Evaluation of MR Elastography as a Noninvasive Diagnostic Test for Spontaneous Intracranial Hypotension.

BACKGROUND AND PURPOSE: Spontaneous intracranial hypotension is a condition resulting from a leak of CSF from the spinal canal arising independent of a medical procedure. Spontaneous intracranial hypotension can present with normal brain MR imaging findings and nonspecific symptoms, leading to the underdiagnosis in some patients and unnecessary invasive myelography in others who are found not to have the condition. Given the likelihood that spontaneous intracranial hypotension alters intracranial biomechanics, the goal of this study was to evaluate MR elastography as a potential noninvasive test to diagnose the condition. MATERIALS AND METHODS: We performed MR elastography in 15 patients with confirmed spontaneous intracranial hypotension from September 2022 to April 2023. Age, sex, symptom duration, and brain MR imaging Bern score were collected. MR elastography data were used to compute stiffness and damping ratio maps, and voxelwise modeling was performed to detect clusters of significant differences in mechanical properties between patients with spontaneous intracranial hypotension and healthy control participants. To evaluate diagnostic accuracy, we summarized each examination by 2 spatial pattern scores (one each for stiffness and damping ratio) and evaluated group-wise discrimination by receiver operating characteristic curve analysis. RESULTS: Patients with spontaneous intracranial hypotension exhibited significant differences in both stiffness and damping ratio (false discovery rate-corrected, Q < 0.05). Pattern analysis discriminated patients with spontaneous intracranial hypotension from healthy controls with an area under the curve of 0.97 overall, and the area under the curve was 0.97 in those without MR imaging findings of spontaneous intracranial hypotension. CONCLUSIONS: Results from this pilot study demonstrate MR elastography as a potential imaging biomarker and a noninvasive method for diagnosing spontaneous intracranial hypotension, including patients with normal brain MR imaging findings.

Prediction of Surgical Outcomes in Normal Pressure Hydrocephalus by MR Elastography.

BACKGROUND AND PURPOSE: Normal pressure hydrocephalus is a treatable cause of dementia associated with distinct mechanical property signatures in the brain as measured by MR elastography. In this study, we tested the hypothesis that specific anatomic features of normal pressure hydrocephalus are associated with unique mechanical property alterations. Then, we tested the hypothesis that summary measures of these mechanical signatures can be used to predict clinical outcomes. MATERIALS AND METHODS: MR elastography and structural imaging were performed in 128 patients with suspected normal pressure hydrocephalus and 44 control participants. Patients were categorized into 4 subgroups based on their anatomic features. Surgery outcome was acquired for 68 patients. Voxelwise modeling was performed to detect regions with significantly different mechanical properties between each group. Mechanical signatures were summarized using pattern analysis and were used as features to train classification models and predict shunt outcomes for 2 sets of feature spaces: a limited 2D feature space that included the most common features found in normal pressure hydrocephalus and an expanded 20-dimensional (20D) feature space that included features from all 4 morphologic subgroups. RESULTS: Both the 2D and 20D classifiers performed significantly better than chance for predicting clinical outcomes with estimated areas under the receiver operating characteristic curve of 0.66 and 0.77, respectively (P < .05, permutation test). The 20D classifier significantly improved the diagnostic OR and positive predictive value compared with the 2D classifier (P < .05, permutation test). CONCLUSIONS: MR elastography provides further insight into mechanical alterations in the normal pressure hydrocephalus brain and is a promising, noninvasive method for predicting surgical outcomes in patients with normal pressure hydrocephalus.

Engineering magnetic anisotropy and exchange couplings in double transition metal MXenes via surface defects.

