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

Generative discriminative models for multivariate inference and statistical mapping in medical imaging

This paper presents a general framework for obtaining interpretable multivariate discriminative models that allow efficient statistical inference for neuroimage analysis. The framework, termed generative discriminative machine (GDM), augments discriminative models with a generative regularization term. We demonstrate that the proposed formulation can be optimized in closed form and in dual space, allowing efficient computation for high dimensional neuroimaging datasets. Furthermore, we provide an analytic estimation of the null distribution of the model parameters, which enables efficient statistical inference and p-value computation without the need for permutation testing. We compared the proposed method with both purely generative and discriminative learning methods in two large structural magnetic resonance imaging (sMRI) datasets of Alzheimer's disease (AD) (n=415) and Schizophrenia (n=853). Using the AD dataset, we demonstrated the ability of GDM to robustly handle confounding variations. Using Schizophrenia dataset, we demonstrated the ability of GDM to handle multi-site studies. Taken together, the results underline the potential of the proposed approach for neuroimaging analyses.Comment: To appear in MICCAI 2018 proceeding

SPoC: A novel framework for relating the amplitude of neuronal oscillations to behaviorally relevant parameters

Previously, modulations in power of neuronal oscillations have been functionally linked to sensory, motor and cognitive operations. Such links are commonly established by relating the power modulations to specific target variables such as reaction times or task ratings. Consequently, the resulting spatio-spectral representation is subjected to neurophysiological interpretation. As an alternative, independent component analysis (ICA) or alternative decomposition methods can be applied and the power of the components may be related to the target variable. In this paper we show that these standard approaches are suboptimal as the first does not take into account the superposition of many sources due to volume conduction, while the second is unable to exploit available information about the target variable. To improve upon these approaches we introduce a novel (supervised) source separation framework called Source Power Comodulation (SPoC). SPoC makes use of the target variable in the decomposition process in order to give preference to components whose power comodulates with the target variable. We present two algorithms that implement the SPoC approach. Using simulations with a realistic head model, we show that the SPoC algorithms are able extract neuronal components exhibiting high correlation of power with the target variable. In this task, the SPoC algorithms outperform other commonly used techniques that are based on the sensor data or ICA approaches. Furthermore, using real electroencephalography (EEG) recordings during an auditory steady state paradigm, we demonstrate the utility of the SPoC algorithms by extracting neuronal components exhibiting high correlation of power with the intensity of the auditory input. Taking into account the results of the simulations and real EEG recordings, we conclude that SPoC represents an adequate approach for the optimal extraction of neuronal components showing coupling of power with continuously changing behaviorally relevant parameters

Evaluation of the passive safety in cars adapted with steering control devices for disabled drivers

The purpose of this research is to analyse the influence of steering control devices for disabled people on passive safety. It is based on the advances made in the modelling and simulation of the driver position and in the suit verification test. The influence of these devices is studied through airbag deployment and/or its influence on driver safety. We characterise the different adaptations that are used in adapted cars that can be found mounted in vehicles in order to generate models that are verified by experimental test. A three-dimensional design software package was used to develop the model. The simulations were generated using a dynamic simulation program employing LS-DYNA finite elements. This program plots the geometry and assigns materials. The airbag is shaped, meshed and folded just as it is mounted in current vehicles. The thermodynamic model of expansion of gases is assigned, and the contact interfaces are defined. Static tests were carried out on the deployment of the airbag to contrast with and to validate the computational models and to measure the behaviour of the airbag when there are steering adaptations mounted in the vehicle. © 2011 Taylor & Francis.Masiá Vañó, J.; Eixerés Tomás, B.; Dols Ruiz, JF. (2011). Evaluation of the passive safety in cars adapted with steering control devices for disabled drivers. International Journal of Crashworthiness. 16(1):75-83. doi:10.1080/13588265.2010.514772S7583161Bedewi, N. E., Marzougui, D., & Motevalli, V. (1996). Evaluation of parameters affecting simulation of airbag deployment and interaction with occupants. International Journal of Crashworthiness, 1(4), 339-354. doi:10.1533/cras.1996.0025Chawla, A., Mukherjee, S., & Sharma, A. (2005). Development of FE meshes for folded airbags. International Journal of Crashworthiness, 10(3), 259-266. doi:10.1533/ijcr.2005.0343Cheng, Z., Rizer, A. L., & Pellettiere, J. A. (2003). Modeling and Simulation of OOP Occupant-Airbag Interaction. SAE Technical Paper Series. doi:10.4271/2003-01-0510Crandall, J. R., Bass, C. R., Pikey, W. D., Miller, H. J., Sikorski, J., & Wilkins, M. (1996). Thoracic response and injury with belt, driver side airbag, and force limited belt restraint systems. International Journal of Crashworthiness, 2(1), 119-132. doi:10.1533/cras.1997.0039Dalrymple, G. (1996). Effects of Assistive Steering Devices on Air Bag Deployment. SAE Technical Paper Series. doi:10.4271/960223Khan, M. U., & Moatamedi, M. (2008). A review of airbag test and analysis. International Journal of Crashworthiness, 13(1), 67-76. doi:10.1080/13588260701731674Khan, M. U., Moatamedi, M., Souli, M., & Zeguer, T. (2008). Multiphysics out of position airbag simulation. International Journal of Crashworthiness, 13(2), 159-166. doi:10.1080/13588260701788385Richert, J., Coutellier, D., Götz, C., & Eberle, W. (2007). Advanced smart airbags: The solution for real-life safety? International Journal of Crashworthiness, 12(2), 159-171. doi:10.1080/13588260701433461Ruff, C., Jost, T., & Eichberger, A. (2007). Simulation of an airbag deployment in out-of-position situations. Vehicle System Dynamics, 45(10), 953-967. doi:10.1080/0042311070153830

