Challenges for biophysical modeling of microstructure

Introducing µGUIDE for quantitative imaging via generalized uncertainty-driven inference using deep learning

This work proposes µGUIDE: a general Bayesian framework to estimate posterior distributions of tissue microstructure parameters from any given biophysical model or signal representation, with exemplar demonstration in diffusion-weighted magnetic resonance imaging. Harnessing a new deep learning architecture for automatic signal feature selection combined with simulation-based inference and efficient sampling of the posterior distributions, µGUIDE bypasses the high computational and time cost of conventional Bayesian approaches and does not rely on acquisition constraints to define model-specific summary statistics. The obtained posterior distributions allow to highlight degeneracies present in the model definition and quantify the uncertainty and ambiguity of the estimated parameters.

Beyond Birth Control: The Neuroscience of Hormonal Contraceptives

Hormonal contraceptives (HCs) are one of the most highly prescribed classes of drugs in the world used for both contraceptive and noncontraceptive purposes. Despite their prevalent use, the impact of HCs on the brain remains inadequately explored. This review synthesizes recent findings on the neuroscience of HCs, with a focus on human structural neuroimaging as well as translational, nonhuman animal studies investigating the cellular, molecular, and behavioral effects of HCs. Additionally, we consider data linking HCs to mood disorders and dysregulation of the hypothalamic-pituitary-adrenal (HPA) axis and stress response as a potential mediator. The review also addresses the unique sensitivity of the adolescent brain to HCs, noting significant changes in brain structure and function when HCs are used during this developmental period. Finally, we discuss potential effects of HCs in combination with smoking-derived nicotine on outcomes of ischemic brain damage. Methodological challenges, such as the variability in HC formulations and user-specific factors, are acknowledged, emphasizing the need for precise and individualized research approaches. Overall, this review underscores the necessity for continued interdisciplinary research to elucidate the neurobiological mechanisms of HCs, aiming to optimize their use and improve women's health.

The Diffusion Exchange Ratio (DEXR): A minimal sampling of diffusion exchange spectroscopy to probe exchange, restriction, and time-dependence

Water exchange is increasingly recognized as an important biological process that can affect the study of biological tissue using diffusion MR. Methods to measure exchange, however, remain immature as opposed to those used to characterize restriction, with no consensus on the optimal pulse sequence (s) or signal model (s). In general, the trend has been towards data-intensive fitting of highly parameterized models. We take the opposite approach and show that a judicious sub-sample of diffusion exchange spectroscopy (DEXSY) data can be used to robustly quantify exchange, as well as restriction, in a data-efficient manner. This sampling produces a ratio of two points per mixing time: (i) one point with equal diffusion weighting in both encoding periods, which gives maximal exchange contrast, and (ii) one point with the same total diffusion weighting in just the first encoding period, for normalization. We call this quotient the Diffusion EXchange Ratio (DEXR). Furthermore, we show that it can be used to probe time-dependent diffusion by estimating the velocity autocorrelation function (VACF) over intermediate to long times (∼2-500ms). We provide a comprehensive theoretical framework for the design of DEXR experiments in the case of static or constant gradients. Data from Monte Carlo simulations and experiments acquired in fixed and viable ex vivo neonatal mouse spinal cord using a permanent magnet system are presented to test and validate this approach. In viable spinal cord, we report the following apparent parameters from just 6 data points: τk=17±4ms, fNG=0.72±0.01, Reff=1.05±0.01μm, and κeff=0.19±0.04μm/ms, which correspond to the exchange time, restricted or non-Gaussian signal fraction, an effective spherical radius, and permeability, respectively. For the VACF, we report a long-time, power-law scaling with ≈t-2.4, which is approximately consistent with disordered domains in 3-D. Overall, the DEXR method is shown to be highly efficient, capable of providing valuable quantitative diffusion metrics using minimal MR data.

Association between seated trunk control and cortical sensorimotor white matter brain changes in patients with chronic low back pain

Trunk control involves integration of sensorimotor information in the brain. Individuals with chronic low back pain (cLBP) have impaired trunk control and show differences in brain structure and function in sensorimotor areas compared with healthy controls (HC). However, the relationship between brain structure and trunk control in this group is not well understood. This cross-sectional study aimed to compare seated trunk control and sensorimotor white matter (WM) structure in people with cLBP and HC and explore relationships between WM properties and trunk control in each group. Thirty-two people with cLBP and 35 HC were tested sitting on an unstable chair to isolate trunk control; performance was measured using the 95% confidence ellipse area (CEA95) of center-of-pressure tracing. A WM network between cortical sensorimotor regions of interest was derived using probabilistic tractography. WM microstructure and anatomical connectivity between cortical sensorimotor regions were assessed. A mixed-model ANOVA showed that people with cLBP had worse trunk control than HC (F = 12.96; p < .001; ηp2 = .091). There were no differences in WM microstructure or anatomical connectivity between groups (p = 0.564 to 0.940). In the cLBP group, WM microstructure was moderately correlated (|r| = .456 to .565; p ≤ .009) with trunk control. Additionally, the cLBP group demonstrated stronger relationships between anatomical connectivity and trunk control (|r| = .377 to .618 p < .034) compared to the HC group. Unique to the cLBP group, WM connectivity between right somatosensory and left motor areas highlights the importance of interhemispheric information exchange for trunk control. Parietal areas associated with attention and spatial reference frames were also relevant to trunk control. These findings suggest that people with cLBP adopt a more cortically driven sensorimotor integration strategy for trunk control. Future research should replicate these findings and identify interventions to effectively modulate this strategy.

