Bellec Pierre, Chu Carlton, Chouinard-Decorte François, Benhajali Yassine, Margulies Daniel S, Craddock R Cameron
The Neuro Bureau, Germany; Centre de Recherche de l'Institut Universitaire de Gériatrie de Montréal, Montréal, Canada; Département d'Informatique et de Recherche Opérationnelle, Université de Montréal, Montréal, Canada.
The Neuro Bureau, Germany; Google DeepMind, London, UK.
Neuroimage. 2017 Jan;144(Pt B):275-286. doi: 10.1016/j.neuroimage.2016.06.034. Epub 2016 Jul 15.
In 2011, the "ADHD-200 Global Competition" was held with the aim of identifying biomarkers of attention-deficit/hyperactivity disorder from resting-state functional magnetic resonance imaging (rs-fMRI) and structural MRI (s-MRI) data collected on 973 individuals. Statisticians and computer scientists were potentially the most qualified for the machine learning aspect of the competition, but generally lacked the specialized skills to implement the necessary steps of data preparation for rs-fMRI. Realizing this barrier to entry, the Neuro Bureau prospectively collaborated with all competitors by preprocessing the data and sharing these results at the Neuroimaging Informatics Tools and Resources Clearinghouse (NITRC) (http://www.nitrc.org/frs/?group_id=383). This "ADHD-200 Preprocessed" release included multiple analytical pipelines to cater to different philosophies of data analysis. The processed derivatives included denoised and registered 4D fMRI volumes, regional time series extracted from brain parcellations, maps of 10 intrinsic connectivity networks, fractional amplitude of low frequency fluctuation, and regional homogeneity, along with grey matter density maps. The data was used by several teams who competed in the ADHD-200 Global Competition, including the winning entry by a group of biostaticians. To the best of our knowledge, the ADHD-200 Preprocessed release was the first large public resource of preprocessed resting-state fMRI and structural MRI data, and remains to this day the only resource featuring a battery of alternative processing paths.
2011年,举办了“注意力缺陷多动障碍200全球竞赛”,目的是从973名个体的静息态功能磁共振成像(rs-fMRI)和结构磁共振成像(s-MRI)数据中识别注意力缺陷多动障碍的生物标志物。统计学家和计算机科学家可能最有资格参与竞赛的机器学习方面,但通常缺乏实施rs-fMRI数据准备必要步骤的专业技能。意识到这一进入障碍后,神经局前瞻性地与所有参赛者合作,对数据进行预处理,并在神经成像信息学工具和资源中心(NITRC)(http://www.nitrc.org/frs/?group_id=383)分享这些结果。这个“ADHD-200预处理版”发布包含多个分析管道,以迎合不同的数据分析理念。处理后的衍生数据包括去噪和配准的4D fMRI体积、从脑部分割提取的区域时间序列、10个内在连接网络的图谱、低频波动分数振幅和区域同质性,以及灰质密度图。几个参与注意力缺陷多动障碍200全球竞赛的团队使用了这些数据,包括一组生物统计学家的获胜作品。据我们所知,ADHD-200预处理版发布是首个大型的预处理静息态fMRI和结构MRI数据的公共资源,至今仍是唯一具有一系列替代处理路径的资源。