Open Database of Epileptic EEG with MRI and Postoperational Assessment of Foci—a Real World Verification for the EEG Inverse Solutions

This paper introduces a freely accessible database http://eeg.pl/epi, containing 23 datasets from patients diagnosed with and operated on for drug-resistant epilepsy. This was collected as part of the clinical routine at the Warsaw Memorial Child Hospital. Each record contains (1) pre-surgical electroencephalography (EEG) recording (10–20 system) with inter-ictal discharges marked separately by an expert, (2) a full set of magnetic resonance imaging (MRI) scans for calculations of the realistic forward models, (3) structural placement of the epileptogenic zone, recognized by electrocorticography (ECoG) and post-surgical results, plotted on pre-surgical MRI scans in transverse, sagittal and coronal projections, (4) brief clinical description of each case. The main goal of this project is evaluation of possible improvements of localization of epileptic foci from the surface EEG recordings. These datasets offer a unique possibility for evaluating different EEG inverse solutions. We present preliminary results from a subset of these cases, including comparison of different schemes for the EEG inverse solution and preprocessing. We report also a finding which relates to the selective parametrization of single waveforms by multivariate matching pursuit, which is used in the preprocessing for the inverse solutions. It seems to offer a possibility of tracing the spatial evolution of seizures in time

Fluorinated tranylcypromine analogues as inhibitors of lysine-specific demethylase 1 (LSD1, KDM1A)

We report a series of tranylcypromine analogues containing a fluorine in the cyclopropyl ring. A number of compounds with additional m- or p- substitution of the aryl ring were micromolar inhibitors of the LSD1 enzyme. In cellular assays, the compounds inhibited the proliferation of acute myeloid leukemia cell lines. Increased levels of the biomarkers H3K4me2 and CD86 were consistent with LSD1 target engagement

Influence of Pacing Mode and Rate on Peripheral Levels of Atrial Natriuretic Peptide (ANP)

The effect of acute modifications of pacing mode and rate on plasma ANP levels was evaluated. ANP was determined in ten resting patients with ODD pacemokers due to binodal disease or intermittent second- and third-degree AV block. At 82/minute pacing rate the ANP plasma levels (normal range 2 to 30 fmol/mL) corresponded to those under AAI (4.05 ± 2.10 fmol/mL) and DDD (4.18 ± 2.02 fmol/mL) pacing, but increased significantly (P < 0.05) during VVI pacing (6.96 ± 3.70 fmol/mL). Acceleration of DDD stimulation frequency from 82 to 113/minutes led to significant increases of ANP levels by the factor of three in all chosen AV delays. The lowest ANP plasma levels were measured of 175 msec AV delay under 82/minute pacing rate in DDD mode. Under 113/minutes the differences of ANP concentration after variations of AV delays were less pronounced. The influences of altered atrial pressure and tension on ANP release are discussed to account for changes in ANP plasma levels following different modes and rates of pacemaker stimulation.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/75366/1/j.1540-8159.1989.tb01862.x.pd

Predicting lethal courses in critically ill COVID-19 patients using a machine learning model trained on patients with non-COVID-19 viral pneumonia

In a pandemic with a novel disease, disease-specific prognosis models are available only with a delay. To bridge the critical early phase, models built for similar diseases might be applied. To test the accuracy of such a knowledge transfer, we investigated how precise lethal courses in critically ill COVID-19 patients can be predicted by a model trained on critically ill non-COVID-19 viral pneumonia patients. We trained gradient boosted decision tree models on 718 (245 deceased) non-COVID-19 viral pneumonia patients to predict individual ICU mortality and applied it to 1054 (369 deceased) COVID-19 patients. Our model showed a significantly better predictive performance (AUROC 0.86 [95% CI 0.86-0.87]) than the clinical scores APACHE2 (0.63 [95% CI 0.61-0.65]), SAPS2 (0.72 [95% CI 0.71-0.74]) and SOFA (0.76 [95% CI 0.75-0.77]), the COVID-19-specific mortality prediction models of Zhou (0.76 [95% CI 0.73-0.78]) and Wang (laboratory: 0.62 [95% CI 0.59-0.65]; clinical: 0.56 [95% CI 0.55-0.58]) and the 4C COVID-19 Mortality score (0.71 [95% CI 0.70-0.72]). We conclude that lethal courses in critically ill COVID-19 patients can be predicted by a machine learning model trained on non-COVID-19 patients. Our results suggest that in a pandemic with a novel disease, prognosis models built for similar diseases can be applied, even when the diseases differ in time courses and in rates of critical and lethal courses