Cerebrospinal fluid phospho-tau T217 outperforms T181 as a biomarker for the differential diagnosis of Alzheimer\u27s disease and PET amyloid-positive patient identification

BACKGROUND: Cerebrospinal fluid biomarker profiles characterized by decreased amyloid-beta peptide levels and increased total and phosphorylated tau levels at threonine 181 (pT181) are currently used to discriminate between Alzheimer\u27s disease and other neurodegenerative diseases. However, these changes are not entirely specific to Alzheimer\u27s disease, and it is noteworthy that other phosphorylated isoforms of tau, possibly more specific for the disease process, have been described in the brain parenchyma of patients. The precise detection of these isoforms in biological fluids remains however a challenge. METHODS: In the present study, we used the latest quantitative mass spectrometry approach, which achieves a sensitive detection in cerebrospinal fluid biomarker of two phosphorylated tau isoforms, pT181 and pT217, and first analyzed a cohort of probable Alzheimer\u27s disease patients and patients with other neurological disorders, including tauopathies, and a set of cognitively normal controls. We then checked the validity of our results on a second cohort comprising cognitively normal individuals and patients with mild cognitive impairments and AD stratified in terms of their amyloid status based on PiB-PET imaging methods. RESULTS: In the first cohort, pT217 but not pT181 differentiated between Alzheimer\u27s disease patients and those with other neurodegenerative diseases and control subjects much more specificity and sensitivity than pT181. T217 phosphorylation was increased by 6.0-fold in patients with Alzheimer\u27s disease whereas T181 phosphorylation was only increased by 1.3-fold, when compared with control subjects. These results were confirmed in the case of a second cohort, in which the pT217 cerebrospinal fluid levels marked out amyloid-positive patients with a sensitivity and a specificity of more than 90% (AUC 0.961; CI 0.874 to 0.995). The pT217 concentrations were also highly correlated with the PiB-PET values (correlation coefficient 0.72; P \u3c 0.001). CONCLUSIONS: Increased cerebrospinal fluid pT217 levels, more than those of pT181, are highly specific biomarkers for detecting both the preclinical and advanced forms of Alzheimer\u27s disease. This finding should greatly improve the diagnosis of Alzheimer\u27s disease, along with the correlations found to exist between pT217 levels and PiB-PET data. It also suggests that pT217 is a promising potential target for therapeutic applications and that a link exists between amyloid and tau pathology

Ensemble learning based on functional connectivity and Riemannian geometry for robust workload estimation

International audienceContext Passive Brain-Computer Interface (pBCI) has recently gained in popularity through its applications, e.g. workload and attention assessment. Nevertheless, one of the main limitations remains the important intra-and inter-subject variability. We propose a robust approach relying on ensemble learning, grounded in functional connectivity and Riemannian geometry to mitigate the high variability of the data with a large and diverse panel of classifiers

Resveratrol Increases Glucose Induced GLP-1 Secretion in Mice: A Mechanism which Contributes to the Glycemic Control

Resveratrol (RSV) is a potent anti-diabetic agent when used at high doses. However, the direct targets primarily responsible for the beneficial actions of RSV remain unclear. We used a formulation that increases oral bioavailability to assess the mechanisms involved in the glucoregulatory action of RSV in high-fat diet (HFD)-fed diabetic wild type mice. Administration of RSV for 5 weeks reduced the development of glucose intolerance, and increased portal vein concentrations of both Glucagon-like peptid-1 (GLP-1) and insulin, and intestinal content of active GLP-1. This was associated with increased levels of colonic proglucagon mRNA transcripts. RSV-mediated glucoregulation required a functional GLP-1 receptor (Glp1r) as neither glucose nor insulin levels were modulated in Glp1r-/- mice. Conversely, levels of active GLP-1 and control of glycemia were further improved when the Dipeptidyl peptidase-4 (DPP-4) inhibitor sitagliptin was co-administered with RSV. In addition, RSV treatment modified gut microbiota and decreased the inflammatory status of mice. Our data suggest that RSV exerts its actions in part through modulation of the enteroendocrine axis in vivo

Les applications des réseaux de neurones en économie, finance, management et environnement : Interview

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Data augmentation in Riemannian space for Brain-Computer Interfaces

International audienceBrain-Computer Interfaces (BCI) try to interpret brain signals , such as EEG , to issue some command or to characterize the cognitive states of the subjects. A strong limitation is that BCI tasks require a high concentration of the user , de facto limiting the length of experiment and the size of the dataset. Furthermore , several BCI paradigms depend on rare events , as for event-related potentials , also reducing the number of training examples available. A common strategy in machine learning when dealing with scarce data is called data augmentation ; new samples are generated by applying chosen transformations on the original dataset. In this contribution , we propose a scheme to adapt data augmentation in EEG-based BCI with a Riemannian standpoint : geometrical properties of EEG covariance matrix are taken into account to generate new training samples. Neural network are good candidates to benefit from such training scheme and a simple multi-layer perceptron offers good results . Experimental validation is conducted on two datasets : an SSVEP experiment with few training samples in each class and an error potential experiment with unbalanced classes (NER Kaggle competition)

