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International Workshop on Ophthalmic Medical Image Analysis

International Workshop on Fetal and Infant Image Analysis

OMIA 2017, FIFI 2017: Fetal, Infant and Ophthalmic Medical Image Analysis pp 14–24Cite as

Assessing Reorganisation of Functional Connectivity in the Infant Brain

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Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 10554))

Abstract

As maturation of neural networks continues throughout childhood, brain lesions insulting immature networks have different impact on brain function than lesions obtained after full network maturation. Thus, longitudinal studies and analysis of spatial and temporal brain signal correlations are a key component to get a deeper understanding of individual maturation processes, their interaction and their link to cognition. Here, we assess the connectivity pattern deviation of developing resting state networks after ischaemic stroke of children between 7 and 17 years. We propose a method to derive a reorganisational score to detect target regions for overtaking affected functional regions within a stroke location. The evaluation is performed using rs-fMRI data of 16 control subjects and 16 stroke patients. The developing functional connectivity affected by ischaemic stroke exhibits significant differences to the control cohort. This suggests an influence of stroke location and developmental stage on regenerating processes and the reorganisational patterns.

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Notes

  1. 1.

    http://surfer.nmr.mgh.harvard.edu [accessed 16th May 2017].

  2. 2.

    http://fsl.fmrib.ox.ac.uk/fsl/fslwiki/FSL [accessed 16th May 2017].

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Acknowledgement

This work was co-funded by the Oesterreichische Nationalbank (Anniversary Fund, project number 15356), by the FWF under KLI 544-B27 and I 2714-B31, by the European Commision FP7-PEOPLE-2013-IAPP 610872 and by ZIT Life Sciences 2014 (1207843).

Author information

Authors and Affiliations

  1. Institute of Computer Aided Automation - Computer Vision Lab, Vienna University of Technology, Vienna, Austria

    Roxane Licandro

  2. Department of Biomedical Imaging and Image-guided Therapy - Computational Imaging Research Lab, Medical University of Vienna, Vienna, Austria

    Roxane Licandro, Karl-Heinz Nenning, Ernst Schwartz, Kathrin Kollndorfer & Georg Langs

  3. Department of Pediatrics and Adolescent Medicine, Medical University of Vienna, Vienna, Austria

    Lisa Bartha-Doering

  4. Department of Radiology, Martinos Center, MGH, Harvard Medical School, Boston, USA

    Hesheng Liu

Authors
  1. Roxane Licandro
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  2. Karl-Heinz Nenning
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  3. Ernst Schwartz
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  4. Kathrin Kollndorfer
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  5. Lisa Bartha-Doering
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  6. Hesheng Liu
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  7. Georg Langs
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Corresponding author

Correspondence to Roxane Licandro .

Editor information

Editors and Affiliations

  1. University College London, London, United Kingdom

    M. Jorge Cardoso

  2. McGill University, Montreal, Canada

    Tal Arbel

  3. University College London, London, United Kingdom

    Andrew Melbourne

  4. Medical University of Vienna, Vienna, Austria

    Hrvoje Bogunovic

  5. Eindhoven University of Technology, Eindhoven, The Netherlands

    Pim Moeskops

  6. Soochow University, Suzhou, China

    Xinjian Chen

  7. Medical University of Vienna, Vienna, Austria

    Ernst Schwartz

  8. University of Iowa, Iowa City, Iowa, USA

    Mona Garvin

  9. Imperial College London, London, United Kingdom

    Emma Robinson

  10. University of Dundee, Dundee, United Kingdom

    Emanuele Trucco

  11. University College London, London, United Kingdom

    Michael Ebner

  12. A*STAR Institute for Infocomm Research, Singapore, Singapore

    Yanwu Xu

  13. Imperial College London, London, United Kingdom

    Antonios Makropoulos

  14. University College London, London, United Kingdom

    Adrien Desjardin

  15. University College London, London, United Kingdom

    Tom Vercauteren

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© 2017 Springer International Publishing AG

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Licandro, R. et al. (2017). Assessing Reorganisation of Functional Connectivity in the Infant Brain. In: Cardoso, M., et al. Fetal, Infant and Ophthalmic Medical Image Analysis. OMIA FIFI 2017 2017. Lecture Notes in Computer Science(), vol 10554. Springer, Cham. https://doi.org/10.1007/978-3-319-67561-9_2

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  • DOI: https://doi.org/10.1007/978-3-319-67561-9_2

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-67560-2

  • Online ISBN: 978-3-319-67561-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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