Ph.D. candidate and CONP Scholar Jessica Royer has contributed a new neuroimaging dataset to the CONP portal. Comprising data from 50 healthy human participants, this open dataset provides a wealth of information spanning anatomical structural sequence data, resting state functional acquisition and diffusion MRI.
These data, acquired in healthy control participants, will not only be a valuable resource for basic questions regarding brain structure and function but will also serve as a good comparator for similar datasets collected in patients with different brain diseases. Jessica is now working on an analogous dataset of patients with forms of epilepsy that do not respond to anti-seizure medication. She hopes to identify differences in brain organisation between the patient and control group, which might provide insight into these forms of epilepsy and lead to better treatments for patients.
Collection and curation of the MICA-MICs dataset has taken 3 years, with the first brain-imaging experiments beginning in 2018. Now, both the raw and processed data are available under open license through the CONP portal and are described in further detail in a preprint that is currently submitted for publication. Processing of the data was based on a fully open pipeline, so that other scientists can replicate their work or adapt the analyses to help answer their own research questions. This pre-processing pipeline was co-developed by Jessica and can be used to process any BIDS-compatible dataset.
For Jessica, open scientific practises are central to the research process. For these large, complex and expensive experiments, sharing the data makes sense to Jessica: “Putting the data out there will help other groups that might not have the resources to collect such data” she says. Working at The Neuro (Montreal Neurological Institute-Hospital), her lab is uniquely positioned to do this research in drug-resistant epilepsy patients given The Neuro’s long-standing expertise in epilepsy, its state of the art facilities, and a large number of patient referrals. By not only sharing the data and tools but also carefully annotating and curating them, Jessica hopes to increase usability by other research teams as much as possible.
Acknowledgements: Several trainees of the Multimodal Imaging and Connectome Analysis Lab (MICA Lab) led by Boris Bernhardt contributed to data collection, pre-processing, and quality control of this dataset, including Shahin Tavakol, Hans Auer, Raul Rodriguez-Cruces, Sara Larivière, Reinder Vos de Wael, Oualid Benkarim, Casey Paquola, and Bo-yong Park. The MICA lab would also like to also highlight the help of Peer Herholz for his contribution to our pre-processing pipeline. This work was supported the Canadian Institute of Health Research (CIHR), Canadian Open Neuroscience Platform, the Canada Research Chairs (CRC) program, and National Sciences and Engineering Research Council of Canada (NSERC).