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Preoperative Assessment of Language Dominance through Combined Resting-State and Task-Based Functional Magnetic Resonance Imaging

  • 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. 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. Thereby, 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. 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:Christian OttORCiD, Katharina Rosengarth, Christian Doenitz, Julius HoehneORCiD, Christina Wendl, Frank Dodoo-Schittko, Elmar Wolfgang LangORCiD, Nils Ole SchmidtORCiDGND, Markus GoldhackerORCiD
DOI:https://doi.org/10.3390/jpm11121342
Titel des übergeordneten Werkes (Englisch):Journal of personalized medicine
Verlag:MDPI
Dokumentart:Artikel aus einer Zeitschrift/Zeitung
Sprache der Veröffentlichung:Englisch
Jahr der Veröffentlichung:2021
Datum der Freischaltung:19.07.2022
Freies Schlagwort / Tag:brain mapping; data-driven analysis; language assessment; resting-state fMRI; task-based fMRI
Jahrgang:11
Ausgabe / Heft:12
Fakultäten / Institute / Einrichtungen:Fakultät Maschinenbau
Begutachtungsstatus:peer-reviewed
Publikationsherkunft:Externe Publikationen
OpenAccess Publikationsweg:Gold Open Access- Erstveröffentlichung in einem/als Open-Access-Medium
Forschungsschwerpunkt:Lebenswissenschaften und Ethik
Lizenz (Deutsch):Keine Lizenz - Es gilt das deutsche Urheberrecht: § 53 UrhG