The Diffusion Exchange Ratio (DEXR): A minimal sampling of diffusion exchange spectroscopy to probe exchange, restriction, and time-dependence

Water exchange is increasingly recognized as an important biological process that can affect the study of biological tissue using diffusion MR. Methods to measure exchange, however, remain immature as opposed to those used to characterize restriction, with no consensus on the optimal pulse sequence(s) or signal model(s). In general, the trend has been towards data-intensive fitting of highly parameterized models. We take the opposite approach and show that a judicious sub-sample of diffusion exchange spectroscopy (DEXSY) data can be used to robustly quantify exchange, as well as restriction, in a data-efficient manner. This sampling produces a ratio of two points per mixing time: (i) one point with equal diffusion weighting in both encoding periods, which gives maximal exchange contrast, and (ii) one point with the same total diffusion weighting in just the first encoding period, for normalization. We call this quotient the Diffusion EXchange Ratio (DEXR). Furthermore, we show that it can be used to probe time-dependent diffusion by estimating the velocity autocorrelation function (VACF) over intermediate to long times (~ 2-500 ms). We provide a comprehensive theoretical framework for the design of DEXR experiments in the case of static or constant gradients. Data from Monte Carlo simulations and experiments acquired in fixed and viable ex vivo neonatal mouse spinal cord using a permanent magnet system are presented to test and validate this approach. In viable spinal cord, we report the following apparent parameters from just 6 data points:τ k = 17 ± 4 m s ,f N G = 0.71 ± 0.01 ,R e f f = 1.10 ± 0.01 μ m , andκ eff = 0.21 ± 0.06 μ m / m s , which correspond to the exchange time, restricted or non-Gaussian signal fraction, an effective spherical radius, and permeability, respectively. For the VACF, we report a long-time, power-law scaling with ≈ t - 2.4 , which is approximately consistent with disordered domains in 3-D. Overall, the DEXR method is shown to be highly efficient, capable of providing valuable quantitative diffusion metrics using minimal MR data.

Investigating apparent differences between standard DKI and axisymmetric DKI and its consequences for biophysical parameter estimates

PURPOSE

The purpose of the study is to identify differences between axisymmetric diffusion kurtosis imaging (DKI) and standard DKI, their consequences for biophysical parameter estimates, and the protocol choice influence on parameter estimation.

METHODS

Noise-free and noisy, synthetic diffusion MRI human brain data is simulated using standard DKI for a standard and the fast "199" acquisition protocol. First the noise-free "baseline" difference between both DKI models is estimated and the influence of fiber complexity is investigated. Noisy data is used to establish the signal-to-noise ratio at which the baseline difference exceeds noise variability. The influence of protocol choices and denoising is investigated. The five axisymmetric DKI tensor metrics (AxTM), the parallel and perpendicular diffusivity and kurtosis and mean of the kurtosis tensor are used to compare both DKI models. Additionally, the baseline difference is also estimated for the five parameters of the WMTI-Watson model.

RESULTS

The parallel and perpendicular kurtosis and all of the WMTI-Watson parameters had large baseline differences. Using a Westin or FA mask reduced the number of voxels with large baseline difference, that is, by selecting voxels with less complex fibers. For the noisy data, precision was worsened by the fast "199" protocol but adaptive denoising can help counteract these effects.

CONCLUSION

For the diffusivities and mean of the kurtosis tensor, axisymmetric DKI with a standard protocol delivers similar results as standard DKI. Fiber complexity is one main driver of the baseline differences. Using the "199" protocol worsens precision in noisy data but adaptive denoising mitigates these effects.

Beyond the surface: how ex-vivo diffusion-weighted imaging reveals large animal brain microstructure and connectivity

Diffusion-weighted Imaging (DWI) is an effective and state-of-the-art neuroimaging method that non-invasively reveals the microstructure and connectivity of tissues. Recently, novel applications of the DWI technique in studying large brains through ex-vivo imaging enabled researchers to gain insights into the complex neural architecture in different species such as those of Perissodactyla (e.g., horses and rhinos), Artiodactyla (e.g., bovids, swines, and cetaceans), and Carnivora (e.g., felids, canids, and pinnipeds). Classical in-vivo tract-tracing methods are usually considered unsuitable for ethical and practical reasons, in large animals or protected species. Ex-vivo DWI-based tractography offers the chance to examine the microstructure and connectivity of formalin-fixed tissues with scan times and precision that is not feasible in-vivo. This paper explores DWI's application to ex-vivo brains of large animals, highlighting the unique insights it offers into the structure of sometimes phylogenetically different neural networks, the connectivity of white matter tracts, and comparative evolutionary adaptations. Here, we also summarize the challenges, concerns, and perspectives of ex-vivo DWI that will shape the future of the field in large brains.

Diffusion MRI of the Hippocampus

The hippocampus is a brain structure that plays key roles in a variety of cognitive processes. Critically, a wide range of neurological disorders are associated with degeneration of the hippocampal microstructure, defined as neurons, dendrites, glial cells, and more. Thus, the hippocampus is a key target for methods that are sensitive to these microscale properties. Diffusion MRI is one such method, which can noninvasively probe neural architecture. Here we review the extensive use of diffusion MRI to capture hippocampal microstructure in both health and disease. The results of these studies indicate that (1) diffusion tensor imaging is sensitive but not specific to the hippocampal microstructure; (2) biophysical modeling of diffusion MRI signals is a promising avenue to capture more specific aspects of the hippocampal microstructure; (3) use of ultra-short diffusion times have shown unique laminar-specific microstructure and response to hippocampal injury; (4) dispersion of microstructure is likely abundant in the hippocampus; and (5) the angular richness of the diffusion MRI signal can be leveraged to improve delineation of the internal hippocampal circuitry. Overall, extant findings suggest that diffusion MRI offers a promising avenue for characterizing hippocampal microstructure.

Sex Differences in the Brain's White Matter Microstructure during Development assessed using Advanced Diffusion MRI Models

Typical sex differences in white matter (WM) microstructure during development are incompletely understood. Here we evaluated sex differences in WM microstructure during typical brain development using a sample of neurotypical individuals across a wide developmental age (N=239, aged 5-22 years). We used the conventional diffusion-weighted MRI (dMRI) model, diffusion tensor imaging (DTI), and two advanced dMRI models, the tensor distribution function (TDF) and neurite orientation dispersion density imaging (NODDI) to assess WM microstructure. WM microstructure exhibited significant, regionally consistent sex differences across the brain during typical development. Additionally, the TDF model was most sensitive in detecting sex differences. These findings highlight the importance of considering sex in neurodevelopmental research and underscore the value of the advanced TDF model.

Diffusion of brain metabolites highlights altered brain microstructure in type C hepatic encephalopathy: a 9.4 T preliminary study

Introduction

Type C hepatic encephalopathy (HE) is a decompensating event of chronic liver disease leading to severe motor and cognitive impairment. The progression of type C HE is associated with changes in brain metabolite concentrations measured by 1H magnetic resonance spectroscopy (MRS), most noticeably a strong increase in glutamine to detoxify brain ammonia. In addition, alterations of brain cellular architecture have been measured ex vivo by histology in a rat model of type C HE. The aim of this study was to assess the potential of diffusion-weighted MRS (dMRS) for probing these cellular shape alterations in vivo by monitoring the diffusion properties of the major brain metabolites.

