Faculty Bibliography

BACKGROUND AND PURPOSE/OBJECTIVE:Mild traumatic brain injury (MTBI) is a common public health concern with potential long-term consequences, yet its underlying pathophysiology remains poorly understood. Clinical heterogeneity of individuals having diverse extent and array of symptoms has impeded the identification of reliable imaging biomarkers. Traditional group-level analyses may obscure biologically meaningful subtypes. This study uses latent class analysis (LCA) to classify MTBI subjects into symptom-defined subgroups and examines corresponding WM microstructural alterations using advanced diffusion MRI. MATERIALS AND METHODS/METHODS:Sixty-one patients with MTBI completed the Rivermead Post-Concussion Symptoms Questionnaire within 1 month of injury. LCA was used to identify symptom-based subgroups. Of these, 54 patients with MTBI underwent multishell diffusion MRI and were compared with 31 controls. WM changes were assessed across subgroups using ROI-based diffusion analyses. RESULTS:LCA identified 3 distinct MTBI subgroups: those with minimal to no symptoms (31.5%), the cognitively symptomatic (38.9%), and the more globally symptomatic (29.6%). The 3 groups were associated with different patterns of diffusion MRI differences compared with controls. The cognitively symptomatic subgroup showed predominantly central WM differences, the globally symptomatic subgroup exhibited more peripheral differences with right-hemisphere predominance and sparing the corpus callosum, marked by reduced fractional anisotropy and kurtosis and elevated diffusivities, and the less symptomatic subgroup demonstrated focal differences in the callosal genu, with increased fractional anisotropy and kurtosis and decreased diffusivity measures. CONCLUSIONS:MTBI comprises biologically distinct phenotypes with subgroup-specific WM signatures on diffusion MRI. Even individuals with minimal to no symptoms show WM differences compared with controls, underscoring the limitations of symptom reporting alone. Integrating symptom-based classification with advanced diffusion MRI may improve diagnostic precision to help risk stratification and provide insight into mechanisms of injury.

Water diffusion gives rise to micron-scale sensitivity of diffusion MRI (dMRI) to cellular-level tissue structure. Precision medicine and quantitative imaging depend on uncovering the information content of dMRI and establishing its parsimonious hardware-independent fingerprint. Based on the rotational SO(3) symmetry, we study the geometry of the dMRI signal and the topology of its acquisition, identify irreducible components and a full set of invariants for the cumulant tensors, and relate them to tissue properties. Including all kurtosis invariants improves multiple sclerosis classification in a cohort of 1189 subjects. We design the shortest acquisitions based on icosahedral vertices to determine the most used invariants in only 1-2 minutes for whole brain. Representing dMRI via scalar invariant maps with definite symmetries will underpin machine learning classifiers of pathology, development, and aging, while fast protocols will enable translation of advanced dMRI into clinic.

PURPOSE/OBJECTIVE: METHODS: RESULTS:Applied to in vivo human MRI with linear tensor encoding on a high-performance gradient system, PIPE evaluates fiber response and fODF parameters for the whole brain in the presence of significant gradient nonlinearities in under 3 min. CONCLUSIONS:

PURPOSE/OBJECTIVE:The dependence of the long-time (tortuosity) limit of the extra-cellular diffusivity on the intra-cellular volume fraction is of fundamental importance for microstructure modeling. While such dependencies have been explored for the white matter, the tortuosity limit in gray matter is unknown due to complex cell composition and geometry. Here we rationalize and validate numerically the analytical relation between the extra-cellular diffusivity and intra-cellular fractions of cell bodies (somas) and neurites. METHODS:The tortuosity relation for extra-cellular diffusivity qualitatively follows from effective medium theory, coarse-grained by diffusion outside somas (spheres) and neurites (cylinders), respectively. This problem is equivalent to finding the overall conductivity in a medium of grains in a matrix, with methodology dating back to the 19th century. We extend the effective medium methodology to populations of impermeable spheres and randomly oriented cylinders with various volume fractions, yielding closed-form expressions corroborated by Monte Carlo simulations. RESULTS:We establish the power-law scaling of the extra-cellular diffusivity with the volume fractions of the extra-soma and extra-neurite spaces. We further evaluate the proposed framework using simulations in realistic tissue geometries, and by applying it to in vivo MRI data. CONCLUSION/CONCLUSIONS:Theory and simulations relate extra-cellular tortuosity to soma and neurite fractions, thereby offering a diffusion MRI protocol design optimized for in vivo assessment of soma size and soma/neurite fractions within clinical scan times. Such in vivo measurements can be used to study development, aging, and neurodegenerative disorders.

