PGSE, OGSE, and sensitivity to axon diameter in diffusion MRI: Insight from a simulation study

Developing an AI-empowered head-only ultra-high-performance gradient MRI system for high spatiotemporal neuroimaging

Recent advances in neuroscience requires high-resolution MRI to decipher the structural and functional details of the brain. Developing a high-performance gradient system is an ongoing effort in the field to facilitate high spatial and temporal encoding. Here, we proposed a head-only gradient system NeuroFrontier, dedicated for neuroimaging with an ultra-high gradient strength of 650 mT/m and 600 T/m/s. The proposed system features in 1) ultra-high power of 7MW achieved by running two gradient power amplifiers using a novel paralleling method; 2) a force/torque balanced gradient coil design with a two-step mechanical structure that allows high-efficiency and flexible optimization of the peripheral nerve stimulation; 3) a high-density integrated RF system that is miniaturized and customized for the head-only system; 4) an AI-empowered compressed sensing technique that enables ultra-fast acquisition of high-resolution images and AI-based acceleration in q-t space for diffusion MRI (dMRI); and 5) a prospective head motion correction technique that effectively corrects motion artifacts in real-time with 3D optical tracking. We demonstrated the potential advantages of the proposed system in imaging resolution, speed, and signal-to-noise ratio for 3D structural MRI (sMRI), functional MRI (fMRI) and dMRI in neuroscience applications of submillimeter layer-specific fMRI and dMRI. We also illustrated the unique strength of this system for dMRI-based microstructural mapping, e.g., enhanced lesion contrast at short diffusion-times or high b-values, and improved estimation accuracy for cellular microstructures using diffusion-time-dependent dMRI or for neurite microstructures using q-space approaches.

Restricted diffusion characteristics in oscillating gradient spin echo with mesoscopic phantom

In diffusion magnetic resonance imaging, oscillating gradient spin echo (OGSE) has an extremely short diffusion time if motion probing gradient (MPG) is applied to the waveform. Further, it can detect microstructural specificity. OGSE changes sensitivity to spin displacement velocity based on the MPG phase. The current study aimed to investigate the restricted diffusion characteristics of each OGSE waveform using the capillary phantom with various b-values, frequencies, and MPG phases. We performed OGSE (b-value = 300, 500, 800, 1200, 1600, and 2000 s/mm2) for the sine and cosine waveforms using the capillary phantom (6, 12, 25, 50, and 100 μm and free water) with a 9.4-T experimental magnetic resonance imaging system and a solenoid coil. We evaluated the axial and radial diffusivity (AD, RD) of each structure size. The output current of the MPG was assessed with an oscilloscope and analyzed with the gradient modulation power spectra by fast Fourier transform. In sine, the sidelobe spectrum was enhanced with increasing frequency, and the central spectrum slightly increased. The difference in RD was detected at 6 and 12 μm; however, it did not depend on the structure scale at 50 or 100 μm and free water. In cosine, the diffusion spectrum was enhanced, whereas the central spectrum decreased with increasing frequency. Both AD and RD in cosine had a frequency dependence, and AD and RD increased with a higher frequency regardless of structure size. AD and RD in either sine or cosine had no evident b-value dependence. We evaluated the OGSE-restricted diffusion characteristics. The measurements obtained diffusion information similar to the pulsed gradient spin echo. Hence, the cosine measurements indicated that a higher frequency could capture faster diffusion within the diffusion phenomena.

Diffusion MRI with free gradient waveforms on a high-performance gradient system: Probing restriction and exchange in the human brain

The dependence of the diffusion MRI signal on the diffusion time carries signatures of restricted diffusion and exchange. Here we seek to highlight these signatures in the human brain by performing experiments using free gradient waveforms designed to be selectively sensitive to the two effects. We examine six healthy volunteers using both strong and ultra-strong gradients (80, 200 and 300 mT/m). In an experiment featuring a large set of 150 gradient waveforms with different sensitivities to restricted diffusion and exchange, our results reveal unique and different time-dependence signatures in grey and white matter. Grey matter was characterised by both restricted diffusion and exchange and white matter predominantly by restricted diffusion. Exchange in grey matter was at least twice as fast as in white matter, across all subjects and all gradient strengths. The cerebellar cortex featured relatively short exchange times (115 ms). Furthermore, we show that gradient waveforms with tailored designs can be used to map exchange in the human brain. We also assessed the feasibility of clinical applications of the method used in this work and found that the exchange-related contrast obtained with a 25-minute protocol at 300 mT/m was preserved in a 4-minute protocol at 300 mT/m and a 10-minute protocol at 80 mT/m. Our work underlines the utility of free waveforms for detecting time dependence signatures due to restricted diffusion and exchange in vivo, which may potentially serve as a tool for studying diseased tissue.

Frequency-dependent diffusion kurtosis imaging in the human brain using an oscillating gradient spin echo sequence and a high-performance head-only gradient

Measuring the time/frequency dependence of diffusion MRI is a promising approach to distinguish between the effects of different tissue microenvironments, such as membrane restriction, tissue heterogeneity, and compartmental water exchange. In this study, we measure the frequency dependence of diffusivity (D) and kurtosis (K) with oscillating gradient diffusion encoding waveforms and a diffusion kurtosis imaging (DKI) model in human brains using a high-performance, head-only MAGNUS gradient system, with a combination of b-values, oscillating frequencies (f), and echo time that has not been achieved in human studies before. Frequency dependence of diffusivity and kurtosis are observed in both global and local white matter (WM) and gray matter (GM) regions and characterized with a power-law model ∼Λ*fθ. The frequency dependences of diffusivity and kurtosis (including changes between fmin and fmax, Λ, and θ) vary over different WM and GM regions, indicating potential microstructural differences between regions. A trend of decreasing kurtosis over frequency in the short-time limit is successfully captured for in vivo human brains. The effects of gradient nonlinearity (GNL) on frequency-dependent diffusivity and kurtosis measurements are investigated and corrected. Our results show that the GNL has prominent scaling effects on the measured diffusivity values (3.5∼5.5% difference in the global WM and 6∼8% difference in the global cortex) and subsequently affects the corresponding power-law parameters (Λ, θ) while having a marginal influence on the measured kurtosis values (<0.05% difference) and power-law parameters (Λ, θ). This study expands previous OGSE studies and further demonstrates the translatability of frequency-dependent diffusivity and kurtosis measurements to human brains, which may provide new opportunities to probe human brain microstructure in health and disease.

Single-subject electroencephalography measurement of interhemispheric transfer time for the in-vivo estimation of axonal morphology

Assessing axonal morphology in vivo opens new avenues for the combined study of brain structure and function. A novel approach has recently been introduced to estimate the morphology of axonal fibers from the combination of magnetic resonance imaging (MRI) data and electroencephalography (EEG) measures of the interhemispheric transfer time (IHTT). In the original study, the IHTT measures were computed from EEG data averaged across a group, leading to bias of the axonal morphology estimates. Here, we seek to estimate axonal morphology from individual measures of IHTT, obtained from EEG data acquired in a visual evoked potential experiment. Subject-specific IHTTs are computed in a data-driven framework with minimal a priori constraints, based on the maximal peak of neural responses to visual stimuli within periods of statistically significant evoked activity in the inverse solution space. The subject-specific IHTT estimates ranged from 8 to 29 ms except for one participant and the between-session variability was comparable to between-subject variability. The mean radius of the axonal radius distribution, computed from the IHTT estimates and the MRI data, ranged from 0 to 1.09 μm across subjects. The change in axonal g-ratio with axonal radius ranged from 0.62 to 0.81 μm-α . The single-subject measurement of the IHTT yields estimates of axonal morphology that are consistent with histological values. However, improvement of the repeatability of the IHTT estimates is required to improve the specificity of the single-subject axonal morphology estimates.

Estimating axon radius using diffusion-relaxation MRI: calibrating a surface-based relaxation model with histology

Axon radius is a potential biomarker for brain diseases and a crucial tissue microstructure parameter that determines the speed of action potentials. Diffusion MRI (dMRI) allows non-invasive estimation of axon radius, but accurately estimating the radius of axons in the human brain is challenging. Most axons in the brain have a radius below one micrometer, which falls below the sensitivity limit of dMRI signals even when using the most advanced human MRI scanners. Therefore, new MRI methods that are sensitive to small axon radii are needed. In this proof-of-concept investigation, we examine whether a surface-based axonal relaxation process could mediate a relationship between intra-axonal T2 and T1 times and inner axon radius, as measured using postmortem histology. A unique in vivo human diffusion-T1-T2 relaxation dataset was acquired on a 3T MRI scanner with ultra-strong diffusion gradients, using a strong diffusion-weighting (i.e., b = 6,000 s/mm2) and multiple inversion and echo times. A second reduced diffusion-T2 dataset was collected at various echo times to evaluate the model further. The intra-axonal relaxation times were estimated by fitting a diffusion-relaxation model to the orientation-averaged spherical mean signals. Our analysis revealed that the proposed surface-based relaxation model effectively explains the relationship between the estimated relaxation times and the histological axon radius measured in various corpus callosum regions. Using these histological values, we developed a novel calibration approach to predict axon radius in other areas of the corpus callosum. Notably, the predicted radii and those determined from histological measurements were in close agreement.

