Rotenberg David J, Chang Qing, Potapova Natalia, Wang Andy, Hon Marcia, Sanches Marcos, Bogetic Nikola, Frias Nathan, Liu Tommy, Behan Brendan, El-Badrawi Rachad, Strother Stephen C, Evans Susan G, Mikkelsen Jordan, Gee Tom, Dong Fan, Arnott Stephen R, Laing Shuai, Dharsee Moyez, Vaccarino Anthony L, Javadi Mojib, Evans Kenneth R, Jankowicz Damian
Krembil Center for Neuroinformatics, Center for Addiction and Mental Health (CAMH), Toronto, ON, Canada.
Dalla Lana School of Public Health, Toronto, ON, Canada.
Front Neuroinform. 2018 Nov 6;12:77. doi: 10.3389/fninf.2018.00077. eCollection 2018.
Investigations of mental illness have been enriched by the advent and maturation of neuroimaging technologies and the rapid pace and increased affordability of molecular sequencing techniques, however, the increased volume, variety and velocity of research data, presents a considerable technical and analytic challenge to curate, federate and interpret. Aggregation of high-dimensional datasets across brain disorders can increase sample sizes and may help identify underlying causes of brain dysfunction, however, additional barriers exist for effective data harmonization and integration for their combined use in research. To help realize the potential of multi-modal data integration for the study of mental illness, the Centre for Addiction and Mental Health (CAMH) constructed a centralized data capture, visualization and analytics environment-the based on the Ontario Brain Institute (OBI) Brain-CODE architecture, towards the curation of a standardized, consolidated psychiatric hospital-wide research dataset, directly coupled to high performance computing resources.
神经成像技术的出现与成熟以及分子测序技术的快速发展和成本降低，丰富了对精神疾病的研究。然而，研究数据的数量、种类和增长速度不断增加，给数据的整理、联合和解释带来了巨大的技术和分析挑战。跨脑疾病的高维数据集聚合可以增加样本量，并有助于识别脑功能障碍的潜在原因。然而，在有效整合和统一数据以便在研究中联合使用方面，还存在其他障碍。为了帮助实现多模态数据整合在精神疾病研究中的潜力，成瘾与心理健康中心（CAMH）基于安大略脑研究所（OBI）的Brain-CODE架构构建了一个集中式数据采集、可视化和分析环境，旨在整理一个标准化、整合的全院范围精神科研究数据集，并直接连接到高性能计算资源。
