A Flexible Framework for Non-Invasive Source Localization in Pediatric Focal Epilepsy Tiferet Levine-Gazit Medical Visio

1. Children’s Hospital Boston A Flexible Framework for Non-InvasiveSource Localization in Pediatric Focal EpilepsyTiferet Levine-GazitMedical Vision GroupCSAIL, MIT

2. Background Focal Epilepsy Epilepsy affects 1% of the population under the age of 20 Seizures prevent healthy development and may cause brain damage Focal epilepsy is triggered by pathological electrical activity in a small clump of neurons Current Treatments 35% of focal epilepsy patients do not respond to medication, and must undergo surgical resection of the epileptic focal points Surgery requires accurate localization of the foci Subdural EEG is currently needed to reconstruct focal sources from voltage measurements, and even with it only about 65% of patients are seizure-free after surgery

3. Project Overview Project Goals More accurate source localization to improve post-surgical prognosis Source localization based on non-invasive tests and scans instead of subdural EEG Contributions Flexible, modular framework for non-invasive source localization based on scalp EEG Incorporates prior information from MRI and other sources Utilizes state-of-the-art patient specific head modeling Allows easy switching of prior map, field-solver, or inverse method Tools for processing raw patient data for use with sophisticated field-solving packages

4. Source Localization The Source Localization Problem EEG gives us voltage readings at electrodes on the scalp From the quasi-static Maxwell equations we get the state equation relating source currents in the head to voltages on the scalp:  ∙ ( V ) =  ∙ JP Forward problem: Find voltages at electrodes given source configuration Inverse problem: Find source configuration given voltages at electrodes The inverse problem is highly ill-posed and must in practice be solved through iterative solution of the forward problem to search for a current configuration that best explains the voltages measured

5. Modeling the Head Head Models In order to solve the forward problem we need a model of the head as a volume conductor Current clinical practice uses simple multishell spherical head models BEM allows realistic modeling of scalp and skull, but not different brain tissues FEM allows realistic modeling of scalp, skull, CSF, GM, and WM Creating a Head Model • We use NeuroFEM as the field-solving package to solve the forward problem on a high-resolution FEM head model • First we segment whole-head MRI using modified watershed segmentation • Then we mesh the head volume, assigning a conductivity to each mesh node based on its tissue classification

6. Modeling the Electrodes Clinical Electrode Placement There are several ways electrodes may be placed in an EEG acquisition The most common method is the 10-20 system, requiring manual placement of 19-32 electrodes based on anatomical landmarks and relative distances Better methods include dense electrode nets with standard locations, electrodes with MR-visible markers, and electrode locations recorded with a 3D tracker Source: Okamoto, et al. NeuroImage, 21(1): 2004.

7. Modeling the Electrodes Aligning Electrodes to Our Head Models • In order to use NeuroFEM, we need to give it the location of each electrode in the reference frame of the head model • To do this, we first build a very simple model of the patient head surface in Slicer • If the 10-20 system or electrode markers were used, we then place fiducials in Slicer at each electrode location, transform the coordinates from Slicer coordinates to NeuroFEM coordinates, and output the electrode file • If a tracker or standard net locations were used, we place fiducials at the four reference points on the Slicer model, and from these four points we find the matrix to transform the given acquisition coordinates to the Slicer reference frame. We use this matrix to transform all electrode coordinates. We then transform the electrodes from Slicer into NeuroFEM and output the electrode file.

8. Solving the Inverse Problem Flexible Framework for Source Localization • In order to solve the EEG inverse problem, one must optimize the dipole configuration through repeated forward simulations • We have implemented and tested several optimization techniques: • Exhaustive search over the entire brain or a specified ROI (good for clinical source localization) • Simultaneous Perturbation Stochastic Approximation (SPSA) for very efficient stochastic optimization (good for research when many localizations must be carried out quickly) • NeuroFEM’s built-in Simplex optimization (useful only if no prior map is to be used) • Other inverse methods may very easily be implemented in this framework, and the framework may be used to compare different methods in a research setting

9. Prior Information Defining Prior Probabilities for Focal-Point Locations There are many methods in the literature for locating focal-point hot-spots and assigning prior probabilities on dipole locations Any such method may be used to define a prior probability map for use within our framework Automatic methods of assigning prior probabilities may look at factors such as: Anatomical considerations for where focal points are likely to lie Machine-vision techniques for locating specific anomalies such as cysts or lesions Asymmetry measures between the two hemispheres, measures of GM/WM blurring, volumetric measures of various structures, measures of cortical thickening, etc. Our framework is very useful in testing and comparing different prior formulations

10. Incorporating Prior Information The Error Function Any inverse method optimizes an error function We incorporate the prior information we wish to use through a prior probability map that is used as a weighted additive Bayesian prior term in the error function Error = Slice from a patient prior probability map

11. Experiments and Results Sanity Checks To verify that a given head model and inverse method works well, the first step is always a sanity check localizing a known simulated dipole We build a head and electrode model, place a dipole with known configuration, and solve the forward problem to obtain simulated voltages for this dipole We then solve the inverse problem with these voltages on the same head model, to make sure we get back the dipole we started with We ran such sanity checks on three different head models – spherical, isotropic FEM, and anisotropic FEM – using various inverse methods such as NeuroFEM Simplex, SPSA, and exhaustive search, in some cases using both deep and surface dipoles. All our sanity checks returned the dipoles we had initially place to very high degree of accuracy

12. Experiments and Results: Robustness Tests of Robustness to Noise Real data always contains noise. It is important to test how various models and methods are affected by this noise. To obtain noisy data with known parameters we again use simulated EEG, but this time artificially add noise in the various inputs used by the source localization software Noise in voltages: Add zero-mean Gaussian noise to the voltage at each electrode, simulating various levels of noise by adjusting the STD of the Gaussian Errors in electrode locations: Add zero-mean Gaussian noise to the three location components of each electrode, simulating various levels of location uncertainty by adjusting the STD of the Gaussian

13. Experiments and Results: Robustness Robustness to Noise in Voltages Two head models – spherical and anisotropic FEM. SPSA optimization. At least ten different localizations for each noise level on each head model Spherical SNR levels (dB): Inf, 38, 24, 18, 4 FEM SNR levels (dB): Inf, 51, 37, 31, 17, 11, -2.6

14. Experiments and Results: Robustness Robustness to Errors in Electrode Locations Two head models – spherical and anisotropic FEM. SPSA optimization. At least fifteen different localizations for each noise level on each head model Spherical FEM • These results indicate the benefits of using accurate electrode placement methods such as electrode nets or tracking devices in clinical EEG acquisitions

15. Experiments and Results: Prior Probabilities Experiments Incorporating a Prior Probability Map Isotropic FEM head model from real patient data with focal cortical dysplasia (FCD) (figure 1) Electrode net aligned to head model from standard locations (figure 2) Prior probability map based on the patient’s anatomy (figure 3): Hot-spots found through asymmetry analysis of MRI data Probabilities assigned according to anatomical considerations based on voxel tissue classifications Simulated dipole within the anomalous region Noise added to voltages, electrode locations, and tissue conductivities (1) (2) (3a) (3b)

16. Experiments and Results: Prior Probabilities Summary of Prior Probability Experiments

17. Conclusion Future Work Spatio-temporal preprocessing of EEG to separate out signal from multiple dipoles Anisotropic conductivity tensors in our head models Inclusion of more inverse methods, prior formulations, and field solvers within our source-localization framework Development of a more automated pipeline with a nice user interface More rigorous testing of different models and methods, together with clinical trials and surgical validation

18. Thank you!