CACTUS: a computational framework for generating realistic white matter microstructure substrates

Monte-Carlo diffusion simulations are a powerful tool for validating tissue microstructure models by generating synthetic diffusion-weighted magnetic resonance images (DW-MRI) in controlled environments. This is fundamental for understanding the link between micrometre-scale tissue properties and DW-MRI signals measured at the millimetre-scale, optimizing acquisition protocols to target microstructure properties of interest, and exploring the robustness and accuracy of estimation methods. However, accurate simulations require substrates that reflect the main microstructural features of the studied tissue. To address this challenge, we introduce a novel computational workflow, CACTUS (Computational Axonal Configurator for Tailored and Ultradense Substrates), for generating synthetic white matter substrates. Our approach allows constructing substrates with higher packing density than existing methods, up to 95% intra-axonal volume fraction, and larger voxel sizes of up to 500μm3 with rich fibre complexity. CACTUS generates bundles with angular dispersion, bundle crossings, and variations along the fibres of their inner and outer radii and g-ratio. We achieve this by introducing a novel global cost function and a fibre radial growth approach that allows substrates to match predefined targeted characteristics and mirror those reported in histological studies. CACTUS improves the development of complex synthetic substrates, paving the way for future applications in microstructure imaging.

Artificial intelligence for diffusion MRI-based tissue microstructure estimation in the human brain: an overview

Artificial intelligence (AI) has made significant advances in the field of diffusion magnetic resonance imaging (dMRI) and other neuroimaging modalities. These techniques have been applied to various areas such as image reconstruction, denoising, detecting and removing artifacts, segmentation, tissue microstructure modeling, brain connectivity analysis, and diagnosis support. State-of-the-art AI algorithms have the potential to leverage optimization techniques in dMRI to advance sensitivity and inference through biophysical models. While the use of AI in brain microstructures has the potential to revolutionize the way we study the brain and understand brain disorders, we need to be aware of the pitfalls and emerging best practices that can further advance this field. Additionally, since dMRI scans rely on sampling of the q-space geometry, it leaves room for creativity in data engineering in such a way that it maximizes the prior inference. Utilization of the inherent geometry has been shown to improve general inference quality and might be more reliable in identifying pathological differences. We acknowledge and classify AI-based approaches for dMRI using these unifying characteristics. This article also highlighted and reviewed general practices and pitfalls involving tissue microstructure estimation through data-driven techniques and provided directions for building on them.

The influence of axonal beading and undulation on axonal diameter mapping

We consider the effect of non-cylindrical axonal shape on axonal diameter mapping with diffusion MRI. Practical sensitivity to axon diameter is attained at strong diffusion weightings b , where the deviation from the 1 / b scaling yields the finite transverse diffusivity, which is then translated into axon diameter. While axons are usually modeled as perfectly straight, impermeable cylinders, the local variations in diameter (caliber variation or beading) and direction (undulation) have been observed in microscopy data of human axons. Here we quantify the influence of cellular-level features such as caliber variation and undulation on axon diameter estimation. For that, we simulate the diffusion MRI signal in realistic axons segmented from 3-dimensional electron microscopy of a human brain sample. We then create artificial fibers with the same features and tune the amplitude of their caliber variations and undulations. Numerical simulations of diffusion in fibers with such tunable features show that caliber variations and undulations result in under- and over-estimation of axon diameters, correspondingly; this bias can be as large as 100%. Given that increased axonal beading and undulations have been observed in pathological tissues, such as traumatic brain injury and ischemia, the interpretation of axon diameter alterations in pathology may be significantly confounded.

Temporal Diffusion Ratio (TDR) for imaging restricted diffusion: Optimisation and pre-clinical demonstration

Temporal Diffusion Ratio (TDR) is a recently proposed dMRI technique (Dell'Acqua et al., proc. ISMRM 2019) which provides contrast between areas with restricted diffusion and areas either without restricted diffusion or with length scales too small for characterisation. Hence, it has a potential for informing on pore sizes, in particular the presence of large axon diameters or other cellular structures. TDR employs the signal from two dMRI acquisitions obtained with the same, large, b-value but with different diffusion gradient waveforms. TDR is advantageous as it employs standard acquisition sequences, does not make any assumptions on the underlying tissue structure and does not require any model fitting, avoiding issues related to model degeneracy. This work for the first time introduces and optimises the TDR method in simulation for a range of different tissues and scanner constraints and validates it in a pre-clinical demonstration. We consider both substrates containing cylinders and spherical structures, representing cell soma in tissue. Our results show that contrasting an acquisition with short gradient duration, short diffusion time and high gradient strength with an acquisition with long gradient duration, long diffusion time and low gradient strength, maximises the TDR contrast for a wide range of pore configurations. Additionally, in the presence of Rician noise, computing TDR from a subset (50% or fewer) of the acquired diffusion gradients rather than the entire shell as proposed originally further improves the contrast. In the last part of the work the results are demonstrated experimentally on rat spinal cord. In line with simulations, the experimental data shows that optimised TDR improves the contrast compared to non-optimised TDR. Furthermore, we find a strong correlation between TDR and histology measurements of axon diameter. In conclusion, we find that TDR has great potential and is a very promising alternative (or potentially complement) to model-based approaches for informing on pore sizes and restricted diffusion in general.

Linear multi-scale modeling of diffusion MRI data: A framework for characterization of oriented structures across length scales

Diffusion-weighted magnetic resonance imaging (DW-MRI) has evolved to provide increasingly sophisticated investigations of the human brain's structural connectome in vivo. Restriction spectrum imaging (RSI) is a method that reconstructs the orientation distribution of diffusion within tissues over a range of length scales. In its original formulation, RSI represented the signal as consisting of a spectrum of Gaussian diffusion response functions. Recent technological advances have enabled the use of ultra-high b-values on human MRI scanners, providing higher sensitivity to intracellular water diffusion in the living human brain. To capture the complex diffusion time dependence of the signal within restricted water compartments, we expand upon the RSI approach to represent restricted water compartments with non-Gaussian response functions, in an extended analysis framework called linear multi-scale modeling (LMM). The LMM approach is designed to resolve length scale and orientation-specific information with greater specificity to tissue microstructure in the restricted and hindered compartments, while retaining the advantages of the RSI approach in its implementation as a linear inverse problem. Using multi-shell, multi-diffusion time DW-MRI data acquired with a state-of-the-art 3 T MRI scanner equipped with 300 mT/m gradients, we demonstrate the ability of the LMM approach to distinguish different anatomical structures in the human brain and the potential to advance mapping of the human connectome through joint estimation of the fiber orientation distributions and compartment size characteristics.

Inversion-Recovery-Prepared Oscillating Gradient Sequence Improves Diffusion-Time Dependency Measurements in the Human Brain

BACKGROUND

Oscillating gradient diffusion MRI (dMRI) enables measurements at a short diffusion-time (t_d ), but it is challenging for clinical systems. Particularly, the low b-value and low resolution may give rise to cerebrospinal fluid (CSF) contamination.

PURPOSE

To assess the effect of CSF partial volume on t_d -dMRI measurements and efficacy of inversion-recovery (IR) prepared oscillating and pulsed gradient dMRI sequence to improve t_d -dMRI measurements in the human brain.

STUDY TYPE

Prospective.

SUBJECTS

Ten normal volunteers and six glioma patients.

FIELD STRENGTH/SEQUENCE

A 3 T; three-dimensional (3D) IR-prepared oscillating gradient-prepared gradient spin-echo (GRASE) and two-dimensional (2D) IR-prepared oscillating gradient echo-planar imaging (EPI) sequences.

ASSESSMENT

We assessed the t_d -dependent patterns of apparent diffusion coefficient (ADC) in several gray and white matter structures, including the hippocampal subfields (head, body, and tail), cortical gray matter, thalamus, and posterior white matter in normal volunteers. Pulsed gradient (0 Hz) and oscillating gradients at frequencies of 20 Hz, 40 Hz, and 60 Hz dMRI were acquired with GRASE and EPI sequences with or without the IR module. We also tested the t_d -dependency patterns in glioma patients using the EPI sequence with or without the IR module.

STATISTICAL TESTS

The differences in ADC across the different t_d s were compared by one-way ANOVA followed by post hoc pairwise t-tests with Bonferroni correction.

RESULTS

In the healthy subjects, brain regions that were possibly contaminated by CSF signals, such as the hippocampus (head, body, and tail) and cortical gray matter, t_d -dependent ADC changes were only significant with the IR-prepared 2D and 3D sequences but not with the non-IR sequences. In brain glioblastomas patients, significantly higher t_d -dependence was observed in the tumor region with the IR module than that without IR (slope = 0.0196 μm² /msec² vs. 0.0034 μm² /msec² ).

