Introduction
Focal cortical dysplasia (FCD) is a relatively common cause of drug-resistant focal epilepsy, and particularly of MRI negative (MRI-) epilepsy. Approximately 15-25% of patients being evaluated for epilepsy surgery have apparently normal MRIs; FCD is diagnosed pathologically in 25-50% of these patients \cite{Blumcke2017,Lerner2009,Bien2009}. Because post-operative seizure outcomes are significantly worse in MRI- compared to MRI positive (MRI+) patients \cite{Téllez-Zenteno2010}, improved identification of subtle FCDs is of great clinical importance as it may lead to better surgical outcomes.
FCDs are variable in their histopathological and radiographic appearances. Pathological abnormalities range from cortical dyslamination in type I, to the additional presence of large dysmorphic neurons in type IIa (FCDIIa), and balloon cells and more prominent dysmyelination in type IIb (FCDIIb) \cite{Blümcke2011}. Typical radiographic findings include changes in cortical thickness, image intensity (including the transmantle sign in FCD type IIb), gray-white junction (GWJ) blurring, and atypical cortical folding patterns \cite{Adler2017a,Kini2016}. These findings, however, exist on a spectrum, with features appearing inconsistently, even within and across FCDIIb lesions \cite{Hong2017}. They can also be extremely subtle, with up to 80% of small bottom-of-sulcus dysplasias being missed on routine visual inspection \cite{Besson2008}.
A number of post-processing methods have therefore been developed to aid in FCD detection. These range from creation of synthetic contrasts to highlight areas of interest (such as the Morphometric Analysis Program (MAP) \cite{Huppertz2005}) to machine-learning based fully automated detection methods \cite{Adler2017,Ahmed2015,Hong2014}. Across these methods, two key challenges have been 1) selection of optimal features to describe FCD lesions, and 2) accounting for variability within and across FCD lesions, as well as in normal cortex. While a wide variety of features have been investigated, most methods have used features derived from either voxel-based morphometry \cite{Huppertz2005,Martin2017} or surface-based morphometry (SBM) (see review in \cite{Kini2016}). To account for normal and pathological variability, large training sets are typically used, with each new patient's data compared to a normative database; performance tends to be dependent on the size of both the lesional and normative training sets \cite{Jin2018}.
In this work, we implemented a novel set of 3D multiscale local image features across multiple MR contrasts. Similar features have been previously shown to efficiently represent the local statistics of natural images \cite{Simoncelli2001}. We show that our feature set encompasses information contained in several measures previously used for FCD detection. We then use a normative modeling approach to characterize normal cortical variability. In this representation, we were able to detect FCDs as outliers in a relatively circumscribed portion of the feature space, but only after accounting for the expected appearance of normal cortex in healthy volunteers at each location. Local normalization corrects for the expected atypical appearance of some normal cortical regions such as the anterior insula, which demonstrate similar underlying features to our FCDs; it also corrects for the initial similarity of our FCD lesions to the expected appearance of normal cortex at their given location.
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-) 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 sagittally.
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.
We wished to estimate overall signal and noise levels and the local signal likelihood at each point in each image to construct SNR-aware local image filters, used subsequently both to correct for MRI coil-induced variations in image intensity and to compute local 3D multi-scale oriented filters to create features on which to base our normative model.