UC San Diego Resting State EEG Data from Patients with Parkinson's Disease

Welcome to the resting state EEG dataset collected at the University of San Diego and curated by Alex Rockhill at the University of Oregon.

Please email [e-mail] before submitting a manuscript to be published in a peer-reviewed journal using this data, we wish to ensure that the data to be analyzed and interpreted with scientific integrity so as not to mislead the public about findings that may have clinical relevance. The purpose of this is to be responsible stewards of the data without an "available upon reasonable request" clause that we feel doesn't fully represent the open-source, reproducible ethos. The data is freely available to download so we cannot stop your publication if we don't support your methods and interpretation of findings, however, in being good data stewards, we would like to offer suggestions in the pre-publication stage so as to reduce conflict in published scientific literature. As far as credit, there is precedent for receiving a mention in the acknowledgements section for reading and providing feedback on the paper or, for more involved consulting, being included as an author may be warranted. The purpose of asking for this is not to inflate our number of authorships; we take ethical considerations of the best way to handle intellectual property in the form of manuscripts very seriously, and, again, sharing is at the discretion of the author although we strongly recommend it. Please be ethical and considerate in your use of this data and all open-source data and be sure to credit authors by citing them.

An example of an analysis that we could consider problematic and would strongly advice to be corrected before submission to a publication would be using machine learning to classify Parkinson's patients from healthy controls using this dataset. This is because there are far too few patients for proper statistics. Parkinson's disease presents heterogeneously across patients, and, with a proper test-training split, there would be fewer than 8 patients in the testing set. Statistics on 8 or fewer patients for such a complicated diease would be inaccurate due to having too small of a sample size. Furthermore, if multiple machine learning algorithms were desired to be tested, a third split would be required to choose the best method, further lowering the number of patients in the testing set. We strongly advise against using any such approach because it would mislead patients and people who are interested in knowing if they have Parkinson's disease.

Note that UPDRS rating scales were collected by laboratory personnel who had completed online training and not a board-certified neurologist. Results should be interpreted accordingly, especially that analyses based largely on these ratings should be taken with the appropriate amount of uncertainty.

In addition to contacting the aforementioned email, please cite the following papers:

Nicko Jackson, Scott R. Cole, Bradley Voytek, Nicole C. Swann. Characteristics of Waveform Shape in Parkinson's Disease Detected with Scalp Electroencephalography. eNeuro 20 May 2019, 6 (3) ENEURO.0151-19.2019; DOI: 10.1523/ENEURO.0151-19.2019.

Swann NC, de Hemptinne C, Aron AR, Ostrem JL, Knight RT, Starr PA. Elevated synchrony in Parkinson disease detected with electroencephalography. Ann Neurol. 2015 Nov;78(5):742-50. doi: 10.1002/ana.24507. Epub 2015 Sep 2. PMID: 26290353; PMCID: PMC4623949.

George JS, Strunk J, Mak-McCully R, Houser M, Poizner H, Aron AR. Dopaminergic therapy in Parkinson's disease decreases cortical beta band coherence in the resting state and increases cortical beta band power during executive control. Neuroimage Clin. 2013 Aug 8;3:261-70. doi: 10.1016/j.nicl.2013.07.013. PMID: 24273711; PMCID: PMC3814961.

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).

Pernet, C. R., Appelhoff, S., Gorgolewski, K. J., Flandin, G.,
Phillips, C., Delorme, A., Oostenveld, R. (2019). EEG-BIDS, an extension to the brain imaging data structure for electroencephalography. Scientific Data, 6, 103. https://doi.org/10.1038/s41597-019-0104-8.

Note: see this discussion on the structure of the json files that is sufficient but not optimal and will hopefully be changed in future versions of BIDS: https://neurostars.org/t/behavior-metadata-without-tsv-event-data-related-to-a-neuroimaging-data/6768/25.