CONCLUSION

The IR-prepared sequence effectively suppressed the CSF partial volume effect and significantly improved the t_d -dependent measurements in the human brain.

EVIDENCE LEVEL

1 TECHNICAL EFFICACY: Stage 1.

Novel insights into axon diameter and myelin content in late childhood and adolescence

White matter microstructural development in late childhood and adolescence is driven predominantly by increasing axon density and myelin thickness. Ex vivo studies suggest that the increase in axon diameter drives developmental increases in axon density observed with pubertal onset. In this cross-sectional study, 50 typically developing participants aged 8-18 years were scanned using an ultra-strong gradient magnetic resonance imaging scanner. Microstructural properties, including apparent axon diameter $({d}_a)$, myelin content, and g-ratio, were estimated in regions of the corpus callosum. We observed age-related differences in ${d}_a$, myelin content, and g-ratio. In early puberty, males had larger ${d}_a$ in the splenium and lower myelin content in the genu and body of the corpus callosum, compared with females. Overall, this work provides novel insights into developmental, pubertal, and cognitive correlates of individual differences in apparent axon diameter and myelin content in the developing human brain.

Mutation-related magnetization-transfer, not axon density, drives white matter differences in premanifest Huntington disease: Evidence from in vivo ultra-strong gradient MRI

White matter (WM) alterations have been observed in Huntington disease (HD) but their role in the disease-pathophysiology remains unknown. We assessed WM changes in premanifest HD by exploiting ultra-strong-gradient magnetic resonance imaging (MRI). This allowed to separately quantify magnetization transfer ratio (MTR) and hindered and restricted diffusion-weighted signal fractions, and assess how they drove WM microstructure differences between patients and controls. We used tractometry to investigate region-specific alterations across callosal segments with well-characterized early- and late-myelinating axon populations, while brain-wise differences were explored with tract-based cluster analysis (TBCA). Behavioral measures were included to explore disease-associated brain-function relationships. We detected lower MTR in patients' callosal rostrum (tractometry: p = .03; TBCA: p = .03), but higher MTR in their splenium (tractometry: p = .02). Importantly, patients' mutation-size and MTR were positively correlated (all p-values < .01), indicating that MTR alterations may directly result from the mutation. Further, MTR was higher in younger, but lower in older patients relative to controls (p = .003), suggesting that MTR increases are detrimental later in the disease. Finally, patients showed higher restricted diffusion signal fraction (FR) from the composite hindered and restricted model of diffusion (CHARMED) in the cortico-spinal tract (p = .03), which correlated positively with MTR in the posterior callosum (p = .033), potentially reflecting compensatory mechanisms. In summary, this first comprehensive, ultra-strong gradient MRI study in HD provides novel evidence of mutation-driven MTR alterations at the premanifest disease stage which may reflect neurodevelopmental changes in iron, myelin, or a combination of these.

Soma and Neurite Density MRI (SANDI) of the in-vivo mouse brain and comparison with the Allen Brain Atlas

Diffusion MRI (dMRI) provides unique insights into the neural tissue milieu by probing interactions between diffusing molecules and tissue microstructure. Most dMRI techniques focus on white matter (WM) tissues, nevertheless, interest in gray matter characterizations is growing. The Soma and Neurite Density MRI (SANDI) methodology harnesses a model incorporating water diffusion in spherical objects (assumed to be associated with cell bodies) and in impermeable "sticks" (assumed to represent neurites), which potentially enables the characterization of cellular and neurite densities. Recognising the importance of rodents in animal models of development, aging, plasticity, and disease, we here employ SANDI for in-vivo preclinical imaging and provide a first validation of the methodology by comparing SANDI metrics with cellular density reflected by the Allen mouse brain atlas. SANDI was implemented on a 9.4T scanner equipped with a cryogenic coil, and in-vivo experiments were carried out on N = 6 mice. Pixelwise, ROI-based, and atlas comparisons were performed, magnitude vs. real-valued analyses were compared, and shorter acquisitions with reduced the number of b-value shells were investigated. Our findings reveal good reproducibility of the SANDI parameters, including the sphere and stick fractions, as well as sphere size (CoV < 7%, 12% and 3%, respectively). Additionally, we find a very good rank correlation between SANDI-driven sphere fraction and Allen mouse brain atlas contrast that represents cellular density. We conclude that SANDI is a viable preclinical MRI technique that can greatly contribute to research on brain tissue microstructure.

Impact of tissue properties on time-dependent alterations in apparent diffusion coefficient: a phantom study using oscillating-gradient spin-echo and pulsed-gradient spin-echo sequences

Purpose

The purpose of this study was to investigate whether the changes in apparent diffusion coefficients (ADCs) due to differences in diffusion time reflect tissue properties in actual measurements of phantoms.

Materials and methods

Various n-alkane phantoms and sucrose/collagen phantoms with various collagen densities were set up with and without polyvinyl alcohol (PVA) foam with an average pore diameter of 300 μm. Thus, n-alkanes or sucrose/collagen represented substrate viscosity and the presence of PVA foam represented tissue structure with septum. Diffusion-weighted images with various diffusion times (7.71–60 ms) were acquired using pulsed-gradient spin-echo (PGSE) and oscillating-gradient spin-echo (OGSE) sequences. The ADCs of the phantoms with and without PVA foam were calculated.

Results

The ADCs of some of the phantoms without PVA decreased with diffusion times decreased. In the n-alkane phantoms, only C₈H₁₈ showed significantly different ADCs depending on the use of PVA foam in the OGSE sequence. On the other hand, sucrose/collagen phantoms showed significant differences according to diffusion time. The ADCs of the phantoms decreased as the molecular size of the n-alkanes or collagen density of the sucrose/collagen phantom increased. Compared to phantoms without PVA foam, the ADC of the phantoms with PVA foam decreased as the diffusion time increased.

Conclusion

Changes in ADCs due to differences in diffusion time reflect tissue properties in actual measurements of phantoms. These changes in ADCs can be used for tissue characterization in vivo.

Towards a representative reference for MRI-based human axon radius assessment using light microscopy

Non-invasive assessment of axon radii via MRI bears great potential for clinical and neuroscience research as it is a main determinant of the neuronal conduction velocity. However, there is a lack of representative histological reference data at the scale of the cross-section of MRI voxels for validating the MRI-visible, effective radius (r_eff). Because the current gold standard stems from neuroanatomical studies designed to estimate the bulk-determined arithmetic mean radius (r_arith) on small ensembles of axons, it is unsuited to estimate the tail-weighted r_eff. We propose CNN-based segmentation on high-resolution, large-scale light microscopy (lsLM) data to generate a representative reference for r_eff. In a human corpus callosum, we assessed estimation accuracy and bias of r_arith and r_eff. Furthermore, we investigated whether mapping anatomy-related variation of r_arith and r_eff is confounded by low-frequency variation of the image intensity, e.g., due to staining heterogeneity. Finally, we analyzed the error due to outstandingly large axons in r_eff. Compared to r_arith, r_eff was estimated with higher accuracy (maximum normalized-root-mean-square-error of r_eff: 8.5 %; r_arith: 19.5 %) and lower bias (maximum absolute normalized-mean-bias-error of r_eff: 4.8 %; r_arith: 13.4 %). While r_arith was confounded by variation of the image intensity, variation of r_eff seemed anatomy-related. The largest axons contributed between 0.8 % and 2.9 % to r_eff. In conclusion, the proposed method is a step towards representatively estimating r_eff at MRI voxel resolution. Further investigations are required to assess generalization to other brains and brain areas with different axon radii distributions.

Test-retest reproducibility of in vivo oscillating gradient and microscopic anisotropy diffusion MRI in mice at 9.4 Tesla

BACKGROUND AND PURPOSE

Microstructure imaging with advanced diffusion MRI (dMRI) techniques have shown increased sensitivity and specificity to microstructural changes in various disease and injury models. Oscillating gradient spin echo (OGSE) dMRI, implemented by varying the oscillating gradient frequency, and microscopic anisotropy (μA) dMRI, implemented via tensor valued diffusion encoding, may provide additional insight by increasing sensitivity to smaller spatial scales and disentangling fiber orientation dispersion from true microstructural changes, respectively. The aims of this study were to characterize the test-retest reproducibility of in vivo OGSE and μA dMRI metrics in the mouse brain at 9.4 Tesla and provide estimates of required sample sizes for future investigations.

METHODS

Twelve adult C57Bl/6 mice were scanned twice (5 days apart). Each imaging session consisted of multifrequency OGSE and μA dMRI protocols. Metrics investigated included μA, linear diffusion kurtosis, isotropic diffusion kurtosis, and the diffusion dispersion rate (Λ), which explores the power-law frequency dependence of mean diffusivity. The dMRI metric maps were analyzed with mean region-of-interest (ROI) and whole brain voxel-wise analysis. Bland-Altman plots and coefficients of variation (CV) were used to assess the reproducibility of OGSE and μA metrics. Furthermore, we estimated sample sizes required to detect a variety of effect sizes.

