Novel surface features for automated detection of focal cortical dysplasias in paediatric epilepsy

Abstract

Focal cortical dysplasia is a congenital abnormality of cortical development and the leading cause of surgically remediable drug-resistant epilepsy in children. Post-surgical outcome is improved by presurgical lesion detection on structural MRI. Automated computational techniques have improved detection of focal cortical dysplasias in adults but have not yet been effective when applied to developing brains. There is therefore a need to develop reliable and sensitive methods to address the particular challenges of a paediatric cohort.We developed a classifier using surface-based features to identify focal abnormalities of cortical development in a paediatric cohort. In addition to established measures, such as cortical thickness, grey-white matter blurring, FLAIR signal intensity, sulcal depth and curvature, our novel features included complementary metrics of surface morphology such as local cortical deformation as well as post-processing methods such as the “doughnut” method - which quantifies local variability in cortical morphometry/MRI signal intensity, and per-vertex interhemispheric asymmetry. A neural network classifier was trained using data from 22 patients with focal epilepsy (mean age = 12.1 ± 3.9, 9 females), after intra- and inter-subject normalisation using a population of 28 healthy controls (mean age = 14.6 ± 3.1, 11 females). Leave-one-out cross-validation was used to quantify classifier sensitivity using established features and the combination of established and novel features.Focal cortical dysplasias in our paediatric cohort were correctly identified with a higher sensitivity (73%) when novel features, based on our approach for detecting local cortical changes, were included, when compared to the sensitivity using only established features (59%). These methods may be applicable to aiding identification of subtle lesions in medication-resistant paediatric epilepsy as well as to the structural analysis of both healthy and abnormal cortical development.

Voxel‐based morphometric magnetic resonance imaging postprocessing in non‐lesional pediatric epilepsy patients using pediatric normal databases

“…applied a surface‐base method to generate multivariate morphometric parameters and used a neural network classifier for automated detection of FCD, which yielded 73% sensitivity in 22 pediatric patients with visible FCD on 1.5 T MRI. Again, the effectiveness of this technique on non‐lesional cases is unknown . Riney et al .…”

Automated detection of focal cortical dysplasia type II with surface‐based magnetic resonance imaging postprocessing and machine learning

“…In brief, the processing involves (1) segmentation of white matter, (2) tessellation of the gray/white matter boundary, (3) inflation of the folded surface tessellation, and (4) automatic correction of topological defects. These steps have been described in detail elsewhere . Reconstruction results of each subject were inspected visually, and any inaccuracies due to imaging artifacts were manually corrected.…”

“…Six cortical features were acquired at each vertex of the 3D cortical reconstruction: cortical thickness, gray‐white matter intensity contrast, curvature, sulcal depth, “doughnut” maps, and local cortical deformation (LCD) . Cortical thickness was calculated as follows.…”

“…Shallow gyral areas of the brain move inward during inflation and have a negative value, whereas deep sulcal areas move outward and have a positive value . Doughnut maps were assessed by measuring cortical thickness and gray‐white matter intensity within a 6‐mm‐radius circle and within the doughnut, where the circle was centered on a vertex on the inflated surface and the doughnut was a surrounding region around the circle . LCD was computed by the sum of intrinsic curvature within a 25‐mm‐radius ring (gray circle) .…”

“…To overcome some of these inherent weaknesses and further increase yield, a multivariate surface‐based morphometry (SBM) approach may be beneficial. Recently, a few studies have reported multivariate SBM approaches combined with machine learning, with high accuracy for automated FCD detection in adult and pediatric patients (some had negative MRI by visual analyses) . However, the previous studies were all based on single centers, using a small cohort of patients with histologically confirmed or radiologically defined FCD, and only 1 study assessed the false‐positive rate in healthy controls …”