The IAS-MEEG Package: A Flexible Inverse Source Reconstruction Platform for Reconstruction and Visualization of Brain Activity from M/EEG Data

We present a standalone Matlab software platform complete with visualization for the reconstruction of the neural activity in the brain from MEG or EEG data. The underlying inversion combines hierarchical Bayesian models and Krylov subspace iterative least squares solvers. The Bayesian framework of the underlying inversion algorithm allows to account for anatomical information and possible a priori belief about the focality of the reconstruction. The computational efficiency makes the software suitable for the reconstruction of lengthy time series on standard computing equipment. The algorithm requires minimal user provided input parameters, although the user can express the desired focality and accuracy of the solution. The code has been designed so as to favor the parallelization performed automatically by Matlab, according to the resources of the host computer. We demonstrate the flexibility of the platform by reconstructing activity patterns with supports of different sizes from MEG and EEG data. Moreover, we show that the software reconstructs well activity patches located either in the subcortical brain structures or on the cortex. The inverse solver and visualization modules can be used either individually or in combination. We also provide a version of the inverse solver that can be used within Brainstorm toolbox. All the software is available online by Github, including the Brainstorm plugin, with accompanying documentation and test data

Sixfold Post-Fracture Mortality in 16-To 30-Year-Old Patients-Suicides, Homicides, and Intoxications Among Leading Causes of Death

Background and Aims: The death of any young individual is associated with the loss of many potentially fulfilling years of life. It has been suggested that the relative mortality of fracture patients may be higher in younger age groups than in older cohorts. We determined the mortality and causes of death in a cohort of 16- to 30-year-old patients that had been hospitalized for fractures. Material and Methods: We collected data using criteria based on the diagnosis code (International Statistical Classification of Diseases and Related Health Problems, 10th Revision), surgical procedure code (Nordic Medico-Statistical Committee), and seven additional characteristics of patients admitted to the trauma ward at the Central Finland Hospital between 2002 and 2008. Patients were then followed to ascertain their mortality status until the end of 2012. Standardized mortality ratios were calculated and causes of death were determined by combining our registry data with data provided by Statistics Finland. Results: During the study, 199 women and 525 men aged 16-30 years had sustained fractures. None of these patients died during the primary hospital stay. At the end of follow-up (mean duration 7.4 years), 6 women and 23 men had died. The standardized mortality ratio for all patients was 6.2 (95% Confidence Interval: 4.3-8.9). Suicides and intoxications comprised over half, and motor vehicle accidents and homicides comprised nearly a third of the post-fracture deaths. Conclusion: We found a concerning increase in mortality among young adults that had been hospitalized due to a fracture compared to the general population that had been standardized by age, sex, and calendar-period. Leading causes of death were suicides and intoxications or motor vehicle accidents and homicides, which may be indicative of depressive disorders or impulse control disorders, respectively. Identification of the underlying psychosocial problems may provide an opportunity for preventive interventions.Peer reviewe

Linear sampling method for identifying cavities in a heat conductor

We consider an inverse problem of identifying the unknown cavities in a heat conductor. Using the Neumann-to-Dirichlet map as an input data, we develop a linear sampling type method for the heat equation. A new feature is that there is a freedom to choose the time variable, which suggests that we have more data than the linear sampling methods for the inverse boundary value problem associated with EIT and inverse scattering problem with near field data

Computing Volume Bounds of Inclusions by EIT Measurements

The size estimates approach for Electrical Impedance Tomography (EIT) allows for estimating the size (area or volume) of an unknown inclusion in an electrical conductor by means of one pair of boundary measurements of voltage and current. In this paper we show by numerical simulations how to obtain such bounds for practical application of the method. The computations are carried out both in a 2D and a 3D setting.Comment: 20 pages with figure

Inverse Modeling for MEG/EEG data

We provide an overview of the state-of-the-art for mathematical methods that are used to reconstruct brain activity from neurophysiological data. After a brief introduction on the mathematics of the forward problem, we discuss standard and recently proposed regularization methods, as well as Monte Carlo techniques for Bayesian inference. We classify the inverse methods based on the underlying source model, and discuss advantages and disadvantages. Finally we describe an application to the pre-surgical evaluation of epileptic patients.Comment: 15 pages, 1 figur

