Suchergebnisse

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

ObjectiveValidating objective, brain-based indices of consciousness in behaviorally unresponsive patients represents a challenge due to the impossibility of obtaining independent evidence through subjective reports. Here we address this problem by first validating a promising metric of consciousnessthe Perturbational Complexity Index (PCI)in a benchmark population who could confirm the presence or absence of consciousness through subjective reports, and then applying the same index to patients with disorders of consciousness (DOCs). MethodsThe benchmark population encompassed 150 healthy controls and communicative brain-injured subjects in various states of conscious wakefulness, disconnected consciousness, and unconsciousness. Receiver operating characteristic curve analysis was performed to define an optimal cutoff for discriminating between the conscious and unconscious conditions. This cutoff was then applied to a cohort of noncommunicative DOC patients (38 in a minimally conscious state [MCS] and 43 in a vegetative state [VS]). ResultsWe found an empirical cutoff that discriminated with 100% sensitivity and specificity between the conscious and the unconscious conditions in the benchmark population. This cutoff resulted in a sensitivity of 94.7% in detecting MCS and allowed the identification of a number of unresponsive VS patients (9 of 43) with high values of PCI, overlapping with the distribution of the benchmark conscious condition. InterpretationGiven its high sensitivity and specificity in the benchmark and MCS population, PCI offers a reliable, independently validated stratification of unresponsive patients that has important physiopathological and therapeutic implications. In particular, the high-PCI subgroup of VS patients may retain a capacity for consciousness that is not expressed in behavior. Ann Neurol 2016;80:718-729 ; Prin "Connage" (Italian Government) ; European Union (EU) ; James S. McDonnell Foundation Scholar Award ; Belgian National Fund for Scientific Research ; Templeton World Charity Foundation ; McDonnell Foundation ; Distinguished Chair in Consciousness Science at the University of Wisconsin ; Univ Milan, Dept Biomed & Clin Sci L Sacco, Via GB Grassi 74, I-20157 Milan, Italy ; Fdn Europea Ric Biomed, Milan, Italy ; Univ Milano Bicocca, Sch Med & Surg, Dept Hlth Sci, Monza, Italy ; Univ Fed Sao Paulo, Inst Sci & Technol, Sao Jose Dos Campos, Brazil ; Fdn Don Gnocchi Onlus, Ist Ricovero & Cura Carattere Sci, Milan, Italy ; Univ Wisconsin, Dept Psychiat, Madison, WI 53706 USA ; Univ Wisconsin, Dept Neurol, Madison, WI 53706 USA ; Univ Liege, Coma Sci Grp, GIGA, Liege, Belgium ; Univ Liege, Dept Neurol, Liege, Belgium ; Univ Hosp Liege, Liege, Belgium ; Grande Osped Metropolitano Niguarda Ca Granda, Neurocrit Care Unit, Azienda Sociosanit Terr, Dept Neurosci, Milan, Italy ; Institute of Science and Technology, Universidade Federal de São Paulo (UNIFESP), São José dos Campos, Brazil ; EU: 600806 ; EU: 686764 ; EU: 720270 ; Web of Science