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Marie-Aurélie Bruno

5 papers in the library · 2,275 citations · publishing 2009-2016

Papers

A Theoretically Based Index of Consciousness Independent of Sensory Processing and Behavior

Science Translational Medicine August 14, 2013 Adenauer G. Casali, Olivia Gosseries, Mario Rosanova et al. 1,299 citations

A new index called the perturbational complexity index (PCI) can objectively measure a person's level of consciousness without requiring them to respond or interact. PCI works by using transcranial magnetic stimulation to briefly perturb the cortex and then measuring the algorithmic complexity of the resulting brain activity patterns. The index was tested on healthy individuals during wakefulness, dreaming, nonrapid eye movement sleep, and under sedation with midazolam, xenon, and propofol, as well as on patients who had emerged from coma. PCI reliably distinguished conscious from unconscious states in single individuals across all conditions, including vegetative state, minimally conscious state, and locked-in syndrome.

Breakdown of within- and between-network Resting State Functional Magnetic Resonance Imaging Connectivity during Propofol-induced Loss of Consciousness

Anesthesiology September 30, 2010 Pierre Boveroux, Audrey Vanhaudenhuyse, Marie-Aurélie Bruno et al. 645 citations

Propofol-induced unconsciousness is linked to decreased connectivity within frontoparietal networks (the default-mode and executive-control networks) and between the thalamus and these networks, with a negative correlation between thalamic and cortical activity emerging during unconsciousness. In contrast, connectivity in low-level sensory cortices (auditory and visual networks) is preserved, including their thalamocortical connections. Loss of consciousness is associated with a breakdown of cross-modal interactions between visual and auditory networks. These findings suggest that unconsciousness results from disrupted communication between sensory and higher-order frontoparietal cortices, preventing conscious perception.

Resting-state Network-specific Breakdown of Functional Connectivity during Ketamine Alteration of Consciousness in Volunteers

Anesthesiology August 9, 2016 Vincent Bonhomme, Audrey Vanhaudenhuyse, Athena Demertzi et al. 206 citations

Ketamine alters consciousness by disrupting connectivity within and between specific resting-state brain networks, particularly the default mode network (DMn) and salience network (SALn), while leaving sensory and motor networks largely intact. In healthy volunteers given stepwise ketamine infusions until they lost responsiveness, DMn connectivity between the medial prefrontal cortex and other network regions decreased (from 0.20 to 0.07), and the normal anticorrelated activity between the DMn and sensory regions reversed (e.g., right sensory cortex shifted from -0.07 to 0.04). SALn connectivity was also suppressed but nonuniformly. These specific changes, including preserved sensory network connectivity, are shared with propofol-induced unconsciousness.

Dualism Persists in the Science of Mind

Annals of the New York Academy of Sciences March 1, 2009 Athena Demertzi, Charlene Liew, Didier Ledoux et al. 125 citations

Attitudes about whether mind and brain are separate or the same thing vary by age, gender, and religious belief. Two surveys—one of university students in Edinburgh (250 people) and another of health-care workers and the public in Liège (1,858 people)—found that dualistic views (seeing mind and brain as distinct) were common. In the Liège survey, younger participants, women, and those with religious beliefs were more likely to endorse separation of mind and brain, survival of a spiritual part after death, and the existence of a soul distinct from the body. Religious belief was the strongest predictor of dualism. Over a third of medical and paramedical professionals also regarded mind and brain as separate, even though most health-care workers rejected that distinction.

"Relevance vector machine" consciousness classifier applied to cerebral metabolism of vegetative and locked-in patients.

Neuroimage May 15, 2011 Christophe L Phillips, Marie-Aurélie Bruno, Pierre Maquet et al.

A machine-learning classifier trained on fluorodeoxyglucose PET brain scans from 37 healthy controls and 13 patients in a vegetative state achieved 100% accuracy in distinguishing between conscious awareness and the vegetative state during cross-validation. When tested on 8 patients with locked-in syndrome, all scans were classified as "conscious" with a mean probability of .95. The authors conclude that relevance vector machine classification of cerebral metabolic images could become a useful tool for automated PET-based diagnosis of altered states of consciousness in coma survivors.