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Abstract #0967

Creating a multi-centre harmonised surface-based MRI dataset for the Multi-centre Epilepsy Lesion Detection Project

Mathilde Ripart1, Hannah Spitzer2, Russell Shinohara3, MELD Consortium4, Torsten Baldeweg1, Sophie Adler1, and Konrad Wagstyl5
1Great Ormond Street Institute for Child Health, UCL, London, United Kingdom, 2Institute of Computational Biology, Helmholtz Zentrum München, Munich, Germany, 3Penn Statistics in Imaging and Visualization Center, University of Pennsylvania, Philadelphia, PA, United States, 4UCL, London, United Kingdom, 5Wellcome Centre for Human Neuroimaging, UCL, London, United Kingdom


The Multi-centre Epilepsy Lesion Detection (MELD) project presents a methodology to harmonise a large heterogenous cohort of surface-based MRI data. Structural features were extracted from T1w and FLAIR images and pre-processed to reduce systematic site, scanner, and age-specific biases. The harmonised dataset enabled the characterisation of subtle radiological markers of focal cortical dysplasia (FCD), a cortical abnormality causing drug-resistant epilepsy. Machine-learning algorithms trained on the harmonised dataset improved the classification of FCD histopathologies. With open-source protocols and code, the MELD preprocessing pipeline offers a reproducible method to prepare large heterogeneous datasets for statistical analysis and machine-learning tasks.

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