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Aging and neurodegenerative diseases lead to decline in thinking and memory ability. The subfields of the hippocampus (HCsf) play important roles in memory formation and recall. Imaging techniques sensitive to the underlying HCsf tissue microstructure can reveal unique structure–function associations and their vulnerability in aging and disease. The goal of this study was to use magnetic resonance elastography (MRE), a noninvasive MR imaging-based technique that can quantitatively image the viscoelastic mechanical properties of tissue to determine the associations of HCsf stiffness with different cognitive domains across the lifespan. Eighty-eight adult participants completed the study (age 23–81 years, male/female 36/51), in which we aimed to determine which HCsf regions most strongly correlated with different memory performance outcomes and if viscoelasticity of specific HCsf regions mediated the relationship between age and performance. Our results revealed that both interference cost on a verbal memory task and relational memory task performance were significantly related to cornu ammonis 1–2 (CA1–CA2) stiffness (p= 0.018 andp= 0.011, respectively), with CA1–CA2 stiffness significantly mediating the relationship between age and interference cost performance (p= 0.031). There were also significant associations between delayed free verbal recall performance and stiffness of both the dentate gyrus–cornu ammonis 3 (DG–CA3;p= 0.016) and subiculum (SUB;p= 0.032) regions. This further exemplifies the functional specialization of HCsf in declarative memory and the potential use of MRE measures as clinical biomarkers in assessing brain health in aging and disease.SIGNIFICANCE STATEMENTHippocampal subfields are cytoarchitecturally unique structures involved in distinct aspects of memory processing. Magnetic resonance elastography is a technique that can noninvasively image tissue viscoelastic mechanical properties, potentially serving as sensitive biomarkers of aging and neurodegeneration related to functional outcomes. High-resolutionin vivoimaging has invigorated interest in determining subfield functional specialization and their differential vulnerability in aging and disease. Applying MRE to probe subfield-specific cognitive correlates will indicate that measures of subfield stiffness can determine the integrity of structures supporting specific domains of memory performance. These findings will further validate our high-resolution MRE method and support the potential use of subfield stiffness measures as clinical biomarkers in classifying aging and disease states.

The quality of language that children hear in their environment is associated with the development of language-related brain regions, in turn promoting vocabulary knowledge. Although informative, it remains unknown how these environmental influences alter the structure of neural tissue and subsequent vocabulary outcomes. The current study uses magnetic resonance elastography (MRE) to examine how children’s language environments underlie brain tissue mechanical properties, characterized as brain tissue stiffness and damping ratio, and promote vocabulary knowledge. Twenty-five children, ages 5–7, had their audio and video recorded while engaging in a play session with their parents. Children also completed the Picture Vocabulary Task (from NIH Toolbox) and participated in an MRI, where MRE and anatomical images were acquired. Higher quality input was associated with greater stiffness in the bilateral inferior frontal gyrus and right superior temporal gyrus, whereas greater vocabulary knowledge was associated with lower damping ratio in the right inferior frontal gyrus. These findings suggest changes in neural tissue composition are sensitive to malleable aspects of the environment, whereas tissue organization is more strongly associated with vocabulary outcome. Notably, these associations were independent of maternal education, suggesting more proximal measures of a child’s environment may be the source of differences in neural tissue structure underlying variability in vocabulary outcomes.

Brain age is a quantitative estimate to explain an individual’s structural and functional brain measurements relative to the overall population and is particularly valuable in describing differences related to developmental or neurodegenerative pathology. Accurately inferring brain age from brain imaging data requires sophisticated models that capture the underlying age-related brain changes. Magnetic resonance elastography (MRE) is a phase contrast MRI technology that uses external palpations to measure brain mechanical properties. Mechanical property measures of viscoelastic shear stiffness and damping ratio have been found to change across the entire life span and to reflect brain health due to neurodegenerative diseases and even individual differences in cognitive function. Here we develop and train a multi-modal 3D convolutional neural network (CNN) to model the relationship between age and whole brain mechanical properties. After training, the network maps the measurements and other inputs to a brain age prediction. We found high performance using the 3D maps of various mechanical properties to predict brain age. Stiffness maps alone were able to predict ages of the test group subjects with a mean absolute error (MAE) of 3.76 years, which is comparable to single inputs of damping ratio (MAE: 3.82) and outperforms single input of volume (MAE: 4.60). Combining stiffness and volume in a multimodal approach performed the best, with an MAE of 3.60 years, whereas including damping ratio worsened model performance. Our results reflect previous MRE literature that had demonstrated that stiffness is more strongly related to chronological age than damping ratio. This machine learning model provides the first prediction of brain age from brain biomechanical data—an advancement towards sensitively describing brain integrity differences in individuals with neuropathology.

