References of "Bonhomme, Vincent"
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See detailFellowship en anesthésie
BONHOMME, Vincent ULiege

Conference (2020, January 18)

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See detailPerturbations in dynamical models of whole-brain activity dissociate between the level and stability of consciousness
Sanz Perl; Pallavicini, Carla; Pérez Ipiña, Ignacio et al

E-print/Working paper (2020)

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See detailBelgian standards for patient safety in anesthesia (Revision 2019)
BONHOMME, Vincent ULiege; Wouters, Patrick; BRICHANT, Jean-François ULiege et al

in Acta Anaesthesiologica Belgica (2020), 71(1), 5-14

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See detail‘Safety First’: A Plea for Realism
Himpe, Dirk; Carlier, Stefaan; Van de Velde, Marc et al

in Acta Anaesthesiologica Belgica (2020), 71(1), 1-3

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See detailGeneral Anesthesia: A Probe to Explore Consciousness
BONHOMME, Vincent ULiege; STAQUET, Cécile ULiege; Montupil, Javier ULiege et al

in Frontiers in Systems Neuroscience (2019)

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See detailBOLD power spectral density differentiates patients with pathological consciousness
Alcauter, Sarael; Carrière, Manon ULiege; Raimondo, Federico ULiege et al

Poster (2019, June 10)

Introduction: Functional connectivity has been successfully used to discriminate non-sedated patients with disorders of consciousness (Demertzi et al., 2015). However, on clinical demand, patients are ... [more ▼]

Introduction: Functional connectivity has been successfully used to discriminate non-sedated patients with disorders of consciousness (Demertzi et al., 2015). However, on clinical demand, patients are evaluated under sedation to restrict motion, which considerably limits the classification of patients based on functional connectivity. It has been previously shown that changes of the frequency properties of spontaneous BOLD signal are of cognitive relevance even in sleeping neonates (Alcauter et al., 2015). We therefore aimed at exploring the automatic discrimination of sedated patients in the clinical entities of minimally consciousness state (MCS) and unresponsive wakefulness syndrome (UWS), based on the frequency profile of the BOLD signal. Methods: Forty-four patients with MCS (n=26) or VS/UWS (n=18), based on the Coma Recovery Scale-Revised (CRS-R), were scanned on a 3T MRI scanner. Images of the whole brain were acquired with BOLD-sensitive sequences (300 volumes, TR=2s, TE=30ms, voxel size=3x3x3 mm3) and a T1 (TR=2.3s, TE=2.47ms, voxel size = 1x1x1.2 mm3). Sedative agents (propofol, sevoflurane, or a combination of both) were administered using the minimum necessary dose. Preprocessing of functional images included slice-time correction, realignment, segmentation, normalisation, and smoothing (6mm FWHM). Noise reduction included detection and regression of motion outliers (ART toolbox), anatomical component-based correction, and regression of motion parameters, no temrporal filtering was applied. The average power density between 0.01 and 0.1 Hz (classic frequency band for resting state analyses) was estimated and divided by the total power density, for each voxel. Supervised classification of patients in MCS or UWS was explored with Support Vector Machine classifiier using stratified 5-fold cross-validation. The clusters with significant differences between groups (p<0.005, uncorrected; cluster size > 10 voxels) in the training sets were selected as features. The 5-fold validation was repeated 20 times to estimate the variability of the classification accuracies and the frequency of each voxel being selected as a relevant feature. Results:The average classification accuracy was 79%±5 (SD), with average sensitivity 76%±10, and specificity 81%±9. The most frequently selected regions as features included the superior parietal lobule (Frequency: 100%; MNI x, y, z (mm): -26, -50, 64), putamen (97%; -30, -6, -8), occipital fusiform gyrus (92%; -34, -70, -20), occipital pole (65%; 22, -98, 16), angular gyrus (54%; -60, -58, 32). Conclusions: The power spectral density of the spontaneous BOLD signal under anesthesia allowed to classify individual patients with MCS and UWS with 79% accuracy. The most frequent selected features included association areas in the parietal and occipital lobes and the putamen. Further validation with independent cohorts is needed to generalize the current findings. Taken together, the use of power spectral density may represent an alternative to functional connectivity to classify patients with consciousness disorders under anesthesia, therefore capturing properties of conscious function beyond reportability. [less ▲]

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See detailChanges in Whole Brain Dynamics and Connectivity Patterns during Sevoflurane- and Propofol-induced Unconsciousness Identified by Functional Magnetic Resonance Imaging
Golkowski, Daniel; Larroque, Stephen Karl ULiege; VANHAUDENHUYSE, Audrey ULiege et al

in Anesthesiology (2019), 130(6), 898-911

Background: A key feature of the human brain is its capability to adapt flexibly to changing external stimuli. This capability can be eliminated by general anesthesia, a state characterized by ... [more ▼]

