1. Multi-Modal Quantitative Analysis of Pediatric Focal Epilepsy Andy Eow Medical Vision Group CSAIL, MIT

2. Project Background Focal Epilepsy 2.5 million Americans suffer from some form of epilepsy. A large percentage being symptomatic partial epilepsy / focal epilepsy. 25% of these people are unresponsive to anti- epileptic medication and surgery is the last alternative. Success of surgery is highly dependent on the surgeon’s ability to locate the epileptic foci. EEG Techniques Sub-dural EEG (Gold Standard) 32-channel Surface EEG 128-channel Surface EEG Sub-dural EEG 32-channel EEG128-channel EEG

3. Project Background EEG Source Localization Repeatedly solving the forward problem to obtain a set of dipole parameters that produces electrical potential that matches observed EEG data. Ill-posed inverse problem where multiple solutions can generate similar scalp potentials. Source localization with scalp EEG and visual correlation with imaging modalities guide placement of subdural grid. Goal Improve EEG source localization with:  Patient-specific Head Model (Accuracy of forward solution)  Hotspots Prior Probability Map (Constraint on inverse solution) Focal cortical dysplasia (FCD) is one of the most epileptogenic lesions associated with early onset medically refractive focal epilepsy. These are the cases which I focused on.

4. Pipeline Framework

5. Patient Example Details 16 years when image acquisition was done. Focal cortical dysplasia in the right inferior frontal lobe T1-weighted & T2-weighted structural MRI volumes T1-weighted MRIT2-weighted MRI

6. Pre-processing Multi-modal Image Registration ITK’s rigid registration framework with a MI metric Tissue Segmentation Intensity Correction (Neil Weisenfeld’s entropy min. implementation) Noise Smoothing (ITK’s Curvature Flow anisotropic diffusion filter) Modified Watershed Segmentation Intensity Correction Noise Smoothing Segmentation Label Map

7. Symmetry Plane Detector Choose arbitrary target symmetry plane. Obtain chiral, I c, from reflection about this plane. Find optimal rigid transformation, T R *, that maps I c to I. “Half” the transform T R * and apply to I c to obtain symmetrically aligned chiral. Aligns plane of maximal inter-hemispheric similarity with target symmetry plane.

8. Symmetry Plane Robustness Assumption In the absence of large pathologies, plane of maximal inter-hemispheric similarity matches the anatomical mid-sagittal plane. MCA Infarction (Worst Case) Left hemisphere largely absent. Left shifting of mid-line structures. Plane of maximal inter-hemispheric similarity no longer coincides well with the mid-sagittal plane. Matches remaining cortical tissues and other major structural features such as the skull, ocular cavities etc. Holistically, symmetry plane detected still reasonable. Unlikely for FCD cases to present with such large pathologies.

9. Asymmetry Analysis Deformation Field, F Find optimal deformation that best matches symmetrically aligned volume and its chiral (about the target symmetry plane) ITK’s non-rigid registration framework with a SSD metric Asymmetry |F|(1 + .F) Emphasis is placed on |F|. Deformation Magnitude, |F| Modified Deformation Divergence, 1+ .F Asymmetry, |F|(1+ .F)

10. Asymmetry Analysis Asymmetry Thresholding:  + S Only voxels of significant asymmetry remaining Hotspots Label Map Clustering of significant asymmetry voxels Delineation of separate hotspot regions Further removal of small noise clusters Thresholded AsymmetryHotspots Label Map

11. Hotspots Prior Probability Map Building the Probability Map … Assigning probability values P hotspot = 0.9 P GM = 0.2 P WM = 0.1 P CSF = 0.01 P outside = 0 Gaussian smoothing Prior Probability Map Hotspots Label Map

12. EEG Source Localization Experiments Simulate dipole within detected hotspot to obtain EEG measurements. Perturbations: EEG voltage noise,  v Electrode location noise,  e Tissue conductivity,  c Results Simulated Noise  v / V e / mm c / % Location Error / mm NeuroFEMPrior Zero NoiseNA 0.07Not necessary Low Noise5 x 10 -4 352Not necessary Medium Noise1 x 10 -3 47.54.90.5 High Noise5 x 10 -3 51014.60.5

13. Future Work Functional Neuroimaging PET / SPECT Detect functional asymmetries such as regions of decreased glucose metabolism for PET and cerebral blood flow for SPECT etc. Complements structural asymmetries provided by MRI. Diffusion-Tensor Imaging Anisotropic patient-specific head model Reduced FA in dysplastic neurons within white-matter tissue structures. Complements structural and functional asymmetries to generate a more comprehensive prior probability map. Detects cortical abnormalities at an earlier stage. Analyze neural connectivity between potential surgical sites and eloquent

14. Thank you!