Wagner, FrancaFrancaWagnerHänggi, MatthiasMatthiasHänggi0000-0001-5845-031XWeck, AnjaAnjaWeckPastore-Wapp, ManuelaManuelaPastore-Wapp0000-0003-2603-8915Wiest, Roland Gerhard RudiRoland Gerhard RudiWiestKiefer, ClausClausKiefer2024-09-022024-09-022020-07-16https://boris-portal.unibe.ch/handle/20.500.12422/36841Predicting 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.en600 - Technology::610 - Medicine & healtharticle10.7892/boris.1458853267821210.1038/s41598-020-68683-y