RESULTS

Bland-Altman plots showed negligible biases between test and retest sessions. ROI-based CVs revealed high reproducibility for most metrics (CVs < 15%). Voxel-wise CV maps revealed high reproducibility for μA (CVs ~ 10%), but low reproducibility for OGSE metrics (CVs ~ 50%).

CONCLUSION

Most of the μA dMRI metrics are reproducible in both ROI-based and voxel-wise analysis, while the OGSE dMRI metrics are only reproducible in ROI-based analysis. Given feasible sample sizes (10-15), μA metrics and OGSE metrics may provide sensitivity to subtle microstructural changes (4-8%) and moderate changes (> 6%), respectively.

Connectome 2.0: Developing the next-generation ultra-high gradient strength human MRI scanner for bridging studies of the micro-, meso- and macro-connectome

The first phase of the Human Connectome Project pioneered advances in MRI technology for mapping the macroscopic structural connections of the living human brain through the engineering of a whole-body human MRI scanner equipped with maximum gradient strength of 300 mT/m, the highest ever achieved for human imaging. While this instrument has made important contributions to the understanding of macroscale connectional topology, it has also demonstrated the potential of dedicated high-gradient performance scanners to provide unparalleled in vivo assessment of neural tissue microstructure. Building on the initial groundwork laid by the original Connectome scanner, we have now embarked on an international, multi-site effort to build the next-generation human 3T Connectome scanner (Connectome 2.0) optimized for the study of neural tissue microstructure and connectional anatomy across multiple length scales. In order to maximize the resolution of this in vivo microscope for studies of the living human brain, we will push the diffusion resolution limit to unprecedented levels by (1) nearly doubling the current maximum gradient strength from 300 mT/m to 500 mT/m and tripling the maximum slew rate from 200 T/m/s to 600 T/m/s through the design of a one-of-a-kind head gradient coil optimized to minimize peripheral nerve stimulation; (2) developing high-sensitivity multi-channel radiofrequency receive coils for in vivo and ex vivo human brain imaging; (3) incorporating dynamic field monitoring to minimize image distortions and artifacts; (4) developing new pulse sequences to integrate the strongest diffusion encoding and highest spatial resolution ever achieved in the living human brain; and (5) calibrating the measurements obtained from this next-generation instrument through systematic validation of diffusion microstructural metrics in high-fidelity phantoms and ex vivo brain tissue at progressively finer scales with accompanying diffusion simulations in histology-based micro-geometries. We envision creating the ultimate diffusion MRI instrument capable of capturing the complex multi-scale organization of the living human brain - from the microscopic scale needed to probe cellular geometry, heterogeneity and plasticity, to the mesoscopic scale for quantifying the distinctions in cortical structure and connectivity that define cyto- and myeloarchitectonic boundaries, to improvements in estimates of macroscopic connectivity.

Diffusion-Weighted Imaging: Recent Advances and Applications

Quantitative diffusion imaging techniques enable the characterization of tissue microstructural properties of the human brain "in vivo", and are widely used in neuroscientific and clinical contexts. In this review, we present the basic physical principles behind diffusion imaging and provide an overview of the current diffusion techniques, including standard and advanced techniques as well as their main clinical applications. Standard diffusion tensor imaging (DTI) offers sensitivity to changes in microstructure due to diseases and enables the characterization of single fiber distributions within a voxel as well as diffusion anisotropy. Nonetheless, its inability to represent complex intravoxel fiber topologies and the limited biological specificity of its metrics motivated the development of several advanced diffusion MRI techniques. For example, high-angular resolution diffusion imaging (HARDI) techniques enabled the characterization of fiber crossing areas and other complex fiber topologies in a single voxel and supported the development of higher-order signal representations aiming to decompose the diffusion MRI signal into distinct microstructure compartments. Biophysical models, often known by their acronym (e.g., CHARMED, WMTI, NODDI, DBSI, DIAMOND) contributed to capture the diffusion properties from each of such tissue compartments, enabling the computation of voxel-wise maps of axonal density and/or morphology that hold promise as clinically viable biomarkers in several neurological and neuroscientific applications; for example, to quantify tissue alterations due to disease or healthy processes. Current challenges and limitations of state-of-the-art models are discussed, including validation efforts. Finally, novel diffusion encoding approaches (e.g., b-tensor or double diffusion encoding) may increase the biological specificity of diffusion metrics towards intra-voxel diffusion heterogeneity in clinical settings, holding promise in neurological applications.

Dissecting whole-brain conduction delays through MRI microstructural measures

Network models based on structural connectivity have been increasingly used as the blueprint for large-scale simulations of the human brain. As the nodes of this network are distributed through the cortex and interconnected by white matter pathways with different characteristics, modeling the associated conduction delays becomes important. The goal of this study is to estimate and characterize these delays directly from the brain structure. To achieve this, we leveraged microstructural measures from a combination of advanced magnetic resonance imaging acquisitions and computed the main determinants of conduction velocity, namely axonal diameter and myelin content. Using the model proposed by Rushton, we used these measures to calculate the conduction velocity and estimated the associated delays using tractography. We observed that both the axonal diameter and conduction velocity distributions presented a rather constant trend across different connection lengths, with resulting delays that scale linearly with the connection length. Relying on insights from graph theory and Kuramoto simulations, our results support the approximation of constant conduction velocity but also show path- and region-specific differences.

Mapping complex cell morphology in the grey matter with double diffusion encoding MR: A simulation study

This paper investigates the impact of cell body (namely soma) size and branching of cellular projections on diffusion MR imaging (dMRI) and spectroscopy (dMRS) signals for both standard single diffusion encoding (SDE) and more advanced double diffusion encoding (DDE) measurements using numerical simulations. The aim is to investigate the ability of dMRI/dMRS to characterize the complex morphology of brain cells focusing on these two distinctive features of brain grey matter. To this end, we employ a recently developed computational framework to create three dimensional meshes of neuron-like structures for Monte Carlo simulations, using diffusion coefficients typical of water and brain metabolites. Modelling the cellular structure as realistically connected spherical soma and cylindrical cellular projections, we cover a wide range of combinations of sphere radii and branching order of cellular projections, characteristic of various grey matter cells. We assess the impact of spherical soma size and branching order on the b-value dependence of the SDE signal as well as the time dependence of the mean diffusivity (MD) and mean kurtosis (MK). Moreover, we also assess the impact of spherical soma size and branching order on the angular modulation of DDE signal at different mixing times, together with the mixing time dependence of the apparent microscopic anisotropy (μA), a promising contrast derived from DDE measurements. The SDE results show that spherical soma size has a measurable impact on both the b-value dependence of the SDE signal and the MD and MK diffusion time dependence for both water and metabolites. On the other hand, we show that branching order has little impact on either, especially for water. In contrast, the DDE results show that spherical soma size has a measurable impact on the DDE signal's angular modulation at short mixing times and the branching order of cellular projections significantly impacts the mixing time dependence of the DDE signal's angular modulation as well as of the derived μA, for both water and metabolites. Our results confirm that SDE based techniques may be sensitive to spherical soma size, and most importantly, show for the first time that DDE measurements may be more sensitive to the dendritic tree complexity (as parametrized by the branching order of cellular projections), paving the way for new ways of characterizing grey matter morphology, non-invasively using dMRS and potentially dMRI.

On the potential for mapping apparent neural soma density via a clinically viable diffusion MRI protocol

•

B-tensor encoding enables estimation of spherical cellular structures in the brain.

•

Spherical compartments may provide markers for apparent neural soma density.

•

Model parameters can be estimated in a fast and robust way using deep learning.

•

Practical acquisition times are achievable on widely available clinical scanners.

Diffusion MRI is a valuable tool for probing tissue microstructure in the brain noninvasively. Today, model-based techniques are widely available and used for white matter characterisation where their development is relatively mature. Conversely, tissue modelling in grey matter is more challenging, and no generally accepted models exist. With advances in measurement technology and modelling efforts, a clinically viable technique that reveals salient features of grey matter microstructure, such as the density of quasi-spherical cell bodies and quasi-cylindrical cell projections, is an exciting prospect. As a step towards capturing the microscopic architecture of grey matter in clinically feasible settings, this work uses a biophysical model that is designed to disentangle the diffusion signatures of spherical and cylindrical structures in the presence of orientation heterogeneity, and takes advantage of B-tensor encoding measurements, which provide additional sensitivity compared to standard single diffusion encoding sequences. For the fast and robust estimation of microstructural parameters, we leverage recent advances in machine learning and replace conventional fitting techniques with an artificial neural network that fits complex biophysical models within seconds. Our results demonstrate apparent markers of spherical and cylindrical geometries in healthy human subjects, and in particular an increased volume fraction of spherical compartments in grey matter compared to white matter. We evaluate the extent to which spherical and cylindrical geometries may be interpreted as correlates of neural soma and neural projections, respectively, and quantify parameter estimation errors in the presence of various departures from the modelling assumptions. While further work is necessary to translate the ideas presented in this work to the clinic, we suggest that biomarkers focussing on quasi-spherical cellular geometries may be valuable for the enhanced assessment of neurodevelopmental disorders and neurodegenerative diseases.

