Pavuluri, KowsalyaDevi

Abstract Vascular brain injury results in loss of structural and functional connectivity and leads to cognitive impairment. Its various manifestations, including microinfarcts, microhaemorrhages and white matter hyperintensities, result in microstructural tissue integrity loss and secondary neurodegeneration. Among these, tissue microstructural alteration is a relatively early event compared with atrophy along the aging and neurodegeneration continuum. Understanding its association with cognition may provide the opportunity to further elucidate the relationship between vascular health and clinical outcomes. Magnetic resonance elastography offers a non-invasive approach to evaluate tissue mechanical properties, providing a window into the microstructural integrity of the brain. This retrospective study evaluated brain stiffness as a potential biomarker for vascular brain injury and its role in mediating the impact of vascular dysfunction on cognitive impairment. Seventy-five participants from the Mayo Clinic Study of Aging underwent brain imaging using a 3T MR imager with a spin-echo echo-planar imaging sequence for magnetic resonance elastography and T1- and T2-weighted pulse sequences. This study evaluated the effects of vascular biomarkers (white matter hyperintensities and cardiometabolic condition score) on brain stiffness using voxelwise analysis. Partial correlation analysis explored associations between brain stiffness, white matter hyperintensities, cardiometabolic condition and global cognition. Mediation analysis determined the role of stiffness in mediating the relationship between vascular biomarkers and cognitive performance. Statistical significance was set at P-values < 0.05. Diagnostic accuracy of magnetic resonance elastography stiffness for white matter hyperintensities and cardiometabolic condition was evaluated using receiver operator characteristic curves. Voxelwise linear regression analysis indicated white matter hyperintensities negatively correlate with brain stiffness, specifically in periventricular regions with high white matter hyperintensity levels. A negative association between cardiovascular risk factors and stiffness was also observed across the brain. No significant patterns of stiffness changes were associated with amyloid load. Global stiffness (µ) negatively correlated with both white matter hyperintensities and cardiometabolic condition when all other covariables including amyloid load were controlled. The positive correlation between white matter hyperintensities and cardiometabolic condition weakened and became statistically insignificant when controlling for other covariables. Brain stiffness and global cognition were positively correlated, maintaining statistical significance after adjusting for all covariables. These findings suggest mechanical alterations are associated with cognitive dysfunction and vascular brain injury. Brain stiffness significantly mediated the indirect effects of white matter hyperintensities and cardiometabolic condition on global cognition. Local cerebrovascular diseases (assessed by white matter hyperintensities) and systemic vascular risk factors (assessed by cardiometabolic condition) impact brain stiffness with spatially and statistically distinct effects. Global brain stiffness is a significant mediator between vascular disease measures and cognitive function, highlighting the value of magnetic resonance elastography-based mechanical assessments in understanding this relationship.

AbstractBackgroundCognitive decline with aging and neurodegeneration is caused by various neurobiological processes at molecular, cellular, and organ levels. Advanced imaging techniques that detect tissue microstructural alterations may predict cognitive decline earlier than atrophy measures. Tissue‐level mechanical properties are thought to primarily reflect the rigidity and tension of extracellular matrix‐cytoskeleton matrix (https://doi.org/10.1038/s41586‐019‐1516‐5) and can be measured noninvasively by Magnetic Resonance Elastography (MRE) (https://doi.org/10.1126/science.7569924). These properties are sensitive to pathophysiological processes that characterize neurological disease including dementias. This study examines the role of medial temporal lobe stiffness in predicting global cognitive decline in participants along the Alzheimer’s disease spectrum.MethodSeventy‐five participants underwent MRE, MRI, amyloid PET imaging, and cognitive assessments with informed consent, and the study was approved by the Mayo Clinic IRB. MRE stiffness maps were calculated by neural network inversion, and medial temporal (MT) stiffness extracted (https://doi.org/10.1002/mrm.29226https://doi.org/10.1016/j.nicl.2023.103328). White matter hyperintensity (WMH) load, average cortical thickness of AD signature regions, PiB‐PET standardized uptake ratio (PiB‐SUVr), and global cognition z‐scores were each computed (https://doi.org/10.1016/j.neurobiolaging.2020.09.005). Linear mixed effect models were used to assess the effects of MT‐stiffness and its interaction with follow‐up time on global cognition. The additional effects of cortical thickness, WMH, and PiB‐SUVr, and their interactions with cognition follow‐up time were evaluated.ResultTable 1 summarizes results from three mixed effect models. MT‐stiffness and its interaction with time were significant predictors in models 1 (MT‐stiffness only) and 2 (MT‐stiffness+cortical thickness). In model 3 (MT‐stiffness+cortical thickness+PiB‐ratio+WMH), MT‐stiffness alone did not reach significance (P= 0.058), but its interaction with time was a significant predictor. Fig.1 shows global cognition z‐scores with age. Figs. 2A,2B, and 2C show global cognition prediction trajectories. These results suggest that softer MT‐stiffness predicts faster cognitive decline. MT‐stiffness was a significant predictor of cognitive score even when controlling for other biomarkers such as cortical thickness, PiB‐SUVr, and WMH. These results indicate that stiffness measurements can add predictive value regarding cognitive decline in addition to atrophy and amyloidosis.ConclusionBrain mechanical alterations capture microstructural changes that may aid in understanding disease progression and provide information about future cognitive decline in elderly participants along the Alzheimer’s disease continuum.

