Faculty Bibliography

Multi-parametric quantitative MRI (qMRI) of the spinal cord is a promising non-invasive tool to probe early microstructural damage in neurological disorders. It is usually performed in vivo by combining acquisitions with multiple signal readouts, which exhibit different thermal noise levels, geometrical distortions and susceptibility to physiological noise. This ultimately hinders joint multi-contrast modelling and makes the geometric correspondence of parametric maps challenging. We propose an approach to overcome these limitations, by implementing state-of-the-art microstructural MRI of the spinal cord with a unified signal readout in vivo (i.e. with matched spatial encoding parameters across a range of imaging contrasts). We base our acquisition on single-shot echo planar imaging with reduced field-of-view, and obtain data from two different vendors (vendor 1: Philips Achieva; vendor 2: Siemens Prisma). Importantly, the unified acquisition allows us to compare signal and noise across contrasts, thus enabling overall quality enhancement via multi-contrast image denoising methods. As a proof-of-concept, here we provide a demonstration with one such method, known as Marchenko-Pastur (MP) Principal Component Analysis (PCA) denoising. MP-PCA is a singular value (SV) decomposition truncation approach that relies on redundant acquisitions, i.e. such that the number of measurements is large compared to the number of components that are maintained in the truncated SV decomposition. Here we used in vivo and synthetic data to test whether a unified readout enables more efficient MP-PCA denoising of less redundant acquisitions, since these can be denoised jointly with more redundant ones. We demonstrate that a unified readout provides robust multi-parametric maps, including diffusion and kurtosis tensors from diffusion MRI, myelin metrics from two-pool magnetisation transfer, and T1 and T2 from relaxometry. Moreover, we show that MP-PCA improves the quality of our multi-contrast acquisitions, since it reduces the coefficient of variation (i.e. variability) by up to 17% for mean kurtosis, 8% for bound pool fraction (myelin-sensitive), and 13% for T1, while enabling more efficient denoising of modalities limited in redundancy (e.g. relaxometry). In conclusion, multi-parametric spinal cord qMRI with unified readout is feasible and provides robust microstructural metrics with matched resolution and distortions, whose quality benefits from multi-contrast denoising methods such as MP-PCA.

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.

The diagnosis of various disorders is hindered by the lack of an imaging technique that reveals the architecture of living tissue at the fine resolution of the associated pathological processes. Indeed, even the most powerful imaging techniques such as MRI can only resolve or visualize biological tissue down to the scale of a cubic millimetre. However, MRI may be able to reveal what happens on a much finer scale, as it is sensitive to the random thermal motion of water molecules and, more importantly, their interactions with surrounding cells constituting the microstructure of the tissue. The gap between being sensitive and specific is bridged by the development of a tissue model that decomposes the MRI signal into components that probe relevant features of the underlying microstructure, typically affected by pathology. Hence, biophysical modelling is potentially a diagnostic tool that allows scientists to identify problems that arise in the unexplored depths of our organs, driving forward treatment and understanding of disease progression. In this chapter, we will introduce the main concepts of multiparametric modelling, lay out a general framework of multi-compartmental models, and discuss limitations and challenges.
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Large-scale consortium efforts such as Enhancing NeuroImaging Genetics through Meta-Analysis (ENIGMA) and other collaborative efforts show that combining statistical data from multiple independent studies can boost statistical power and achieve more accurate estimates of effect sizes, contributing to more reliable and reproducible research. A meta- analysis would pool effects from studies conducted in a similar manner, yet to date, no such harmonized protocol exists for resting state fMRI (rsfMRI) data. Here, we propose an initial pipeline for multi-site rsfMRI analysis to allow research groups around the world to analyze scans in a harmonized way, and to perform coordinated statistical tests. The challenge lies in the fact that resting state fMRI measurements collected by researchers over the last decade vary widely, with variable quality and differing spatial or temporal signal-to-noise ratio (tSNR). An effective harmonization must provide optimal measures for all quality data. Here we used rsfMRI data from twenty-two independent studies with approximately fifty corresponding T1-weighted and rsfMRI datasets each, to (A) review and aggregate the state of existing rsfMRI data, (B) demonstrate utility of principal component analysis (PCA)-based denoising and (C) develop a deformable ENIGMA EPI template based on the representative anatomy that incorporates spatial distortion patterns from various protocols and populations.

