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Post-processing methods may aid in FCD detection. Some methods create synthetic contrasts to highlight areas of interest (such as the Morphometric Analysis Program (MAP) \cite{Huppertz2005}, but often suffer from excessive false positive rates, requiring expert interpretation \cite{Martin2017}. Several fully automated machine learning-based FCD detection methods also have been described with good sensitivity but with variable or unreported specificities. These generally require larger training sets and more complex data processing, limiting widespread adoption \cite{Adler2017,Ahmed2015,Hong2014}. Both sets of techniques often utilize features derived from either voxel-based morphometry \cite{Huppertz2005,Martin2017} or surface-based morphometry (SBM), although a variety of other features also have been proposed (see review in \cite{Kini2016}).
In this work, we add to the literature by using normative modeling across multiple MR contrasts and spatial scales to characterize variability across normal cortex, allowing for more straightforward estimation of similarities and differences between normal cortex and FCD lesions. We found that while FCDs appear as outliers, so do several "normal" brain regions; FCDs appear quite similar in underlying features to some of these regions, particularly in paralimbic areas such as the anterior insula. After local normalization, FCDs appear more distinct from these areas, which allows for more effective automated detection of FCD lesions in our case series.
Materials and Methods
Study Participants
From our surgical epilepsy imaging database, we retrospectively identified 15 consecutive patients undergoing presurgical evaluation for drug-resistant focal epilepsy from 2014-2019 with: 1) radiologically apparent (MRI+) or histologically proven (MRI+ or MRI negative (MRI-)) FCDs; and 2) our standard 3T MRI epilepsy structural imaging protocol. Patients were excluded if they underwent other MR imaging protocols or had low image quality on visual inspection. The control group consisted of 30 healthy volunteers (HVs) scanned using the same imaging protocol with no previous history of neurologic, psychiatric, or other significant medical illnesses that may affect the central nervous system. Data were collected at the National Institutes of Health (NIH) Clinical Center (Bethesda, MD). All participants were enrolled in an Institutional Review Board-approved research protocol; informed consent was obtained from all participants.
Lesion Labels
For MRI+ patients, lesions were traced in the volume using the Analysis of Functional NeuroImages (AFNI) software package \cite{Cox1996a} by an experienced neurologist using the T1, informed by the T2 and FLAIR images when necessary. For MRI- patients, the postoperative T1 was registered to the preoperative T1 in the same manner as described below for T2 and FLAIR images; the resected region was manually traced using AFNI. Lesion masks were mapped onto the smoothed white matter surface using AFNI's 3dVol2Surf function.
MRI Acquisition Protocol
All participants were scanned on a Philips Achieva 3T MRI scanner in the NIH Clinical Center Radiology Department as follows: 1) 3D T1 weighted MPRAGE (T1): TR = 6.8–7.2, TE = 3.2 ms, TI = 900ms, flip angle = 90, voxel size = 0.75 x 0.75 x 0.8, acceleration factor 2 in slice direction, acquisition time = 7:02 min; 2) 3D T2 weighted FSE (T2): TR = 2500, TE = 225–245, voxel size = 1 x 1 x 2 or 1 x 1 x 1, acceleration factor 2 in slice and phase directions, acquisition time = 5:03 min; and 3) 3D FLAIR: TR = 4800, TE = 271–415, TI = 1600, voxel size = 0.9 x 0.9 x 1, acceleration factor 2 in slice direction, 2.6 in phase direction, acquisition time = 6:10 min. All scans were acquired sagitally.
Image Preprocessing
For each individual subject, T
2 and FLAIR images were co-registered to the T
1 with an affine transformation using a normalized mutual information cost function and resampled to the T
1 grid using AFNI. Registered images were visually assessed for alignment. Cortical reconstruction was performed using T
1 and T
2 images as input to FreeSurfer's standard processing pipeline with
FreeSurfer v6.0.0 \cite{Dale1999,Fischl1999}. Results were visually inspected and manually corrected as needed. Cortical surfaces were resampled to a standard mesh using the AFNI SUMA package
\cite{Cox1996a} to allow comparison of corresponding vertices across individuals. Intensity correction was implemented using an in-house edge-aware intensity correction procedure (see supplementary methods for details, Figure S1).