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

Assessing Drivers’ Vigilance State During Monotonous Driving

The differential effects of three hours of monotonous daytime driving on subjective (sleepiness, inattention, monotony), performance (choice reaction time), and physiological (EEG alpha power, P300-amplitude, heart rate) vigilance measures were examined. A linear degradation of drivers’ subjective state, mean long reaction times (as opposed to short ones), P300-amplitude and parietal alpha power with time spent on the highway was identified. An improvement of the subjective measures towards the end of the driving task was not accompanied by any improvement in performance or physiological measures. This dissociation of self-assessment and objective vigilance measures has important implications for the design of modern driver assistant systems that aim to adapt to the driver’s state

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

Relationship between regional white matter hyperintensities and alpha oscillations in older adults

Aging is associated with increased white matter hyperintensities (WMHs) and with alterations of alpha oscillations (7–13 Hz). However, a crucial question remains, whether changes in alpha oscillations relate to aging per se or whether this relationship is mediated by age-related neuropathology like WMHs. Using a large cohort of cognitively healthy older adults (N=907, 60-80 years), we assessed relative alpha power, alpha peak frequency, and long-range temporal correlations (LRTC) from resting-state EEG. We further associated these parameters with voxel-wise WMHs from 3T MRI. We found that a higher prevalence of WMHs in the superior and posterior corona radiata as well as in the thalamic radiation was related to elevated alpha power, with the strongest association in the bilateral occipital cortex. In contrast, we observed no significant relation of the WMHs probability with alpha peak frequency and LRTC. Finally, higher age was associated with elevated alpha power via total WMH volume. We suggest that an elevated alpha power is a consequence of WMH affecting a spatial organization of alpha sources

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