The following scripts accompany the publication: "Novel surface features for automated detection of focal cortical dysplasias in paediatric epilepsy." http://dx.doi.org/10.1016/j.nicl.2016.12.030 Briefly, the scripts calculate surface-based structural MRI features from cortical reconstructions and use these data to train a supervised neural network classifier to identify lesion-like vertices. In the sample used, the leave-one-out classifier was able to correctly identify FCDs in 73% of patients.
Please send any queries to kw350@cam.ac.uk or sophie.adler.13@ucl.ac.uk.
The original scans could not be shared publicly, but the matrix of subjects' morphological data, along with lesion/non-lesion labelling of each vertex, can be freely downloaded from: https://doi.org/10.17863/CAM.6923
The scripts are numbered 1-10 Pre-script steps:
- You need to have FreeSurfer cortical reconstructions of all your participants (https://surfer.nmr.mgh.harvard.edu/). It is important that these are checked and edits are done to correct the surfaces.
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it is important to check that the FLAIR scan is correctly coregistered to the T1 scan and therefore to the surfaces.
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With volumetric FLAIR, the recon-all process included the FLAIR scan. If volumetric FLAIR is unavailable, supplementary script 1 will coregister the FLAIR scan after the recon-all step (Supplementary_script_1). Further analyses need to be made to assess whether non-volumetric FLAIR is sufficient.
- Create manual lesion labels of the FCDs.
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this can be done in FreeSurfer
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after creating the labels, they need to be converted to .mgh files for compatibility with the rest of the scripts (see Supplementary_script_2).
Script 1: This script does the following
- Sample FLAIR at 25%, 50%, 75% of the cortical thickness, at the grey-white matter boundary, and 0.5mm and 1mm subcortically
- Smooth the following features using a 10mm gaussian kernel:
- cortical thickness
- FLAIR at 25%, 50%, 75% of the cortical thickness, at the grey-white matter boundary, and 0.5mm and 1mm subcortically
- grey-white matter intensity contrast
- Calculate curvature for use in script 2 - calculation of local cortical deformation
- Convert curvature and sulcal depth and lgi to .mgh file type
Script 2: This script calculates local cortical deformation
Script 3: This script calculates the Doughnut method
Script 4: Smoothing of local cortical deformation and doughnut metrics
Script 5: Intra-subject normalisation of features
Script 6: Move features to template space (this involves flipping the right hemisphere features so that everything is moved to the left hemisphere)
Script 7: Inter-subject normalisation of features for the classifier
Script 8: Neural Network classifier (including principal component analysis for determining number of nodes in classifier)
Script 9: Clustering of classifier output
Script 10: Ranking of top 5 clusters