Generating Diffusion MRI scalar maps from T1 weighted images using generative adversarial networks

Diffusion magnetic resonance imaging (diffusion MRI) is a non-invasive microstructure assessment technique. Scalar measures, such as FA (fractional anisotropy) and MD (mean diffusivity), quantifying micro-structural tissue properties can be obtained using diffusion models and data processing pipelines. However, it is costly and time consuming to collect high quality diffusion data. Here, we therefore demonstrate how Generative Adversarial Networks (GANs) can be used to generate synthetic diffusion scalar measures from structural T1-weighted images in a single optimized step. Specifically, we train the popular CycleGAN model to learn to map a T1 image to FA or MD, and vice versa. As an application, we show that synthetic FA images can be used as a target for non-linear registration, to correct for geometric distortions common in diffusion MRI

Hamiltonian Formalism in Quantum Mechanics

Heisenberg motion equations in Quantum mechanics can be put into the Hamilton form. The difference between the commutator and its principal part, the Poisson bracket, can be accounted for exactly. Canonical transformations in Quantum mechanics are not, or at least not what they appear to be; their properties are formulated in a series of Conjectures

Learning-based Ensemble Average Propagator Estimation

By capturing the anisotropic water diffusion in tissue, diffusion magnetic resonance imaging (dMRI) provides a unique tool for noninvasively probing the tissue microstructure and orientation in the human brain. The diffusion profile can be described by the ensemble average propagator (EAP), which is inferred from observed diffusion signals. However, accurate EAP estimation using the number of diffusion gradients that is clinically practical can be challenging. In this work, we propose a deep learning algorithm for EAP estimation, which is named learning-based ensemble average propagator estimation (LEAPE). The EAP is commonly represented by a basis and its associated coefficients, and here we choose the SHORE basis and design a deep network to estimate the coefficients. The network comprises two cascaded components. The first component is a multiple layer perceptron (MLP) that simultaneously predicts the unknown coefficients. However, typical training loss functions, such as mean squared errors, may not properly represent the geometry of the possibly non-Euclidean space of the coefficients, which in particular causes problems for the extraction of directional information from the EAP. Therefore, to regularize the training, in the second component we compute an auxiliary output of approximated fiber orientation (FO) errors with the aid of a second MLP that is trained separately. We performed experiments using dMRI data that resemble clinically achievable $q$-space sampling, and observed promising results compared with the conventional EAP estimation method.Comment: Accepted by MICCAI 201

Justice and the Human Alarm System: The Impact of Exclamation Points and Flashing Lights on the Justice Judgment Process

Extending theory within the justice domain and work on the human alarm system, the current paper argues that the process by which justice judgments are formed may be influenced reliably by the activation of psychological systems that people use to detect and handle alarming situations. Building on this analysis, it is further proposed that if this line of reasoning is true then presenting alarm-related stimuli, such as exclamation points and flashing lights, to people should lead to more extreme judgments about subsequent justice-related events than not presenting these alarming stimuli. Findings collected using different experimental paradigms provide evidence supporting these predictions both inside and outside the psychology lab. Implications for the social psychology of justice and the human alarm system literature are discussed

Some Siegel modular standard L-values, and Shafarevich–Tate groups

AbstractWe explain how the Bloch–Kato conjecture leads us to the following conclusion: a large prime dividing a critical value of the L-function of a classical Hecke eigenform f of level 1, should often also divide certain ratios of critical values for the standard L-function of a related genus two (and in general vector-valued) Hecke eigenform F. The relation between f and F (Harderʼs conjecture in the vector-valued case) is a congruence involving Hecke eigenvalues, modulo the large prime. In the scalar-valued case we prove the divisibility, subject to weak conditions. In two instances in the vector-valued case, we confirm the divisibility using elaborate computations involving special differential operators. These computations do not depend for their validity on any unproved conjecture