Two-dimensional (2D) materials have been experimentally proven to manifest almost all types of material properties observed in bulk materials. However, 2D magnetism was elusive until recently. In this work, we used an approach that synergistically uses density functional theory, and Monte Carlo methods to investigate the magnetic and electronic properties of magnetic double transition metal MXene alloys (Hf2MnC2O2and Hf2VC2O2) by exploiting realistic surface terminations via creating surface defects including oxygen vacancies and H adatoms. We found that introducing surface oxygen vacancies or hydrogen adatoms is able to modify the electronic structures, magnetic anisotropies, and exchange couplings. Depending on the defect concentration, a ferromagnetic half-metallic state can be realized for both Hf2VC2O2and Hf2MnC2O2. Bare Hf2VC2O2exhibits easy-axis anisotropy, whereas bare Hf2MnC2O2exhibits easy-plane anisotropy; however, defects can change the latter to easy-axis anisotropy, which is preferable for spintronics applications. The considered defects were found to modify the magnetic anisotropy by as much as 300%. Defects also produce an inhomogeneous pattern of exchange couplings, which can further enhance the Curie temperature. In particular, Hf2MnC2O2H0.22was predicted to have a Curie temperature of about 171 K due to a combination of easy-axis anisotropy and a connected network of enhanced exchange couplings. Our calculations suggest a route toward engineering exchange couplings and magnetic anisotropy to improve magnetic properties.

Quantum vortex melting and superconductor insulator transition in a 2D Josephson junction array in a perpendicular magnetic field via diffusion Monte Carlo.

In this study, we simulated a quantum rotor model describing a Josephson junction array (JJA) in the phase representation at zero temperature in a perpendicular magnetic field [Formula: see text] (in units of [Formula: see text]) on a [Formula: see text] square lattice with spacing a for [Formula: see text]. The superconductor-insulator transition (SIT) is tuned by the ratio of charging energy to Josephson coupling, U/J. Abrupt drops in the magnetization values were observed in the bigger lattices at certain values of B and U/J caused by the formation of vortices. Increasing U/J at a fixed B field causes quantum vortex melting. The magnetization drops to zero around [Formula: see text] indicating SIT. For B = 0.1 the SIT occurs without an intermediate vortex state and the magnetization scales as [Formula: see text], whereas for B = 0.4 the scaling is [Formula: see text] during the vortex melting. For B between 0.1 and 0.4 the scaling is not clear. We used the diffusion Monte Carlo (DMC) method with a guiding wavefunction optimized using the variational Monte Carlo (VMC) method. The ground state energy is calculated easily in DMC and its error estimates were generally smaller than [Formula: see text], both with and without the guiding wavefunction. Quantities like magnetization and vorticity that do not commute with the Hamiltonian were calculated using an efficient forward walking algorithm. Their estimates are affected severely in absence of the guiding wavefunction. With the guiding wavefunction, errors for the magnetization were generally less than [Formula: see text] and going up to [Formula: see text] percent around the phase transition from the Meissner to the vortex state, and without the guiding wavefunction errors were generally higher than [Formula: see text] and going up to [Formula: see text] around the critical point.

Dielectric and diamagnetic susceptibilities near percolative superconductor-insulator transitions.

Coarse-grained superconductor-insulator composites exhibit a superconductor-insulator transition governed by classical percolation, which should be describable by networks of inductors and capacitors. We study several classes of random inductor-capacitor networks on square lattices. We present a unifying framework for defining electric and magnetic response functions, and we extend the Frank-Lobb bond-propagation algorithm to compute these quantities by network reduction. We confirm that the superfluid stiffness scales approximately as [Formula: see text] as the superconducting bond fraction p approaches the percolation threshold p c . We find that the diamagnetic susceptibility scales as [Formula: see text] below percolation, and as [Formula: see text] above percolation. For models lacking self-capacitances, the electric susceptibility scales as [Formula: see text]. Including a self-capacitance on each node changes the critical behavior to approximately [Formula: see text].

Critical exponents of dynamical conductivity in 2D percolative superconductor-insulator transitions: three universality classes.

We simulate three types of random inductor-capacitor (LC) networks on [Formula: see text] square lattices. We calculate the dynamical conductivity using an equation-of-motion method in which timestep error is eliminated and windowing error is minimized. We extract the critical exponent a such that [Formula: see text] at low frequencies. The results suggest that there are three different universality classes. The [Formula: see text] model, with capacitances from each site to ground, has a = 0.314(4). The [Formula: see text] model, with capacitances along bonds, has a = 0. The [Formula: see text] model, with both types of capacitances, has a = 0.304(1). This implies that classical percolative 2D superconductor-insulator transitions (SITs) generically have [Formula: see text] as [Formula: see text]. Therefore, any experiments that give a constant conductivity as [Formula: see text] must be explained in terms of quantum effects.