Sleep disorders affect a large portion of the global population and are strong predictors of morbidity and all-cause mortality. Sleep staging segments a period of sleep into a sequence of phases providing the basis for most clinical decisions in sleep medicine. Manual sleep staging is difficult and time-consuming as experts must evaluate hours of polysomnography (PSG) recordings with electroencephalography (EEG) and electrooculography (EOG) data for each patient. Here, we present U-Sleep, a publicly available, ready-to-use deep-learning-based system for automated sleep staging (sleep.ai.ku.dk). U-Sleep is a fully convolutional neural network, which was trained and evaluated on PSG recordings from 15,660 participants of 16 clinical studies. It provides accurate segmentations across a wide range of patient cohorts and PSG protocols not considered when building the system. U-Sleep works for arbitrary combinations of typical EEG and EOG channels, and its special deep learning architecture can label sleep stages at shorter intervals than the typical 30 s periods used during training. We show that these labels can provide additional diagnostic information and lead to new ways of analyzing sleep. U-Sleep performs on par with state-of-the-art automatic sleep staging systems on multiple clinical datasets, even if the other systems were built specifically for the particular data. A comparison with consensus-scores from a previously unseen clinic shows that U-Sleep performs as accurately as the best of the human experts. U-Sleep can support the sleep staging workflow of medical experts, which decreases healthcare costs, and can provide highly accurate segmentations when human expertize is lacking.

Differentiation of the minimally conscious state (MCS) and the unresponsive wakefulness syndrome (UWS) is a persistent clinical challenge [1]. Based on positron emission tomography (PET) studies with [(18)F]-fluorodeoxyglucose (FDG) during sleep and anesthesia, the global cerebral metabolic rate of glucose has been proposed as an indicator of consciousness [2, 3]. Likewise, FDG-PET may contribute to the clinical diagnosis of disorders of consciousness (DOCs) [4, 5]. However, current methods are non-quantitative and have important drawbacks deriving from visually guided assessment of relative changes in brain metabolism [4]. We here used FDG-PET to measure resting state brain glucose metabolism in 131 DOC patients to identify objective quantitative metabolic indicators and predictors of awareness. Quantitation of images was performed by normalizing to extracerebral tissue. We show that 42% of normal cortical activity represents the minimal energetic requirement for the presence of conscious awareness. Overall, the cerebral metabolic rate accounted for the current level, or imminent return, of awareness in 94% of the patient population, suggesting a global energetic threshold effect, associated with the reemergence of consciousness after brain injury. Our data further revealed that regional variations relative to the global resting metabolic level reflect preservation of specific cognitive or sensory modules, such as vision and language comprehension. These findings provide a simple and objective metabolic marker of consciousness, which can readily be implemented clinically. The direct correlation between brain metabolism and behavior further suggests that DOCs can fundamentally be understood as pathological neuroenergetic conditions and provide a unifying physiological basis for these syndromes.

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Abstract. To achieve sparse parametrizations that allows intuitive analysis, we aim to represent deformation with a basis containing interpretable elements, and we wish to use elements that have the description capacity to represent the deformation compactly. To accomplish this, we introduce in this paper higher-order momentum distributions in the LDDMM registration framework. While the zeroth order moments previously used in LDDMM only describe local displacement, the first-order momenta that are proposed here represent a basis that allows local description of affine transformations and subsequent compact description of non-translational movement in a globally non-rigid deformation. The resulting representation contains directly interpretable information from both mathematical and modeling perspectives. We develop the mathematical construction of the registration framework with higher-order momenta, we show the implications for sparse image registration and deformation description, and we provide examples of how the parametrization enables registration with a very low number of parameters. The capacity and interpretability of the parametrization using higher-order momenta lead to natural modeling of articulated movement, and the method promises to be useful for quantifying ventricle expansion and progressing atrophy during Alzheimer's disease.Key words. LDDMM, diffeomorphic registration, RHKS, kernels, momentum, computational anatomy AMS subject classifications. 65D18, 65K10, 41A151. Introduction. In many image registration applications, we wish to describe the deformation using as few parameters as possible and with a representation that allows intuitive analysis: we search for parametrizations with basis elements that have the capacity to describe deformation sparsely while being directly interpretable. For instance, we wish to use such a representation to compactly describe the progressive atrophy that occurs in the human brain suffering from Alzheimer's disease and that can be detected by the expansion of the ventricles [19,13].Image registration algorithms often represent translational movement in a dense sampling of the image domain. Such approaches fail to satisfy the above goals: low dimensional deformations such as expansion of the ventricles will not be represented sparsely; the registration algorithm must optimize a large number of parameters; and the expansion cannot easily be interpreted from the registration result.In this paper, we use higher-order momentum distributions in the LDDMM registration framework to obtain a deformation parametrization that increases the capacity of sparse approaches with a basis consisting of interpretable elements. We show how the higher-order representation model locally affine transformations, and we use the compact deformation description to register points and images using very few parameters. We illustrate how the deformation coded by the higher-order momenta can be directly interpreted and that it represents information directly useful in applications: with low ...

BackgroundSalivary gland hypofunction and xerostomia are major complications following radiotherapy for head and neck cancer and may lead to debilitating oral disorders and impaired quality of life. Currently, only symptomatic treatment is available. However, mesenchymal stem cell (MSC) therapy has shown promising results in preclinical studies. Objectives are to assess safety and efficacy in a first-in-man trial on adipose-derived MSC therapy (ASC) for radiation-induced xerostomia.MethodsThis is a single-center, phase I/II, randomized, placebo-controlled, double-blinded clinical trial. A total of 30 patients are randomized in a 1:1 ratio to receive ultrasound-guided, administered ASC or placebo to the submandibular glands. The primary outcome is change in unstimulated whole salivary flow rate. The secondary outcomes are safety, efficacy, change in quality of life, qualitative and quantitative measurements of saliva, as well as submandibular gland size, vascularization, fibrosis, and secretory tissue evaluation based on contrast-induced magnetic resonance imaging (MRI) and core-needle samples. The assessments are performed at baseline (1 month prior to treatment) and 1 and 4 months following investigational intervention.DiscussionThe trial is the first attempt to evaluate the safety and efficacy of adipose-derived MSCs (ASCs) in patients with radiation-induced xerostomia. The results may provide evidence for the effectiveness of ASC in patients with salivary gland hypofunction and xerostomia and deliver valuable information for the design of subsequent trials.Trial registrationEudraCT, Identifier: 2014-004349-29. Registered on 1 April 2015.ClinicalTrials.gov, Identifier: NCT02513238. First received on 2 July 2015.The trial is prospectively registered.Electronic supplementary materialThe online version of this article (doi:10.1186/s13063-017-1856-0) contains supplementary material, which is available to authorized users.