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Eigenvector centrality mapping for ultrahigh resolution fMRI data of the human brain

Gabriele Lohmann, Alexander Loktyushin, Johannes Stelzer, Klaus Scheffler
doi: https://doi.org/10.1101/494732
Gabriele Lohmann
1Department of Biomedical Magnetic Resonance Imaging, University Hospital Tübingen, Hoppe-Seyler-Strasse 3, 72076 Tübingen, Germany
2Magnetic Resonance Centre, Max-Planck-Institute for Biological Cybernetics, Max-Planck-Ring 11, 72076 Tübingen, Germany
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  • For correspondence: gabriele.lohmann@tuebingen.mpg.de
Alexander Loktyushin
2Magnetic Resonance Centre, Max-Planck-Institute for Biological Cybernetics, Max-Planck-Ring 11, 72076 Tübingen, Germany
3Max-Planck-Institute for Intelligent Systems, Max-Planck-Ring 4, 72076 Tübingen, Germany
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Johannes Stelzer
1Department of Biomedical Magnetic Resonance Imaging, University Hospital Tübingen, Hoppe-Seyler-Strasse 3, 72076 Tübingen, Germany
2Magnetic Resonance Centre, Max-Planck-Institute for Biological Cybernetics, Max-Planck-Ring 11, 72076 Tübingen, Germany
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Klaus Scheffler
1Department of Biomedical Magnetic Resonance Imaging, University Hospital Tübingen, Hoppe-Seyler-Strasse 3, 72076 Tübingen, Germany
2Magnetic Resonance Centre, Max-Planck-Institute for Biological Cybernetics, Max-Planck-Ring 11, 72076 Tübingen, Germany
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Abstract

Eigenvector centrality mapping (ECM) is a popular technique for analyzing fMRI data of the human brain. It is used to obtain maps of functional hubs in networks of the brain in a manner similar to Google’s PageRank algorithm. ECM attributes a score to the time course of each voxel that reflects its centrality within the network. Voxels that are strongly correlated with many other voxels that are themselves strongly correlated with other voxels receive high scores. Currently, there exist two different implementations ECM, one of which is very fast but limited to one particular type of correlation metric whose interpretation can be problematic. The second implementation supports many different metrics, but it is computationally costly and requires a very large main memory. Here we propose two new implementations of the ECM approach that resolve these issues. The first is based on a new correlation metric that we call “ReLU correlation (RLC)”. The second method is based on matrix projections. We demonstrate the use of both techniques on standard fMRI data, as well as on high-resolution fMRI data acquired at 9.4 Tesla.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-ND 4.0 International license.
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Posted December 13, 2018.
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Eigenvector centrality mapping for ultrahigh resolution fMRI data of the human brain
Gabriele Lohmann, Alexander Loktyushin, Johannes Stelzer, Klaus Scheffler
bioRxiv 494732; doi: https://doi.org/10.1101/494732
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Eigenvector centrality mapping for ultrahigh resolution fMRI data of the human brain
Gabriele Lohmann, Alexander Loktyushin, Johannes Stelzer, Klaus Scheffler
bioRxiv 494732; doi: https://doi.org/10.1101/494732

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