Bundle-Specific Axon Diameter Index as a New Contrast to Differentiate White Matter Tracts

In the central nervous system of primates, several pathways are characterized by different spectra of axon diameters. In vivo methods, based on diffusion-weighted magnetic resonance imaging, can provide axon diameter index estimates non-invasively. However, such methods report voxel-wise estimates, which vary from voxel-to-voxel for the same white matter bundle due to partial volume contributions from other pathways having different microstructure properties. Here, we propose a novel microstructure-informed tractography approach, COMMITAxSize, to resolve axon diameter index estimates at the streamline level, thus making the estimates invariant along trajectories. Compared to previously proposed voxel-wise methods, our formulation allows the estimation of a distinct axon diameter index value for each streamline, directly, furnishing a complementary measure to the existing calculation of the mean value along the bundle. We demonstrate the favourable performance of our approach comparing our estimates with existing histologically-derived measurements performed in the corpus callosum and the posterior limb of the internal capsule. Overall, our method provides a more robust estimation of the axon diameter index of pathways by jointly estimating the microstructure properties of the tissue and the macroscopic organisation of the white matter connectivity.

A rapid-onset diffusion functional MRI signal reflects neuromorphological coupling dynamics

Functional Magnetic Resonance Imaging (fMRI) has transformed our understanding of brain function in-vivo. However, the neurovascular coupling mechanisms underlying fMRI are somewhat "distant" from neural activity. Interestingly, evidence from Intrinsic Optical Signals (IOSs) indicates that neural activity is also coupled to (sub)cellular morphological modulations. Diffusion-weighted functional MRI (dfMRI) experiments have been previously proposed to probe such neuromorphological couplings, but the underlying mechanisms have remained highly contested. Here, we provide the first direct link between in vivo ultrafast dfMRI signals upon rat forepaw stimulation and IOSs in acute slices stimulated optogenetically. We reveal a hitherto unreported rapid onset (<100 ms) dfMRI signal component which (i) agrees with fast-rising IOSs dynamics; (ii) evidences a punctate quantitative correspondence to the stimulation period; and (iii) is rather insensitive to a vascular challenge. Our findings suggest that neuromorphological coupling can be detected via dfMRI signals, auguring well for future mapping of neural activity more directly compared with blood-oxygenation-level-dependent mechanisms.

The sensitivity of diffusion MRI to microstructural properties and experimental factors

Diffusion MRI is a non-invasive technique to study brain microstructure. Differences in the microstructural properties of tissue, including size and anisotropy, can be represented in the signal if the appropriate method of acquisition is used. However, to depict the underlying properties, special care must be taken when designing the acquisition protocol as any changes in the procedure might impact on quantitative measurements. This work reviews state-of-the-art methods for studying brain microstructure using diffusion MRI and their sensitivity to microstructural differences and various experimental factors. Microstructural properties of the tissue at a micrometer scale can be linked to the diffusion signal at a millimeter-scale using modeling. In this paper, we first give an introduction to diffusion MRI and different encoding schemes. Then, signal representation-based methods and multi-compartment models are explained briefly. The sensitivity of the diffusion MRI signal to the microstructural components and the effects of curvedness of axonal trajectories on the diffusion signal are reviewed. Factors that impact on the quality (accuracy and precision) of derived metrics are then reviewed, including the impact of random noise, and variations in the acquisition parameters (i.e., number of sampled signals, b-value and number of acquisition shells). Finally, yet importantly, typical approaches to deal with experimental factors are depicted, including unbiased measures and harmonization. We conclude the review with some future directions and recommendations on this topic.

Probing neural tissues at small scales: Recent progress of oscillating gradient spin echo (OGSE) neuroimaging in humans

The detection sensitivity of diffusion MRI (dMRI) is dependent on diffusion times. A shorter diffusion time can increase the sensitivity to smaller length scales. However, the conventional dMRI uses the pulse gradient spin echo (PGSE) sequence that probes relatively long diffusion times only. To overcome this, the oscillating gradient spin echo (OGSE) sequence has been developed to probe much shorter diffusion times with hardware limitations on preclinical and clinical MRI systems. The OGSE sequence has been previously used on preclinical animal MRI systems. Recently, several studies have translated the OGSE sequence to humans on clinical MRI systems and achieved new information that is invisible using conventional PGSE sequence. This paper provides an overview of the recent progress of the OGSE neuroimaging in humans, including the technical improvements in the translation of the OGSE sequence to human imaging and various applications in different neurological disorders and stroke. Some possible future directions of the OGSE sequence are also discussed.

A simple estimate of axon size with diffusion MRI

Noninvasive estimation of mean axon diameter presents a new opportunity to explore white matter plasticity, development, and pathology. Several diffusion-weighted MRI (DW-MRI) methods have been proposed to measure the average axon diameter in white matter, but they typically require many diffusion encoding measurements and complicated mathematical models to fit the signal to multiple tissue compartments, including intra- and extra-axonal spaces. Here, Monte Carlo simulations uncovered a straightforward DW-MRI metric of axon diameter: the change in radial apparent diffusion coefficient estimated at different effective diffusion times, ΔD_⊥. Simulations indicated that this metric increases monotonically within a relevant range of effective mean axon diameter while being insensitive to changes in extra-axonal volume fraction, axon diameter distribution, g-ratio, and influence of myelin water. Also, a monotonic relationship was found to exist for signals coming from both intra- and extra-axonal compartments. The slope in ΔD_⊥ with effective axon diameter increased with the difference in diffusion time of both oscillating and pulsed gradient diffusion sequences.

Axon diameter index estimation independent of fiber orientation distribution using high-gradient diffusion MRI

Axon diameter mapping using high-gradient diffusion MRI has generated great interest as a noninvasive tool for studying trends in axonal size in the human brain. One of the main barriers to mapping axon diameter across the whole brain is accounting for complex white matter fiber configurations (e.g., crossings and fanning), which are prevalent throughout the brain. Here, we present a framework for generalizing axon diameter index estimation to the whole brain independent of the underlying fiber orientation distribution using the spherical mean technique (SMT). This approach is shown to significantly benefit from the use of real-valued diffusion data with Gaussian noise, which reduces the systematic bias in the estimated parameters resulting from the elevation of the noise floor when using magnitude data with Rician noise. We demonstrate the feasibility of obtaining whole-brain orientationally invariant estimates of axon diameter index and relative volume fractions in six healthy human volunteers using real-valued diffusion data acquired on a dedicated high-gradient 3-Tesla human MRI scanner with 300 mT/m maximum gradient strength. The trends in axon diameter index are consistent with known variations in axon diameter from histology and demonstrate the potential of this generalized framework for revealing coherent patterns in axonal structure throughout the living human brain. The use of real-valued diffusion data provides a viable solution for eliminating the Rician noise floor and should be considered for all spherical mean approaches to microstructural parameter estimation.

Double diffusion encoding and applications for biomedical imaging

Diffusion Magnetic Resonance Imaging (dMRI) is one of the most important contemporary non-invasive modalities for probing tissue structure at the microscopic scale. The majority of dMRI techniques employ standard single diffusion encoding (SDE) measurements, covering different sequence parameter ranges depending on the complexity of the method. Although many signal representations and biophysical models have been proposed for SDE data, they are intrinsically limited by a lack of specificity. Advanced dMRI methods have been proposed to provide additional microstructural information beyond what can be inferred from SDE. These enhanced contrasts can play important roles in characterizing biological tissues, for instance upon diseases (e.g. neurodegenerative, cancer, stroke), aging, learning, and development. In this review we focus on double diffusion encoding (DDE), which stands out among other advanced acquisitions for its versatility, ability to probe more specific diffusion correlations, and feasibility for preclinical and clinical applications. Various DDE methodologies have been employed to probe compartment sizes (Section 3), decouple the effects of microscopic diffusion anisotropy from orientation dispersion (Section 4), probe displacement correlations, study exchange, or suppress fast diffusing compartments (Section 6). DDE measurements can also be used to improve the robustness of biophysical models (Section 5) and study intra-cellular diffusion via magnetic resonance spectroscopy of metabolites (Section 7). This review discusses all these topics as well as important practical aspects related to the implementation and contrast in preclinical and clinical settings (Section 9) and aims to provide the readers a guide for deciding on the right DDE acquisition for their specific application.

