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Outcome prediction with resting-state functional connectivity after cardiac arrest.

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dc.contributor.authorWagner, Franca
dc.contributor.authorHänggi, Matthias
dc.contributor.authorWeck, Anja
dc.contributor.authorPastore-Wapp, Manuela
dc.contributor.authorWiest, Roland Gerhard Rudi
dc.contributor.authorKiefer, Claus
dc.date.accessioned2024-09-02T16:08:55Z
dc.date.available2024-09-02T16:08:55Z
dc.date.issued2020-07-16
dc.description.abstractPredicting outcome in comatose patients after successful cardiopulmonary resuscitation is challenging. Our primary aim was to assess the potential contribution of resting-state-functional magnetic resonance imaging (RS-fMRI) in predicting neurological outcome. RS-fMRI was used to evaluate functional and effective connectivity within the default mode network in a cohort of 90 comatose patients and their impact on functional neurological outcome after 3 months. The RS-fMRI processing protocol comprises the evaluation of functional and effective connectivity within the default mode network. Seed-to-voxel and ROI-to-ROI feature analysis was performed as starting point for a supervised machine-learning approach. Classification of the Cerebral Performance Category (CPC) 1-3 (good to acceptable outcome) versus CPC 4-5 (adverse outcome) achieved a positive predictive value of 91.7%, sensitivity of 90.2%, and accuracy of 87.8%. A direct link to the level of consciousness and outcome after 3 months was identified for measures of segregation in the precuneus, in medial and right frontal regions. Thalamic connectivity appeared significantly reduced in patients without conscious response. Decreased within-network connectivity in the default mode network and within cortico-thalamic circuits correlated with clinical outcome after 3 months. Our results indicate a potential role of these markers for decision-making in comatose patients early after cardiac arrest.
dc.description.noteFranca Wagner and Matthias Hänggi contributed equally.
dc.description.sponsorshipUniversitätsinstitut für Diagnostische und Interventionelle Neuroradiologie
dc.description.sponsorshipUniversitätsklinik für Intensivmedizin
dc.identifier.doi10.7892/boris.145885
dc.identifier.pmid32678212
dc.identifier.publisherDOI10.1038/s41598-020-68683-y
dc.identifier.urihttps://boris-portal.unibe.ch/handle/20.500.12422/36841
dc.language.isoen
dc.publisherSpringer Nature
dc.relation.ispartofScientific reports
dc.relation.issn2045-2322
dc.relation.organizationDCD5A442BADDE17DE0405C82790C4DE2
dc.relation.organizationDCD5A442C011E17DE0405C82790C4DE2
dc.subject.ddc600 - Technology::610 - Medicine & health
dc.titleOutcome prediction with resting-state functional connectivity after cardiac arrest.
dc.typearticle
dspace.entity.typePublication
oaire.citation.issue1
oaire.citation.startPage11695
oaire.citation.volume10
oairecerif.author.affiliationUniversitätsinstitut für Diagnostische und Interventionelle Neuroradiologie
oairecerif.author.affiliationUniversitätsklinik für Intensivmedizin
oairecerif.author.affiliationUniversitätsklinik für Intensivmedizin
oairecerif.author.affiliationUniversitätsinstitut für Diagnostische und Interventionelle Neuroradiologie
oairecerif.author.affiliationUniversitätsinstitut für Diagnostische und Interventionelle Neuroradiologie
oairecerif.author.affiliationUniversitätsinstitut für Diagnostische und Interventionelle Neuroradiologie
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unibe.date.licenseChanged2020-08-12 07:11:56
unibe.description.ispublishedpub
unibe.eprints.legacyId145885
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unibe.subtype.articlejournal

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