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Preoperative assessment of language dominance through combined resting-state and task-based functional magnetic resonance imaging

  • Background: Brain lesions in language-related cortical areas remain a challenge in the clinical routine. In recent years the resting-state fMRI (rs-fMRI) was shown to be a feasible method for preoperative language assessment. The aim of this study was to examine whether language-related resting-state components, which have been obtained using a data-driven independent-component-based identification algorithm, can be supportive in determining language dominance in the left or right hemisphere. Methods: Twenty patients suffering from brain lesions close to supposed language relevant cortical areas were included. Rs-fMRI and task-based (tb-fMRI) were performed for the purpose of preoperative language assessment. Tb-fMRI included a verb generation task with an appropriate control condition (a syllable switching task) to decompose language critical and language supportive processes. Subsequently, the best fitting ICA component for the resting-state language network (RSLN) referential to general linear models (GLMs) of the tb-fMRI (including models with and without linguistic control conditions) was identified using an algorithm based on the Dice-index. Results: The RSLNs associated with GLMs using a linguistic control condition led to significantly higher laterality indices than GLM baseline contrasts. LIs derived from GLM contrasts with and without control conditions alone did not differ significantly. Conclusion: In general, the results suggest that determining language dominance in the human brain is feasible both with tb-fMRI and rs-fMRI, and in particular, the combination of both approaches yields a higher specificity in preoperative language assessment. Moreover, we can conclude that the choice of the language mapping paradigm is crucial for the mentioned benefits.

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Metadaten
Verfasserangaben:C. Ott, K. Rosengarth, Christian Doenitz, J. Hoehne, C. Wendl, Frank Dodoo-Schittko, E. Lang, Nils Ole SchmidtORCiDGND, Markus GoldhackerORCiD
DOI:https://doi.org/10.1016/j.bas.2021.100523
Titel des übergeordneten Werkes (Englisch):Brain and Spine
Verlag:Elsevier
Dokumentart:Konferenzveröffentlichung (Präsentation, Abstract)
Sprache der Veröffentlichung:Englisch
Jahr der Veröffentlichung:2021
Datum der Freischaltung:19.07.2022
Jahrgang:1
Ausgabe / Heft:Suppl. 2
Aufsatznummer:100523
Fakultäten / Institute / Einrichtungen:Fakultät Maschinenbau
Begutachtungsstatus:peer-reviewed
Publikationsherkunft:Externe Publikationen
OpenAccess Publikationsweg:Gold Open Access- Erstveröffentlichung in einem/als Open-Access-Medium
Forschungsschwerpunkt:Digitalisierung
Lizenz (Deutsch):Creative Commons - CC BY - Namensnennung 4.0 International