Abstracts

Rationale: Until recently, functional imaging techniques have been developed predominantly to improve visualization of epileptogenic foci in grey matter for focal-onset epilepsy. However, few techniques assess the degree of ictal-associated connectivity between different zones in the epileptogenic cortex through white matter (WM) axonal paths, thus neglecting a crucial step towards understanding epileptic networks. Subtracted Post-Ictal Diffusion Tensor Imaging (spiDTI) is a novel imaging technique with the objective of identifying transient water diffusion changes during the early post-ictal state in such pathways.Methods: A post-ictal (up to 4hrs following seizure termination) and an inter-ictal (no electrocerebral seizures for at least 24hrs) high resolution DTI datasets were acquired. Imaging parameters consisted of 2mm-thick oblique slices in 60 non-collinear directions with a diffusion factor of 900 s/mm2 in a 3T MRI scanner. Eddy current and motion corrections were applied to these volumetric datasets using TORTOISE v2.0.1. The resulting volumes were registered to a SPGR MRI dataset. Fractional anisotropy (FA) measures were obtained using FSL FMRIB’s Diffusion Toolbox (Oxford, UK, 2015). Comparisons among the inter-ictal and post-ictal FA were achieved through the development of a custom program in MATLAB v2014. Specifically, a “voxel per voxel” subtraction was accomplished between the interictal and post-ictal FA values following a Gaussian filter. Finally, statistical cleaning of the subtracted FA values was performed where a voxel normalization and a threshold of greater than 1.5 standard deviations were applied to differentiate substantial changes to create the spiDTI signals.Results: The protocol was applied to 5 patients at Rush University Medical Center. SpiDTI demonstrated a decrease in FA following stereotypic complex partial seizures without generalization secondarily for each patient. SpiDTI regions of interest demonstrated a statistically different anisotropic to isotropic diffusion difference (p<0.05). The spiDTI datasets complemented grey matter dominant imaging modalities (subtracted ictal SPECT co-registered to MRI and MEG data) acquired for all patients. A fused multi-modality neuroimaging dataset was generated for each patient. This functionally-rich pre-implantation dataset facilitated guidance of responsive neurostimulation system (NeuroPace, Inc) depth leads implanted in WM for each of the 5 patients.Conclusions: SpiDTI is a novel imaging technique that can facilitate identifying ictal-associated WM propagation pathways for patients who have not generalized secondarily. Furthermore, this method can be used to predict optimal depth placement implant sites for direct neurostimulation therapy where depth electrode lead placement is critical for interfacing with extensive WM connected epileptogenic regions.