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Resolving large-scale networks in ultra-high field fMRI (9.4T) of the human brain

MPG-Autoren
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Stelzer,  J
Max Planck Institute for Biological Cybernetics, Max Planck Society;
Department High-Field Magnetic Resonance, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Ehses,  P
Department High-Field Magnetic Resonance, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Bause,  J
Max Planck Institute for Biological Cybernetics, Max Planck Society;
Department High-Field Magnetic Resonance, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Scheffler,  K
Max Planck Institute for Biological Cybernetics, Max Planck Society;
Department High-Field Magnetic Resonance, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Lohmann,  G
Department High-Field Magnetic Resonance, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Zitation

Stelzer, J., Ehses, P., Bause, J., Scheffler, K., & Lohmann, G. (2016). Resolving large-scale networks in ultra-high field fMRI (9.4T) of the human brain. Poster presented at 46th Annual Meeting of the Society for Neuroscience (Neuroscience 2016), San Diego, CA, USA.


Zitierlink: http://hdl.handle.net/21.11116/0000-0000-7AD4-8
Zusammenfassung
The combination of ultra-high field fMRI with state-of-the art network approaches offers a unique window for studying human brain function at the mesoscopic level. In this study, we used a 9.4T MRI system to acquire functional data at submillimetre resolution, covering more than 500000 voxels in fronto-parietal areas. As experimental manipulation we tested a simple 2-back against an 0-back memory task, as in the human connectome project. We analysed the data with a network-based method which we specifically tailored for ultra-high-resolution data on the single subject level, named “task-induced edge density” or “TED”. Our method aims to detect task-dependent changes in synchronization across the entire brain. The algorithm operates on the voxel level and does not require any presegmentation or spatial smoothing of the data. Our method reveals widespread changes in the network configuration across the two memory tasks. A large proportion of grey matter voxels changes its connectivity to the rest of the brain between the two tasks. Thus our findings suggest that vast parts of the cortex might subserve the underlying brain functions. Interestingly, a distributed subset of areas appears to change its connectivity to an especially large number of voxels, possibly indicating key areas or super-hubs within the network. We further discuss the fine structure of the connectivity patterns, such as the formation of subnetworks on smaller spatial scales and the relation to the underlying anatomical structure. The present results distinctively favour a more integrative rather than segregative view of brain function, which appears to be wide-spread instead of sparse. However, our results also raise other issues of interpretability due to the sheer extent of the involved brain areas.