PURPOSE/OBJECTIVE:We aim to inform the design of new diffusion MRI (dMRI) approaches for microvasculature quantification that enhance the biological specificity of imaging towards cancer. METHODS: RESULTS: CONCLUSIONS:

PURPOSE/OBJECTIVE: METHODS: RESULTS:Applied to in vivo human MRI with linear tensor encoding on a high-performance gradient system, PIPE evaluates fiber response and fODF parameters for the whole brain in the presence of significant gradient nonlinearities in under 3 min. CONCLUSIONS:

BACKGROUND AND PURPOSE/OBJECTIVE:Mild traumatic brain injury (MTBI) is a common public health concern with potential long-term consequences, yet its underlying pathophysiology remains poorly understood. Clinical heterogeneity of individuals having diverse extent and array of symptoms has impeded the identification of reliable imaging biomarkers. Traditional group-level analyses may obscure biologically meaningful subtypes. This study uses latent class analysis (LCA) to classify MTBI subjects into symptom-defined subgroups and examines corresponding WM microstructural alterations using advanced diffusion MRI. MATERIALS AND METHODS/METHODS:Sixty-one MTBI patients within one month of injury completed the Rivermead Post-Concussion Symptoms Questionnaire (RPQ). LCA was used to identify symptom-based subgroups. Of these, 54 MTBI patients underwent multi-shell diffusion MRI and were compared with 31 controls. WM changes were assessed across subgroups using ROI-based diffusion analyses. RESULTS:LCA identified three distinct MTBI subgroups: those with minimal to no symptoms (31.5%), the cognitively symptomatic (38.9%), and the more globally symptomatic (29.6%). The three groups were associated with different patterns of diffusion MRI differences compared with controls. The cognitively symptomatic subgroup showed predominantly central WM differences, the globally symptomatic subgroup exhibited more peripheral differences with right-hemisphere predominance and sparing the corpus callosum, marked by reduced fractional anisotropy and kurtosis and elevated diffusivities, the less symptomatic subgroup demonstrated focal differences in the callosal genu, with increased fractional anisotropy and kurtosis and decreased diffusivity measures. CONCLUSIONS:MTBI comprises biologically distinct phenotypes with subgroup-specific WM signatures on diffusion MRI. Even individuals with minimal to no symptoms show WM differences compared with controls, underscoring the limitations of symptom reporting alone. Integrating symptom-based classification with advanced diffusion MRI may improve diagnostic precision to help risk stratification and provide insight into mechanisms of injury. ABBREVIATIONS/BACKGROUND:LCA = latent class analysis; MTBI = mild traumatic brain injury; RPQ = Rivermead post-concussion symptoms questionnaire.

Expansion of diffusion MRI (dMRI) both into the realm of strong gradients, and into accessible imaging with portable low-field devices, brings about the challenge of gradient nonlinearities. Spatial variations of the diffusion gradients make diffusion weightings and directions non-uniform across the field of view, and deform perfect shells in the q-space designed for isotropic directional coverage. Such imperfections hinder parameter estimation: Anisotropic shells hamper the deconvolution of fiber orientation distribution function (fODF), while brute-force retraining of a nonlinear regressor for each unique set of directions and diffusion weightings is computationally inefficient. Here we propose a protocol-independent parameter estimation (PIPE) method that enables fast parameter estimation for the most general case where the scan in each voxel is acquired with a different protocol in q-space. PIPE applies for any spherical convolution-based dMRI model, irrespective of its complexity, which makes it suitable both for white and gray matter in the brain or spinal cord, and for other tissues where fiber bundles have the same properties within a voxel (fiber response), but are distributed with an arbitrary fODF. In vivo human MRI experiments on a high-performance system show that PIPE can map fiber response and fODF parameters for the whole brain in the presence of significant gradient nonlinearities in under 3 minutes. PIPE enables fast parameter estimation in the presence of arbitrary gradient nonlinearities, eliminating the need to arrange dMRI in shells or to retrain the estimator for different protocols in each voxel. PIPE applies for any model based on a convolution of a voxel-wise fiber response and fODF, and data from varying b-tensor shapes, diffusion/echo times, and other scan parameters.

Evaluating tissue microstructure and membrane integrity in the living human brain through diffusion-water exchange imaging is challenging due to requirements for a high signal-to-noise ratio and short diffusion times dictated by relatively fast exchange processes. The goal of this work was to demonstrate the feasibility of in vivo imaging of tissue micro-geometries and water exchange within the brain gray matter using the state-of-the-art Connectome 2.0 scanner equipped with an ultra-high-performance gradient system (maximum gradient strength=500 mT/m, maximum slew rate=600 T/m/s). We performed diffusion MRI measurements in 15 healthy volunteers at multiple diffusion times (13-30 ms) and b-values up to 17.5 ms/μm². The anisotropic Kärger model was applied to estimate the apparent exchange time between intra-neurite and extracellular water in gray matter. The estimated exchange time across the cortical ribbon was around (median±interquartile range) 13±8 ms on Connectome 2.0, substantially faster than that measured using an imaging protocol compatible with Connectome 1.0-alike systems on the same cohort. Our investigation suggested that the apparent exchange time estimation using a Connectome 1.0 compatible protocol was more prone to residual noise floor biases due to the small time-dependent signal contrasts across diffusion times when the exchange is fast (≤20 ms). Furthermore, spatial variation of exchange time was observed across the cortex, where the motor cortex, somatosensory cortex and visual cortex exhibit longer apparent exchange times compared to other cortical regions. Non-linear fitting for the anisotropic Kärger model was accelerated 100 times using a GPU-based pipeline compared to the conventional CPU-based approach. This study highlighted the importance of the chosen diffusion times and measures to address Rician noise in dMRI data, which can have a substantial impact on the estimated apparent exchange time and require extra attention when comparing the results between various hardware setups.