HUP iEEG Epilepsy Dataset

<h1>HUP iEEG dataset</h1>

This dataset was prepared for release as part of a manuscript by Bernabei & Li et al., Brain (2023). A subset of the data has been featured in Kini & Bernabei et al., Brain (2019) [1], and Bernabei & Sinha et al., Brain (2022) [2].

<h3>Dataset description</h3>
These files contain de-identified patient data collected as part of surgical treatment for drug resistant epilepsy at the Hospital of the University of Pennsylvania. Each of the 58 subjects underwent intracranial EEG with subdural grid, strip, and depth electrodes (ECoG) or purely stereotactically-placed depth electrodes (SEEG). Each patient also underwent subsequent treatment with surgical resection or laser ablation. Electrophysiologic data for both interictal and ictal periods is available, as are electrode localizations in ICBM152 MNI space. Furthermore, clinically-determined seizure onset channels are provided, as are channels which overlap with the resection/ablation zone, which was rigorously determined by segmenting the resection cavity.

<h3>BIDS Conversion</h3>
MNE-BIDS was used to convert the dataset into BIDS format.

<h3>References</h3>
[1] Kini L.*, Bernabei J.M.*, Mikhail F., Hadar P., Shah P., Khambhati A., Oechsel K., Archer R., Boccanfuso J.A., Conrad E., Stein J., Das S., Kheder A., Lucas T.H., Davis K.A., Bassett D.S., Litt B., Virtual resection predicts surgical outcome for drug resistant epilepsy. Brain, 2019.

[2] Bernabei J.M.*, Sinha N.*, Arnold T.C., Conrad E., Ong I., Pattnaik A.R., Stein J.M., Shinohara R.T., Lucas T.H., Bassett D.S., Davis K.A., Litt B., Normative intracranial EEG maps epileptogenic tissues in focal epilepsy. Brain, 2022

[3] Appelhoff, S., Sanderson, M., Brooks, T., Vliet, M., Quentin, R., Holdgraf, C., Chaumon, M., Mikulan, E., Tavabi, K., Höchenberger, R., Welke, D., Brunner, C., Rockhill, A., Larson, E., Gramfort, A. and Jas, M. (2019). MNE-BIDS: Organizing electrophysiological data into the BIDS format and facilitating their analysis. Journal of Open Source Software 4: (1896). https://doi.org/10.21105/joss.01896

[4] Holdgraf, C., Appelhoff, S., Bickel, S., Bouchard, K., D'Ambrosio, S., David, O., … Hermes, D. (2019). iEEG-BIDS, extending the Brain Imaging Data Structure specification to human intracranial electrophysiology. Scientific Data, 6, 102. https://doi.org/10.1038/s41597-019-0105-7

<h1>HUP iEEG数据集</h1>

本数据集由Bernabei与Li等人筹备发布，相关研究成果发表于《Brain》2023年期刊。该数据集的部分子集曾被收录于Kini与Bernabei等人2019年发表于《Brain》的论文[1]，以及Bernabei与Sinha等人2022年发表于《Brain》的论文[2]。

<h3>数据集描述</h3>
本数据集文件包含宾夕法尼亚大学附属医院针对药物难治性癫痫患者开展外科治疗过程中采集的去标识化患者数据。58名受试者均接受了硬膜下栅格、条状及深部电极记录的颅内脑电图（electrocorticography, ECoG），或仅接受立体定向放置的深部电极脑电图（stereoelectroencephalography, SEEG）。所有受试者后续均接受了手术切除或激光消融治疗。数据集包含发作间期与发作期的电生理数据，同时提供电极在ICBM152 MNI空间中的定位信息。此外，数据集还提供了临床判定的癫痫发作起始电极通道，以及与手术切除/消融区域重叠的电极通道——该区域通过对切除腔室进行分割得到，判定过程严谨规范。

<h3>BIDS格式转换</h3>
本数据集采用MNE-BIDS工具转换为脑成像数据结构（Brain Imaging Data Structure, BIDS）格式。

<h3>参考文献</h3>
[1] Kini L.*, Bernabei J.M.*, Mikhail F., Hadar P., Shah P., Khambhati A., Oechsel K., Archer R., Boccanfuso J.A., Conrad E., Stein J., Das S., Kheder A., Lucas T.H., Davis K.A., Bassett D.S., Litt B. 虚拟切除可预测药物难治性癫痫的手术预后. 《Brain》, 2019.
[2] Bernabei J.M.*, Sinha N.*, Arnold T.C., Conrad E., Ong I., Pattnaik A.R., Stein J.M., Shinohara R.T., Lucas T.H., Bassett D.S., Davis K.A., Litt B. 标准化颅内脑电图图谱定位局灶性癫痫的致痫组织. 《Brain》, 2022.
[3] Appelhoff S, Sanderson M, Brooks T, Vliet M, Quentin R, Holdgraf C, Chaumon M, Mikulan E, Tavabi K, Höchenberger R, Welke D, Brunner C, Rockhill A, Larson E, Gramfort A, Jas M. MNE-BIDS：将电生理数据整理为BIDS格式并助力分析. 《开源软件期刊》, 2019, 4(1896). https://doi.org/10.21105/joss.01896
[4] Holdgraf C, Appelhoff S, Bickel S, Bouchard K, D'Ambrosio S, David O, et al. iEEG-BIDS：将脑成像数据结构规范扩展至人类颅内电生理领域. 《科学数据》, 2019, 6:102. https://doi.org/10.1038/s41597-019-0105-7

搜集汇总

背景与挑战

背景概述

HUP iEEG Epilepsy Dataset是一个包含58名药物难治性癫痫患者颅内脑电图（iEEG）数据的数据集，数据来自宾夕法尼亚大学医院的手术治疗，并已去标识化。该数据集提供发作间期和发作期的生理数据、电极定位信息以及临床相关的癫痫发作起始和切除区域通道，遵循BIDS格式，适用于癫痫机制和手术治疗效果的研究。