Assessment of White Matter Injury and Outcome in Severe Brain Trauma. A Prospective Multicenter Cohort

in Anesthesiology (2012), 117(6), 1300-1310

Diffusion Tensor Imaging to Predict Long-term Outcome after Cardiac Arrest: A Bicentric Pilot Study.

in Anesthesiology (2012), 117(6), 1311-1321

BACKGROUND:: Prognostication in comatose survivors of cardiac arrest is a major clinical challenge. The authors' objective was to determine whether an assessment with diffusion tensor imaging, a brain ... [more ▼]

BACKGROUND:: Prognostication in comatose survivors of cardiac arrest is a major clinical challenge. The authors' objective was to determine whether an assessment with diffusion tensor imaging, a brain magnetic resonance imaging sequence, increases the accuracy of 1 yr functional outcome prediction in cardiac arrest survivors. METHODS:: Prospective, observational study in two intensive care units. Fifty-seven comatose survivors of cardiac arrest underwent brain magnetic resonance imaging. Fractional anisotropy (FA), a diffusion tensor imaging value, was measured in predefined white matter regions, and apparent diffusion coefficient was assessed in predefined grey matter regions. Prediction of unfavorable outcome at 1 yr was compared using four prognostic models: FA global, FA selected, apparent diffusion coefficient, and clinical classifiers. RESULTS:: Of the 57 patients included in the study, 49 had an unfavorable outcome at 12 months. Areas under the receiver operating characteristic curve (95% CI) to predict unfavorable outcome for the FA global, FA selected, clinical, and apparent diffusion coefficient models were 0.92 (0.82-0.98), 0.96 (0.87-0.99), 0.78 (0.65-0.88), and 0.86 (0.74-0.94), respectively. The FA selected model had the best overall accuracy for predicting outcome, with a score above 0.44 having 94% (95% CI, 83-99%) sensitivity and 100% (95% CI, 63-100%) specificity for the prediction of unfavorable outcome. CONCLUSION:: Quantitative diffusion tensor imaging indicates that white matter damage is widespread after cardiac arrest. A prognostic model based on FA values in selected white matter tracts seems to predict accurately 1 yr functional outcome. These preliminary results need to be confirmed in a larger population. [less ▲]

Brain functional integration decreases during propofol-induced loss of consciousness.

in NeuroImage (2011), 57(1), 198-205

Consciousness has been related to the amount of integrated information that the brain is able to generate. In this paper, we tested the hypothesis that the loss of consciousness caused by propofol ... [more ▼]

Consciousness has been related to the amount of integrated information that the brain is able to generate. In this paper, we tested the hypothesis that the loss of consciousness caused by propofol anesthesia is associated with a significant reduction in the capacity of the brain to integrate information. To assess the functional structure of the whole brain, functional integration and partial correlations were computed from fMRI data acquired from 18 healthy volunteers during resting wakefulness and propofol-induced deep sedation. Total integration was significantly reduced from wakefulness to deep sedation in the whole brain as well as within and between its constituent networks (or systems). Integration was systematically reduced within each system (i.e., brain or networks), as well as between networks. However, the ventral attentional network maintained interactions with most other networks during deep sedation. Partial correlations further suggested that functional connectivity was particularly affected between parietal areas and frontal or temporal regions during deep sedation. Our findings suggest that the breakdown in brain integration is the neural correlate of the loss of consciousness induced by propofol. They stress the important role played by parietal and frontal areas in the generation of consciousness. [less ▲]

Neural correlates mediating the consolidation of visuomotor adapted actions : A study on the role of night sleep versus the passage of daytime

in NeuroImage (2009), 45(Suppl. 1),