Public MEG Dataset
Magnetoencephalography (MEG) is a functional neuroimaging technique that records the magnetic fields generated by natural brain activity using highly sensitive magnetometers, typically superconducting quantum unit interference devices (SQUID), and produces images of brain activity. MEG has been widely used in basic research on perception and cognitive processes, as well as for locating areas affected by pathology before surgical removal, determining the function of different parts of the brain, and providing neural feedback. It can be applied to both clinical discovery of abnormal locations and simple measurement of brain activity in experiments.
1. The Open MEG Archive (OMEGA)
The OMEGA database was established through collaboration between McGill University's McConnell Brain Imaging Centre and the University of Montreal. The database consists of multimodal datasets from 220 subjects, including approximately 300 resting-state MEG data: 182 healthy controls and 38 patients with ADHD or chronic pain.
This dataset has several features: · The first open data repository dedicated entirely to MEG that conforms to BIDS standards · All data can be directly read using the open-source EEG/MEG analysis toolkit Brainstorm · All data includes T1 structural imaging for each subject · All data contains anonymous demographic questionnaires for each subject · Provides an upload feature for inclusion in its own database
Database project homepage: https://www.mcgill.ca/bic/resources/omega
For more detailed information about this database, see this article: http://dx.doi.org/10.1016/j.neuroimage.2015.04.028
This database uses an authorized access mechanism; apply for authorization at: https://www.mcgill.ca/bic/omega-registration
Note: BIDS stands for Brain Imaging Data Structure, which is a standard structure for brain imaging data; see this articlehttps://www.nature.com/articles/sdata2018110 for more information about BIDS.
2. Human Connectome Project (HCP)
The HCP project was established by Professor Kamil Ugurbil from the University of Minnesota and Professor David Van Essen from the University of Washington. Using greatly improved methods for data collection, analysis, and sharing, HCP provides data and discoveries that greatly enhance our understanding of brain structure, function, connectivity, and their relationship to behavior.
This dataset includes 3T and 7T MRI data as well as MEG data from 1200 subjects aged between 22 and 35 years old.
Database project homepage: https://www.humanconnectome.org/study/hcp-young-adult Database access address: https://www.humanconnectome.org/study/hcp-young-adult/document/1200-subjects-data-release
3. Cambridge Center for Ageing Neuroscience (CAMCAN)
CAMCAN is a large collaborative research project at the University of Cambridge launched in October 2010 with substantial initial funding from the Biotechnology and Biological Sciences Research Council (BBSRC), followed by support from the Medical Research Council's Cognition and Brain Sciences Unit (CBU) and Horizon2020 LifeBrain project funded by European Union.
The project uses epidemiological, cognitive, and neuroimaging data to understand how individuals can best preserve cognitive abilities into old age. This database contains detailed data from CAMCAN projects I & II including over 700 MRI/fMRI/MEG datasets collected using Elekta Neuromag Vectorview system with a total of 306 channels. See below figure for detailed statistics.
Database project homepage: https://camcan-archive.mrc-cbu.cam.ac.uk/dataaccess/ For more detailed information about this database see this article:http://www.sciencedirect.com/science/article/pii/S1053811915008150 The database uses an authorized access mechanism; apply for authorization at: https://camcan-archive.mrc-cbu.cam.ac.uk/dataaccess/datarequest.php