<?xml version="1.0" encoding="utf-8"?>
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  <doc>
    <id>4572</id>
    <completedYear/>
    <publishedYear>2021</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue>Suppl. 2</issue>
    <volume>1</volume>
    <type>conferencepresentation</type>
    <publisherName>Elsevier</publisherName>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Preoperative assessment of language dominance through combined resting-state and task-based functional magnetic resonance imaging</title>
    <abstract language="eng">Background: &#13;
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.&#13;
&#13;
Methods: &#13;
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.&#13;
&#13;
Results: &#13;
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.&#13;
&#13;
Conclusion: &#13;
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.</abstract>
    <parentTitle language="eng">Brain and Spine</parentTitle>
    <identifier type="doi">10.1016/j.bas.2021.100523</identifier>
    <enrichment key="opus.import.date">2022-06-30T19:37:23+00:00</enrichment>
    <enrichment key="opus.source">sword</enrichment>
    <enrichment key="opus.import.user">importuser</enrichment>
    <enrichment key="BegutachtungStatus">peer-reviewed</enrichment>
    <licence>Creative Commons - CC BY - Namensnennung 4.0 International</licence>
    <author>Christian Ott</author>
    <author>K. Rosengarth</author>
    <author>Christian Doenitz</author>
    <author>Julius Hoehne</author>
    <author>C. Wendl</author>
    <author>Frank Dodoo-Schittko</author>
    <author>Elmar Wolfgang Lang</author>
    <author>Nils Ole Schmidt</author>
    <author>Markus Goldhacker</author>
    <collection role="institutes" number="FAKMB">Fakultät Maschinenbau</collection>
    <collection role="othpublikationsherkunft" number="">Externe Publikationen</collection>
    <collection role="oaweg" number="">Gold Open Access- Erstveröffentlichung in einem/als Open-Access-Medium</collection>
    <collection role="othforschungsschwerpunkt" number="16311">Digitalisierung</collection>
    <collection role="persons" number="goldhackerbics">Goldhacker, Markus (Prof. Dr.) - Brain-Computer Interface and Cognitive Systems Lab</collection>
  </doc>
</export-example>