Machine learning based white matter models with permeability: An experimental study in cuprizone treated in-vivo mouse model of axonal demyelination

The intra-axonal water exchange time (τ_i), a parameter associated with axonal permeability, could be an important biomarker for understanding and treating demyelinating pathologies such as Multiple Sclerosis. Diffusion-Weighted MRI (DW-MRI) is sensitive to changes in permeability; however, the parameter has so far remained elusive due to the lack of general biophysical models that incorporate it. Machine learning based computational models can potentially be used to estimate such parameters. Recently, for the first time, a theoretical framework using a random forest (RF) regressor suggests that this is a promising new approach for permeability estimation. In this study, we adopt such an approach and for the first time experimentally investigate it for demyelinating pathologies through direct comparison with histology. We construct a computational model using Monte Carlo simulations and an RF regressor in order to learn a mapping between features derived from DW-MRI signals and ground truth microstructure parameters. We test our model in simulations, and find strong correlations between the predicted and ground truth parameters (intra-axonal volume fraction f: R² =0.99, τ_i: R² =0.84, intrinsic diffusivity d: R² =0.99). We then apply the model in-vivo, on a controlled cuprizone (CPZ) mouse model of demyelination, comparing the results from two cohorts of mice, CPZ (N=8) and healthy age-matched wild-type (WT, N=8). We find that the RF model estimates sensible microstructure parameters for both groups, matching values found in literature. Furthermore, we perform histology for both groups using electron microscopy (EM), measuring the thickness of the myelin sheath as a surrogate for exchange time. Histology results show that our RF model estimates are very strongly correlated with the EM measurements (ρ = 0.98 for f, ρ = 0.82 for τ_i). Finally, we find a statistically significant decrease in τ_i in all three regions of the corpus callosum (splenium/genu/body) of the CPZ cohort (<τ_i>=310ms/330ms/350ms) compared to the WT group (<τ_i>=370ms/370ms/380ms). This is in line with our expectations that τ_i is lower in regions where the myelin sheath is damaged, as axonal membranes become more permeable. Overall, these results demonstrate, for the first time experimentally and in vivo, that a computational model learned from simulations can reliably estimate microstructure parameters, including the axonal permeability .

Technological Advances of Magnetic Resonance Imaging in Today's Health Care Environment

Today's health care environment is shifting rapidly, driven by demographic change and high economic pressures on the system. Furthermore, modern precision medicine requires highly accurate and specific disease diagnostics in a short amount of time. Future imaging technology must adapt to these challenges.Demographic change necessitates scanner technologies tailored to the needs of an aging and increasingly multimorbid patient population. Accordingly, examination times have to be short enough that diagnostic images can be generated even for patients who can only lie in the scanner for a short time because of pain or with low breath-hold capacity.For economic reasons, the rate of nondiagnostic scans due to artifacts should be reduced as far as possible. As imaging plays an increasingly pivotal role in clinical-therapeutic decision making, magnetic resonance (MR) imaging facilities are confronted with an ever-growing number of patients, emphasizing the need for faster acquisitions while maintaining image quality.Lastly, modern precision medicine requires high and standardized image quality as well as quantifiable data in order to develop image-based biomarkers on which subsequent treatment management can rely.In recent decades, a variety of approaches have addressed the challenges of high throughput, demographic change, and precision medicine in MR imaging. These include field strength, gradient, coil and sequence development, as well as an increasing consideration of artificial intelligence. This article reviews state-of-the art MR technology and discusses future implementation from the perspective of what we know today.

Time-dependent diffusion in undulating thin fibers: Impact on axon diameter estimation

Diffusion MRI may enable non-invasive mapping of axonal microstructure. Most approaches infer axon diameters from effects of time-dependent diffusion on the diffusion-weighted MR signal by modeling axons as straight cylinders. Axons do not, however, propagate in straight trajectories, and so far the impact of the axonal trajectory on diameter estimation has been insufficiently investigated. Here, we employ a toy model of axons, which we refer to as the undulating thin fiber model, to analyze the impact of undulating trajectories on the time dependence of diffusion. We study time-dependent diffusion in the frequency domain and characterize the diffusion spectrum by its height, width, and low-frequency behavior (power law exponent). Results show that microscopic orientation dispersion of the thin fibers is the main parameter that determines the characteristics of the diffusion spectra. At lower frequencies (longer diffusion times), straight cylinders and undulating thin fibers can have virtually identical spectra. If the straight-cylinder assumption is used to interpret data from undulating thin axons, the diameter is overestimated by an amount proportional to the undulation amplitude and microscopic orientation dispersion of the fibers. At higher frequencies (shorter diffusion times), spectra from cylinders and undulating thin fibers differ. The low-frequency behavior of the spectra from the undulating thin fibers may also differ from that of cylinders, because the power law exponent of undulating fibers can reach values below 2 for experimentally relevant frequency ranges. In conclusion, we argue that the non-straight nature of axonal trajectories should not be overlooked when analyzing and interpreting diffusion MRI data.

Nonivasive quantification of axon radii using diffusion MRI

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.

Oscillating diffusion-encoding with a high gradient-amplitude and high slew-rate head-only gradient for human brain imaging

PURPOSE

We investigate the importance of high gradient-amplitude and high slew-rate on oscillating gradient spin echo (OGSE) diffusion imaging for human brain imaging and evaluate human brain imaging with OGSE on the MAGNUS head-gradient insert (200 mT/m amplitude and 500 T/m/s slew rate).

METHODS

Simulations with cosine-modulated and trapezoidal-cosine OGSE at various gradient amplitudes and slew rates were performed. Six healthy subjects were imaged with the MAGNUS gradient at 3T with OGSE at frequencies up to 100 Hz and b = 450 s/mm² . Comparisons were made against standard pulsed gradient spin echo (PGSE) diffusion in vivo and in an isotropic diffusion phantom.

RESULTS

Simulations show that to achieve high frequency and b-value simultaneously for OGSE, high gradient amplitude, high slew rates, and high peripheral nerve stimulation limits are required. A strong linear trend for increased diffusivity (mean: 8-19%, radial: 9-27%, parallel: 8-15%) was observed in normal white matter with OGSE (20 Hz to 100 Hz) as compared to PGSE. Linear fitting to frequency provided excellent correlation, and using a short-range disorder model provided radial long-term diffusivities of D_∞,MD = 911 ± 72 µm² /s, D_∞,PD = 1519 ± 164 µm² /s, and D_∞,RD = 640 ± 111 µm² /s and correlation lengths of l_{c ,MD} = 0.802 ± 0.156 µm, l_{c ,PD} = 0.837 ± 0.172 µm, and l_{c ,RD} = 0.780 ± 0.174 µm. Diffusivity changes with OGSE frequency were negligible in the phantom, as expected.

CONCLUSION

The high gradient amplitude, high slew rate, and high peripheral nerve stimulation thresholds of the MAGNUS head-gradient enables OGSE acquisition for in vivo human brain imaging.

Diffusion time dependency along the human corpus callosum and exploration of age and sex differences as assessed by oscillating gradient spin-echo diffusion tensor imaging

Conventional diffusion imaging uses pulsed gradient spin echo (PGSE) waveforms with diffusion times of tens of milliseconds (ms) to infer differences of white matter microstructure. The combined use of these long diffusion times with short diffusion times (<10 ​ms) enabled by oscillating gradient spin echo (OGSE) waveforms can enable more sensitivity to changes of restrictive boundaries on the scale of white matter microstructure (e.g. membranes reflecting the axon diameters). Here, PGSE and OGSE images were acquired at 4.7 ​T from 20 healthy volunteers aged 20-73 years (10 males). Mean, radial, and axial diffusivity, as well as fractional anisotropy were calculated in the genu, body and splenium of the corpus callosum (CC). Monte Carlo simulations were also conducted to examine the relationship of intra- and extra-axonal radial diffusivity with diffusion time over a range of axon diameters and distributions. The results showed elevated diffusivities with OGSE relative to PGSE in the genu and splenium (but not the body) in both males and females, but the OGSE-PGSE difference was greater in the genu for males. Females showed positive correlations of OGSE-PGSE diffusivity difference with age across the CC, whereas there were no such age correlations in males. Simulations of radial diffusion demonstrated that for axon sizes in human brain both OGSE and PGSE diffusivities were dominated by extra-axonal water, but the OGSE-PGSE difference nonetheless increased with area-weighted outer-axon diameter. Therefore, the lack of OGSE-PGSE difference in the body is not entirely consistent with literature that suggests it is composed predominantly of axons with large diameter. The greater OGSE-PGSE difference in the genu of males could reflect larger axon diameters than females. The OGSE-PGSE difference correlation with age in females could reflect loss of smaller axons at older ages. The use of OGSE with short diffusion times to sample the microstructural scale of restriction implies regional differences of axon diameters along the corpus callosum with preliminary results suggesting a dependence on age and sex.

