Journal
NEUROIMAGE-CLINICAL
Volume 14, Issue -, Pages 18-27Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.nicl.2016.12.030
Keywords
FCD; Intractable epilepsy; Structural MRI; Automated classification; Paediatric
Categories
Funding
- National Institute for Health Research Biomedical Research Centre at Great Ormond Street Hospital for Children NHS Foundation Trust and University College London
- Rosetrees Trust [A711]
- James Baird Fund
- Wellcome Trust [WT095692MA]
- Wellcome Trust
- Bernard Wolfe Health Neuroscience Fund
- MRC [G0300117, G1002276] Funding Source: UKRI
- Action Medical Research [2214] Funding Source: researchfish
- Medical Research Council [G1002276, G0300117] Funding Source: researchfish
- National Institute for Health Research [NF-SI-0515-10073] Funding Source: researchfish
- Rosetrees Trust [M413] Funding Source: researchfish
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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. Wedeveloped 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 aswell as post-processingmethods 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-validationwas 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. (C) 2017 The Authors. Published by Elsevier Inc.
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