Long Range Magnetic Order and the Darwin Lagrangian

We simulate a finite system of $N$ confined electrons with inclusion of the Darwin magnetic interaction in two- and three-dimensions. The lowest energy states are located using the steepest descent quenching adapted for velocity dependent potentials. Below a critical density the ground state is a static Wigner lattice. For supercritical density the ground state has a non-zero kinetic energy. The critical density decreases with $N$ for exponential confinement but not for harmonic confinement. The lowest energy state also depends on the confinement and dimension: an antiferromagnetic cluster forms for harmonic confinement in two dimensions.Comment: 5 figure

Retinotopic organization of human visual cortex mapped with positron-emission tomography

The retinotopic organization of primary visual cortex was mapped in normal human volunteers. Positron-emission tomographic measurements of regional cerebral blood flow were employed to detect focal functional brain activation. Oxygen-15-labeled water, delivered by intravenous bolus, was used as the blood flow tracer to allow multiple stimulated- state (n = 5) and control-state (n = 3) measurements to be acquired for each of 7 subjects. Responses were identified by applying a maximum- detection algorithm to subtraction-format images of the stimulus- induced change in cerebral blood flow. Response locales were described using a standardized system of stereotactic coordinates. Changes in stimulus location (macular, perimacular, peripheral, upper-field, lower- field) caused systematic, highly significant changes in response locale within visual cortex. Discrete extrastriate visual responses were also observed

Retinotopic organization of human visual cortex mapped with positron-emission tomography

The retinotopic organization of primary visual cortex was mapped in normal human volunteers. Positron-emission tomographic measurements of regional cerebral blood flow were employed to detect focal functional brain activation. Oxygen-15-labeled water, delivered by intravenous bolus, was used as the blood flow tracer to allow multiple stimulated- state (n = 5) and control-state (n = 3) measurements to be acquired for each of 7 subjects. Responses were identified by applying a maximum- detection algorithm to subtraction-format images of the stimulus- induced change in cerebral blood flow. Response locales were described using a standardized system of stereotactic coordinates. Changes in stimulus location (macular, perimacular, peripheral, upper-field, lower- field) caused systematic, highly significant changes in response locale within visual cortex. Discrete extrastriate visual responses were also observed

Training for the future

Aim The aim of this prospective study was to examine the association between behavioural problems and medical and psychological outcomes in clinically treated children and adolescents with asthma. Methods Patients (n=134) were recruited from two high-altitude asthma clinics in Switzerland and one asthma clinic in the Netherlands. Outcome measures were Asthma Control Test (ACT), Paediatric Asthma Quality of Life Questionnaire (PAQLQ(S)), forced expiratory volume in 1sec (FEV1) and fractional concentration of exhaled nitric oxide (FeNO). Parents completed the Child Behaviour Checklist (CBCL) (predictor variable). Data were collected at the start and end of treatment. Multiple regression analysis was used while adjusting for demographic variables, clinic and length of stay. Results More severe internalizing behavioural problems were associated with less improvement of total quality of life (t=2.26, p=0.03) and the domains symptoms (t=2.04, p=0.04) and emotions (t=2.3, p=0.02) after clinical treatment. Behavioural problems were not associated with a change of lung function measurements (FEV1 and FeNO) and asthma control (ACT) during treatment. Conclusion A focus of healthcare professionals on the treatment of internalizing behavioural problems may optimize the quality of life in clinically treated youth with asthma

The construct of institutional distance through the lens of different institutional perspectives:Review, analysis, and recommendations

This paper presents a review and critique of the 20-year-old literature on institutional distance, which has greatly proliferated. We start with a discussion of the three institutional perspectives that have served as a theoretical foundation for this construct: organizational institutionalism, institutional economics, and comparative institutionalism. We use this as an organizing framework to describe the different ways in which institutional distance has been conceptualized and measured, and to analyze the most common organizational outcomes that have been linked to institutional distance, as well as the proposed explanatory mechanisms of those effects. We substantiate our qualitative review with a meta-analysis, which synthesizes the main findings in this area of research. Building on our review and previous critical work, we note key ambiguities in the institutional distance literature related to underlying theoretical perspectives and associated mechanisms, distance versus profile effects, and measurement. We conclude with actionable recommendations for improving institutional distance research