At very low diffusion weighting the diffusion MRI signal is affected by intravoxel incoherent motion (IVIM) caused by dephasing of magnetization due to incoherent blood flow in capillaries or other sources of microcirculation. While IVIM measurements at low diffusion weightings have been frequently used to investigate perfusion in the body as well as in malignant tissue, the effect and origin of IVIM in normal brain tissue is not completely established. We investigated the IVIM effect on the brain diffusion MRI signal in a cohort of 137 radiologically-normal patients (62 male; mean age = 50.2 ± 17.8, range = 18 to 94). We compared the diffusion tensor parameters estimated from a mono-exponential fit at b = 0 and 1000 s/mm² versus at b = 250 and 1000 s/mm². The asymptotic fitting method allowed for quantitative assessment of the IVIM signal fraction f* in specific brain tissue and regions. Our results show a mean (median) percent difference in the mean diffusivity of about 4.5 (4.9)% in white matter (WM), about 7.8 (8.7)% in cortical gray matter (GM), and 4.3 (4.2)% in thalamus. Corresponding perfusion fraction f* was estimated to be 0.033 (0.032) in WM, 0.066 (0.065) in cortical GM, and 0.033 (0.030) in the thalamus. The effect of f* with respect to age was found to be significant in cortical GM (Pearson correlation ρ = 0.35, p = 3*10^-5) and the thalamus (Pearson correlation ρ = 0.20, p = 0.022) with an average increase in f* of 5.17*10^-4/year and 3.61*10^-4/year, respectively. Significant correlations between f* and age were not observed for WM, and corollary analysis revealed no effect of gender on f*. Possible origins of the IVIM effect in normal brain tissue are discussed.

While diffusion MRI (dMRI) is currently the method of choice to non-invasively probe tissue microstructure and study structural connectivity in the brain, its spatial resolution is limited and its results need structural validation. Current ex vivo methods employed to provide 3D fiber orientations have limitations, including tissue-distorting sample preparation, small field of view or inability to quantify 3D fiber orientation distributions. 3D fiber orientation in tissue sections can be obtained from 3D scanning small-angle X-ray scattering (3D sSAXS) by analyzing the anisotropy of scattering signals. Here we adapt the 3D sSAXS method for use in brain tissue, exploiting the high sensitivity of the SAXS signal to the ordered molecular structure of myelin. We extend the characterization of anisotropy from vectors to tensors, employ the Funk-Radon-Transform for converting scattering information to real space fiber orientations, and demonstrate the feasibility of the method in thin sections of mouse brain with minimal sample preparation. We obtain a second rank tensor representing the fiber orientation distribution function (fODF) for every voxel, thereby generating fODF maps. Finally, we illustrate the potential of 3D sSAXS by comparing the result with diffusion MRI fiber orientations in the same mouse brain. We show a remarkably good correspondence, considering the orthogonality of the two methods, i.e. the different physical processes underlying the two signals. 3D sSAXS can serve as validation method for microstructural MRI, and can provide novel microstructural insights for the nervous system, given the method's orthogonality to dMRI, high sensitivity to myelin sheath's orientation and abundance, and the possibility to extract myelin-specific signal and to perform micrometer-resolution scanning.

Introduction: Although often characterized as a disease of white matter, gray matter (GM) pathology has been shown to play an important role in multiple sclerosis (MS).
Objective(s): We used diffusion kurtosis imaging (DKI), a clinically feasible extension of diffusion tensor imaging (DTI) to characterize pathology in cortical and subcortical GM regions in MS patients compared to controls and study how selected DKI parameters correlate with disease severity in comparison to traditional volumetric approaches.
Method(s): 36 MS patients and 24 age and gender matched controls were enrolled in the study. MS patients completed a Patient Determined Disease Steps Score (PDDS). All patients received MRI on a 3T MR Scanner (Siemens, Skyra, or Prisma), which included whole brain 3D magnetization-prepared rapid gradientecho (MPRAGE) (1 mm³ isotropic resolution) for extracting volumetrics and monopolar diffusion-weighted echo-planar imaging (EPI) (voxel size = 1.7 x 1.7 x 3 mm³, b=0, 250, 1000, and 2000 s/m² along 84 directions, TE/TR = 100/3500 ms, GRAPPA with acceleration 2, and multiband 2) for deriving diffusion metrics. Volume metrics from automatic segmentation from MPRAGE and diffusion metrics which included mean diffusivity (MD), mean kurtosis (MK), and fractional anisotropy (FA) were derived for 7 subcortical and 5 cortical GM regions. We determined the partial correlations between PDDS and either GM volume or diffusion parameters covarying for gender and age. We also determined the differences in volume and diffusion metrics between MS patients and controls using ANCOVA with age as the covariate.
Result(s): We observed statistically significant differences in volumes between MS patients and controls for the amygdala, caudate, putamen, nucleus accumbens, cingulate lobe, and subcortical gray volumes with p-values ranging from 0.001 to 0.044. Statistically significant group differences were observed in a majority of the ROI for FA, MD, and MK. Overall, FA was increased, MD was increased, and MK was decreased for most ROI in MS patients compared to controls. There was an increased number of significant partial correlations between PDDS and diffusion metrics compared to PDDS and volume metrics, specifically positive correlations for occipital lobe MD and FA and negative correlations for hippocampal FA.
Conclusion(s): Our results suggest that DKI metrics are sensitive to changes in GM and complimentary to GM volumetrics as an index of GM pathology

