A multi-domain prognostic model of disorder of consciousness using resting-state fMRI and laboratory parameters
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ORIGINAL RESEARCH
A multi-domain prognostic model of disorder of consciousness using resting-state fMRI and laboratory parameters Yamei Yu 1 & Fanxia Meng 1 & Li Zhang 2 & Xiaoyan Liu 1 & Yuehao Wu 1 & Sicong Chen 3 & Xufei Tan 1 & Xiaoxia Li 1 & Sheng Kuang 4 & Yu Sun 5,6 & Benyan Luo 1 Accepted: 31 August 2020 # Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract Objectives Although laboratory parameters have long been recognized as indicators of outcome of traumatic brain injury (TBI), it remains a challenge to predict the recovery of disorder of consciousness (DOC) in severe brain injury including TBI. Recent advances have shown an association between alterations in brain connectivity and recovery from DOC. In the present study, we developed a prognostic model of DOC recovery via a combination of laboratory parameters and resting-state functional magnetic resonance imaging (fMRI). Methods Fifty-one patients with DOC (age = 52.3 ± 15.2 y, male/female = 31/20) were recruited from Hangzhou Hospital of Zhejiang CAPR and were sub-grouped into conscious (n = 34) and unconscious (n = 17) groups based upon their Glasgow Outcome Scale-Extended (GOS-E) scores at 12-month follow-ups after injury. Resting-state functional connectivity, network nodal measures (centrality), and laboratory parameters were obtained from each patient and served as features for support vector machine (SVM) classifications. Results We found that functional connectivity was the most accurate single-domain model (ACC: 70.1% ± 4.5%, P = 0.038, 1000 permutations), followed by degree centrality, betweenness centrality, and laboratory parameters. The stacked multi-domain prognostic model (ACC: 73.4% ± 3.1%, P = 0.005, 1000 permutations) combining all singledomain models yielded a significantly higher accuracy compared to that of the best-performing single-domain model (P = 0.002). Conclusion Our results suggest that laboratory parameters only contribute to the outcome prediction of DOC patients, whereas combining information from neuroimaging and clinical parameters may represent a strategy to achieve a more accurate prognostic model, which may further provide better guidance for clinical management of DOC patients. Keywords Disorder of consciousness . Resting-state functional connectivity . Laboratory parameters . Machine learning
Electronic supplementary material The online version of this article (https://doi.org/10.1007/s11682-020-00390-8) contains supplementary material, which is available to authorized users. * Yu Sun [email protected] * Benyan Luo [email protected] 1
Department of Neurology and Brain Medical Centre, The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou 310000, China
2
Department of Rehabilitation Medicine, Zhejiang Provincial People’s Hospital, People’s Hospital of Hangzhou Medical College, Hangzhou, China
3
Department of Neurology, Huzhou central hospital, Huzhou, Zhejiang, China
4
Data Science Group, The Ant Financial (Hang Zhou) Network Technology Co. Ltd, Hangzho
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