PURPOSE: Magnetic resonance elastography (MRE) uses phase-contrast MRI to generate mechanical property maps of the in vivo brain through imaging of tissue deformation from induced mechanical vibration. The mechanical property estimation process in MRE can be susceptible to noise from physiological and mechanical sources encoded in the phase, which is expected to be highly correlated. This correlated noise has yet to be characterized in brain MRE, and its effects on mechanical property estimates computed using inversion algorithms are undetermined. METHODS: To characterize the effects of signal noise in MRE, we conducted three experiments quantifying (1) physiomechanical sources of signal noise, (2) physiological noise due to cardiac-induced movement, and (3) impact of correlated noise on mechanical property estimates. We employ a correlation length metric to estimate the extent that correlated signal persists in MRE images and demonstrate the effect of correlated noise on property estimates through simulations. RESULTS: We found that both physiological noise and vibration noise were greater than image noise and were spatially correlated across all subjects. Added physiological and vibration noise to simulated data resulted in property maps with higher error than equivalent levels of Gaussian noise. CONCLUSION: Our work provides the foundation to understand contributors to brain MRE data quality and provides recommendations for future work to correct for signal noise in MRE.

MR elastography (MRE) is a technique to characterize brain mechanical properties in vivo. Due to the need to capture tissue deformation in multiple directions over time, MRE is an inherently long acquisition, which limits achievable resolution and use in challenging populations. The purpose of this work is to develop a method for accelerating MRE acquisition by using low-rank image reconstruction to exploit inherent spatiotemporal correlations in MRE data.The proposed MRE sampling and reconstruction method, OSCILLATE (Observing Spatiotemporal Correlations for Imaging with Low-rank Leveraged Acceleration in Turbo Elastography), involves alternating which k-space points are sampled between each repetition by a reduction factor, RDecomposition of MRE displacement data demonstrated that, on average, 96.1% of all energy from an MRE dataset is captured at rank L = 12 (reduced from a full rank of 24). Retrospectively undersampling data with ROSCILLATE produces whole-brain MRE data at 2 mm isotropic resolution in 1 min 48 s.

Cerebral palsy is a neurodevelopmental movement disorder that affects coordination and balance. Therapeutic treatments for balance deficiencies in this population primarily focus on the musculoskeletal system, whereas the neural basis of balance impairment is often overlooked. Magnetic resonance elastography (MRE) is an emerging technique that has the ability to sensitively assess microstructural brain health through in vivo measurements of neural tissue stiffness. Using magnetic resonance elastography, we have previously measured significantly softer grey matter in children with cerebral palsy as compared with typically developing children. To further allow magnetic resonance elastography to be a clinically useful tool in rehabilitation, we aim to understand how brain stiffness in children with cerebral palsy is related to dynamic balance reaction performance as measured through anterior and posterior single-stepping thresholds, defined as the standing perturbation magnitudes that elicit anterior or posterior recovery steps. We found that global brain stiffness is significantly correlated with posterior stepping thresholds ( P = .024) such that higher brain stiffness was related to better balance recovery. We further identified specific regions of the brain where stiffness was correlated with stepping thresholds, including the precentral and postcentral gyri, the precuneus and cuneus, and the superior temporal gyrus. Identifying brain regions affected in cerebral palsy and related to balance impairment can help inform rehabilitation strategies targeting neuroplasticity to improve motor function.

