Faculty Bibliography

Axon caliber plays a crucial role in determining conduction velocity and, consequently, in the timing and synchronization of neural activation. Noninvasive measurement of axon radii could have significant impact on the understanding of healthy and diseased neural processes. Until now, accurate axon radius mapping has eluded in vivo neuroimaging, mainly due to a lack of sensitivity of the MRI signal to micron-sized axons. Here, we show how - when confounding factors such as extra-axonal water and axonal orientation dispersion are eliminated - heavily diffusion-weighted MRI signals become sensitive to axon radii. However, diffusion MRI is only capable of estimating a single metric, the effective radius, representing the entire axon radius distribution within a voxel that emphasizes the larger axons. Our findings, both in rodents and humans, enable noninvasive mapping of critical information on axon radii, as well as resolve the long-standing debate on whether axon radii can be quantified.

22q11.2 deletion syndrome, or 22q11.2DS, is a genetic syndrome associated with high rates of schizophrenia and autism spectrum disorders, in addition to widespread structural and functional abnormalities throughout the brain. Experimental animal models have identified neuronal connectivity deficits, e.g., decreased axonal length and complexity of axonal branching, as a primary mechanism underlying atypical brain development in 22q11.2DS. However, it is still unclear whether deficits in axonal morphology can also be observed in people with 22q11.2DS. Here, we provide an unparalleled in vivo characterization of white matter microstructure in participants with 22q11.2DS (12-15 years) and those undergoing typical development (8-18 years) using a customized magnetic resonance imaging scanner which is sensitive to axonal morphology. A rich array of diffusion MRI metrics are extracted to present microstructural profiles of typical and atypical white matter development, and provide new evidence of connectivity differences in individuals with 22q11.2DS. A recent, large-scale consortium study of 22q11.2DS identified higher diffusion anisotropy and reduced overall diffusion mobility of water as hallmark microstructural alterations of white matter in individuals across a wide age range (6-52 years). We observed similar findings across the white matter tracts included in this study, in addition to identifying deficits in axonal morphology. This, in combination with reduced tract volume measurements, supports the hypothesis that abnormal microstructural connectivity in 22q11.2DS may be mediated by densely packed axons with disproportionately small diameters. Our findings provide insight into the in vivo white matter phenotype of 22q11.2DS, and promote the continued investigation of shared features in neurodevelopmental and psychiatric disorders.

PURPOSE/OBJECTIVE:The general utility of diffusion kurtosis imaging (DKI) is challenged by its poor robustness to imaging artifacts and thermal noise that often lead to implausible kurtosis values. THEORY AND METHODS/UNASSIGNED:A robust scalar kurtosis index can be estimated from powder-averaged diffusion-weighted data. We introduce a novel DKI estimator that uses this scalar kurtosis index as a proxy for the mean kurtosis to regularize the fit. RESULTS:The regularized DKI estimator improves the robustness and reproducibility of the kurtosis metrics and results in parameter maps with enhanced quality and contrast. CONCLUSION/CONCLUSIONS:Our novel DKI estimator promotes the wider use of DKI in clinical research and potentially diagnostics by improving the reproducibility and precision of DKI fitting and, as such, enabling enhanced visual, quantitative, and statistical analyses of DKI parameters.

Development of therapies for neurological disorders depends on our ability to non-invasively diagnose and monitor the progression of underlying pathologies at the cellular level. Physics and physiology limit the resolution of human MRI to be orders of magnitude coarser than cell dimensions. Here we identify and quantify the MRI signal coming from within micrometer-thin axons in human white matter tracts in vivo, by utilizing the sensitivity of diffusion MRI to Brownian motion of water molecules restricted by cell walls. We study a specific power-law scaling of the diffusion MRI signal with the diffusion weighting, predicted for water confined to narrow axons, and quantify axonal water fraction and orientation dispersion.

Diffusion MRI fiber tractography is sensitive to noise and artifacts in diffusion-weighted images, and these challenges can propagate into fiber-orientation estimation and the tractography process. In this “Did You Know” communication, we synthesize evidence that state-of-the-art preprocessing improves tractography anatomical fidelity and test-retest reproducibility compared to minimally processed data. We summarize best-practice preprocessing – including denoising, motion and eddy current correction, EPI distortion correction, and Gibbs ringing removal – along with additional and emerging steps, and highlight integrated, publicly available pipelines that implement these methods in standardized, containerized workflows. We also outline practical acquisition and data-handling considerations that maximize the benefits of modern processing, providing a foundation for reliable tractography-based studies of the brain.

PURPOSE/OBJECTIVE:Rotational invariants (RIs) are at the root of many dMRI applications. Among others, they are presented as a sensible way of reducing the dimensionality of biophysical models. While thermal noise impact on diffusion metrics has been well studied, little is known on its effect on spherical harmonics-based RI (RISH) features and derived markers. In this work, we evaluate the effect of noise on RISH features and downstream Standard Model Imaging (SMI) estimates. THEORY AND METHODS/METHODS:Using simulated and test/retest multishell MRI data, we assess the accuracy and precision of RISH features and SMI parameters in the presence of thermal noise, as well as its robustness to variations in protocol design. We further propose and evaluate correction strategies that bypass the need of rotational invariant features as an intermediate step. RESULTS:Both RISH features and SMI estimates are impacted by SNR-dependent Rician biases. However, higher-order RISH features are susceptible to a secondary noise-related source of bias, which not only depends on SNR, but also protocol and underlying microstructure. Rician bias-correcting techniques are insufficient to maximize the accuracy of RISH and SMI features, or to ensure consistency across protocols. SMI estimators that avoid RISH features by fitting the model to the directional diffusion MRI data outperform RISH-based approaches in accuracy, repeatability, and reproducibility across acquisition protocols. CONCLUSIONS:RISH features are increasingly used in dMRI analysis, yet they are prone to various sources of noise that lower their accuracy and reproducibility. Understanding the impact of noise and mitigating such biases is critical to maximize the validity, repeatability, and reproducibility of dMRI studies.