BACKGROUND AND PURPOSE/OBJECTIVE:Working memory impairment is one of the most troubling and persistent symptoms after mild traumatic brain injury (MTBI). Here we investigate how working memory deficits relate to detectable WM microstructural injuries to discover robust biomarkers that allow early identification of patients with MTBI at the highest risk of working memory impairment. MATERIALS AND METHODS/METHODS:Multi-shell diffusion MR imaging was performed on a 3T scanner with 5 b-values. Diffusion metrics of fractional anisotropy, diffusivity and kurtosis (mean, radial, axial), and WM tract integrity were calculated. Auditory-verbal working memory was assessed using the Wechsler Adult Intelligence Scale, 4th ed, subtests: 1) Digit Span including Forward, Backward, and Sequencing; and 2) Letter-Number Sequencing. We studied 19 patients with MTBI within 4 weeks of injury and 20 healthy controls. Tract-Based Spatial Statistics and ROI analyses were performed to reveal possible correlations between diffusion metrics and working memory performance, with age and sex as covariates. RESULTS:= .04), mainly present in the right superior longitudinal fasciculus, which was not observed in healthy controls. Patients with MTBI also appeared to lose the normal associations typically seen in fractional anisotropy and axonal water fraction with Letter-Number Sequencing. Tract-Based Spatial Statistics results also support our findings. CONCLUSIONS:Differences between patients with MTBI and healthy controls with regard to the relationship between microstructure measures and working memory performance may relate to known axonal perturbations occurring after injury.

Tissue microstructure modeling of diffusion MRI signal is an active research area striving to bridge the gap between macroscopic MRI resolution and cellular-level tissue architecture. Such modeling in neuronal tissue relies on a number of assumptions about the microstructural features of axonal fiber bundles, such as the axonal shape (e.g., perfect cylinders) and the fiber orientation dispersion. However, these assumptions have not yet been validated by sufficiently high-resolution 3-dimensional histology. Here, we reconstructed sequential scanning electron microscopy images in mouse brain corpus callosum, and introduced a random-walker (RaW)-based algorithm to rapidly segment individual intra-axonal spaces and myelin sheaths of myelinated axons. Confirmed by a segmentation based on human annotations initiated with conventional machine-learning-based carving, our semi-automatic algorithm is reliable and less time-consuming. Based on the segmentation, we calculated MRI-relevant estimates of size-related parameters (inner axonal diameter, its distribution, along-axon variation, and myelin g-ratio), and orientation-related parameters (fiber orientation distribution and its rotational invariants; dispersion angle). The reported dispersion angle is consistent with previous 2-dimensional histology studies and diffusion MRI measurements, while the reported diameter exceeds those in other mouse brain studies. Furthermore, we calculated how these quantities would evolve in actual diffusion MRI experiments as a function of diffusion time, thereby providing a coarse-graining window on the microstructure, and showed that the orientation-related metrics have negligible diffusion time-dependence over clinical and pre-clinical diffusion time ranges. However, the MRI-measured inner axonal diameters, dominated by the widest cross sections, effectively decrease with diffusion time by ~ 17% due to the coarse-graining over axonal caliber variations. Furthermore, our 3d measurement showed that there is significant variation of the diameter along the axon. Hence, fiber orientation dispersion estimated from MRI should be relatively stable, while the "apparent" inner axonal diameters are sensitive to experimental settings, and cannot be modeled by perfectly cylindrical axons.