Methods

The bile duct-ligated (BDL) rat model of type C HE was used. Five animals were scanned before surgery and 6- to 7-week post-BDL surgery, with each animal being used as its own control. 1H-MRS was performed in the hippocampus (SPECIAL, TE = 2.8 ms) and dMRS in a voxel encompassing the entire brain (DW-STEAM, TE = 15 ms, diffusion time = 120 ms, maximum b-value = 25 ms/μm2) on a 9.4 T scanner. The in vivo MRS acquisitions were further validated with histological measures (immunohistochemistry, Golgi-Cox, electron microscopy).

Results

The characteristic 1H-MRS pattern of type C HE, i.e., a gradual increase of brain glutamine and a decrease of the main organic osmolytes, was observed in the hippocampus of BDL rats. Overall increased metabolite diffusivities (apparent diffusion coefficient and intra-stick diffusivity-Callaghan's model, significant for glutamine, myo-inositol, and taurine) and decreased kurtosis coefficients were observed in BDL rats compared to control, highlighting the presence of osmotic stress and possibly of astrocytic and neuronal alterations. These results were consistent with the microstructure depicted by histology and represented by a decline in dendritic spines density in neurons, a shortening and decreased number of astrocytic processes, and extracellular edema.

Discussion

dMRS enables non-invasive and longitudinal monitoring of the diffusion behavior of brain metabolites, reflecting in the present study the globally altered brain microstructure in BDL rats, as confirmed ex vivo by histology. These findings give new insights into metabolic and microstructural abnormalities associated with high brain glutamine and its consequences in type C HE.

Lessons on brain edema in HE: from cellular to animal models and clinical studies

Brain edema is considered as a common feature associated with hepatic encephalopathy (HE). However, its central role as cause or consequence of HE and its implication in the development of the neurological alterations linked to HE are still under debate. It is now well accepted that type A and type C HE are biologically and clinically different, leading to different manifestations of brain edema. As a result, the findings on brain edema/swelling in type C HE are variable and sometimes controversial. In the light of the changing natural history of liver disease, better description of the clinical trajectory of cirrhosis and understanding of molecular mechanisms of HE, and the role of brain edema as a central component in the pathogenesis of HE is revisited in the current review. Furthermore, this review highlights the main techniques to measure brain edema and their advantages/disadvantages together with an in-depth description of the main ex-vivo/in-vivo findings using cell cultures, animal models and humans with HE. These findings are instrumental in elucidating the role of brain edema in HE and also in designing new multimodal studies by performing in-vivo combined with ex-vivo experiments for a better characterization of brain edema longitudinally and of its role in HE, especially in type C HE where water content changes are small.

Insights and improvements in correspondence between axonal volume fraction measured with diffusion-weighted MRI and electron microscopy

Biophysical diffusion-weighted imaging (DWI) models are increasingly used in neuroscience to estimate the axonal water fraction (f AW ), which in turn is key for noninvasive estimation of the axonal volume fraction (f A ). These models require thorough validation by comparison with a reference method, for example, electron microscopy (EM). While EM studies often neglect the unmyelinated axons and solely report the fraction of myelinated axons, in DWI both myelinated and unmyelinated axons contribute to the DWI signal. However, DWI models often include simplifications, for example, the neglect of differences in the compartmental relaxation times or fixed diffusivities, which in turn might affect the estimation off AW . We investigate whether linear calibration parameters (scaling and offset) can improve the comparability between EM- and DWI-based metrics off A . To this end, we (a) used six DWI models based on the so-called standard model of white matter (WM), including two models with fixed compartmental diffusivities (e.g., neurite orientation dispersion and density imaging, NODDI) and four models that fitted the compartmental diffusivities (e.g., white matter tract integrity, WMTI), and (b) used a multimodal data set including ex vivo diffusion DWI and EM data in mice with a broad dynamic range of fibre volume metrics. We demonstrated that the offset is associated with the volume fraction of unmyelinated axons and the scaling factor is associated with different compartmentalT 2 and can substantially enhance the comparability between EM- and DWI-based metrics off A . We found that DWI models that fitted compartmental diffusivities provided the most accurate estimates of the EM-basedf A . Finally, we introduced a more efficient hybrid calibration approach, where only the offset is estimated but the scaling is fixed to a theoretically predicted value. Using this approach, a similar one-to-one correspondence to EM was achieved for WMTI. The method presented can pave the way for use of validated DWI-based models in clinical research and neuroscience.

White matter microstructural plasticity associated with educational intervention in reading disability

Children's reading progress typically slows during extended breaks in formal education, such as summer vacations. This stagnation can be especially concerning for children with reading difficulties or disabilities, such as dyslexia, because of the potential to exacerbate the skills gap between them and their peers. Reading interventions can prevent skill loss and even lead to appreciable gains in reading ability during the summer. Longitudinal studies relating intervention response to brain changes can reveal educationally relevant insights into rapid learning-driven brain plasticity. The current work focused on reading outcomes and white matter connections, which enable communication among the brain regions required for proficient reading. We collected reading scores and diffusion-weighted images at the beginning and end of summer for 41 children with reading difficulties who had completed either 1st or 2nd grade. Children were randomly assigned to either receive an intensive reading intervention (n = 26; Seeing Stars from Lindamood-Bell which emphasizes orthographic fluency) or be deferred to a wait-list group (n = 15), enabling us to analyze how white matter properties varied across a wide spectrum of skill development and regression trajectories. On average, the intervention group had larger gains in reading compared to the non-intervention group, who declined in reading scores. Improvements on a proximal measure of orthographic processing (but not other more distal reading measures) were associated with decreases in mean diffusivity within core reading brain circuitry (left arcuate fasciculus and left inferior longitudinal fasciculus) and increases in fractional anisotropy in the left corticospinal tract. Our findings suggest that responses to intensive reading instruction are related predominantly to white matter plasticity in tracts most associated with reading.

Current Auditory Hallucinations Are Not Associated With Specific White Matter Diffusion Alterations in Schizophrenia

Background and Hypothesis

Studies have linked auditory hallucinations (AH) in schizophrenia spectrum disorders (SCZ) to altered cerebral white matter microstructure within the language and auditory processing circuitry (LAPC). However, the specificity to the LAPC remains unclear. Here, we investigated the relationship between AH and DTI among patients with SCZ using diffusion tensor imaging (DTI).

Study Design

We included patients with SCZ with (AH+; n = 59) and without (AH-; n = 81) current AH, and 140 age- and sex-matched controls. Fractional anisotropy (FA), mean diffusivity (MD), radial diffusivity (RD), and axial diffusivity (AD) were extracted from 39 fiber tracts. We used principal component analysis (PCA) to identify general factors of variation across fiber tracts and DTI metrics. Regression models adjusted for sex, age, and age2 were used to compare tract-wise DTI metrics and PCA factors between AH+, AH-, and healthy controls and to assess associations with clinical characteristics.

