Multi-stage Biomarker Models for Progression Estimation in Alzheimer’s Disease

The estimation of disease progression in Alzheimer’s disease (AD) based on a vector of quantitative biomarkers is of high interest to clinicians, patients, and biomedical researchers alike. In this work, quantile regression is employed to learn statistical models describing the evolution of such biomarkers. Two separate models are constructed using (1) subjects that progress from a cognitively normal (CN) stage to mild cognitive impairment (MCI) and (2) subjects that progress from MCI to AD during the observation window of a longitudinal study. These models are then automatically combined to develop a multi-stage disease progression model for the whole disease course. A probabilistic approach is derived to estimate the current disease progress (DP) and the disease progression rate (DPR) of a given individual by fitting any acquired biomarkers to these models. A particular strength of this method is that it is applicable even if individual biomarker measurements are missing for the subject. Employing cognitive scores and image-based biomarkers, the presented method is used to estimate DP and DPR for subjects from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). Further, the potential use of these values as features for different classification tasks is demonstrated. For example, accuracy of 64% is reached for CN vs. MCI vs. AD classification

Natural recovery of genetic diversity by gene flow in reforested areas of the endemic Canary Island pine, Pinus canariensis

The endemic pine, Pinus canariensis, forms one of the main forest ecosystems in the Canary Islands. In this archipelago, pine forest is a mosaic of natural stands (remnants of past forest overexploitation) and artificial stands planted from the 1940's. The genetic makeup of the artificially regenerated forest is of some concern. The use of reproductive material with uncontrolled origin or from a reduced number of parental trees may produce stands ill adapted to local conditions or unable to adapt in response to environmental change. The genetic diversity within a transect of reforested stands connecting two natural forest fragments has been studied with nuclear and chloroplast microsatellites. Little genetic differentiation and similar levels of genetic diversity to the surrounding natural stands were found for nuclear markers. However, chloroplast microsatellites presented lower haplotype diversity in reforested stands, and this may be a consequence of the lower effective population size of the chloroplast genome, meaning chloroplast markers have a higher sensitivity to bottlenecks. Understory natural regeneration within the reforestation was also analysed to study gene flow from natural forest into artificial stands. Estimates of immigration rate into artificially regenerated forest were high (0.68-0.75), producing a significant increase of genetic diversity (both in chloroplast and nuclear microsatellites), which indicates the capacity for genetic recovery for P. canariensis reforestations surrounded by larger natural stands

Spectral Graph Convolutions for Population-based Disease Prediction

Exploiting the wealth of imaging and non-imaging information for disease prediction tasks requires models capable of representing, at the same time, individual features as well as data associations between subjects from potentially large populations. Graphs provide a natural framework for such tasks, yet previous graph-based approaches focus on pairwise similarities without modelling the subjects' individual characteristics and features. On the other hand, relying solely on subject-specific imaging feature vectors fails to model the interaction and similarity between subjects, which can reduce performance. In this paper, we introduce the novel concept of Graph Convolutional Networks (GCN) for brain analysis in populations, combining imaging and non-imaging data. We represent populations as a sparse graph where its vertices are associated with image-based feature vectors and the edges encode phenotypic information. This structure was used to train a GCN model on partially labelled graphs, aiming to infer the classes of unlabelled nodes from the node features and pairwise associations between subjects. We demonstrate the potential of the method on the challenging ADNI and ABIDE databases, as a proof of concept of the benefit from integrating contextual information in classification tasks. This has a clear impact on the quality of the predictions, leading to 69.5% accuracy for ABIDE (outperforming the current state of the art of 66.8%) and 77% for ADNI for prediction of MCI conversion, significantly outperforming standard linear classifiers where only individual features are considered.Comment: International Conference on Medical Image Computing and Computer-Assisted Interventions (MICCAI) 201

Disorders of sex development: challenges for the future

No abstract available

A novel grading biomarker for the prediction of conversion from mild cognitive impairment to Alzheimer's disease

