Adding dynamics to the Human Connectome Project with MEG

Larson-Prior, L.J.; Oostenveld, R.; Della Penna, S.; Michalareas, Giorgos; Prior, F.; Babajani-Feremi, A.; Schoffelen, J.-M.; Marzetti, L.; de Pasquale, F.; Di Pompeo, F.; Stout, J.; Woolrich, M.; Luo, Q.; Bucholz, R.; Fries, Pascal; Pizzella, V.; Romani, G.L.; Corbetta, M.; Snyder, A.Z.; WU-Minn HCP Consortium

doi:10.1016/j.neuroimage.2013.05.056

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Adding dynamics to the Human Connectome Project with MEG

MPS-Authors

Michalareas, Giorgos
Ernst Strüngmann Institute (ESI) for Neuroscience in Cooperation with Max Planck Society, Max Planck Society;
Fries Lab, Ernst Strüngmann Institute (ESI) for Neuroscience in Cooperation with Max Planck Society, Max Planck Society;

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Fries, Pascal
Ernst Strüngmann Institute (ESI) for Neuroscience in Cooperation with Max Planck Society, Max Planck Society;
Fries Lab, Ernst Strüngmann Institute (ESI) for Neuroscience in Cooperation with Max Planck Society, Max Planck Society;

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https://www.sciencedirect.com/science/article/pii/S1053811913005508
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Larson-Prior_2013_AddingDynamics_manuscript.pdf
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Citation

Larson-Prior, L., Oostenveld, R., Della Penna, S., Michalareas, G., Prior, F., Babajani-Feremi, A., et al. (2013). Adding dynamics to the Human Connectome Project with MEG. NeuroImage, 80, 190-201. doi:10.1016/j.neuroimage.2013.05.056.

Cite as: https://hdl.handle.net/21.11116/0000-000B-6135-C

Abstract

The Human Connectome Project (HCP) seeks to map the structural and functional connections between network elements in the human brain. Magnetoencephalography (MEG) provides a temporally rich source of information on brain network dynamics and represents one source of functional connectivity data to be provided by the HCP. High quality MEG data will be collected from 50 twin pairs both in the resting state and during performance of motor, working memory and language tasks. These data will be available to the general community. Additionally, using the cortical parcellation scheme common to all imaging modalities, the HCP will provide processing pipelines for calculating connection matrices as a function of time and frequency. Together with structural and functional data generated using magnetic resonance imaging methods, these data represent a unique opportunity to investigate brain network connectivity in a large cohort of normal adult human subjects. The analysis pipeline software and the dynamic connectivity matrices that it generates will all be made freely available to the research community.