Study Results

Widespread differences relative to controls were observed for MD and RD in patients without current AH. Only limited differences in 2 fiber tracts were observed between AH+ and controls. Unimodal PCA factors based on MD, RD, and AD, as well as multimodal PCA factors, differed significantly relative to controls for AH-, but not AH+. We did not find any significant associations between PCA factors and clinical characteristics.

Conclusions

Contrary to previous studies, DTI metrics differed mainly in patients without current AH compared to controls, indicating a widespread neuroanatomical distribution. This challenges the notion that altered DTI metrics within the LAPC is a specific feature underlying AH.

Associations of prior concussion severity with brain microstructure using mean apparent propagator magnetic resonance imaging

Magnetic resonance imaging (MRI) diffusion studies have shown chronic microstructural tissue abnormalities in athletes with history of concussion, but with inconsistent findings. Concussions with post-traumatic amnesia (PTA) and/or loss of consciousness (LOC) have been connected to greater physiological injury. The novel mean apparent propagator (MAP) MRI is expected to be more sensitive to such tissue injury than the conventional diffusion tensor imaging. This study examined effects of prior concussion severity on microstructure with MAP-MRI. Collegiate-aged athletes (N = 111, 38 females; ≥6 months since most recent concussion, if present) completed semistructured interviews to determine the presence of prior concussion and associated injury characteristics, including PTA and LOC. MAP-MRI metrics (mean non-Gaussian diffusion [NG Mean], return-to-origin probability [RTOP], and mean square displacement [MSD]) were calculated from multi-shell diffusion data, then evaluated for associations with concussion severity through group comparisons in a primary model (athletes with/without prior concussion) and two secondary models (athletes with/without prior concussion with PTA and/or LOC, and athletes with/without prior concussion with LOC only). Bayesian multilevel modeling estimated models in regions of interest (ROI) in white matter and subcortical gray matter, separately. In gray matter, the primary model showed decreased NG Mean and RTOP in the bilateral pallidum and decreased NG Mean in the left putamen with prior concussion. In white matter, lower NG Mean with prior concussion was present in all ROI across all models and was further decreased with LOC. However, only prior concussion with LOC was associated with decreased RTOP and increased MSD across ROI. Exploratory analyses conducted separately in male and female athletes indicate associations in the primary model may differ by sex. Results suggest microstructural measures in gray matter are associated with a general history of concussion, while a severity-dependent association of prior concussion may exist in white matter.

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.

Probing brain tissue microstructure with MRI: principles, challenges, and the role of multidimensional diffusion-relaxation encoding

Diffusion MRI uses the random displacement of water molecules to sensitize the signal to brain microstructure and to properties such as the density and shape of cells. Microstructure modeling techniques aim to estimate these properties from acquired data by separating the signal between virtual tissue 'compartments' such as the intra-neurite and the extra-cellular space. A key challenge is that the diffusion MRI signal is relatively featureless compared with the complexity of brain tissue. Another challenge is that the tissue microstructure is wildly different within the gray and white matter of the brain. In this review, we use results from multidimensional diffusion encoding techniques to discuss these challenges and their tentative solutions. Multidimensional encoding increases the information content of the data by varying not only the b-value and the encoding direction but also additional experimental parameters such as the shape of the b-tensor and the echo time. Three main insights have emerged from such encoding. First, multidimensional data contradict common model assumptions on diffusion and T2 relaxation, and illustrates how the use of these assumptions cause erroneous interpretations in both healthy brain and pathology. Second, many model assumptions can be dispensed with if data are acquired with multidimensional encoding. The necessary data can be easily acquired in vivo using protocols optimized to minimize Cramér-Rao lower bounds. Third, microscopic diffusion anisotropy reflects the presence of axons but not dendrites. This insight stands in contrast to current 'neurite models' of brain tissue, which assume that axons in white matter and dendrites in gray matter feature highly similar diffusion. Nevertheless, as an axon-based contrast, microscopic anisotropy can differentiate gray and white matter when myelin alterations confound conventional MRI contrasts.

Subject classification and cross-time prediction based on functional connectivity and white matter microstructure features in a rat model of Alzheimer's using machine learning

BACKGROUND

The pathological process of Alzheimer's disease (AD) typically takes decades from onset to clinical symptoms. Early brain changes in AD include MRI-measurable features such as altered functional connectivity (FC) and white matter degeneration. The ability of these features to discriminate between subjects without a diagnosis, or their prognostic value, is however not established.

METHODS

The main trigger mechanism of AD is still debated, although impaired brain glucose metabolism is taking an increasingly central role. Here, we used a rat model of sporadic AD, based on impaired brain glucose metabolism induced by an intracerebroventricular injection of streptozotocin (STZ). We characterized alterations in FC and white matter microstructure longitudinally using functional and diffusion MRI. Those MRI-derived measures were used to classify STZ from control rats using machine learning, and the importance of each individual measure was quantified using explainable artificial intelligence methods.

RESULTS

Overall, combining all the FC and white matter metrics in an ensemble way was the best strategy to discriminate STZ rats, with a consistent accuracy over 0.85. However, the best accuracy early on was achieved using white matter microstructure features, and later on using FC. This suggests that consistent damage in white matter in the STZ group might precede FC. For cross-timepoint prediction, microstructure features also had the highest performance while, in contrast, that of FC was reduced by its dynamic pattern which shifted from early hyperconnectivity to late hypoconnectivity.

CONCLUSIONS

Our study highlights the MRI-derived measures that best discriminate STZ vs control rats early in the course of the disease, with potential translation to humans.

Implications of fitting a two-compartment model in single-shell diffusion MRI

It is becoming increasingly common for studies to fit single-shell diffusion MRI data to a two-compartment model, which comprises a hindered cellular compartment and a freely diffusing isotropic compartment. These studies consistently find that the fraction of the isotropic compartment (f) is sensitive to white matter (WM) conditions and pathologies, although the actual biological source of changes infhas not been validated. In this work we put aside the biological interpretation offand study the sensitivity implications of fitting single-shell data to a two-compartment model. We identify a nonlinear transformation between the one-compartment model (diffusion tensor imaging, DTI) and a two-compartment model in which the mean diffusivities of both compartments are effectively fixed. While the analytic relationship implies that fitting this two-compartment model does not offer any more information than DTI, it explains why metrics derived from a two-compartment model can exhibit enhanced sensitivity over DTI to certain types of WM processes, such as age-related WM differences. The sensitivity enhancement should not be viewed as a substitute for acquiring multi-shell data. Rather, the results of this study provide insight into the consequences of choosing a two-compartment model when only single-shell data is available.

