Serial Correlations in Single-Subject fMRI with Sub-Second TR

When performing statistical analysis of single-subject fMRI data, serial correlations need to be taken into account to allow for valid inference. Otherwise, the variability in the parameter estimates might be under-estimated resulting in increased false-positive rates. Serial correlations in fMRI data are commonly characterized in terms of a first-order autoregressive (AR) process and then removed via pre-whitening. The required noise model for the pre-whitening depends on a number of parameters, particularly the repetition time (TR). Here we investigate how the sub-second temporal resolution provided by simultaneous multislice (SMS) imaging changes the noise structure in fMRI time series. We fit a higher-order AR model and then estimate the optimal AR model order for a sequence with a TR of less than 600 ms providing whole brain coverage. We show that physiological noise modelling successfully reduces the required AR model order, but remaining serial correlations necessitate an advanced noise model. We conclude that commonly used noise models, such as the AR(1) model, are inadequate for modelling serial correlations in fMRI using sub-second TRs. Rather, physiological noise modelling in combination with advanced pre-whitening schemes enable valid inference in single-subject analysis using fast fMRI sequences

Warum soziale Netzwerke nicht nur zur sozialen Belastung werden: Ein Beitrag aus sozialpsychologischer Perspektive

Internetbasierte soziale Netzwerke sind ein generationenübergreifendes Gesellschaftsphänomen, das zum Gegenstand einer Reihe von (sozial-)psychologischen Fragestellungen geworden ist. Die im Folgenden skizzierten Forschungsbereiche und Erkenntnisse sollen eine erste Orientierung bieten und Schlaglichter auf positive und auf negative Aspekte sozialer Netzwerke für das psychische Wohlbefinden werfen

Mass flow, energy flow and costs of the German building stock

On behalf of a committee of the German parliament, a study of the mass, energy and monetary flows of the German building stock has been initated. The approach was macro-economic (top-down) for the calculation of the overall flows and process oriented (bottom-up)for the detailed flows created by new construction, refurbishment, demolition and utilisation of buildings. The building stock has been modelled with a stochastic replacement model for a reference population of 160 building materials. Specific data sets were used for the upstream and downstream parts. A scenario for the development of the building stock and the induced mass, energy and monetary flows was established

Accelerated mapping of magnetic susceptibility using 3D planes-on-a-paddlewheel (POP) EPI at ultra-high field strength

With the advent of ultra-high field MRI scanners in clinical research, susceptibility based MRI has recently gained increasing interest because of its potential to assess subtle tissue changes underlying neurological pathologies/disorders. Conventional, but rather slow, three-dimensional (3D) spoiled gradient-echo (GRE) sequences are typically employed to assess the susceptibility of tissue. 3D echo-planar imaging (EPI) represents a fast alternative but generally comes with echo-time restrictions, geometrical distortions and signal dropouts that can become severe at ultra-high fields. In this work we assess quantitative susceptibility mapping (QSM) at 7T using non-Cartesian 3D EPI with a planes-on-a-paddlewheel (POP) trajectory, which is created by rotating a standard EPI readout train around its own phase encoding axis. We show that the threefold accelerated non-Cartesian 3D POP EPI sequence enables very fast, whole brain susceptibility mapping at an isotropic resolution of 1mm and that the high image quality has sufficient signal-to-noise ratio in the phase data for reliable QSM processing. The susceptibility maps obtained were comparable with regard to QSM values and geometric distortions to those calculated from a conventional 4min 3D GRE scan using the same QSM processing pipeline

Comparison of data-driven uncertainty quantification methods for a carbon dioxide storage benchmark scenario

A variety of methods is available to quantify uncertainties arising with\-in the modeling of flow and transport in carbon dioxide storage, but there is a lack of thorough comparisons. Usually, raw data from such storage sites can hardly be described by theoretical statistical distributions since only very limited data is available. Hence, exact information on distribution shapes for all uncertain parameters is very rare in realistic applications. We discuss and compare four different methods tested for data-driven uncertainty quantification based on a benchmark scenario of carbon dioxide storage. In the benchmark, for which we provide data and code, carbon dioxide is injected into a saline aquifer modeled by the nonlinear capillarity-free fractional flow formulation for two incompressible fluid phases, namely carbon dioxide and brine. To cover different aspects of uncertainty quantification, we incorporate various sources of uncertainty such as uncertainty of boundary conditions, of conceptual model definitions and of material properties. We consider recent versions of the following non-intrusive and intrusive uncertainty quantification methods: arbitary polynomial chaos, spatially adaptive sparse grids, kernel-based greedy interpolation and hybrid stochastic Galerkin. The performance of each approach is demonstrated assessing expectation value and standard deviation of the carbon dioxide saturation against a reference statistic based on Monte Carlo sampling. We compare the convergence of all methods reporting on accuracy with respect to the number of model runs and resolution. Finally we offer suggestions about the methods' advantages and disadvantages that can guide the modeler for uncertainty quantification in carbon dioxide storage and beyond