欢迎使用圣地亚哥大学采集、俄勒冈大学Alex Rockhill整理的静息态脑电图（Electroencephalogram, EEG）数据集。

若您计划使用本数据撰写手稿并提交至同行评审期刊发表，请先发送邮件至[e-mail]。我们希望确保数据的分析与解读符合科学诚信原则，避免误导公众对可能具有临床相关性的研究结果产生误解。此举旨在成为负责任的数据管理者，摒弃"合理请求即可获取"的条款——我们认为该条款未能充分体现开源、可复现的理念。本数据可免费下载，因此即便我们不认可您的方法与结果解读，也无法阻止您的发表；但作为尽责的数据管理者，我们希望在预发表阶段提供建议，以减少已发表科学文献中的冲突。关于署名，若我们对论文进行审阅并提供反馈，通常会在致谢部分提及；若涉及更深入的咨询，则可能需要将我们列为作者。此举并非为了增加署名数量；我们高度重视以符合伦理的方式处理手稿形式的知识产权，且再次强调，尽管我们强烈建议分享，但最终决定权仍在作者手中。请您在使用本数据及所有开源数据时秉持伦理与审慎原则，并务必通过引用致谢作者。

我们认为以下分析方法存在问题，强烈建议在投稿前修正：使用本数据集通过机器学习（machine learning）对帕金森病患者与健康对照者进行分类。原因在于，本数据中的患者数量远不足以支持可靠的统计分析。帕金森病在患者间表现出异质性，即使进行合理的训练-测试集划分，测试集中的患者数量也将不足8例。对于如此复杂的疾病，基于8例或更少样本的统计分析会因样本量过小而不准确。此外，若需测试多种机器学习算法，则需进行第三次划分以选择最优方法，这将进一步减少测试集中的患者数量。我们强烈反对使用此类方法，因其会误导患者及希望了解自身是否患有帕金森病的人群。

请注意，统一帕金森病评定量表（Unified Parkinson's Disease Rating Scale, UPDRS）由完成在线培训的实验室人员而非board认证的神经科医生采集。因此，研究结果应相应解读，尤其是主要基于这些评分的分析需考虑适当的不确定性。

除联系上述邮箱外，请引用以下文献：

Nicko Jackson, Scott R. Cole, Bradley Voytek, Nicole C. Swann. 头皮脑电图（EEG）检测到的帕金森病患者脑电波形特征. eNeuro, 2019年5月20日, 6(3): ENEURO.0151-19.2019; DOI: 10.1523/ENEURO.0151-19.2019.

Swann NC, de Hemptinne C, Aron AR, Ostrem JL, Knight RT, Starr PA. 脑电图检测到的帕金森病患者同步性升高. 《神经病学年鉴》, 2015年11月, 78(5):742-50. doi: 10.1002/ana.24507. Epub 2015年9月2日. PMID: 26290353; PMCID: PMC4623949.

George JS, Strunk J, Mak-McCully R, Houser M, Poizner H, Aron AR. 帕金森病中的多巴胺能治疗降低静息态皮质β波段相干性并增加执行控制期间的皮质β波段功率. 《临床神经影像》, 2013年8月8日, 3:261-70. doi: 10.1016/j.nicl.2013.07.013. PMID: 24273711; PMCID: PMC3814961.

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: 将电生理数据组织为BIDS格式并促进其分析. 《开源软件期刊》4: (1896).

Pernet, C. R., Appelhoff, S., Gorgolewski, K. J., Flandin, G., Phillips, C., Delorme, A., Oostenveld, R. (2019). EEG-BIDS：脑影像数据结构对脑电图的扩展. 《科学数据》6: 103. https://doi.org/10.1038/s41597-019-0104-8.

注：关于JSON文件结构的讨论见此链接，该结构虽可用但并非最优，有望在未来BIDS版本中改进：https://neurostars.org/t/behavior-metadata-without-tsv-event-data-related-to-a-neuroimaging-data/6768/25.

搜集汇总

背景与挑战

背景概述

该数据集是来自加州大学圣地亚哥分校的帕金森病患者静息态脑电图（EEG）数据，包含31名参与者（包括患者和健康对照）的原始EEG记录，遵循BIDS标准组织，总大小为545MB。数据集强调由于样本量有限，不建议用于机器学习分类分析，并提供了相关研究论文引用，以支持科学使用的完整性。