Fast Predictive Simple Geodesic Regression

Deformable image registration and regression are important tasks in medical image analysis. However, they are computationally expensive, especially when analyzing large-scale datasets that contain thousands of images. Hence, cluster computing is typically used, making the approaches dependent on such computational infrastructure. Even larger computational resources are required as study sizes increase. This limits the use of deformable image registration and regression for clinical applications and as component algorithms for other image analysis approaches. We therefore propose using a fast predictive approach to perform image registrations. In particular, we employ these fast registration predictions to approximate a simplified geodesic regression model to capture longitudinal brain changes. The resulting method is orders of magnitude faster than the standard optimization-based regression model and hence facilitates large-scale analysis on a single graphics processing unit (GPU). We evaluate our results on 3D brain magnetic resonance images (MRI) from the ADNI datasets.Comment: 19 pages, 10 figures, 13 table

Multi-Channel Stochastic Variational Inference for the Joint Analysis of Heterogeneous Biomedical Data in Alzheimer's Disease

The joint analysis of biomedical data in Alzheimer's Disease (AD) is important for better clinical diagnosis and to understand the relationship between biomarkers. However, jointly accounting for heterogeneous measures poses important challenges related to the modeling of the variability and the interpretability of the results. These issues are here addressed by proposing a novel multi-channel stochastic generative model. We assume that a latent variable generates the data observed through different channels (e.g., clinical scores, imaging, ...) and describe an efficient way to estimate jointly the distribution of both latent variable and data generative process. Experiments on synthetic data show that the multi-channel formulation allows superior data reconstruction as opposed to the single channel one. Moreover, the derived lower bound of the model evidence represents a promising model selection criterion. Experiments on AD data show that the model parameters can be used for unsupervised patient stratification and for the joint interpretation of the heterogeneous observations. Because of its general and flexible formulation, we believe that the proposed method can find important applications as a general data fusion technique.Comment: accepted for presentation at MLCN 2018 workshop, in Conjunction with MICCAI 2018, September 20, Granada, Spai

CSF and Brain Structural Imaging Markers of the Alzheimer's Pathological Cascade

10.1371/journal.pone.0047406PLoS ONE712

Comparison of variables associated with cerebrospinal fluid neurofilament, total-tau, and neurogranin

INTRODUCTION: Three cerebrospinal fluid (CSF) markers of neurodegeneration (N) (neurofilament light [NfL], total-tau [T-tau], and neurogranin [Ng]) have been proposed under the AT(N) scheme of the National Institute on Aging-Alzheimer's Association Research Framework. METHODS: We examined, in a community-based population (N = 777, aged 50-95) (1) what variables were associated with each of the CSF (N) markers, and (2) whether the variables associated with each marker differed by increased brain amyloid. CSF T-tau was measured with an automated electrochemiluminescence Elecsys immunoassay; NfL and Ng were measured with in-house enzyme-linked immunosorbent assays. RESULTS: Multiple variables were differentially associated with CSF NfL and T-tau levels, but not Ng. Most associations were attenuated after adjustment for age and sex. T-tau had the strongest association with cognition in the presence of amyloidosis, followed by Ng. Variables associations with NfL did not differ by amyloid status. DISCUSSION: Understanding factors that influence CSF (N) markers will assist in the interpretation and utility of these markers in clinical practice

Multiple landmark detection using multi-agent reinforcement learning

The detection of anatomical landmarks is a vital step for medical image analysis and applications for diagnosis, interpretation and guidance. Manual annotation of landmarks is a tedious process that requires domain-specific expertise and introduces inter-observer variability. This paper proposes a new detection approach for multiple landmarks based on multi-agent reinforcement learning. Our hypothesis is that the position of all anatomical landmarks is interdependent and non-random within the human anatomy, thus finding one landmark can help to deduce the location of others. Using a Deep Q-Network (DQN) architecture we construct an environment and agent with implicit inter-communication such that we can accommodate K agents acting and learning simultaneously, while they attempt to detect K different landmarks. During training the agents collaborate by sharing their accumulated knowledge for a collective gain. We compare our approach with state-of-the-art architectures and achieve significantly better accuracy by reducing the detection error by 50%, while requiring fewer computational resources and time to train compared to the naïve approach of training K agents separately. Code and visualizations available: https://github.com/thanosvlo/MARL-for-Anatomical-Landmark-Detectio

Combining Anomaly and Z' Mediation of Supersymmetry Breaking

We propose a scenario in which the supersymmetry breaking effect mediated by an additional U(1)' is comparable with that of anomaly mediation. We argue that such a scenario can be naturally realized in a large class of models. Combining anomaly with Z' mediation allows us to solve the tachyonic slepton problem of the former and avoid significant fine tuning in the latter. We focus on an NMSSM-like scenario where U(1)' gauge invariance is used to forbid a tree-level mu term, and present concrete models, which admit successful dynamical electroweak symmetry breaking. Gaugino masses are somewhat lighter than the scalar masses, and the third generation squarks are lighter than the first two. In the specific class of models under consideration, the gluino is light since it only receives a contribution from 2-loop anomaly mediation, and it decays dominantly into third generation quarks. Gluino production leads to distinct LHC signals and prospects of early discovery. In addition, there is a relatively light Z', with mass in the range of several TeV. Discovering and studying its properties can reveal important clues about the underlying model.Comment: Minor changes: references added, typos corrected, journal versio

Atrophy in the parahippocampal gyrus as an early biomarker of Alzheimer’s disease

The main aim of the present study was to compare volume differences in the hippocampus and parahippocampal gyrus as biomarkers of Alzheimer’s disease (AD). Based on the previous findings, we hypothesized that there would be significant volume differences between cases of healthy aging, amnestic mild cognitive impairment (aMCI), and mild AD. Furthermore, we hypothesized that there would be larger volume differences in the parahippocampal gyrus than in the hippocampus. In addition, we investigated differences between the anterior, middle, and posterior parts of both structures. We studied three groups of participants: 18 healthy participants without memory decline, 18 patients with aMCI, and 18 patients with mild AD. 3 T T1-weighted MRI scans were acquired and gray matter volumes of the anterior, middle, and posterior parts of both the hippocampus and parahippocampal gyrus were measured using a manual tracing approach. Volumes of both the hippocampus and parahippocampal gyrus were significantly different between the groups in the following order: healthy > aMCI > AD. Volume differences between the groups were relatively larger in the parahippocampal gyrus than in the hippocampus, in particular, when we compared healthy with aMCI. No substantial differences were found between the anterior, middle, and posterior parts of both structures. Our results suggest that parahippocampal volume discriminates better than hippocampal volume between cases of healthy aging, aMCI, and mild AD, in particular, in the early phase of the disease. The present results stress the importance of parahippocampal atrophy as an early biomarker of AD

(Extra)Ordinary Gauge/Anomaly Mediation

We study anomaly mediation models with gauge mediation effects from messengers which have a general renormalizable mass matrix with a supersymmetry-breaking spurion. Our models lead to a rich structure of supersymmetry breaking terms in the visible sector. We derive sum rules among the soft scalar masses for each generation. Our sum rules for the first and second generations are the same as those in general gauge mediation, but the sum rule for the third generation is different because of the top Yukawa coupling. We find the parameter space where the tachyonic slepton problem is solved. We also explore the case in which gauge mediation causes the anomalously small gaugino masses. Since anomaly mediation effects on the gaugino masses exist, we can obtain viable mass spectrum of the visible sector fields.Comment: 24 pages, 10 figure