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

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

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

Narrow genetic base in forest restoration with holm oak (Quercus ilex L.) in Sicily

In order to empirically assess the effect of actual seed sampling strategy on genetic diversity of holm oak (Quercus ilex) forestations in Sicily, we have analysed the genetic composition of two seedling lots (nursery stock and plantation) and their known natural seed origin stand by means of six nuclear microsatellite loci. Significant reduction in genetic diversity and significant difference in genetic composition of the seedling lots compared to the seed origin stand were detected. The female and the total effective number of parents were quantified by means of maternity assignment of seedlings and temporal changes in allele frequencies. Extremely low effective maternity numbers were estimated (Nfe $\approx$ 2-4) and estimates accounting for both seed and pollen donors gave also low values (Ne $\approx$ 35-50). These values can be explained by an inappropriate forestry seed harvest strategy limited to a small number of spatially close trees

A semi-supervised large margin algorithm for white matter hyperintensity segmentation

Precise detection and quantification of white matter hyperintensities (WMH) is of great interest in studies of neurodegenerative diseases (NDs). In this work, we propose a novel semi-supervised large margin algorithm for the segmentation of WMH. The proposed algorithm optimizes a kernel based max-margin objective function which aims to maximize the margin averaged over inliers and outliers while exploiting a limited amount of available labelled data. We show that the learning problem can be formulated as a joint framework learning a classifier and a label assignment simultaneously, which can be solved efficiently by an iterative algorithm. We evaluate our method on a database of 280 brain Magnetic Resonance (MR) images from subjects that either suffered from subjective memory complaints or were diagnosed with NDs. The segmented WMH volumes correlate well with the standard clinical measurement (Fazekas score), and both the qualitative visualization results and quantitative correlation scores of the proposed algorithm outperform other well known methods for WMH segmentation

Standardized evaluation of algorithms for computer-aided diagnosis of dementia based on structural MRI: the CADDementia challenge

Algorithms for computer-aided diagnosis of dementia based on structural MRI have demonstrated high performance in the literature, but are difficult to compare as different data sets and methodology were used for evaluation. In addition, it is unclear how the algorithms would perform on previously unseen data, and thus, how they would perform in clinical practice when there is no real opportunity to adapt the algorithm to the data at hand. To address these comparability, generalizability and clinical applicability issues, we organized a grand challenge that aimed to objectively compare algorithms based on a clinically representative multi-center data set. Using clinical practice as the starting point, the goal was to reproduce the clinical diagnosis. Therefore, we evaluated algorithms for multi-class classification of three diagnostic groups: patients with probable Alzheimer's disease, patients with mild cognitive impairment and healthy controls. The diagnosis based on clinical criteria was used as reference standard, as it was the best available reference despite its known limitations. For evaluation, a previously unseen test set was used consisting of 354 T1-weighted MRI scans with the diagnoses blinded. Fifteen research teams participated with a total of 29 algorithms. The algorithms were trained on a small training set (n = 30) and optionally on data from other sources (e.g., the Alzheimer's Disease Neuroimaging Initiative, the Australian Imaging Biomarkers and Lifestyle flagship study of aging). The best performing algorithm yielded an accuracy of 63.0% and an area under the receiver-operating-characteristic curve (AUC) of 78.8%. In general, the best performances were achieved using feature extraction based on voxel-based morphometry or a combination of features that included volume, cortical thickness, shape and intensity. The challenge is open for new submissions via the web-based framework: http://caddementia.grand-challenge.org

Automatic Methods of Gas Analysis Depending upon Thermal Conductivity

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Genetic component of flammability variation in a Mediterranean shrub

Recurrent fires impose a strong selection pressure in many ecosystems worldwide. In such ecosystems, plant flammability is of paramount importance because it enhances population persistence, particularly in non‐resprouting species. Indeed, there is evidence of phenotypic divergence of flammability under different fire regimes. Our general hypothesis is that flammability‐enhancing traits are adaptive; here, we test whether they have a genetic component. To test this hypothesis, we used the postfire obligate seeder Ulex parviflorus from sites historically exposed to different fire recurrence. We associated molecular variation in potentially adaptive loci detected with a genomic scan (using AFLP markers) with individual phenotypic variability in flammability across fire regimes. We found that at least 42% of the phenotypic variation in flammability was explained by the genetic divergence in a subset of AFLP loci. In spite of generalized gene flow, the genetic variability was structured by differences in fire recurrence. Our results provide the first field evidence supporting that traits enhancing plant flammability have a genetic component and thus can be responding to natural selection driven by fire. These results highlight the importance of flammability as an adaptive trait in fire‐prone ecosystems

Instantiated mixed effects modeling of Alzheimer's disease markers

The assessment and prediction of a subject's current and future risk of developing neurodegenerative diseases like Alzheimer's disease are of great interest in both the design of clinical trials as well as in clinical decision making. Exploring the longitudinal trajectory of markers related to neurodegeneration is an important task when selecting subjects for treatment in trials and the clinic, in the evaluation of early disease indicators and the monitoring of disease progression. Given that there is substantial intersubject variability, models that attempt to describe marker trajectories for a whole population will likely lack specificity for the representation of individual patients. Therefore, we argue here that individualized models provide a more accurate alternative that can be used for tasks such as population stratification and a subject-specific prognosis. In the work presented here, mixed effects modeling is used to derive global and individual marker trajectories for a training population. Test subject (new patient) specific models are then instantiated using a stratified “marker signature” that defines a subpopulation of similar cases within the training database. From this subpopulation, personalized models of the expected trajectory of several markers are subsequently estimated for unseen patients. These patient specific models of markers are shown to provide better predictions of time-to-conversion to Alzheimer's disease than population based models

Sex. Dev.

Campomelic dysplasia (MIM 114290) is a severe malformation syndrome frequently accompanied by male-to-female sex reversal. Causative are mutations within the SOX9 gene on 17q24.3 as well as chromosomal aberrations (translocations, inversions or deletions) in the vicinity of SOX9 . Here, we report on a patient with muscular hypotonia, craniofacial dysmorphism, cleft palate, brachydactyly, malformations of thoracic spine, and gonadal dysgenesis with female external genitalia and müllerian duct derivatives in the presence of a male karyotype. X-ray examination and clinical examinations revealed no signs of campomelia. The combination of molecular cytogenetic analysis and array CGH revealed an unbalanced translocation between one chromosome 7 and one chromosome 17 [46,XY,t(7; 17)(q33;q24).ish t(7; 17) (wcp7+,wcp17+;wcp7+wcp17+)] with a deletion of approximately 4.2 Mb located about 0.5 Mb upstream of SOX9 . STS analysis confirmed the deletion of chromosome 17, which has occurred de novo on the paternal chromosome. The proximal breakpoint on chromosome 17 is localized outside the known breakpoint cluster regions. The deletion on chromosome 17q24 removes several genes. Among these genes PRKAR1A is deleted. Inactivating mutations of PRKAR1A cause Carney complex. To our knowledge, this is the first report of a patient with acampomelic campomelic dysplasia, carrying both a deletion and a translocation