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71. Jahrestagung der Deutschen Gesellschaft für Neurochirurgie (DGNC)
9. Joint Meeting mit der Japanischen Gesellschaft für Neurochirurgie

Deutsche Gesellschaft für Neurochirurgie (DGNC) e. V.

21.06. - 24.06.2020

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Resting-state fMRI – robustness of language assessment in tumour patients

Resting-state fMRI – Robustheit der Bestimmung von Sprachnetzwerken bei Tumorpatienten

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  • presenting/speaker Christian Ott - Universitätsklinikum Regensburg, Klinik und Poliklinik für Neurochirurgie, Regensburg, Deutschland
  • Katharina Rosengarth - Universitätsklinikum Regensburg, Klinik und Poliklinik für Neurochirurgie, Regensburg, Deutschland
  • Markus Goldhacker - Universitätsklinikum Regensburg, Klinik und Poliklinik für Neurochirurgie, Regensburg, Deutschland
  • Julius Höhne - Universitätsklinikum Regensburg, Klinik und Poliklinik für Neurochirurgie, Regensburg, Deutschland
  • Elmar Lang - Universität Regensburg, Experimentelle Psychologie, Regensburg, Deutschland
  • Alexander Brawanski - Universitätsklinikum Regensburg, Klinik und Poliklinik für Neurochirurgie, Regensburg, Deutschland
  • Nils Ole Schmidt - Universitätsklinikum Regensburg, Klinik und Poliklinik für Neurochirurgie, Regensburg, Deutschland

Deutsche Gesellschaft für Neurochirurgie. 71. Jahrestagung der Deutschen Gesellschaft für Neurochirurgie (DGNC), 9. Joint Meeting mit der Japanischen Gesellschaft für Neurochirurgie. sine loco [digital], 21.-24.06.2020. Düsseldorf: German Medical Science GMS Publishing House; 2020. DocP062

doi: 10.3205/20dgnc350, urn:nbn:de:0183-20dgnc3505

Published: June 26, 2020

© 2020 Ott et al.
This is an Open Access article distributed under the terms of the Creative Commons Attribution 4.0 License. See license information at http://creativecommons.org/licenses/by/4.0/.

Outline

Text

Objective: In recent years, resting-state functional magnetic resonance imaging (RS-fMRI) has been shown to be a promising and feasible method for preoperative language assessment. The use in preoperative language assessment remains a controversial subject due to mass effect and edema caused by the tumor.This study examines whether the results for the language-related resting-state components obtained using an independent data-driven component-based identification algorithm based on a template derived from healthy volunteers compared to the results using the same algorithm but excluding the tumor area and the displaced tumor surrounding areas are substantially different.

Methods: 20 patients with brain lesions close to supposedly language-relevant cortical areas were included in the study. Preoperative language assessment was performed with RS-fMRI. The best fitting independent component analysis (ICA) component for the resting-state language network (RSLN) referential to a template for the language network derived from healthy volunteers were subsequently identified using an algorithm based on the Dice index. The same procedure was performed using an additional individual template (a sphere) to mask the tumor and its potentially displaced surroundings.

Results: RSLNs associated with both methods seemed to be reasonable. There was no significant difference in the results with or without masking the tumor and its surroundings.

Conclusion: Using an independent data-driven component-based identification algorithm based on a predefined template seems to be robust enough for clinical purposes for language assessment in tumor patients regardless of mass effect and edema due to the lesion.