Akustische Tomographie zur gleichzeitigen Bestimmung von Temperatur- und Strömungsfeldern in Innenräumen

Das Verfahren der akustischen Laufzeittomographie nutzt die Abhängigkeit der Schallgeschwindigkeit von den Parametern Temperatur und Strömung entlang des Ausbreitungsweges akustischer Signale, um diese Parameter zu bestimmen. Es wird ein Algorithmus vorgestellt, der eine tomographische Rekonstruktion der 2-dimensionalen Strömungsfelder innerhalb eines Messgebietes erlaubt, wobei die räumliche Auflösung des Vektorfeldes der Auflösung des Temperaturfeldes entspricht. Neben Ergebnissen von Simulationen verschiedener Strömungssituationen wird eine Anwendung vorgestellt, welches die Anwendbarkeit des Verfahrens zur Detektion von Strömungs- und Temperaturverteilung in einem abgeschlossenen Raum demonstriert.Acoustic travel time tomography uses the dependency of sound speed from temperature and flow properties along the propagation path to measure these parameters. An algorithm is introduced which is capable of resolving the two-dimensional flow field within a certain measuring area comparable to the resolution of the temperature field. Different flow fields have been simulated in order to show the reconstruction properties of the algorithm. Furthermore an experiment has been carried out, which demonstrates the applicability of the acoustic tomographic method to detect temperature and flow fields indoor

Croconaine-Based Polymer Particles as Contrast Agents for Photoacoustic Imaging

In the development and optimization of imaging methods, photoacoustic imaging (PAI) has become a powerful tool for preclinical biomedical diagnosis and detection of cancer. PAI probes can improve contrast and help identify pathogenic tissue. Such contrast agents must meet several requirements: they need to be biocompatible, and absorb strongly in the near-infrared (NIR) range, while relaxing the photoexcited state thermally and not radiatively. In this work, polymer nanoparticles are produced with croconaine as a monomer unit. Small molecular croconaine dyes are known to act as efficient pigments, which do not show photoluminescence. Here, for the first time croconaine copolymer nanoparticles are produced from croconic acid and a range of aromatic diamines. Following a dispersion polymerization protocol, this approach yields monodisperse particles of adjustable size. All synthesized polymers exhibit broad absorption within the NIR spectrum and therefore represent suitable candidates as contrast agents for PAI. The optical properties of these polymer particles are discussed with respect to the relation between particle size and outstanding photoacoustic performance. Biocompatibility of the polymer particles is demonstrated in cell viability experiments. © The Authors. Published by Wiley-VCH Gmb

Aerosol microphysical impact on summertime convective precipitation in the Rocky Mountain region

We present an aerosol-cloud-precipitation modeling study of convective clouds using the Weather Research and Forecasting model fully coupled with Chemistry (WRF-Chem) version 3.1.1. Comparison of the model output with measurements from a research site in the Rocky Mountains in Colorado revealed that the fraction of organics in the model is underpredicted. This is most likely due to missing processes in the aerosol module in the model version used, such as new particle formation and growth of secondary organic aerosols. When boundary conditions and domain-wide initial conditions of aerosol loading are changed in the model (factors of 0.1, 0.2, and 10 of initial aerosol mass of SO4-2, NH4+, and NO3-), the domain-wide precipitation changes by about 5%. Analysis of the model results reveals that the Rocky Mountain region and Front Range environment is not conducive for convective invigoration to play a major role, in increasing precipitation, as seen in some other studies. When localized organic aerosol emission are increased to mimic new particle formation, the resulting increased aerosol loading leads to increases in domain-wide precipitation, opposite to what is seen in the model simulations with changed boundary and initial conditions