Fiber-specific age-related differences in the white matter of healthy adults uncovered by fixel-based analysis

Diffusion magnetic resonance imaging studies often investigate white matter (WM) microstructural degeneration in aging by probing WM regions that exhibit negative age associations of fractional anisotropy (FA). However, WM regions in which FA is unassociated with age are not necessarily "spared" in aging. Besides the confound of inter-participant heterogeneity, FA conflates all intravoxel fiber populations and does not allow the detection of individual fiber-specific age associations. In this study of 541 healthy adults aged 36-100 years, we use fixel-based analysis to investigate age associations among each "fixel" within a voxel, representing individual fiber populations. We find age associations of fixel-based measures that indicate age-related differences in individual fiber populations amid complex fiber architectures. Different crossing fiber populations exhibit different slopes of age associations. Our findings may provide evidence of selective degeneration of intravoxel WM fibers in aging, which does not necessarily manifest as a change in FA and therefore escapes notice if conventional voxel-based analyses are relied upon alone.

Tract- and gray matter- based spatial statistics show white matter and gray matter microstructural differences in autistic males

Background

Autism spectrum disorder (ASD) is a neurodevelopmental condition commonly studied in the context of early childhood. As ASD is a life-long condition, understanding the characteristics of brain microstructure from adolescence into adulthood and associations to clinical features is critical for improving outcomes across the lifespan. In the current work, we utilized Tract Based Spatial Statistics (TBSS) and Gray Matter Based Spatial Statistics (GBSS) to examine the white matter (WM) and gray matter (GM) microstructure in neurotypical (NT) and autistic males.

Methods

Multi-shell diffusion MRI was acquired from 78 autistic and 81 NT males (12-to-46-years) and fit to the DTI and NODDI diffusion models. TBSS and GBSS were performed to analyze WM and GM microstructure, respectively. General linear models were used to investigate group and age-related group differences. Within the ASD group, relationships between WM and GM microstructure and measures of autistic symptoms were investigated.

Results

All dMRI measures were significantly associated with age across WM and GM. Significant group differences were observed across WM and GM. No significant age-by-group interactions were detected. Within the ASD group, positive relationships with WM microstructure were observed with ADOS-2 Calibrated Severity Scores.

Conclusion

Using TBSS and GBSS our findings provide new insights into group differences of WM and GM microstructure in autistic males from adolescence into adulthood. Detection of microstructural differences across the lifespan as well as their relationship to the level of autistic symptoms will deepen to our understanding of brain-behavior relationships of ASD and may aid in the improvement of intervention options for autistic adults.

Mapping tissue microstructure across the human brain on a clinical scanner with soma and neurite density image metrics

Soma and neurite density image (SANDI) is an advanced diffusion magnetic resonance imaging biophysical signal model devised to probe in vivo microstructural information in the gray matter (GM). This model requires acquisitions that include b values that are at least six times higher than those used in clinical practice. Such high b values are required to disentangle the signal contribution of water diffusing in soma from that diffusing in neurites and extracellular space, while keeping the diffusion time as short as possible to minimize potential bias due to water exchange. These requirements have limited the use of SANDI only to preclinical or cutting-edge human scanners. Here, we investigate the potential impact of neglecting water exchange in the SANDI model and present a 10-min acquisition protocol that enables to characterize both GM and white matter (WM) on 3 T scanners. We implemented analytical simulations to (i) evaluate the stability of the fitting of SANDI parameters when diminishing the number of shells; (ii) estimate the bias due to potential exchange between neurites and extracellular space in such reduced acquisition scheme, comparing it with the bias due to experimental noise. Then, we demonstrated the feasibility and assessed the repeatability and reproducibility of our approach by computing microstructural metrics of SANDI with AMICO toolbox and other state-of-the-art models on five healthy subjects. Finally, we applied our protocol to five multiple sclerosis patients. Results suggest that SANDI is a practical method to characterize WM and GM tissues in vivo on performant clinical scanners.

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.

Comparison of different neurite density metrics with brain asymmetry evaluation

The standard diffusion MRI model with intra- and extra-axonal water pools offers a set of microstructural parameters describing brain white matter architecture. However, non-linearities in the standard model and diffusion data contamination by noise and imaging artefacts make estimation of diffusion metrics challenging. In order to develop reliable diffusion approaches and to avoid computational model degeneracy, additional theoretical assumptions allowing stable numerical implementations are required. Advanced diffusion approaches allow for estimation of intra-axonal water fraction (AWF), describing a key structural characteristic of brain tissue. AWF can be interpreted as an indirect measure or proxy of neurite density and has a potential as useful clinical biomarker. Established diffusion approaches such as white matter tract integrity, neurite orientation dispersion and density imaging (NODDI), and spherical mean technique provide estimates of AWF within their respective theoretical frameworks. In the present study, we estimated AWF metrics using different diffusion approaches and compared measures of brain asymmetry between the different metrics in a sub-sample of 182 subjects from the UK Biobank. Multivariate decomposition by mean of linked independent component analysis revealed that the various AWF proxies derived from the different diffusion approaches reflect partly non-overlapping variance of independent components, with distinct anatomical distributions and sensitivity to age. Further, voxel-wise analysis revealed age-related differences in AWF-based brain asymmetry, indicating less apparent left-right hemisphere difference with higher age. Finally, we demonstrated that NODDI metrics suffer from a quite strong dependence on used numerical algorithms and post-processing pipeline. The analysis based on AWF metrics strongly depends on the used diffusion approach and leads to poorly reproducible results.

C-NODDI: a constrained NODDI model for axonal density and orientation determinations in cerebral white matter

Purpose

Neurite orientation dispersion and density imaging (NODDI) provides measures of neurite density and dispersion through computation of the neurite density index (NDI) and the orientation dispersion index (ODI). However, NODDI overestimates the cerebrospinal fluid water fraction in white matter (WM) and provides physiologically unrealistic high NDI values. Furthermore, derived NDI values are echo-time (TE)-dependent. In this work, we propose a modification of NODDI, named constrained NODDI (C-NODDI), for NDI and ODI mapping in WM.