Abstract High-fidelity control of spin ensemble dynamics is essential for many research areas, spanning from quantum computing and radio-frequency (RF) engineering to NMR spectroscopy and imaging. However, attaining robust and high-fidelity spin operations remains an unmet challenge. Using an evolutionary algorithm and artificial intelligence (AI), we designed new RF pulses with customizable spatial or temporal field inhomogeneity compensation. Compared with the standard RF shapes, the new AI-generated pulses show superior performance for bandwidth, robustness, and tolerance to field imperfections. As a benchmark, we constructed a spin entanglement operator for the weakly coupled two-spin-1/2 system of 13CHCl3, achieving high-fidelity transformations under multiple inhomogeneity sources. We then generated band-selective and ultra-broadband RF pulses typical of biomolecular NMR spectroscopy. When implemented in multipulse NMR experiments, the AI-generated pulses significantly increased the sensitivity of medium-size and large protein spectra relative to standard pulse sequences. Finally, we applied the new pulses to typical imaging experiments, showing a remarkable tolerance to changes in the RF field. These AI-generated RF pulses can be directly implemented in quantum information, NMR spectroscopy of biomolecules, magnetic resonance imaging techniques for in vivo and materials sciences.

Water devoid RF pulses designed by an evolutionary algorithm and AI improve solvent suppression and increase the NMR spectra sensitivity.

PurposeInversion algorithms used to convert acquired MR elastography wave data into material property estimates often assume that the underlying materials are locally homogeneous. Here we evaluate the impact of that assumption on stiffness estimates in gray‐matter regions of interest in brain MR elastography.MethodsWe describe an updated neural network inversion framework using finite‐difference model–derived data to train convolutional neural network inversion algorithms. Neural network inversions trained on homogeneous simulations (homogeneous learned inversions [HLIs]) or inhomogeneous simulations (inhomogeneous learned inversions [ILIs]) are generated with a variety of kernel sizes. These inversions are evaluated in a brain MR elastography simulation experiment and in vivo in a test–retest repeatability experiment including 10 healthy volunteers.ResultsIn simulation and in vivo, HLI and ILI with small kernels produce similar results. As kernel size increases, the assumption of homogeneity has a larger effect, and HLI and ILI stiffness estimates show larger differences. At each inversion's optimal kernel size in simulation (7 × 7 × 7 for HLI, 11 × 11 × 11 for ILI), ILI is more sensitive to true changes in stiffness in gray‐matter regions of interest in simulation. In vivo, there is no difference in the region‐level repeatability of stiffness estimates between the inversions, although ILI appears to better maintain the stiffness map structure as kernel size increases, while decreasing the spatial variance in stiffness estimates.ConclusionsThis study suggests that inhomogeneous inversions provide small but significant benefits even when large stiffness gradients are absent.

Abstract High-fidelity control of spin ensemble dynamics is essential to many fields, spanning from quantum computing to optical, coherent, and nuclear magnetic resonance (NMR) spectroscopy and imaging (MRI). However, attaining robust and high-fidelity quantum spin operations remains an unmet challenge. Using a combination of an evolutionary algorithm and artificial intelligence, we designed time-optimal, radio frequency (RF) pulses with tunable spatial or temporal field inhomogeneity compensation and fidelity for unitary operations up to 0.9999. As a benchmark, we constructed a spin entanglement operator and a programmable quantum state creator for a weakly-coupled two-spin-1/2 system. We achieved high-fidelity transformations under multiple inhomogeneity sources. The newly designed RF pulses are more robust and less prone to imperfection than the commonly used shapes for basic liquid-state NMR experiments and reduce RF artifacts in MRI. This new strategy will enable the design of efficient quantum computing opera-tors as well as new spectroscopic and imaging techniques.