerical solution • Pseudo-sphere approach • Conclusion Conclusion Solving the « Forward Problem » is not very exciting neither easy but it is crucial for any attempt at source estimation. The key elements are the head model and, from it, the solution used. Model : simple define 3 sphere shell realistic extract volume surfaces most realistic extract volume conductivity Solution : analytic easy and quick but anatomical errors numeric slower, more anatomically accurate but numerical erros So far we still have NOT localised anything… Source Estimation in EEG Part II : The inverse problem Agenda Part II : Solving the inverse problem • Introduction • Equivalent current dipole solution • Distributed linear solution • Other solutions • Conclusion Inverse Problem Solving v f (r , j ) v, potential at the electrodes, (Nel x 1) , source location, r where [rx , ry , rz]’ j , source orientation & amplitude [jx , jy , jz]’ f, solution of the forward problem , additive noise, (Nel x 1) Function f is • linear w.r.t. the source orientation & amplitude • non-linear w.r.t. the source location Parameters When Ns sources are present the problem to solve is Ns v f (rl , j l ) l 1 For each source, there are 6 parameters : or • 3 for the location, [x y z] coordinates, • 3 for the orientation and amplitude, [jx jy jz] components • 3 for the location, [x y z] coordinates • 2 for the orientation, [q j] angles • 1 for the amplitude, j intensity/strength Parameters, cont’d The inverse problem is « ill posed », i.e. in general there is no unique solution: • Number and location of active sources are unknown! • Measurements from just Ne electrodes. To uniquely solve the inverse problem assumptions/constraints on the solution MUST be adopted. Those constraints define the form of the solution ! Inverse Problem, example Example : 4 different networks but with the same measurable output: 2V and 4Ω. 2V 12 Ω 6 Ω 12 Ω 6 Ω 12 Ω 2V 6Ω 4Ω 6V S1 3V S2 If we constrain the solution to have • the smallest source • the smallest but deeper source • source along the 12Ω resistor S3 : solution S3 solution S2 solution S1 Equivalent Current Dipole With the ECD solution : A priori fixed number of sources considered, usually less than 10 over-determined but nonlinear problem iterative fitting of the 6 parameters of each source. Problem : How many ECDs a priori ? The number of sources limited : 6xNs < Ne Advantage : Simple focused solution. But is a single (or 2 or 3 or…) dipole(s) representative of the cortical activity ? Distributed Linear solution With the DL solution : “All” possible (fixed) source locations (>103) considered simultaneously largely under-determined but linear problem : v Lj external constraints required to calculate a unique solution Problem : What should be the constraints ? Advantage : 3D voxelwise results. Agenda Part II : Solving the inverse problem • Introduction • Equivalent current dipole solution • Distributed linear solution • Other solutions • Conclusion ECD solution For Ns sources, the problem can be rewritten as Ns v f (rl , j l ) L (rl ) j l L j Ns l 1 l 1 which is an over-determined but non-linear problem. Once the location is fixed ˆ j Lv The cost function to be optimised is : 2 2 cost v vˆ v Lˆ j v LL v To be iteratively minimised only w.r.t the i.e. 3 parameters per source. 2 rl, ECD solution, cont’d For Ns sources, the cost function can be minimised for the rl using any nonlinear procedure, e.g. gradient descent, simplex algorithm, etc. cost v LL v 2 At each iteration the leadfield L must be recalculated (many times) as the source locations are modifed Once the location of the sources is determined, their intensity is obtained by ˆ j Lv ECD solution, cont’d The iterative optimisation procedure can only find a local minimum the starting location(s) used can influence the solution found ! 1D example of optimisation problem: Cost function For an ECD solution, initialise the dipoles • at multiple random locations and repeat the fitting procedure focal cluster of solutions ? • at a «guessed» solution spot. Value of parameter Local minimum Local minimum Global minimum ECD applications Epileptic patient: • EEG recorded from 21 electrodes • FDG-PET with electrode markers EEG power EEG data ECD applications, cont’d PET scan used to: - generate a pseudo-sphere model - define the electrodes location ECD applications, cont’d First peak, above F4 Second and third peak ECD applications, cont’d Second source First source Third source Hypometabolic region ECD limitations • How many dipoles ? The more sources, the better the fit… in a mathematical sense !!! • Is a dipole, i.e. a punctual source, the right model for a patch of activated cortex ? • What about the influence of the noise ? Find the confidence interval. • (Is the sECD a good approach ? Given that you find what you put in.) Agenda Part II : Solving the inverse problem • Introduction • Equivalent current dipole solution • Distributed linear solution • Other solutions • Conclusion Distributed Linear solution With the DL solution : “All” possible (fixed) source locations (>103) considered simultaneously largely under-determined but linear problem : v Lj