The Imaging Database for Epilepsy And Surgery (IDEAS)

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

癫痫与手术成像数据库（Imaging Database for Epilepsy And Surgery, IDEAS）

作者：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

磁共振成像（Magnetic Resonance Imaging, MRI）是识别多种神经系统疾病脑部异常的关键工具。在局灶性癫痫中，MRI用于检测大脑结构异常。对于隐匿性病灶，若视觉检查未发现明显异常，机器学习与人工智能算法可提升病灶检出效能。此类方法的成功与否，取决于训练数据的规模与质量。

本文公开了一个开源数据集，包含442名药物难治性局灶性癫痫患者的预处理MRI扫描数据与详细人口统计学信息，这些患者均接受了神经外科切除术。MRI扫描数据包括术前3D T1序列、如有则包含3D FLAIR序列，同时还包含人工审核的完整表面重建结果与体积分割数据。人口统计学信息涵盖年龄、性别、癫痫发病年龄、手术部位、切除标本的组织病理学结果、伴/不伴意识受损的局灶性癫痫发作的发生情况与频率、局灶性继发全面强直-阵挛发作情况、手术时使用的抗癫痫药物（Anti-seizure Medications, ASMs）数量，以及总计1764患者-年的术后随访数据。尤为重要的是，本数据集还包含基于术后成像勾勒出的切除区域掩码。

为验证数据的真实性，我们成功复现了既往研究中癫痫术后无发作预后约50%的长期结局结果。我们的成像数据也复现了患者组相较于健康对照的脑萎缩群体水平特征。本队列的手术切除部位主要集中于颞叶与额叶。

我们期望本公开共享的数据集能够推动计算方法在临床神经病学领域的开发与应用。

https://arxiv.org/abs/2406.06731

本OpenNeuro发布版本仅包含原始T1w与FLAIR扫描数据。完整的处理后数据（包括切除区域掩码与其他人口统计学信息）可通过以下链接获取：https://www.cnnp-lab.com/ideas-data

- BIDS数据集：https://figshare.com/s/07fca72410094bc49506 ，采用BIDS格式组织的原始T1w与FLAIR扫描，包含NIfTI文件与json描述文件
- 切除区域掩码数据集：https://figshare.com/s/31ab43d1829b12ac13e8 ，IDEAS队列的切除区域掩码，包含原生空间与FreeSurfer orig.mgz空间格式
- FreeSurfer颅骨剥离脑数据：https://figshare.com/s/39b61a1df5fa8443e3c4 ，FreeSurfer处理得到的颅骨剥离后的脑部NIfTI格式文件
- FreeSurfer原始空间转换数据：https://figshare.com/s/f13391a4161b807ce6b0 ，转换为NIfTI格式的FreeSurfer orig.mgz文件
- FreeSurfer重建结果压缩包：https://figshare.com/s/b13b8bb41390d3f7a088 ，FreeSurfer表面与体积重建结果压缩文件
- FreeSurfer统计量表：https://figshare.com/s/010142dd51e37ba4e4e2 ，Desikan-Killiany脑图谱（Desikan-Killiany atlas）分区的FreeSurfer皮层厚度、体积与表面积数据
- 临床与人口统计学元数据表：https://figshare.com/s/bab70268afeb1071202b ，包含患者临床与人口统计学相关元数据
- 手术切除区域占比表：https://figshare.com/s/097ba0e254e36f0eee52 ，说明Desikan-Killiany脑图谱中各脑区被手术切除比例的表格
- Z分数统计量表：https://figshare.com/s/8c086fc295a75f85e628 ，Desikan-Killiany脑图谱分区的FreeSurfer皮层厚度、体积与表面积数据，以标准对照群体为参照得到的Z分数
- 群体效应量表：https://figshare.com/s/323db205354788c4d1f0 ，患者组与健康对照组的群体效应量差异数据

搜集汇总

背景与挑战

背景概述

该数据集是一个开放源码的影像数据库，包含442名药物难治性局灶性癫痫患者的预处理MRI扫描和详细临床信息，旨在促进计算神经学方法的发展。数据包括术前3D T1和FLAIR影像、手动表面重建、体积分割以及术后切除掩膜，支持机器学习和AI算法在癫痫病变检测中的应用。