Diffusion-weighted Magnetic Resonance Imaging (dMRI) has emerged as an imaging modality of interest to measure axon diameters noninvasively. The previously observed b ^-½ power law scaling suggests that high b-value dMRI signals originate from water confined within "stick" geometries, representing impermeable cellular processes. A key assumption is that any deviation from this power law at high b-values-modeled as a non-zero perpendicular intracellular diffusivity-must be specifically axonal in origin. Recent developments in axon diameter mapping build upon such assumptions, thereby neglecting the possibility that other cellular structures, such as glial processes, may also exhibit similar "stick"-like characteristics. This explorative study investigates the validity of axon diameter mapping by evaluating its robustness to experimental variation. In particular, it compares the mapping of the axon diameter using the zeroth- (spherical mean) and second-order (spherical variance) rotationally invariant spherical harmonic (RISH) features. As a condition for validity, axon diameter should be robust to such variations in RISH order. A novel log-linear estimator with a closed-form solution for computationally efficient axon diameter mapping is introduced, which can be applied with a minimum of two high b-value measurements. Using this estimator, it was observed that axon diameter measurements vary with RISH order, suggesting that high b-value signals from non-axonal cellular sources may confound axon diameter mapping. Monte Carlo simulations show that such dependence on RISH order could be explained by the presence of glial processes. Overall, these results highlight the need for caution in the interpretation of dMRI-derived "axon" diameter.

Optimal treatment of glioma has been a subject of debate over the last few decades, since maximal resection can improve survival, whereas preservation of functional peritumoral brain tissue minimizes the risk of postoperative neurological deficits. Our preliminary study uses tractography and neural tissue microstructure modeling based on diffusion MRI to quantify progressive axonal degeneration in proximity to frontotemporal glioma. For this, we sample major white matter pathways that traverse peritumoral penumbra at two time points. The results show a pattern of decreased intra-axonal water fraction beyond anatomical MRI abnormalities, which may indicate a tumor invasion of normal-appearing white matter that potentially advocates supratotal resection.

The development of noninvasive MRI biomarkers as surrogates of histopathological features in kidney tissue requires detailed explorations of contrast. Therefore, we studied kidney diffusion kurtosis imaging (DKI) with a wide array of encodings, including flow compensation, variable directional sampling, and cardiac gating regimes. Twelve healthy volunteers underwent DKI at 5-10 diffusion weightings (b-values) ranging from 0 to 1200 smm^-2 with 12 or 30 directional samplings, bipolar or flow-compensated diffusion gradient waveforms, and at systolic or diastolic cardiac phases. DKI biomarkers, mean diffusivity (MD) and kurtosis (MK), were interrogated using a directionally robust fitting algorithm compared to conventional fits. The combination of flow compensation and cardiac triggering at the diastolic phase in the kidneys reduced flow effects on DKI. In systole, flow-compensated waveforms significantly reduced MD and MK for both cortex and medulla: cortex MD: 3.00 versus 2.55 μm² ms^-1, medulla MD: 2.80 versus 2.39 μm² ms^-1, cortex MK: 0.58 versus 0.45, and medulla MK: 0.60 versus 0.47 (all p < 0.05). Flow suppression alleviated requirements for processing the DKI at higher minimum b-values, as neither MD nor MK significantly differed at the diastolic phase for minimum b-values of 0 versus 200 smm^-2: cortex MD: 2.30 versus 2.28 μm² ms^-1, p = 0.278; medulla MD: 2.29 versus 2.28 μm² ms^-1, p = 0.437; cortex MK: 0.37 versus 0.36, p = 0.308; and medulla MK: 0.40 versus 0.40, p = 0.904. Flow-compensated waveforms mitigate cardiac and respiratory motion-related artifacts at higher diffusion encodings in addition to microcirculation effects. The robust fitting initially developed for brain DKI is highly applicable to the kidneys because it disentangles tissue-specific directional diffusion information from artifacts.

Ultra low-field (ULF) MRI is an accessible neuroimaging modality that can bridge healthcare disparities and advance population-level brain health research. However, the inherently low signal-to-noise ratio of ULF-MRI often necessitates reductions in spatial resolution and, combined with the field-dependency of MRI contrast, challenges the accurate extraction of clinically relevant brain morphology. We evaluate the current state of ULF-MRI brain volumetry utilizing techniques for enhancing spatial resolution and leveraging recent advancements in brain segmentation. This is based on the agreement between ULF and corresponding high-field (HF) MRI brain volumes, and test-retest repeatability for multiple ULF scans. In this study, we find that accurate brain volumes can be measured from ULF-MRIs when combining orthogonal imaging directions for T₂-weighted images to form a higher resolution image volume. We also demonstrate that not all orthogonal imaging directions contribute equally to volumetric accuracy and provide a recommended scan protocol given the constraints of the current technology.