Methods

Using NODDI and C-NODDI, we investigated age-related alterations in WM in a cohort of 58 cognitively unimpaired adults. Further, NDI values derived using NODDI or C-NODDI were correlated with the neurofilament light chain (NfL) concentration levels, a plasma biomarker of axonal degeneration. Finally, we investigated the TE dependence of NODDI or C-NODDI derived NDI and ODI.

Results

ODI derived values using both approaches were virtually identical, exhibiting constant trends with age. Further, our results indicated a quadratic relationship between NDI and age suggesting that axonal maturation continues until middle age followed by a decrease. This quadratic association was notably significant in several WM regions using C-NODDI, while limited to a few regions using NODDI. Further, C-NODDI-NDI values exhibited a stronger correlation with NfL concentration levels as compared to NODDI-NDI, with lower NDI values corresponding to higher levels of NfL. Finally, we confirmed the previous finding that NDI estimation using NODDI was dependent on TE, while NDI derived values using C-NODDI exhibited lower sensitivity to TE in WM.

Conclusion

C-NODDI provides a complementary method to NODDI for determination of NDI in white matter.

Bio-psycho-social factors' associations with brain age: a large-scale UK Biobank diffusion study of 35,749 participants

Brain age refers to age predicted by brain features. Brain age has previously been associated with various health and disease outcomes and suggested as a potential biomarker of general health. Few previous studies have systematically assessed brain age variability derived from single and multi-shell diffusion magnetic resonance imaging data. Here, we present multivariate models of brain age derived from various diffusion approaches and how they relate to bio-psycho-social variables within the domains of sociodemographic, cognitive, life-satisfaction, as well as health and lifestyle factors in midlife to old age (N = 35,749, 44.6-82.8 years of age). Bio-psycho-social factors could uniquely explain a small proportion of the brain age variance, in a similar pattern across diffusion approaches: cognitive scores, life satisfaction, health and lifestyle factors adding to the variance explained, but not socio-demographics. Consistent brain age associations across models were found for waist-to-hip ratio, diabetes, hypertension, smoking, matrix puzzles solving, and job and health satisfaction and perception. Furthermore, we found large variability in sex and ethnicity group differences in brain age. Our results show that brain age cannot be sufficiently explained by bio-psycho-social variables alone. However, the observed associations suggest to adjust for sex, ethnicity, cognitive factors, as well as health and lifestyle factors, and to observe bio-psycho-social factor interactions' influence on brain age in future studies.

Unraveling multi-fixel microstructure with tractography and angular weighting

Recent advances in MRI technology have enabled richer multi-shell sequences to be implemented in diffusion MRI, allowing the investigation of both the microscopic and macroscopic organization of the brain white matter and its complex network of neural fibers. The emergence of advanced diffusion models has enabled a more detailed analysis of brain microstructure by estimating the signal received from a voxel as the combination of responses from multiple fiber populations. However, disentangling the individual microstructural properties of different macroscopic white matter tracts where those pathways intersect remains a challenge. Several approaches have been developed to assign microstructural properties to macroscopic streamlines, but often present shortcomings. ROI-based heuristics rely on averages that are not tract-specific. Global methods solve a computationally-intensive global optimization but prevent the use of microstructural properties not included in the model and often require restrictive hypotheses. Other methods use atlases that might not be adequate in population studies where the shape of white matter tracts varies significantly between patients. We introduce UNRAVEL, a framework combining the microscopic and macroscopic scales to unravel multi-fixel microstructure by utilizing tractography. The framework includes commonly-used heuristics as well as a new algorithm, estimating the microstructure of a specific white matter tract with angular weighting. Our framework grants considerable freedom as the inputs required, a set of streamlines defining a tract and a multi-fixel diffusion model estimated in each voxel, can be defined by the user. We validate our approach on synthetic data and in vivo data, including a repeated scan of a subject and a population study of children with dyslexia. In each case, we compare the estimation of microstructural properties obtained with angular weighting to other commonly-used approaches. Our framework provides estimations of the microstructure at the streamline level, volumetric maps for visualization and mean microstructural values for the whole tract. The angular weighting algorithm shows increased accuracy, robustness to uncertainties in its inputs and maintains similar or better reproducibility compared to commonly-used analysis approaches. UNRAVEL will provide researchers with a flexible and open-source tool enabling them to study the microstructure of specific white matter pathways with their diffusion model of choice.

Quantitative MRI in leukodystrophies

Leukodystrophies constitute a large and heterogeneous group of genetic diseases primarily affecting the white matter of the central nervous system. Different disorders target different white matter structural components. Leukodystrophies are most often progressive and fatal. In recent years, novel therapies are emerging and for an increasing number of leukodystrophies trials are being developed. Objective and quantitative metrics are needed to serve as outcome measures in trials. Quantitative MRI yields information on microstructural properties, such as myelin or axonal content and condition, and on the chemical composition of white matter, in a noninvasive fashion. By providing information on white matter microstructural involvement, quantitative MRI may contribute to the evaluation and monitoring of leukodystrophies. Many distinct MR techniques are available at different stages of development. While some are already clinically applicable, others are less far developed and have only or mainly been applied in healthy subjects. In this review, we explore the background, current status, potential and challenges of available quantitative MR techniques in the context of leukodystrophies.

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 Value of Various Post-Processing Modalities of Diffusion Weighted Imaging in the Detection of Multiple Sclerosis

Diffusion tensor imaging (DTI) showed its adequacy in evaluating the normal-appearing white matter (NAWM) and lesions in the brain that are difficult to evaluate with routine clinical magnetic resonance imaging (MRI) in multiple sclerosis (MS). Recently, MRI systems have been developed with regard to software and hardware, leading to different proposed diffusion analysis methods such as diffusion tensor imaging, q-space imaging, diffusional kurtosis imaging, neurite orientation dispersion and density imaging, and axonal diameter measurement. These methods have the ability to better detect in vivo microstructural changes in the brain than DTI. These different analysis modalities could provide supplementary inputs for MS disease characterization and help in monitoring the disease’s progression as well as treatment efficacy. This paper reviews some of the recent diffusion MRI methods used for the assessment of MS in vivo.

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 that are 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 gradient waveforms with different sensitivities to restricted diffusion and exchange (150 samples), our results reveal unique time-dependence signatures in grey and white matter, where the former is characterised by both restricted diffusion and exchange and the latter predominantly exhibits restricted diffusion. Furthermore, we show that gradient waveforms with independently varying sensitivities to restricted diffusion and exchange can be used to map exchange in the human brain. We consistently find that exchange in grey matter is at least twice as fast as in white matter, across all subjects and all gradient strengths. The shortest exchange times observed in this study were in the cerebellar cortex (115 ms). We also assess the feasibility of future clinical applications of the method used in this work, where we find that the grey-white matter exchange contrast obtained with a 25-minute 300 mT/m protocol is preserved by a 4-minute 300 mT/m and a 10-minute 80 mT/m protocol. 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.