Estimating axon conduction velocity in vivo from microstructural MRI

The conduction velocity (CV) of action potentials along axons is a key neurophysiological property central to neural communication. The ability to estimate CV in humans in vivo from non-invasive MRI methods would therefore represent a significant advance in neuroscience. However, there are two major challenges that this paper aims to address: (1) Much of the complexity of the neurophysiology of action potentials cannot be captured with currently available MRI techniques. Therefore, we seek to establish the variability in CV that can be captured when predicting CV purely from parameters that have been reported to be estimatable from MRI: inner axon diameter (AD) and g-ratio. (2) errors inherent in existing MRI-based biophysical models of tissue will propagate through to estimates of CV, the extent to which is currently unknown. Issue (1) is investigated by performing a sensitivity analysis on a comprehensive model of axon electrophysiology and determining the relative sensitivity to various morphological and electrical parameters. The investigations suggest that 85% of the variance in CV is accounted for by variation in AD and g-ratio. The observed dependency of CV on AD and g-ratio is well characterised by the previously reported model by Rushton. Issue (2) is investigated through simulation of diffusion and relaxometry MRI data for a range of axon morphologies, applying models of restricted diffusion and relaxation processes to derive estimates of axon volume fraction (AVF), AD and g-ratio and estimating CV from the derived parameters. The results show that errors in the AVF have the biggest detrimental impact on estimates of CV, particularly for sparse fibre populations (AVF<0.3). For our equipment set-up and acquisition protocol, CV estimates are most accurate (below 5% error) where AVF is above 0.3, g-ratio is between 0.6 and 0.85 and AD is high (above 4μm). CV estimates are robust to errors in g-ratio estimation but are highly sensitive to errors in AD estimation, particularly where ADs are small. We additionally show CV estimates in human corpus callosum in a small number of subjects. In conclusion, we demonstrate accurate CV estimates are possible in regions of the brain where AD is sufficiently large. Problems with estimating ADs for smaller axons presents a problem for estimating CV across the whole CNS and should be the focus of further study.

Single- and double-Diffusion encoding MRI for studying ex vivo apparent axon diameter distribution in spinal cord white matter

Mapping average axon diameter (AAD) and axon diameter distribution (ADD) in neuronal tissues non-invasively is a challenging task that may have a tremendous effect on our understanding of the normal and diseased central nervous system (CNS). Water diffusion is used to probe microstructure in neuronal tissues, however, the different water populations and barriers that are present in these tissues turn this into a complex task. Therefore, it is not surprising that recently we have witnessed a burst in the development of new approaches and models that attempt to obtain, non-invasively, detailed microstructural information in the CNS. In this work, we aim at challenging and comparing the microstructural information obtained from single diffusion encoding (SDE) with double diffusion encoding (DDE) MRI. We first applied SDE and DDE MR spectroscopy (MRS) on microcapillary phantoms and then applied SDE and DDE MRI on an ex vivo porcine spinal cord (SC), using similar experimental conditions. The obtained diffusion MRI data were fitted by the same theoretical model, assuming that the signal in every voxel can be approximated as the superposition of a Gaussian-diffusing component and a series of restricted components having infinite cylindrical geometries. The diffusion MRI results were then compared with histological findings. We found a good agreement between the fittings and the experimental data in white matter (WM) voxels of the SC in both diffusion MRI methods. The microstructural information and apparent AADs extracted from SDE MRI were found to be similar or somewhat larger than those extracted from DDE MRI especially when the diffusion time was set to 40 ms. The apparent ADDs extracted from SDE and DDE MRI show reasonable agreement but somewhat weaker correspondence was observed between the diffusion MRI results and histology. The apparent subtle differences between the microstructural information obtained from SDE and DDE MRI are briefly discussed.

Diffusion dispersion imaging: Mapping oscillating gradient spin-echo frequency dependence in the human brain

PURPOSE

Oscillating gradient spin-echo (OGSE) diffusion MRI provides information about the microstructure of biological tissues by means of the frequency dependence of the apparent diffusion coefficient (ADC). ADC dependence on OGSE frequency has been explored in numerous rodent studies, but applications in the human brain have been limited and have suffered from low contrast between different frequencies, long scan times, and a limited exploration of the nature of the ADC dependence on frequency.

THEORY AND METHODS

Multiple frequency OGSE acquisitions were acquired in healthy subjects at 7T to explore the power-law frequency dependence of ADC, the "diffusion dispersion." Furthermore, a method for optimizing the estimation of the ADC difference between different OGSE frequencies was developed, which enabled the design of a highly efficient protocol for mapping diffusion dispersion.

RESULTS

For the first time, evidence of a linear dependence of ADC on the square root of frequency in healthy human white matter was obtained. Using the optimized protocol, high-quality, full-brain maps of apparent diffusion dispersion rate were also demonstrated at an isotropic resolution of 2 mm in a scan time of 6 min.

CONCLUSIONS

This work sheds light on the nature of diffusion dispersion in the healthy human brain and introduces full-brain diffusion dispersion mapping at clinically relevant scan times. These advances may lead to new biomarkers of pathology or improved microstructural modeling.

ActiveAxADD : Toward non-parametric and orientationally invariant axon diameter distribution mapping using PGSE

PURPOSE

Non-invasive axon diameter distribution (ADD) mapping using diffusion MRI is an ill-posed problem. Current ADD mapping methods require knowledge of axon orientation before performing the acquisition. Instead, ActiveAx uses a 3D sampling scheme to estimate the orientation from the signal, providing orientationally invariant estimates. The mean diameter is estimated instead of the distribution for the solution to be tractable. Here, we propose an extension (ActiveAx_ADD ) that provides non-parametric and orientationally invariant estimates of the whole distribution.

THEORY

The accelerated microstructure imaging with convex optimization (AMICO) framework accelerates mean diameter estimation using a linear formulation combined with Tikhonov regularization to stabilize the solution. Here, we implement a new formulation (ActiveAx_ADD ) that uses Laplacian regularization to provide robust estimates of the whole ADD.

METHODS

The performance of ActiveAx_ADD was evaluated using Monte Carlo simulations on synthetic white matter samples mimicking axon distributions reported in histological studies.

RESULTS

ActiveAx_ADD provided robust ADD reconstructions when considering the isolated intra-axonal signal. However, our formulation inherited some common microstructure imaging limitations. When accounting for the extra axonal compartment, estimated ADDs showed spurious peaks and increased variability because of the difficulty of disentangling intra and extra axonal contributions.

CONCLUSION

Laplacian regularization solves the ill-posedness regarding the intra axonal compartment. ActiveAx_ADD can potentially provide non-parametric and orientationally invariant ADDs from isolated intra-axonal signals. However, further work is required before ActiveAx_ADD can be applied to real data containing extra-axonal contributions, as disentangling the 2 compartment appears to be an overlooked challenge that affects microstructure imaging methods in general.

The Dmipy Toolbox: Diffusion MRI Multi-Compartment Modeling and Microstructure Recovery Made Easy

Non-invasive estimation of brain microstructure features using diffusion MRI (dMRI)—known as Microstructure Imaging—has become an increasingly diverse and complicated field over the last decades. Multi-compartment (MC)-models, representing the measured diffusion signal as a linear combination of signal models of distinct tissue types, have been developed in many forms to estimate these features. However, a generalized implementation of MC-modeling as a whole, providing deeper insights in its capabilities, remains missing. To address this fact, we present Diffusion Microstructure Imaging in Python (Dmipy), an open-source toolbox implementing PGSE-based MC-modeling in its most general form. Dmipy allows on-the-fly implementation, signal modeling, and optimization of any user-defined MC-model, for any PGSE acquisition scheme. Dmipy follows a “building block”-based philosophy to Microstructure Imaging, meaning MC-models are modularly constructed to include any number and type of tissue models, allowing simultaneous representation of a tissue's diffusivity, orientation, volume fractions, axon orientation dispersion, and axon diameter distribution. In particular, Dmipy is geared toward facilitating reproducible, reliable MC-modeling pipelines, often allowing the whole process from model construction to parameter map recovery in fewer than 10 lines of code. To demonstrate Dmipy's ease of use and potential, we implement a wide range of well-known MC-models, including IVIM, AxCaliber, NODDI(x), Bingham-NODDI, the spherical mean-based SMT and MC-MDI, and spherical convolution-based single- and multi-tissue CSD. By allowing parameter cascading between MC-models, Dmipy also facilitates implementation of advanced approaches like CSD with voxel-varying kernels and single-shell 3-tissue CSD. By providing a well-tested, user-friendly toolbox that simplifies the interaction with the otherwise complicated field of dMRI-based Microstructure Imaging, Dmipy contributes to more reproducible, high-quality research.

