Lateral frontoparietal effective connectivity differentiates and predicts state of consciousness in a cohort of patients with traumatic disorders of consciousness

被引:0
|
作者
Ihalainen, Riku [1 ,2 ]
Annen, Jitka [3 ,4 ,5 ]
Gosseries, Olivia [3 ,4 ]
Cardone, Paolo [3 ,4 ]
Panda, Rajanikant [3 ,4 ]
Martial, Charlotte [3 ,4 ]
Thibaut, Aurore [3 ,4 ]
Laureys, Steven [3 ,6 ,7 ]
Chennu, Srivas [2 ]
机构
[1] Beth Israel Deaconess Med Ctr, Dept Anesthesia Crit Care & Pain Med, Boston, MA USA
[2] Univ Kent, Sch Comp, Canterbury, England
[3] Univ Hosp Liege, GIGA Consciousness Res Unit, Coma Sci Grp, Liege, Belgium
[4] Univ Hosp Liege, Ctr Cerveau 2, Liege, Belgium
[5] Univ Ghent, Dept Data Anal, Ghent, Belgium
[6] CERVO Brain Res Ctr, Quebec City, PQ, Canada
[7] Hangzhou Normal Univ, Consciousness Sci Inst, Hangzhou, Zhejiang, Peoples R China
来源
PLOS ONE | 2024年 / 19卷 / 07期
关键词
DEFAULT-MODE NETWORK; PROPOFOL-INDUCED LOSS; VEGETATIVE STATE; FUNCTIONAL CONNECTIVITY; BRAIN ACTIVITY; RESTING STATE; AWARENESS; EEG; RESPONSES; COMA;
D O I
10.1371/journal.pone.0298110
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
Neuroimaging studies have suggested an important role for the default mode network (DMN) in disorders of consciousness (DoC). However, the extent to which DMN connectivity can discriminate DoC states-unresponsive wakefulness syndrome (UWS) and minimally conscious state (MCS)-is less evident. Particularly, it is unclear whether effective DMN connectivity, as measured indirectly with dynamic causal modelling (DCM) of resting EEG can disentangle UWS from healthy controls and from patients considered conscious (MCS+). Crucially, this extends to UWS patients with potentially "covert" awareness (minimally conscious star, MCS*) indexed by voluntary brain activity in conjunction with partially preserved frontoparietal metabolism as measured with positron emission tomography (PET+ diagnosis; in contrast to PET- diagnosis with complete frontoparietal hypometabolism). Here, we address this gap by using DCM of EEG data acquired from patients with traumatic brain injury in 11 UWS (6 PET- and 5 PET+) and in 12 MCS+ (11 PET+ and 1 PET-), alongside with 11 healthy controls. We provide evidence for a key difference in left frontoparietal connectivity when contrasting UWS PET- with MCS+ patients and healthy controls. Next, in a leave-one-subject-out cross-validation, we tested the classification performance of the DCM models demonstrating that connectivity between medial prefrontal and left parietal sources reliably discriminates UWS PET- from MCS+ patients and controls. Finally, we illustrate that these models generalize to an unseen dataset: models trained to discriminate UWS PET- from MCS+ and controls, classify MCS* patients as conscious subjects with high posterior probability (pp > .92). These results identify specific alterations in the DMN after severe brain injury and highlight the clinical utility of EEG-based effective connectivity for identifying patients with potential covert awareness.
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页数:25
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