Traumatic brain injury (TBI) is a major health concern affecting both military and civilian populations. Despite notable advances in TBI research in recent years, there remains a significant gap in linking the impulsive loadings from a blast or a blunt impact to the clinical injury patterns observed in TBI. Synthetic head models or phantoms can be used to establish this link as they can be constructed with geometry, anatomy, and material properties that match the human brain, and can be used as an alternative to animal models. This study presents one such phantom called the Anthropomorphic Neurologic Gyrencephalic Unified Standard (ANGUS) phantom, which is an idealized gyrencephalic brain phantom composed of polyacrylamide gel. Here we mechanically characterized the ANGUS phantom using tagged magnetic resonance imaging (MRI) and magnetic resonance elastography (MRE), and then compared the outcomes to data obtained in healthy volunteers. The direct comparison between the phantom’s response and the data from a cohort of in vivo human subjects demonstrate that the ANGUS phantom may be an appropriate model for bulk tissue response and gyral dynamics of the human brain under small amplitude linear impulses. However, the phantom’s response differs from that of the in vivo human brain under rotational impacts, suggesting avenues for future improvements to the phantom.

Off-resonance related geometric distortion can impact quantitative MRI techniques, such as magnetic resonance elastography (MRE), and result in errors to these otherwise sensitive metrics of brain health. MRE is a phase contrast technique to determine the mechanical properties of tissue by imaging shear wave displacements and estimating tissue stiffness through inverse solution of Navier's equation. In this study, we systematically examined the quantitative effects of distortion and corresponding correction approaches on MRE measurements through a series of simulations, phantom models, and in vivo brain experiments. We studied two different k-space trajectories, echo-planar imaging and spiral, and we determined that readout time, off-resonance gradient strength, and the combination of readout direction and off-resonance gradient direction, impact the estimated mechanical properties. Images were also processed through traditional distortion correction pipelines, and we found that each of the correction mechanisms works well for reducing stiffness errors, but are limited in cases of very large distortion. The ability of MRE to detect subtle changes to neural tissue health relies on accurate, artifact-free imaging, and thus off-resonance related geometric distortion must be considered when designing sequences and protocols by limiting readout time and applying correction where appropriate.

The white matter tracts of brain tissue consist of highly-aligned, myelinated fibers; white matter is structurally anisotropic and is expected to exhibit anisotropic mechanical behavior. In vivo mechanical properties of tissue can be imaged using magnetic resonance elastography (MRE). MRE can detect and monitor natural and disease processes that affect tissue structure; however, most MRE inversion algorithms assume locally homogenous properties and/or isotropic behavior, which can cause artifacts in white matter regions. A heterogeneous, model-based transverse isotropic implementation of a subzone-based nonlinear inversion (TI-NLI) is demonstrated. TI-NLI reconstructs accurate maps of the shear modulus, damping ratio, shear anisotropy, and tensile anisotropy of in vivo brain tissue using standard MRE motion measurements and fiber directions estimated from diffusion tensor imaging (DTI). TI-NLI accuracy was investigated with using synthetic data in both controlled and realistic settings: excellent quantitative and spatial accuracy was observed and cross-talk between estimated parameters was minimal. Ten repeated, in vivo, MRE scans acquired from a healthy subject were co-registered to demonstrate repeatability of the technique. Good resolution of anatomical structures and bilateral symmetry were evident in MRE images of all mechanical property types. Repeatability was similar to isotropic MRE methods and well within the limits required for clinical success. TI-NLI MRE is a promising new technique for clinical research into anisotropic tissues such as the brain and muscle.

Agarose phantoms are one type of phantom commonly used in developing in vivo brain magnetic resonance elastography (MRE) sequences because they are inexpensive and easy to work with, store, and dispose of; however, protocols for creating agarose phantoms are non-standardized and often result in inconsistent phantoms with significant variability in mechanical properties. Many magnetic resonance imaging (MRI) and ultrasound studies use phantoms, but often these phantoms are not tailored for desired mechanical properties and as such are too stiff or not mechanically consistent enough to be used in MRE. In this work, we conducted a systematic study of agarose phantom creation parameters to identify those factors that are most conducive to producing mechanically consistent agarose phantoms for MRE research. We found that cooling rate and liquid temperature affected phantom homogeneity. Phantom stiffness is affected by agar concentration (quadratically), by final liquid temperature and salt content in phantoms, and by the interaction of these two metrics each with stir rate. We captured and quantified the implied relationships with a regression model that can be used to estimate stiffness of resulting phantoms. Additionally, we characterized repeatability, stability over time, impact on MR signal parameters, and differences in agar gel microstructure. This protocol and regression model should prove beneficial in future MRE development studies that use phantoms to determine stiffness measurement accuracy.