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Current Directions in Biomedical Engineering

Joint Journal of the German Society for Biomedical Engineering in VDE and the Austrian and Swiss Societies for Biomedical Engineering

Editor-in-Chief: Dössel, Olaf

Editorial Board: Augat, Peter / Buzug, Thorsten M. / Haueisen, Jens / Jockenhoevel, Stefan / Knaup-Gregori, Petra / Kraft, Marc / Lenarz, Thomas / Leonhardt, Steffen / Malberg, Hagen / Penzel, Thomas / Plank, Gernot / Radermacher, Klaus M. / Schkommodau, Erik / Stieglitz, Thomas / Urban, Gerald A.

Open Access
Online
ISSN
2364-5504
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Methodological aspects of analyzing high resolved brain connectivity for multiple subjects

Britta Pester
  • Corresponding author
  • Institute of Medical Statistics, Computer Sciences and Documentation, Jena University Hospital, Friedrich Schiller University, Jena, Germany, Bachstr. 18, 07743 Jena
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/ Christoph Schmidt
  • Institute of Medical Statistics, Computer Sciences and Documentation, Jena University Hospital, Friedrich Schiller University, Jena, Germany
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/ Karl-Jürgen Bär
  • Department of Psychiatry, Jena University Hospital, Friedrich Schiller University, Jena, Germany
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/ Lutz Leistritz
  • Institute of Medical Statistics, Computer Sciences and Documentation, Jena University Hospital, Friedrich Schiller University, Jena, Germany
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Published Online: 2017-09-07 | DOI: https://doi.org/10.1515/cdbme-2017-0088

Abstract

Analyzing directed interactions within brain networks of high spatial resolution is always associated with a limited interpretability due to the high amount of possible connections. Here, module detection algorithms have proven to helpfully subsume the information of the resulting networks for each proband. However, the between-subject comparison of clusters is not straightforward since identified modules are not matched to each other across different subjects. Tensor decomposition has successfully been applied for the detection of group-wide connectivity patterns. Yet, a thorough investigation of the effect of the involved analysis parameters and data properties on decomposition results has still been missing. In this study we filled this gap and found that - given appropriate parameter choices - tensor decomposition of functional connectivity data reveals meaningful, group-specific insights into the brain's information processing.

Keywords: Large scale Granger causality; parallel factor analysis; network analysis; module detection; fMRI

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Published Online: 2017-09-07


Citation Information: Current Directions in Biomedical Engineering, Volume 3, Issue 2, Pages 417–421, ISSN (Online) 2364-5504, DOI: https://doi.org/10.1515/cdbme-2017-0088.

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©2017 Britta Pester et al., published by De Gruyter. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. BY-NC-ND 4.0

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