Vaccarino Anthony L, Beaton Derek, Black Sandra E, Blier Pierre, Farzan Farnak, Finger Elizabeth, Foster Jane A, Freedman Morris, Frey Benicio N, Gilbert Evans Susan, Ho Keith, Javadi Mojib, Kennedy Sidney H, Lam Raymond W, Lang Anthony E, Lasalandra Bianca, Latour Sara, Masellis Mario, Milev Roumen V, Müller Daniel J, Munoz Douglas P, Parikh Sagar V, Placenza Franca, Rotzinger Susan, Soares Claudio N, Sparks Alana, Strother Stephen C, Swartz Richard H, Tan Brian, Tartaglia Maria Carmela, Taylor Valerie H, Theriault Elizabeth, Turecki Gustavo, Uher Rudolf, Zinman Lorne, Evans Kenneth R
Indoc Research, Toronto, ON, Canada.
Data Science and Advanced Analytics, St. Michael's Hospital, Unity Health Toronto, Toronto, ON, Canada.
Front Psychiatry. 2022 Feb 7;13:816465. doi: 10.3389/fpsyt.2022.816465. eCollection 2022.
The Ontario Brain Institute's "Brain-CODE" is a large-scale informatics platform designed to support the collection, storage and integration of diverse types of data across several brain disorders as a means to understand underlying causes of brain dysfunction and developing novel approaches to treatment. By providing access to aggregated datasets on participants with and without different brain disorders, Brain-CODE will facilitate analyses both within and across diseases and cover multiple brain disorders and a wide array of data, including clinical, neuroimaging, and molecular. To help achieve these goals, consensus methodology was used to identify a set of core demographic and clinical variables that should be routinely collected across all participating programs. Establishment of Common Data Elements within Brain-CODE is critical to enable a high degree of consistency in data collection across studies and thus optimize the ability of investigators to analyze pooled participant-level data within and across brain disorders. Results are also presented using selected common data elements pooled across three studies to better understand psychiatric comorbidity in neurological disease (Alzheimer's disease/amnesic mild cognitive impairment, amyotrophic lateral sclerosis, cerebrovascular disease, frontotemporal dementia, and Parkinson's disease).
安大略脑研究所的“Brain-CODE”是一个大规模信息学平台,旨在支持跨多种脑部疾病收集、存储和整合各类数据,以此来了解脑功能障碍的潜在病因并开发新的治疗方法。通过提供对患有和未患有不同脑部疾病参与者的汇总数据集的访问,Brain-CODE将促进疾病内部和跨疾病的分析,并涵盖多种脑部疾病和大量数据,包括临床、神经影像和分子数据。为帮助实现这些目标,采用了共识方法来确定一组应在所有参与项目中常规收集的核心人口统计学和临床变量。在Brain-CODE中建立通用数据元素对于在各项研究的数据收集中实现高度一致性至关重要,从而优化研究人员分析脑部疾病内部和跨脑部疾病的汇总参与者层面数据的能力。还使用了在三项研究中汇总的选定通用数据元素来呈现结果,以便更好地了解神经疾病(阿尔茨海默病/遗忘型轻度认知障碍、肌萎缩侧索硬化症、脑血管疾病、额颞叶痴呆和帕金森病)中的精神共病情况。