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

Geodesic Information Flows: Spatially-Variant Graphs and Their Application to Segmentation and Fusion

Clinical annotations, such as voxel-wise binary or probabilistic tissue segmentations, structural parcellations, pathological regionsof- interest and anatomical landmarks are key to many clinical studies. However, due to the time consuming nature of manually generating these annotations, they tend to be scarce and limited to small subsets of data. This work explores a novel framework to propagate voxel-wise annotations between morphologically dissimilar images by diffusing and mapping the available examples through intermediate steps. A spatially-variant graph structure connecting morphologically similar subjects is introduced over a database of images, enabling the gradual diffusion of information to all the subjects, even in the presence of large-scale morphological variability. We illustrate the utility of the proposed framework on two example applications: brain parcellation using categorical labels and tissue segmentation using probabilistic features. The application of the proposed method to categorical label fusion showed highly statistically significant improvements when compared to state-of-the-art methodologies. Significant improvements were also observed when applying the proposed framework to probabilistic tissue segmentation of both synthetic and real data, mainly in the presence of large morphological variability

Enrichment of clinical trials in MCI due to AD using markers of amyloid and neurodegeneration

Objective: To investigate the effect of enriching mild cognitive impairment (MCI) clinical trials using combined markers of amyloid pathology and neurodegeneration. Methods: We evaluate an implementation of the recent National Institute for Aging–Alzheimer's Association (NIA-AA) diagnostic criteria for MCI due to Alzheimer disease (AD) as inclusion criteria in clinical trials and assess the effect of enrichment with amyloid (A+), neurodegeneration (N+), and their combination (A+N+) on the rate of clinical progression, required sample sizes, and estimates of trial time and cost. Results: Enrichment based on an individual marker (A+ or N+) substantially improves all assessed trial characteristics. Combined enrichment (A+N+) further improves these results with a reduction in required sample sizes by 45% to 60%, depending on the endpoint. Conclusions: Operationalizing the NIA-AA diagnostic criteria for clinical trial screening has the potential to substantially improve the statistical power of trials in MCI due to AD by identifying a more rapidly progressing patient population

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

Introducing coherent time control to cavity magnon-polariton modes

By connecting light to magnetism, cavity magnon-polaritons (CMPs) can link quantum computation to spintronics. Consequently, CMP-based information processing devices have emerged over the last years, but have almost exclusively been investigated with single-tone spectroscopy. However, universal computing applications will require a dynamic and on-demand control of the CMP within nanoseconds. Here, we perform fast manipulations of the different CMP modes with independent but coherent pulses to the cavity and magnon system. We change the state of the CMP from the energy exchanging beat mode to its normal modes and further demonstrate two fundamental examples of coherent manipulation. We first evidence dynamic control over the appearance of magnon-Rabi oscillations, i.e., energy exchange, and second, energy extraction by applying an anti-phase drive to the magnon. Our results show a promising approach to control building blocks valuable for a quantum internet and pave the way for future magnon-based quantum computing research

Measuring primordial gravitational waves from CMB B-modes in cosmologies with generalized expansion histories

We evaluate our capability to constrain the abundance of primordial tensor perturbations in cosmologies with generalized expansion histories in the epoch of cosmic acceleration. Forthcoming satellite and sub-orbital experiments probing polarization in the CMB are expected to measure the B-mode power in CMB polarization, coming from PGWs on the degree scale, as well as gravitational lensing on arcmin scales; the latter is the main competitor for the measurement of PGWs, and is directly affected by the underlying expansion history, determined by the presence of a DE component. In particular, we consider early DE possible scenarios, in which the expansion history is substantially modified at the epoch in which the CMB lensing is most relevant. We show that the introduction of a parametrized DE may induce a variation as large as 30% in the ratio of the power of lensing and PGWs on the degree scale. We find that adopting the nominal specifications of upcoming satellite measurements the constraining power on PGWs is weakened by the inclusion of the extra degrees of freedom, resulting in a reduction of about 10% of the upper limits on r in fiducial models with no GWs, as well as a comparable increase in the error bars in models with non-zero r. Moreover, we find that the inclusion of sub-orbital CMB experiments, capable of mapping the B-mode power up to the angular scales affected by lensing, can restore the forecasted performances with a cosmological constant. Finally, we show how the combination of CMB data with Type Ia SNe, BAO and Hubble constant allows to constrain simultaneously r and the DE quantities in the parametrization we consider, consisting of present abundance and first redshift derivative of the energy density. We compare this study with results obtained using the forecasted lensing potential measurement precision from CMB satellite observations, finding consistent results.Comment: 17 pages, 9 figures, accepted for publication by JCAP. Modified version after the referee's comment

Lack of clustering in low-redshift 21-cm intensity maps cross-correlated with 2dF galaxy densities

We report results from 21-cm intensity maps acquired from the Parkes radio telescope and cross-correlated with galaxy maps from the 2dF galaxy survey. The data span the redshift range $0.057<z<0.098$ and cover approximately 1,300 square degrees over two long fields. Cross correlation is detected at a significance of $5.18\sigma$. The amplitude of the cross-power spectrum is low relative to the expected dark matter power spectrum, assuming a neutral hydrogen (HI) bias and mass density equal to measurements from the ALFALFA survey. The decrement is pronounced and statistically significant at small scales. At $k\sim1.5$ $h \mathrm{Mpc^{-1}}$, the cross power spectrum is more than a factor of 6 lower than expected, with a significance of $14.8\,\sigma$. This decrement indicates either a lack of clustering of neutral hydrogen (HI), a small correlation coefficient between optical galaxies and HI, or some combination of the two. Separating 2dF into red and blue galaxies, we find that red galaxies are much more weakly correlated with HI on $k\sim1.5$ $h \mathrm{Mpc^{-1}}$ scales, suggesting that HI is more associated with blue star-forming galaxies and tends to avoid red galaxies.Comment: 12 pages, 3 figures; fixed typo in meta-data title and paper author