The goal of the Canadian Dementia Imaging Protocol (CDIP) is to harmonize magnetic resonance imaging acquisitions in the context of studying primary and secondary causes of morbidity of neurodegeneration. The CDIP updates previous protocols, taking into account the capacity of new generation devices and advancements in magnetic resonance imaging techniques. We have deployed this standardized protocol on multiple platforms in the Canadian research context, as well as at international partners sites that wished to join our efforts.
The Canadian Open Neuroscience Platform (CONP) aims to bring together many of the country’s leading scientists in basic and clinical neuroscience to form an interactive network of collaborations in brain research, interdisciplinary student training, international partnerships, clinical translation and open publishing. The platform will provide a unified interface to the research community and will propel Canadian neuroscience research into a new era of open neuroscience research with the sharing of both data and methods, the creation of large-scale databases, the development of standards for sharing, the facilitation of advanced analytic strategies, the open dissemination to the global community of both neuroscience data and methods, and the establishment of training programs for the next generation of computational neuroscience researchers. CONP aims to remove the technical barriers to practicing open science and improve the accessibility and reusability of neuroscience research to accelerate the pace of discovery.
The MEDICS Laboratory platform proposes a new approach to image analysis. The pipelining engine is entirely modular and flexible. You can put together any number of processes and chain them together in any order you want. No programming necessary. It is also directly connected to the MEDICS database, a comprehensive repository of people, images and clinical data. The interface allows you to filter through that data and assemble it into Datasets using a simple interface. Your Datasets can be saved and used with more than one Pipeline, allowing comparative studies of your Pipeline's performance. Next, by combining Pipelines with Datasets, you now have a fully working processing unit called an Instance. Instances can be RUN, an action that will produce results in the form of new Datasets. Finally, results, not just those produced at the end of a Pipeline but also the ones at any intermediary step inside it, can easily be viewed, scored and printed into a comprehensive report format.