external constraints required to calculate a unique solution Problem : What should be the constraints ? Advantage : three-dimensional voxelwise results. Constraints and priors “Hard” (anatomical) : •Orientation ( cortical sheet) •Location (in gray matter) •Local spatial coherence included in lead field matrix L : v = L j spatially informed basis functions (sIBF) Bs: j=Bs ks “Soft” (functional or other probabilistic) : •Other source priors weighting matrix H : reduced weight at priors Example of “Soft” priors: - location prior as found by other modalities (PET/fMRI), or defined “by hand” - long/short distance spatial coherence between/inside areas - depth constraints Weighted Minimum Norm solution The linear instantaneous problem: v=Lj+ with var( ) = C can be solved using a Weighted Minimum Norm (WMN) approach: ˆj arg min C 1 / 2 Lj v 2 2 2 2 H 1 j H 2 j H 3 j ... 2 1 2 2 2 3 which can also be expressed as, where Vk H kT H k ˆj arg min Constrained solution Lj v T C 1 Lj v j T 21V1 22V2 23V3 ... j Sources likelihood Sources priors WMN solution, cont’d Simplified form of equation: • only one source constraints H is employed, and • the corresponding hyperparameter is simply used to take into account the noise contained in the data C ˆ j arg min noise variancecovariance matrix 1 / 2 Lj v 2 Hj 2 balancing between model and constraint fit 2 Noise regularisation With known and C , and spatial basis function Bs such that j=Bs ks , the solution of the WMN problem is ˆ j B s TB s v TB s T Bs L C L Bs H H Bs 1 2 T Bs ] 1 T Bs L C 1 WMN & Bayesian estimate Under Gaussian assumptions for the distributions of j and , the WMN solution ˆ j L C L H H T 1 2 T ] 1 T L C 1 v is connected to the Bayesian estimate. The conditional expectation (or posterior mean) of the sources is j given by E ( j ,v ) L C T 1 ] 1 1 T 1 j 1 T L C LC v C j LT LC j L C ] v where C j is the prior covariance of the sources. C j1 2 (H T H ) or Then j N (0,C j ) where 1 2 2 2 C V V j 1 1 2 2 3V 3 ... Hierarchical “parametric empirical Bayes” approach In the context of a 2-level hierarchical parametric empirical Bayes (PEB) approach, the source localisation problem v Lj can be expressed as: C j By defining C v Lj 1 , 1 N (0,C ) j 0 2 , 2 N (0,C j ) µ1C1 µ2C 2 µ3C 3 .. 1Ce1 2Ce2 3Ce3 ... and using an EM algorithm, j , i and µi can be jointly estimated. Hierachical PEB, cont’d Hierarchical PEB, cont’d As Cj (and C) is defined as a linear combination of users defined covariance basis functions, the true source (and noise) covariance matrix can be more precisely approximated. Interpretation: With the 2-level approach, the unknown parameters j are assumed to have zero mean (due to the shrinkage priors at the 2nd level) and some variance Cj. To render some location more likely to be active, the local variance can be increased: the activity at some location is less constrained (larger variance), thus more likely to be different from zero. Restricted Maximum Likelihood solution The 2-level hierachical model can be collapsed into: v 1 L 2 L0 1 L2 Because of the 2nd level shrinkage prior, there are no “fixed effects” and only “random effects”. The covariance partitioning implied is E vv t C v C LC j Lt t C LC L i ei i i i i The only unkowns are the hyperparameters i and i, which can be estimated through a ReML procedure. Somatosensory ERP Simple right wrist electrical stimulation P45 59 electrodes, ERP data Stim N20 ERP at electrode CP5 Somatosensory ERP ERP,, cont’d cont ’d Somatosensory Scalp mapping of the average N20 peak nose right ear left ear 1 0 -1 Somatosensory ERP, cont’d Reconstruction of N20 without (left) and with (below) location prior. Location prior Advantages of DL solutions Hierachical PEB and ReML (EM) seem a good approach to EEG source localisation as: • LE and RMSE greatly reduced by the introduction of accurate location priors • accurate and inaccurate location priors used simultaneously no effect of inaccurate location priors on the source reconstruction • accurate noise variance estimate, on average Interestingly, the seeded ECD approach is simply an over constrained DL solution. Limitations of DL solutions The solution calculated totally relies on the constraints employed, therefore • if only inaccurate priors are used wrong/biased solution • in general, without location priors distributed/blurred solution spurious sources, depending on the threshold used Using a single constraint is not advisable... Agenda Part II : Solving the inverse problem • Introduction • Equivalent current dipole solution • Distributed linear solution • Other solutions • Conclusions Conclusions Depending on • the data available, • your knowledge about the activation and • the assumptions you can/want to make you should decide wich approach or solution to use. There does NOT exist any ideal and general solution so far… Special thanks to: • Prof. Karl J. Firston (a) • Prof. Mick D. Rugg (b) • Dr. Pierre Maquet (c) • Dr. Jeremie Mattout (a) • Dr. Olivier David (a) (a) Wellcome Department of Imaging Neuroscience, University College London, UK. (b) University of California Irvine, USA. (c) Cyclotron Research Centre, University of Liège, Belgium.