Common Data Elements to Facilitate Sharing and Re-use of Participant-Level Data: Assessment of Psychiatric Comorbidity Across Brain Disorders

• Published in: Frontiers in psychiatry 2022-02, Vol.13, p.816465-816465
• Main Authors: 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
• Format: Article
• Language: English
• Subjects: common data elements; data sharing; major depressive disorder; neurological disorders; pooled participant data; psychiatric comorbidity; Psychiatry
• ISSN: 1664-0640; 1664-0640
• DOI: 10.3389/fpsyt.2022.816465

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).