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Microdialysis enables the chemistry of the extracellular interstitial space to be monitored. Use of this technique in patients with acute brain injury has increased our understanding of the pathophysiology of several acute neurological disorders. In 2004, a consensus document on the clinical application of cerebral microdialysis was published. Since then, there have been significant advances in the clinical use of microdialysis in neurocritical care. The objective of this review is to report on the International Microdialysis Forum held in Cambridge, UK, in April 2014 and to produce a revised and updated consensus statement about its clinical use including technique, data interpretation, relationship with outcome, role in guiding therapy in neurocritical care and research applications., We gratefully acknowledge financial support for participants as follows: P.J.H.-National Institute for Health Research (NIHR) Professorship and the NIHR Biomedical Research Centre, Cambridge; I.J. Medical Research Council (G1002277 ID 98489); A. H.-Medical Research Council, Royal College of Surgeons of England; K.L.H.C.-NIHR Biomedical Research Centre, Cambridge (Neuroscience Theme; Brain Injury and Repair Theme); M.G.B.-Wellcome Trust Dept Health Healthcare Innovation Challenge Fund (HICF-0510-080); L. H.-The Swedish Research Council, VINNOVA and Uppsala Berzelii Technology Centre for Neurodiagnostics; S. M.-Fondazione IRCCS Ca Granda Ospedale Maggiore Policlinico; D.K.M.-NIHR Senior Investigator Award to D.K.M., NIHR Cambridge Biomedical Research Centre (Neuroscience Theme), FP7 Program of the European Union; M. O.-Swiss National Science Foundation and the Novartis Foundation for Biomedical Research; J.S.-Fondo de Investigacion Sanitaria (Instituto de Salud Carlos III) (PI11/00700) co-financed by the European Regional Development; M.S.-NIHR University College London Hospitals Biomedical Research Centre; N. S.-Fondazione IRCCS Ca Granda Ospedale Maggiore Policlinico.

In this paper, we consider modeling the nonparametric component in partially linear models (PLMs) using linear sparse representations, e.g., wavelet expansions. Two types of representations are investigated, namely, orthogonal bases (complete) and redundant overcomplete expansions. For bases, we introduce a regularized estimator of the nonparametric part. The important contribution here is that the nonparametric part can be parsimoniously estimated by choosing an appropriate penalty function for which the hard and soft thresholding estimators are special cases. This allows us to represent in an effective manner a broad class of signals, including stationary and/or nonstationary signals and avoids excessive bias in estimating the parametric component. We also give a fast estimation algorithm. The method is then generalized to handle the case of overcomplete representations. A large-scale simulation study is conducted to illustrate the finite sample properties of the estimator. The estimator is finally applied to real neurophysiological functional magnetic resonance imaging (MRI) data sets that are suspected to contain both smooth and transient drift features.

National audience; Les processus à longue mémoire peuvent sérieusement compromettre l'estimation des paramètres des modèles linéaires ainsi que les variances associées. Les modèles classiques ARMA peuvent fournir une approche adéquate au problème de courte dépendance pour les systèmes linéaires invariants dans le temps. Cependant, ils s'avèrent inefficaces pour des bruits invariants d'échelle. Ici, nous présentons une nouvelle approche de moindres-carrés pondérés dans le domaine des ondelettes (MCPO). Celle-ci fournit un estimateur du maximum de vraissemblance (MV) approché des paramètres du signal et du bruit. Les propriétés statistiques de cet estimateur, y compris une approximation des limites de Cramèr-Rao, sont établies d'un point de vue théorique puis comparées à ses performances empiriques pour diverses situations de données simulées. En comparaison aux moindres carrés ordinaires (MCO) ou encore aux estimateurs type ARMA, on montre que le MCPO est plus efficace et permet un excellent contrôle des risques de 1ère et 2nde espèce. La méthode est ensuite illustrée sur des séquences neurophysiologiques d'IRMf.