Parameter estimation for WMTI-Watson model of white matter using encoder-decoder recurrent neural network

PURPOSE

Biophysical modeling of the diffusion MRI (dMRI) signal provides estimates of specific microstructural tissue properties. Although non-linear least squares (NLLS) is the most widespread fitting method, it suffers from local minima and high computational cost. Deep learning approaches are steadily replacing NLLS, but come with the limitation that the model needs to be retrained for each acquisition protocol and noise level. In this study, a novel fitting approach was proposed based on the encoder-decoder recurrent neural network (RNN) to accelerate model estimation with good generalization to various datasets.

METHODS

The white matter tract integrity (WMTI)-Watson model as an implementation of the Standard Model of diffusion in white matter derives its parameters indirectly from the diffusion and kurtosis tensors (DKI). The RNN-based solver, which estimates the WMTI-Watson model from DKI, is therefore more readily translatable to various data, irrespective of acquisition protocols as long as the DKI was pre-computed from the signal. An embedding approach was also used to render the model insensitive to potential differences in distributions between training data and experimental data. The analytical solution, NLLS, RNN-, and a multilayer perceptron (MLP)-based methods were evaluated on synthetic and in vivo datasets of rat and human brain.

RESULTS

The proposed RNN solver showed highly reduced computation time over the analytical solution and NLLS, with similar accuracy but improved robustness, and superior generalizability over MLP.

CONCLUSION

The RNN estimator can be easily applied to various datasets without retraining, which shows great potential for a widespread use.

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.

Applications of advanced diffusion MRI in early brain development: a comprehensive review

Brain development follows a protracted developmental timeline with foundational processes of neurodevelopment occurring from the third trimester of gestation into the first decade of life. Defining structural maturational patterns of early brain development is a critical step in detecting divergent developmental trajectories associated with neurodevelopmental and psychiatric disorders that arise later in life. While considerable advancements have already been made in diffusion magnetic resonance imaging (dMRI) for pediatric research over the past three decades, the field of neurodevelopment is still in its infancy with remarkable scientific and clinical potential. This comprehensive review evaluates the application, findings, and limitations of advanced dMRI methods beyond diffusion tensor imaging, including diffusion kurtosis imaging (DKI), constrained spherical deconvolution (CSD), neurite orientation dispersion and density imaging (NODDI) and composite hindered and restricted model of diffusion (CHARMED) to quantify the rapid and dynamic changes supporting the underlying microstructural architectural foundations of the brain in early life.

Axial and radial axonal diffusivities and radii from single encoding strongly diffusion-weighted MRI

We enable the estimation of the per-axon axial diffusivity from single encoding, strongly diffusion-weighted, pulsed gradient spin echo data. Additionally, we improve the estimation of the per-axon radial diffusivity compared to estimates based on spherical averaging. The use of strong diffusion weightings in magnetic resonance imaging (MRI) allows to approximate the signal in white matter as the sum of the contributions from only axons. At the same time, spherical averaging leads to a major simplification of the modeling by removing the need to explicitly account for the unknown distribution of axonal orientations. However, the spherically averaged signal acquired at strong diffusion weightings is not sensitive to the axial diffusivity, which cannot therefore be estimated although needed for modeling axons - especially in the context of multi-compartmental modeling. We introduce a new general method for the estimation of both the axial and radial axonal diffusivities at strong diffusion weightings based on kernel zonal modeling. The method could lead to estimates that are free from partial volume bias with gray matter or other isotropic compartments. The method is tested on publicly available data from the MGH Adult Diffusion Human Connectome project. We report reference values of axonal diffusivities based on 34 subjects, and derive estimates of axonal radii from only two shells. The estimation problem is also addressed from the angle of the required data preprocessing, the presence of biases related to modeling assumptions, current limitations, and future possibilities.

Diffusion radiomics for subtyping and clustering in autism spectrum disorder: A preclinical study

Autism spectrum disorder (ASD) is a highly prevalent, heterogenous neurodevelopmental disorder. Neuroimaging methods such as functional, structural, and diffusion MRI have been used to identify candidate imaging biomarkers for ASD, but current findings remain non-specific and likely arise from the heterogeneity present in ASD. To account for this, efforts to subtype ASD have emerged as a potential strategy for both the study of ASD and advancement of tailored behavioral therapies and therapeutics. Towards these ends, to improve upon current neuroimaging methods, we propose combining biologically sensitive neurite orientation dispersion and density index (NODDI) diffusion MR imaging with radiomics image processing to create a new methodological approach that, we hypothesize, can sensitively and specifically capture neurobiology. We demonstrate this method can sensitively distinguish differences between four genetically distinct rat models of ASD (Fmr1, Pten, Nrxn1, Disc1). Further, we demonstrate diffusion radiomic analyses hold promise for subtyping in ASD as we show unsupervised clustering of NODDI radiomic data generates clusters specific to the underlying genetic differences between the animal models. Taken together, our findings suggest the unique application of radiomic analysis on NODDI diffusion MRI may have the capacity to sensitively and specifically disambiguate the neurobiological heterogeneity present in the ASD population.

Fiber-specific structural properties relate to reading skills in children and adolescents

Recent studies suggest that the cross-sectional relationship between reading skills and white matter microstructure, as indexed by fractional anisotropy, is not as robust as previously thought. Fixel-based analyses yield fiber-specific micro- and macrostructural measures, overcoming several shortcomings of the traditional diffusion tensor model. We ran a whole-brain analysis investigating whether the product of fiber density and cross-section (FDC) related to single-word reading skills in a large, open, quality-controlled dataset of 983 children and adolescents ages 6-18. We also compared FDC between participants with (n = 102) and without (n = 570) reading disabilities. We found that FDC positively related to reading skills throughout the brain, especially in left temporoparietal and cerebellar white matter, but did not differ between reading proficiency groups. Exploratory analyses revealed that among metrics from other diffusion models - diffusion tensor imaging, diffusion kurtosis imaging, and neurite orientation dispersion and density imaging - only the orientation dispersion and neurite density indexes from NODDI were associated (inversely) with reading skills. The present findings further support the importance of left-hemisphere dorsal temporoparietal white matter tracts in reading. Additionally, these results suggest that future DWI studies of reading and dyslexia should be designed to benefit from advanced diffusion models, include cerebellar coverage, and consider continuous analyses that account for individual differences in reading skill.

