The Imaging Database for Epilepsy And Surgery (IDEAS)

Peter N. Taylor, Yujiang Wang, Callum Simpson, Vytene Janiukstyte, Jonathan Horsley, Karoline Leiberg, Beth Little, Harry Clifford, Sophie Adler, Sjoerd B. Vos, Gavin P Winston, Andrew W McEvoy, Anna Miserocchi, Jane de Tisi, John S Duncan

Magnetic resonance imaging (MRI) is a crucial tool to identify brain abnormalities in a wide range of neurological disorders. In focal epilepsy MRI is used to identify structural cerebral abnormalities. For covert lesions, machine learning and artificial intelligence algorithms may improve lesion detection if abnormalities are not evident on visual inspection. The success of this approach depends on the volume and quality of training data.
Herein, we release an open-source dataset of preprocessed MRI scans from 442 individuals with drug-refractory focal epilepsy who had neurosurgical resections, and detailed demographic information. The MRI scan data includes the preoperative 3D T1 and where available 3D FLAIR, as well as a manually inspected complete surface reconstruction and volumetric parcellations. Demographic information includes age, sex, age of onset of epilepsy, location of surgery, histopathology of resected specimen, occurrence and frequency of focal seizures with and without impairment of awareness, focal to bilateral tonic-clonic seizures, number of anti-seizure medications (ASMs) at time of surgery, and a total of 1764 patient years of post-surgical follow up. Crucially, we also include resection masks delineated from post-surgical imaging.
To demonstrate the veracity of our data, we successfully replicated previous studies showing long-term outcomes of seizure freedom in the range of around 50%. Our imaging data replicates findings of group level atrophy in patients compared to controls. Resection locations in the cohort were predominantly in the temporal and frontal lobes.
We envisage our dataset, shared openly with the community, will catalyse the development and application of computational methods in clinical neurology.

https://arxiv.org/abs/2406.06731

This release on OpenNeuro includes only raw T1w and FLAR scans. Fully processed data, including resection masks and other demographic information can be found at the following locations: https://www.cnnp-lab.com/ideas-data


- Bids https://figshare.com/s/07fca72410094bc49506 
Raw T1w and FLAIR scans organised in BIDS format. Nifti and json descriptors included

- Masks
https://figshare.com/s/31ab43d1829b12ac13e8 
Resection masks for IDEAS cohort in native, and freesurfer orig.mgz space

- Freesurfer_brain
https://figshare.com/s/39b61a1df5fa8443e3c4 
skullstripped brain from freesurfer in nifti format

- Freesurfer_orig 
https://figshare.com/s/f13391a4161b807ce6b0 
freesurfer orig.mgz converted to nifti format

- Freesurfer_zip
https://figshare.com/s/b13b8bb41390d3f7a088 
freesurfer surface and volumetric reconstructions

- Tables_stats_freesurfer
https://figshare.com/s/010142dd51e37ba4e4e2 
Freesurfer thickness, volume, and surface areas for the Desikan-Kiliany parcellation.

- Tables_metadata
https://figshare.com/s/bab70268afeb1071202b 
clinical and demographic metadata

- Table_resected
https://figshare.com/s/097ba0e254e36f0eee52 
table indicating the percentage of each brain region in the Desikan-Kiliany atlas subsequently resected by surgery.

- Tables_zscores
https://figshare.com/s/8c086fc295a75f85e628 
Freesurfer thickness, volume, and surface areas for the Desikan-Kiliany parcellation, z-scored against normative controls post-combat.

- Tables_group_effect
https://figshare.com/s/323db205354788c4d1f0 
Group effect size differences to controls