Suchergebnisse

Detection and interpretation of signs of "covert command following" in patients with disorders of consciousness (DOC) remains a challenge for clinicians. In this study, we used a tactile P3-based BCI in 12 patients without behavioral command following, attempting to establish "covert command following." These results were then confronted to cerebral metabolism preservation as measured with glucose PET (FDG-PET). One patient showed "covert command following" (i.e., above-threshold BCI performance) during the active tactile paradigm. This patient also showed a higher cerebral glucose metabolism within the language network (presumably required for command following) when compared with the other patients without "covert command-following" but having a cerebral glucose metabolism indicative of minimally conscious state. Our results suggest that the P3-based BCI might probe "covert command following" in patients without behavioral response to command and therefore could be a valuable addition in the clinical assessment of patients with DOC. ; The study was supported by the University and University Hospital of Liège, the French Speaking Community Concerted Research Action (ARC 12-17/01), the Belgian National Funds for Scientific Research (FRS-FNRS), Human Brain Project (EU-H2020-fetflagship-hbp-sga1-ga720270), Luminous project (EU-H2020-fetopen-ga686764), the James McDonnell Foundation, Mind Science Foundation, IAP research network P7/06 of the Belgian Government (Belgian Science Policy), the European Commission, the Public Utility Foundation Université Européenne du Travail, Fondazione Europea di Ricerca Biomedica, the Bial Foundation, Belgian National Plan Cancer (139), ECH2020 project ComaWare. CC is a Marie Sklodowska-Curie fellow (H2020-MSCA-IF-2016-ADOC-752686).

Low-level states of consciousness are characterized by disruptions of brain activity that sustain arousal and awareness. Yet, how structural, dynamical, local and network brain properties interplay in the different levels of consciousness is unknown. Here, we study fMRI brain dynamics from patients that suffered brain injuries leading to a disorder of consciousness and from healthy subjects undergoing propofol-induced sedation. We show that pathological and pharmacological low-level states of consciousness display less recurrent, less connected and more segregated synchronization patterns than conscious state. We use whole-brain models built upon healthy and injured structural connectivity to interpret these dynamical effects. We found that low-level states of consciousness were associated with reduced network interactions, together with more homogeneous and more structurally constrained local dynamics. Notably, these changes lead the structural hub regions to lose their stability during low-level states of consciousness, thus attenuating the differences between hubs and non-hubs brain dynamics. ; A.L.G. and G.D. were supported by Swiss National Science Foundation Sinergia grant no. 170873. A.P.A. and G.D. received funding from the FLAG-ERA JTC (PCI2018- 092891). G.D., S.L. and G.Z.L. received funding from the European Union's Horizon 2020 Framework Programme for Research and Innovation under the Specific Grant Agreement No. 785907 (Human Brain Project SGA2) and No. 945539 (Human Brain Project SGA3). G.D. acknowledges the Spanish Ministry Project PSI2016-75688-P (AEI/ FEDER), the Catalan Research Group Support 2017 SGR 1545, and AWAKENING (PID2019-105772GB-I00, AEI FEDER EU) funded by the Spanish Ministry of Science, Innovation and Universities (MCIU), State Research Agency (AEI) and European Regional Development Funds (FEDER). The study was further supported by the University and University Hospital of Liège, the Belgian National Funds for Scientific Research (FRS-FNRS), the European Space Agency (ESA) and the ...

Funding Information: This work was supported by the Institute for Information and Communications Technology Planning and Evaluation (IITP) funded by the Korean government (Nos. 2017-0-00451; 2017-0-01779; 2019-0-00079; 2019-0-01371; and 2021-0-02068), the University and University Hospital of Liège, Belgian National Fund for Scientific Research (F.R.S-FNRS), the Italian Ministry of Health, GR-2016–02361494 (to S.C.), the Canadian Institute for Advanced Research (CIFAR) (to M.M.), European Union's Horizon 2020 Framework Program for Research and Innovation under the Specific Grant Agreement (No. 945539, Human Brain Project SGA3) (to M.M. and S.L.), BIAL Foundation, AstraZeneca Foundation, Fund Generate, King Baudouin Foundation, DOCMA project [EU-H2020-MSCA-RISE-–778234], James McDonnell Foundation, Mind Science Foundation, Fondazione Europea di Ricerca Biomedica, National Institutes of Health (No. R01MH064498), Academy of Finland (Nos. 265680 and 294625), Tiny Blue Dot Foundation (to M.M.), and grant EraPerMed JTC 2019 "PerBrain" (to M.R.). L.R.D.S. and R.P. are PhD fellows, O.G. and A. T. are research associates, and S.L. is research director at the F.R.S.–FNRS. We thank S. Lapuschkin for sharing the code; further, we thank all the healthy participants, patients, and their families who participated in this study. Publisher Copyright: © 2022, The Author(s). ; Consciousness can be defined by two components: arousal (wakefulness) and awareness (subjective experience). However, neurophysiological consciousness metrics able to disentangle between these components have not been reported. Here, we propose an explainable consciousness indicator (ECI) using deep learning to disentangle the components of consciousness. We employ electroencephalographic (EEG) responses to transcranial magnetic stimulation under various conditions, including sleep (n = 6), general anesthesia (n = 16), and severe brain injury (n = 34). We also test our framework using resting-state EEG under general anesthesia (n = 15) and severe brain injury (n = 34). ECI simultaneously quantifies arousal and awareness under physiological, pharmacological, and pathological conditions. Particularly, ketamine-induced anesthesia and rapid eye movement sleep with low arousal and high awareness are clearly distinguished from other states. In addition, parietal regions appear most relevant for quantifying arousal and awareness. This indicator provides insights into the neural correlates of altered states of consciousness. ; Peer reviewed