OBJECTIVE: Identifying mild cognitive impairment (MCI) subjects who will progress to Alzheimer's disease is not only crucial in clinical practice, but also has a significant potential to enrich clinical trials. The purpose of this study is to develop an effective biomarker for an accurate prediction of MCI-to-AD conversion from magnetic resonance (MR) images. METHODS: We propose a novel grading biomarker for the prediction of MCI-to-AD conversion. First, we comprehensively study the effects of several important factors on the performance in the prediction task including registration accuracy, age correction, feature selection and the selection of training data. Based on the studies of these factors, a grading biomarker is then calculated for each MCI subject using sparse representation techniques. Finally, the grading biomarker is combined with age and cognitive measures to provide a more accurate prediction of MCI-to-AD conversion. RESULTS: Using the ADNI dataset, the proposed global grading biomarker achieved an area under the receiver operating characteristic curve (AUC) in the range of 79%-81% for the prediction of MCI-to-AD conversion within 3 years in 10-fold cross validations. The classification AUC further increases to 84%-92% when age and cognitive measures are combined with the proposed grading biomarker. CONCLUSION: The obtained accuracy of the proposed biomarker benefits from the contributions of different factors: a tradeoff registration level to align images to the template space; the removal of the normal aging effect; selection of discriminative voxels; the calculation of the grading biomarker using AD and normal control groups; the integration of sparse representation technique and the combination of cognitive measures. SIGNIFICANCE: The evaluation on the ADNI dataset shows the efficacy of the proposed biomarker and demonstrates a significant contribution in accurate prediction of MCI-to-AD conversion

Efficient Multi-Scale 3D CNN with Fully Connected CRF for Accurate Brain Lesion Segmentation

We propose a dual pathway, 11-layers deep, three-dimensional Convolutional Neural Network for the challenging task of brain lesion segmentation. The devised architecture is the result of an in-depth analysis of the limitations of current networks proposed for similar applications. To overcome the computational burden of processing 3D medical scans, we have devised an efficient and effective dense training scheme which joins the processing of adjacent image patches into one pass through the network while automatically adapting to the inherent class imbalance present in the data. Further, we analyze the development of deeper, thus more discriminative 3D CNNs. In order to incorporate both local and larger contextual information, we employ a dual pathway architecture that processes the input images at multiple scales simultaneously. For post-processing of the networks soft segmentation, we use a 3D fully connected Conditional Random Field which effectively removes false positives. Our pipeline is extensively evaluated on three challenging tasks of lesion segmentation in multi-channel MRI patient data with traumatic brain injuries, brain tumors, and ischemic stroke. We improve on the state-of-the-art for all three applications, with top ranking performance on the public benchmarks BRATS 2015 and ISLES 2015. Our method is computationally efficient, which allows its adoption in a variety of research and clinical settings. The source code of our implementation is made publicly available

Self Adversarial Training for Human Pose Estimation

This paper presents a deep learning based approach to the problem of human pose estimation. We employ generative adversarial networks as our learning paradigm in which we set up two stacked hourglass networks with the same architecture, one as the generator and the other as the discriminator. The generator is used as a human pose estimator after the training is done. The discriminator distinguishes ground-truth heatmaps from generated ones, and back-propagates the adversarial loss to the generator. This process enables the generator to learn plausible human body configurations and is shown to be useful for improving the prediction accuracy.Comment: CVPR 2017 Workshop on Visual Understanding of Humans in Crowd Scene and the 1st Look Into Person (LIP) Challeng

Theory of a cylindrical Langmuir probe parallel to the magnetic field and its calibration with interferometry

International audienceA theory for data interpretation is presented for a cylindrical Langmuir probe in plasma parallel to the magnetic field direction. The theory is tested in a linear low-temperature plasma device Aline, in a capacitive radio-frequency (RF) discharge. The probe is placed on a 3D manipulator and a position scan is performed. To exclude strong RF perturbations the probe is RF compensated. Using the theory electron densities are obtained from the current at the plasma potential, where no sheath is present. Results are calibrated by line-integrated density measurements of a 26.5 GHz microwave interferometer. Reasonable agreement is observed for probe and interferometer measurements. Furthermore, preceding, more general probe theory is compared to the one developed in the current work and the application limits are discussed