Background: A key feature of the human brain is its capability to adapt flexibly to changing external stimuli. This capability can be eliminated by general anesthesia, a state characterized by unresponsiveness, amnesia, and (most likely) unconsciousness. Previous studies demonstrated decreased connectivity within the thalamus, frontoparietal, and default mode networks during general anesthesia. We hypothesized that these alterations within specific brain networks lead to a change of communication between networks and their temporal dynamics. Methods: We conducted a pooled spatial independent component analysis of resting-state functional magnetic resonance imaging data obtained from 16 volunteers during propofol and 14 volunteers during sevoflurane general anesthesia that have been previously published. Similar to previous studies, mean z-scores of the resulting spatial maps served as a measure of the activity within a network. Additionally, correlations of associated time courses served as a measure of the connectivity between networks. To analyze the temporal dynamics of between-network connectivity, we computed the correlation matrices during sliding windows of 1 min and applied k-means clustering to the matrices during both general anesthesia and wakefulness. Results: Within-network activity was decreased in the default mode, attentional, and salience networks during general anesthesia (P < 0.001, range of median changes: –0.34, –0.13). Average between-network connectivity was reduced during general anesthesia (P < 0.001, median change: –0.031). Distinct between-network connectivity patterns for both wakefulness and general anesthesia were observed irrespective of the anesthetic agent (P < 0.001), and there were fewer transitions in between-network connectivity patterns during general anesthesia (P < 0.001, median number of transitions during wakefulness: 4 and during general anesthesia: 0). Conclusions: These results suggest that (1) higher-order brain regions play a crucial role in the generation of specific between-network connectivity patterns and their dynamics, and (2) the capability to interact with external stimuli is represented by complex between-network connectivity patterns. [less ▲]

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See detailTwo different methods to assess sympathetic tone during general anesthesia lead to different findings.
DEFRESNE, Aline ULiege; Harrison, Michael; Clement, Francois et al

in Journal of Clinical Monitoring and Computing (2019)

Noxious stimulation influences the autonomic nervous system activity. Sympathetic tone monitoring is currently used to assess the adequacy of the balance between nociception and anti-nociception during ... [more ▼]

Noxious stimulation influences the autonomic nervous system activity. Sympathetic tone monitoring is currently used to assess the adequacy of the balance between nociception and anti-nociception during general anesthesia. The Surgical Plethysmographic Index (SPI) and the EBMi software (Custos(c)) are commercial devices that use different algorithms to measure it. We aimed at determining whether those devices provide similar information during routine surgical procedures under general anesthesia. Data acquired during a previously published study in patients undergoing surgery under general anesthesia were retrospectively analyzed and passed through the EBMi software. The occurrence of EBMi alarms of increased sympathetic tone was compared to the occurrence of SPI values >/= 60, a commonly recommended intraoperative SPI threshold. Trends in classical parameters of sympathetic tone during the 5 min preceding a SPI >/= 60, namely blood pressure, heart rate, and plethysmographic pulse amplitude were assessed. SPI >/= 60 episodes (n = 307) were more frequent than EBMi alerts (n = 240). Approximately 70% of EBMi alerts occurred during periods where the SPI was below 60. Among all episodes of SPI >/= 60, absence of any EBMi alerts was much more frequent than the inverse. A majority, but not all SPI >/= 60 episodes were consistently preceded by an increase in heart rate and/or a decrease in pulse amplitude. Blood pressure did not significantly change before SPI >/= 60. Longer SPI >/= 60 episodes were associated with lower anti-nociception anesthetic regimen. Different methods of sympathetic tone assessment during general anesthesia provide conflicting information. Prospective studies should be undertaken to clarify the clinical indications of both techniques. [less ▲]

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See detailVon Willebrand disease
BONHOMME, Vincent ULiege; DEFRESNE, Aline ULiege; MAQUOI, Isabelle ULiege et al

in Anästhesiologie und Intensivmedizin (2019), 33(3), 463-9

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See detailThe Raw and Processed Electroencephalogram as a Monitoring and Diagnostic Tool.
Montupil, Javier ULiege; DEFRESNE, Aline ULiege; BONHOMME, Vincent ULiege

in Journal of cardiothoracic and vascular anesthesia (2019), 33 Suppl 1

In this narrative review, different aspects of electroencephalogram (EEG) monitoring during anesthesia are approached, with a special focus on cardiothoracic and vascular anesthesia, from the basic ... [more ▼]

In this narrative review, different aspects of electroencephalogram (EEG) monitoring during anesthesia are approached, with a special focus on cardiothoracic and vascular anesthesia, from the basic principles to more sophisticated diagnosis and monitoring utilities. The available processed EEG-derived indexes of the depth of the hypnotic component of anesthesia have well-defined limitations and usefulness. They prevent intraoperative awareness with recall in specific patient populations and under a specific anesthetic regimen. They prevent intraoperative overdose, and they shorten recovery times. They also help to avoid lengthy intraoperative periods of suppression activity, which are known to be deleterious in terms of outcome. Other than those available indexes, the huge amount of information contained in the EEG currently is being used only partially. Several other areas of interest regarding EEG during anesthesia have emerged in terms of anesthesia mechanisms elucidation, nociception monitoring, and diagnosis or prevention of brain insults. [less ▲]

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See detailTargeting the autonomic nervous system for nociception monitoring
BONHOMME, Vincent ULiege

Conference (2018, November 09)

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See detailVentilatory management in neuroanesthesia
BONHOMME, Vincent ULiege

Conference (2018, November 08)

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See detailBrain effects of anesthesia : is ketamine back sheep ?
BONHOMME, Vincent ULiege

Conference (2018, November 08)

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See detailBrain effects of ketamine. Implications on clinical practice
BONHOMME, Vincent ULiege

Conference (2018, August 16)

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See detailBrain effects of anesthetic agents : still a long way to a complete
BONHOMME, Vincent ULiege

Conference (2018, April 29)

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See detailPostoperative delirium and cognitive decline
BONHOMME, Vincent ULiege

Conference (2018, March 17)

Detailed reference viewed: 9 (5 ULiège)