Transfer Learning for SSVEP-based BCI using Riemannian similarities between users

International audienceBrain-Computer Interfaces (BCI) face a great challenge: how to harness the wide variability of brain signals from a user to another. The most visible problem is the lack of a sound framework to capture the specificity of a user brain waves. A first attempt to leverage this issue is to design user-specific spatial filters, carefully adjusted with a lengthy calibration phase. A second, more recent, opening is the systematic study of brain signals through their covariance, in an appropriate space from a geometric point of view. Riemannian geometry allows to efficiently characterize the variability of inter-subject EEG, even with noisy or scarce data. This contribution is the first attempt for SSVEP-based BCI to make the most of the available data from a user, relying on Riemannian geometry to estimate the similarity with a multiuser dataset. The proposed method is built in the framework of transfer learning and borrows the notion of composite mean to partition the space. This method is evaluated on 12 subjects performing an SSVEP task for the control of an exoskeleton arm and the results show the contribution of Riemannian geometry and of the user-specific composite mean, whereas there is only a few data available for a subject

The Vibe: A Versatile Vision-to-Audition Sensory Substitution Device

We describe a sensory substitution scheme that converts a video stream into an audio stream in real-time. It was initially developed as a research tool for studying human ability to learn new ways of perceiving the world: the Vibe can give us the ability to learn a kind of ‘vision’ by audition. It converts a video stream into a continuous stereophonic audio signal that conveys information coded from the video stream. The conversion from the video stream to the audio stream uses a kind of retina with receptive fields. Each receptive field controls a sound source and the user listens to a sound that is a mixture of all these sound sources. Compared to other existing vision-to-audition sensory substitution devices, the Vibe is highly versatile in particular because it uses a set of configurable units working in parallel. In order to demonstrate the validity and interest of this method of vision to audition conversion, we give the results of an experiment involving a pointing task to targets memorised through visual perception or through their auditory conversion by the Vibe. This article is also an opportunity to precisely draw the general specifications of this scheme in order to prepare its implementation on an autonomous/mobile hardware

On the need for metrics in dictionary learning assessment

International audienceDictionary-based approaches are the focus of a growing attention in the signal processing community, often achieving state of the art results in several application fields. Albeit their success , the criteria introduced so far for the assessment of their performances suffer from several shortcomings. The scope of this paper is to conduct a thorough analysis of these criteria and to highlight the need for principled criteria, enjoying the properties of metrics. Henceforth we introduce new criteria based on transportation like metrics and discuss their behaviors w.r.t the literature

Subspace metrics for multivariate dictionaries and application to EEG

International audienceOvercomplete representations and dictionary learning algorithms are attracting a growing interest in the machine learning community. This paper addresses the emerging problem of comparing multivari-ate overcomplete dictionaries. Despite a recurrent need to rely on a distance for learning or assessing multivariate overcomplete dictionaries , no metrics in their underlying spaces have yet been proposed. Henceforth we propose to study overcomplete representations from the perspective of matrix manifolds. We consider distances between multivariate dictionaries as distances between their spans which reveal to be elements of a Grassmannian manifold. We introduce set-metrics defined on Grassmannian spaces and study their properties both theoretically and numerically. Thanks to the introduced met-rics, experimental convergences of dictionary learning algorithms are assessed on synthetic datasets. Set-metrics are embedded in a clustering algorithm for a qualitative analysis of real EEG signals for Brain-Computer Interfaces (BCI). The obtained clusters of subjects are associated with subject performances. This is a major method-ological advance to understand the BCI-inefficiency phenomenon and to predict the ability of a user to interact with a BCI

Using Riemannian geometry for SSVEP-based Brain Computer Interface

Challenges for the next generation of Brain Computer Interfaces (BCI) are to mitigate the common sources of variability (electronic, electrical, biological) and to develop online and adaptive systems following the evolution of the subject's brain waves. Studying electroencephalographic (EEG) signals from their associated covariance matrices allows the construction of a representation which is invariant to extrinsic perturbations. As covariance matrices should be estimated, this paper first presents a thorough study of all estimators conducted on real EEG recording. Working in Euclidean space with covariance matrices is known to be error-prone, one might take advantage of algorithmic advances in Riemannian geometry and matrix manifold to implement methods for Symmetric Positive-Definite (SPD) matrices. Nonetheless, existing classification algorithms in Riemannian spaces are designed for offline analysis. We propose a novel algorithm for online and asynchronous processing of brain signals, borrowing principles from semi-unsupervised approaches and following a dynamic stopping scheme to provide a prediction as soon as possible. The assessment is conducted on real EEG recording: this is the first study on Steady-State Visually Evoked Potential (SSVEP) experimentations to exploit online classification based on Riemannian geometry. The proposed online algorithm is evaluated and compared with state-of-the-art SSVEP methods, which are based on Canonical Correlation Analysis (CCA). It is shown to improve both the classification accuracy and the information transfer rate in the online and asynchronous setup