Regularized Linear Inversion with Randomized Singular Value Decomposition

In this work, we develop efficient solvers for linear inverse problems based on randomized singular value decomposition (RSVD). This is achieved by combining RSVD with classical regularization methods, e.g., truncated singular value decomposition, Tikhonov regularization, and general Tikhonov regularization with a smoothness penalty. One distinct feature of the proposed approach is that it explicitly preserves the structure of the regularized solution in the sense that it always lies in the range of a certain adjoint operator. We provide error estimates between the approximation and the exact solution under canonical source condition, and interpret the approach in the lens of convex duality. Extensive numerical experiments are provided to illustrate the efficiency and accuracy of the approach.Comment: 20 pages, 4 figure

Antitumor effect of oncolytic virus and paclitaxel encapsulated in extracellular vesicles for lung cancer treatment

Standard of care for cancer is commonly a combination of surgery with radiotherapy or chemoradiotherapy. However, in some advanced cancer patients this approach might still remaininefficient and may cause many side effects, including severe complications and even death. Oncolytic viruses exhibit different anti-cancer mechanisms compared with conventional therapies, allowing the possibility for improved effect in cancer therapy. Chemotherapeutics combined with oncolytic viruses exhibit stronger cytotoxic responses and oncolysis. Here, we have investigated the systemic delivery of the oncolytic adenovirus and paclitaxel encapsulated in extracellular vesicles (EV) formulation that, in vitro, significantly increased the transduction ratio and the infectious titer when compared with the virus and paclitaxel alone. We demonstrated that the obtained EV formulation reduced the in vivo tumor growth in animal xenograft model of human lung cancer. Indeed, we found that combined treatment of oncolytic adenovirus and paclitaxel encapsulated in EV has enhanced anticancer effects both in vitro and in vivo in lung cancer models. Transcriptomic comparison carried out on the explanted xenografts from the different treatment groups revealed that only 5.3% of the differentially expressed genes were overlapping indicating that a de novo genetic program is triggered by the presence of the encapsulated paclitaxel: this novel genetic program might be responsible of the observed enhanced antitumor effect. Our work provides a promising approach combining anticancer drugs and viral therapies by intravenous EV delivery as a strategy for the lung cancer treatment.Peer reviewe

RF thermal and new cold part design studies on TTF-III input coupler for Project-X

RF power coupler is one of the key components in superconducting (SC) linac. It provides RF power to the SC cavity and interconnects different temperature layers (1.8K, 4.2K, 70K and 300K). TTF-III coupler is one of the most promising candidates for the High Energy (HE) linac of Project X, but it cannot meet the average power requirements because of the relatively high temperature rise on the warm inner conductor, some design modifications will be required. In this paper, we describe our simulation studies on the copper coating thickness on the warm inner conductor with RRR value of 10 and 100. Our purpose is to rebalance the dynamic and static loads, and finally lower the temperature rise along the warm inner conductor. In addition, to get stronger coupling, better power handling and less multipacting probability, one new cold part design was proposed using 60mm coaxial line; the corresponding multipacting simulation studies have also been investigated.Comment: 5 pages, 12 figures. Submitted to Chinese Physics C (Formerly High Energy Physics and Nuclear Physics

Hierarchical Bayesian level set inversion

The level set approach has proven widely successful in the study of inverse problems for inter- faces, since its systematic development in the 1990s. Re- cently it has been employed in the context of Bayesian inversion, allowing for the quantification of uncertainty within the reconstruction of interfaces. However the Bayesian approach is very sensitive to the length and amplitude scales in the prior probabilistic model. This paper demonstrates how the scale-sensitivity can be cir- cumvented by means of a hierarchical approach, using a single scalar parameter. Together with careful con- sideration of the development of algorithms which en- code probability measure equivalences as the hierar- chical parameter is varied, this leads to well-defined Gibbs based MCMC methods found by alternating Metropolis-Hastings updates of the level set function and the hierarchical parameter. These methods demon- strably outperform non-hierarchical Bayesian level set methods