Abstract
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).
Highlights
The Ontario Brain Institute’s (OBI) “Brain-CODE” informatics platform [1] was designed to support the collection, integration, sharing, and analysis of diverse types of patient-level data across several brain disorders, including neurodevelopmental disorders, cerebral palsy, epilepsy, major depressive disorder (MDD, www.canbind.ca), concussion, and neurodegenerative/neurovascular cognitive disorders
Low item-total correlations (r < 0.3) were noted for the “Sleep” domain in Alzheimer’s disease (AD)/mild cognitive impairment (MCI), cerebrovascular disease (CVD), frontotemporal dementia (FTD), and Parkinson’s disease (PD), which were questionable within the amyotrophic lateral sclerosis (ALS) (r = 0.33) and MDD (r = 0.31) cohorts
Other items identified as having low item-total correlations, included “Appetite” in AD/MCI (r = 0.19), PD (r = 0.26), which were questionable in CVD (r = 0.35) and MDD (r = 0.30); “Concentration” in ALS (r = 0.24); and FTD (r = 0.32); Self-perception in ALS (r = 0.26), CVD (r = 0.35), FTD (r = 0.31)
Summary
The Ontario Brain Institute’s (OBI) “Brain-CODE” informatics platform (www.braincode.ca) [1] was designed to support the collection, integration, sharing, and analysis of diverse types of patient-level data across several brain disorders, including neurodevelopmental disorders (www.pond-network.ca), cerebral palsy (www.cpnet.canchild.ca), epilepsy (www.eplink.ca), major depressive disorder (MDD, www.canbind.ca), concussion (www.connectontario.ca), and neurodegenerative/neurovascular cognitive disorders (www.ondri.ca). These programs provide an opportunity to facilitate collaboration across disorders; with pooling of data across these programs expanding the utility of the individual datasets to support cross-disease comparisons and generalizability of findings. We present here a summary of how we determined and used the Brain-CODE CDEs, including an example using CDEs pooled across three programs to assess symptoms of depression and anxiety across neurological diseases (Alzheimer’s disease/amnesic mild cognitive impairment, amyotrophic lateral sclerosis, cerebrovascular disease, frontotemporal dementia and Parkinson’s disease), as well as major depressive disorder (MDD)
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