Oscillating-gradient diffusion magnetic resonance imaging detects acute subcellular structural changes in the mouse forebrain after neonatal hypoxia-ischemia

The recently developed oscillating-gradient diffusion MRI (OG-dMRI) technique extends our ability to examine brain structures at different spatial scales. In this study, we investigated the sensitivity of OG-dMRI in detecting cellular and subcellular structural changes in a mouse model of neonatal hypoxia ischemia (HI). Neonatal mice received unilateral HI injury or sham injury at postnatal day 10, followed by in vivo T₂-weighted and diffusion MRI of the brains at 3-6 h and 24 h after HI. Apparent diffusion coefficient (ADC) maps were acquired using conventional pulsed-gradient dMRI (PG-dMRI) and OG-dMRI with oscillating frequencies from 50 to 200 Hz. Pathology at cellular and subcellular levels was evaluated using neuronal, glial, and mitochondrial markers. We found significantly higher rates of ADC increase with oscillating frequencies (Δ_fADC) in the ipsilateral edema region, compared to the contralateral side, starting as early as 3 h after HI. Even in injured regions that showed no apparent change in PG-ADC or pseudo-normalized PG-ADC measurements, Δ_fADC remained significantly elevated. Histopathology showed swelling of sub-cellular structures in these regions with no apparent whole-cell level change. These results suggest that OG-dMRI is sensitive to subcellular structural changes in the brain after HI and is less susceptible to pseudo-normalization than PG-dMRI.

Diffusion MRI in the brain - Theory and concepts

Over the past two decades, diffusion MRI has become an essential tool in neuroimaging investigations. This is due to its sensitivity to the motion of water molecules as they diffuse through the microstructural environment, allowing diffusion MRI to be used as a 'probe' of tissue microstructure. Furthermore, this sensitivity is strongly direction-dependent, notably in brain white matter, due to the alignment of structures that restrict or hinder the motion of water molecules, notably axonal membranes. This provides a means of inferring the orientation of fibres in vivo, and by use of appropriate fibre-tracking algorithms, of delineating the path of white matter tracts in the brain. The ability to perform so-called tractography in humans in vivo non-invasively is unique to diffusion MRI, and is now used in applications such as neurosurgery planning and more broadly within investigations of brain connectomics. This review describes the theory and concepts of diffusion MRI and describes its most important areas of application in the brain, with a strong focus on tractography.

Towards a 'resolution limit' for DW-MRI tumor microstructural models: A simulation study investigating the feasibility of distinguishing between microstructural changes

PURPOSE

To determine the feasibility of extracting sufficiently precise estimates of cell radius, R, and intracellular volume fraction, fi , from DW-MRI data in order to distinguish between specific microstructural changes tissue may undergo, specifically focusing on cell death in tumors.

METHODS

Simulations with optimized and non-optimized clinical acquisitions were performed for a range of microstructures, using a two-compartment model. The ability to distinguish between (i) cell shrinkage with cell density constant, mimicking apoptosis, and (ii) cell size constant with cell density decreasing, mimicking loss of cells, was evaluated based on the precision of simulated parameter estimates. Relationships between parameter precision, SNR, and the magnitude of specific parameter changes, were used to infer SNR requirements for detecting changes.

RESULTS

Accuracy and precision depended on microstructural properties, SNR, and the acquisition protocol. The main benefit of optimized acquisitions tended to be improved accuracy and precision of R, particularly for small cells. In most cases considered, higher SNR was required for detecting changes in R than for changes in fi .

CONCLUSIONS

Given the relative changes in R and fi due to apoptosis, simulations indicate that, for a range of microstructures, detecting changes in R require higher SNR than detecting changes in fi , and that such SNR is typically not achieved in clinical data. This suggests that if apoptotic cell size decreases are to be detected in clinical settings, improved SNR is required. Comparing measurement precision with the magnitude of expected biological changes should form part of the validation process for potential biomarkers.

Imaging brain microstructure with diffusion MRI: practicality and applications

This article gives an overview of microstructure imaging of the brain with diffusion MRI and reviews the state of the art. The microstructure-imaging paradigm aims to estimate and map microscopic properties of tissue using a model that links these properties to the voxel scale MR signal. Imaging techniques of this type are just starting to make the transition from the technical research domain to wide application in biomedical studies. We focus here on the practicalities of both implementing such techniques and using them in applications. Specifically, the article summarizes the relevant aspects of brain microanatomy and the range of diffusion-weighted MR measurements that provide sensitivity to them. It then reviews the evolution of mathematical and computational models that relate the diffusion MR signal to brain tissue microstructure, as well as the expanding areas of application. Next we focus on practicalities of designing a working microstructure imaging technique: model selection, experiment design, parameter estimation, validation, and the pipeline of development of this class of technique. The article concludes with some future perspectives on opportunities in this topic and expectations on how the field will evolve in the short-to-medium term.

Quantifying brain microstructure with diffusion MRI: Theory and parameter estimation

We review, systematize and discuss models of diffusion in neuronal tissue, by putting them into an overarching physical context of coarse-graining over an increasing diffusion length scale. From this perspective, we view research on quantifying brain microstructure as occurring along three major avenues. The first avenue focusses on transient, or time-dependent, effects in diffusion. These effects signify the gradual coarse-graining of tissue structure, which occurs qualitatively differently in different brain tissue compartments. We show that transient effects contain information about the relevant length scales for neuronal tissue, such as the packing correlation length for neuronal fibers, as well as the degree of structural disorder along the neurites. The second avenue corresponds to the long-time limit, when the observed signal can be approximated as a sum of multiple nonexchanging anisotropic Gaussian components. Here, the challenge lies in parameter estimation and in resolving its hidden degeneracies. The third avenue employs multiple diffusion encoding techniques, able to access information not contained in the conventional diffusion propagator. We conclude with our outlook on future directions that could open exciting possibilities for designing quantitative markers of tissue physiology and pathology, based on methods of studying mesoscopic transport in disordered systems.

The role of diffusion MRI in neuroscience

Diffusion-weighted imaging has pushed the boundaries of neuroscience by allowing us to examine the white matter microstructure of the living human brain. By doing so, it has provided answers to fundamental neuroscientific questions, launching a new field of research that had been largely inaccessible. We briefly summarize key questions that have historically been raised in neuroscience concerning the brain's white matter. We then expand on the benefits of diffusion-weighted imaging and its contribution to the fields of brain anatomy, functional models and plasticity. In doing so, this review highlights the invaluable contribution of diffusion-weighted imaging in neuroscience, presents its limitations and proposes new challenges for future generations who may wish to exploit this powerful technology to gain novel insights.

Modelling white matter with spherical deconvolution: How and why?

Since the realization that diffusion MRI can probe the microstructural organization and orientation of biological tissue in vivo and non-invasively, a multitude of diffusion imaging methods have been developed and applied to study the living human brain. Diffusion tensor imaging was the first model to be widely adopted in clinical and neuroscience research, but it was also clear from the beginning that it suffered from limitations when mapping complex configurations, such as crossing fibres. In this review, we highlight the main steps that have led the field of diffusion imaging to move from the tensor model to the adoption of diffusion and fibre orientation density functions as a more effective way to describe the complexity of white matter organization within each brain voxel. Among several techniques, spherical deconvolution has emerged today as one of the main approaches to model multiple fibre orientations and for tractography applications. Here we illustrate the main concepts and the reasoning behind this technique, as well as the latest developments in the field. The final part of this review provides practical guidelines and recommendations on how to set up processing and acquisition protocols suitable for spherical deconvolution.

Modeling conduction delays in the corpus callosum using MRI-measured g-ratio

Conduction of action potentials along myelinated axons is affected by their structural features, such as the axonal g-ratio, the ratio between the inner and outer diameters of the myelin sheath surrounding the axon. The effect of g-ratio variance on conduction properties has been quantitatively evaluated using single-axon models. It has recently become possible to estimate a g-ratio weighted measurement in vivo using quantitative MRI. Nevertheless, it is still unclear whether the variance in the g-ratio in the healthy human brain leads to significant differences in conduction velocity. In this work we tested whether the g-ratio MRI measurement can be used to predict conduction delays in the corpus callosum. We present a comprehensive framework in which the structural properties of fibers (i.e. length and g-ratio, measured using MRI), are incorporated in a biophysical model of axon conduction, to model conduction delays of long-range white matter fibers. We applied this framework to the corpus callosum, and found conduction delay estimates that are compatible with previously estimated values of conduction delays. We account for the variance in the velocity given the axon diameter distribution in the splenium, mid-body and genu, to further compare the fibers within the corpus callosum. Conduction delays have been suggested to increase with age. Therefore, we investigated whether there are differences in the g-ratio and the fiber length between young and old adults, and whether this leads to a difference in conduction speed and delays. We found very small differences between the predicted delays of the two groups in the motor fibers of the corpus callosum. We also found that the motor fibers of the corpus callosum have the fastest conduction estimates. Using the axon diameter distributions, we found that the occipital fibers have the slowest estimations, while the frontal and motor fiber tracts have similar estimates. Our study provides a framework for predicting conduction latencies in vivo. The framework could have major implications for future studies of white matter diseases and large range network computations. Our results highlight the need for improving additional in vivo measurements of white matter microstructure.