Distinguishing type II focal cortical dysplasias from normal cortex: A novel normative modeling approach

• Published in: NeuroImage clinical 2021-01, Vol.30, p.102565-102565, Article 102565
• Main Authors: Snyder, Kathryn, Whitehead, Emily P., Theodore, William H., Zaghloul, Kareem A., Inati, Souheil J., Inati, Sara K.
• Format: Article
• Language: English
• Subjects: Epilepsy; Focal cortical dysplasia; Machine learning; Regular; Structural MRI
• ISSN: 2213-1582; 2213-1582
• DOI: 10.1016/j.nicl.2021.102565

•Multiscale image filters provide a good representation of local cortical appearance.•Most FCD lesions and some normal cortical regions appear as outliers in our model.•FCDs appear similar to the anterior insula and some paralimbic cortical regions.•Our constrained outlier detection approach allows for automated FCD detection. Focal cortical dysplasias (FCDs) are a common cause of apparently non-lesional drug-resistant focal epilepsy. Visual detection of subtle FCDs on MRI is clinically important and often challenging. In this study, we implement a set of 3D local image filters adapted from computer vision applications to characterize the appearance of normal cortex surrounding the gray-white junction. We create a normative model to serve as the basis for a novel multivariate constrained outlier approach to automated FCD detection. Standardized MPRAGE, T2 and FLAIR MR images were obtained in 15 patients with radiologically or histologically diagnosed FCDs and 30 healthy volunteers. Multiscale 3D local image filters were computed for each MR contrast then sampled onto the gray-white junction surface. Using an iterative Gaussianization procedure, we created a normative model of cortical variability in healthy volunteers, allowing for identification of outlier regions and estimates of similarity in normal cortex and FCD lesions. We used a constrained outlier approach following local normalization to automatically detect FCD lesions based on projection onto the mean FCD feature vector. FCDs as well as some normal cortical regions such as primary sensorimotor and paralimbic regions appear as outliers. Regions such as the paralimbic regions and the anterior insula have similar features to FCDs. Our constrained outlier approach allows for automated FCD detection with 80% sensitivity and 70% specificity. A normative model using multiscale local image filters can be used to describe the normal cortical variability. Although FCDs appear similar to some cortical regions such as the anterior insula and paralimbic cortices, they can be identified using a constrained outlier detection approach. Our method for detecting outliers and estimating similarity is generic and could be extended to identification of other types of lesions or atypical cortical areas.