Multicompartmental models and diffusion abnormalities in paediatric mild traumatic brain injury

The underlying pathophysiology of paediatric mild traumatic brain injury and the time-course for biological recovery remains widely debated, with clinical care principally informed by subjective self-report. Similarly, clinical evidence indicates that adolescence is a risk factor for prolonged recovery, but the impact of age-at-injury on biomarkers has not been determined in large, homogeneous samples. The current study collected diffusion MRI data in consecutively recruited patients (n = 203; 8-18 years old) and age and sex-matched healthy controls (n = 170) in a prospective cohort design. Patients were evaluated subacutely (1-11 days post-injury) as well as at 4 months post-injury (early chronic phase). Healthy participants were evaluated at similar times to control for neurodevelopment and practice effects. Clinical findings indicated persistent symptoms at 4 months for a significant minority of patients (22%), along with residual executive dysfunction and verbal memory deficits. Results indicated increased fractional anisotropy and reduced mean diffusivity for patients, with abnormalities persisting up to 4 months post-injury. Multicompartmental geometric models indicated that estimates of intracellular volume fractions were increased in patients, whereas estimates of free water fractions were decreased. Critically, unique areas of white matter pathology (increased free water fractions or increased neurite dispersion) were observed when standard assumptions regarding parallel diffusivity were altered in multicompartmental models to be more biologically plausible. Cross-validation analyses indicated that some diffusion findings were more reproducible when ∼70% of the total sample (142 patients, 119 controls) were used in analyses, highlighting the need for large-sample sizes to detect abnormalities. Supervised machine learning approaches (random forests) indicated that diffusion abnormalities increased overall diagnostic accuracy (patients versus controls) by ∼10% after controlling for current clinical gold standards, with each diffusion metric accounting for only a few unique percentage points. In summary, current results suggest that novel multicompartmental models are more sensitive to paediatric mild traumatic brain injury pathology, and that this sensitivity is increased when using parameters that more accurately reflect diffusion in healthy tissue. Results also indicate that diffusion data may be insufficient to achieve a high degree of objective diagnostic accuracy in patients when used in isolation, which is to be expected given known heterogeneities in pathophysiology, mechanism of injury and even criteria for diagnoses. Finally, current results indicate ongoing clinical and physiological recovery at 4 months post-injury.

Estimating axial diffusivity in the NODDI model

To estimate microstructure-related parameters from diffusion MRI data, biophysical models make strong, simplifying assumptions about the underlying tissue. The extent to which many of these assumptions are valid remains an open research question. This study was inspired by the disparity between the estimated intra-axonal axial diffusivity from literature and that typically assumed by the Neurite Orientation Dispersion and Density Imaging (NODDI) model (d_∥=1.7μm²/ms). We first demonstrate how changing the assumed axial diffusivity results in considerably different NODDI parameter estimates. Second, we illustrate the ability to estimate axial diffusivity as a free parameter of the model using high b-value data and an adapted NODDI framework. Using both simulated and in vivo data we investigate the impact of fitting to either real-valued or magnitude data, with Gaussian and Rician noise characteristics respectively, and what happens if we get the noise assumptions wrong in this high b-value and thus low SNR regime. Our results from real-valued human data estimate intra-axonal axial diffusivities of ∼2-2.5μm²/ms, in line with current literature. Crucially, our results demonstrate the importance of accounting for both a rectified noise floor and/or a signal offset to avoid biased parameter estimates when dealing with low SNR data.

Stepwise Stochastic Dictionary Adaptation Improves Microstructure Reconstruction with Orientation Distribution Function Fingerprinting

Fitting of the multicompartment biophysical model of white matter is an ill-posed optimization problem. One approach to make it computationally tractable is through Orientation Distribution Function (ODF) Fingerprinting. However, the accuracy of this method relies solely on ODF dictionary generation mechanisms which either sample the microstructure parameters on a multidimensional grid or draw them randomly with a uniform distribution. In this paper, we propose a stepwise stochastic adaptation mechanism to generate ODF dictionaries tailored specifically to the diffusion-weighted images in hand. The results we obtained on a diffusion phantom and in vivo human brain images show that our reconstructed diffusivities are less noisy and the separation of a free water fraction is more pronounced than for the prior (uniform) distribution of ODF dictionaries.

Advanced spinal cord MRI in multiple sclerosis: Current techniques and future directions

Spinal cord magnetic resonance imaging (MRI) has a central role in multiple sclerosis (MS) clinical practice for diagnosis and disease monitoring. Advanced MRI sequences capable of visualizing and quantifying tissue macro- and microstructure and reflecting different pathological disease processes have been used in MS research; however, the spinal cord remains under-explored, partly due to technical obstacles inherent to imaging this structure. We propose that the study of the spinal cord merits equal ambition in overcoming technical challenges, and that there is much information to be exploited to make valuable contributions to our understanding of MS. We present a narrative review on the latest progress in advanced spinal cord MRI in MS, covering in the first part structural, functional, metabolic and vascular imaging methods. We focus on recent studies of MS and those making significant technical steps, noting the challenges that remain to be addressed and what stands to be gained from such advances. Throughout we also refer to other works that presend more in-depth review on specific themes. In the second part, we present several topics that, in our view, hold particular potential. The need for better imaging of gray matter is discussed. We stress the importance of developing imaging beyond the cervical spinal cord, and explore the use of ultra-high field MRI. Finally, some recommendations are given for future research, from study design to newer developments in analysis, and the need for harmonization of sequences and methods within the field. This review is aimed at researchers and clinicians with an interest in gaining an overview of the current state of advanced MRI research in this field and what is primed to be the future of spinal cord imaging in MS research.

Neurite Exchange Imaging (NEXI): A minimal model of diffusion in gray matter with inter-compartment water exchange

Biophysical models of diffusion in white matter have been center-stage over the past two decades and are essentially based on what is now commonly referred to as the "Standard Model" (SM) of non-exchanging anisotropic compartments with Gaussian diffusion. In this work, we focus on diffusion MRI in gray matter, which requires rethinking basic microstructure modeling blocks. In particular, at least three contributions beyond the SM need to be considered for gray matter: water exchange across the cell membrane - between neurites and the extracellular space; non-Gaussian diffusion along neuronal and glial processes - resulting from structural disorder; and signal contribution from soma. For the first contribution, we propose Neurite Exchange Imaging (NEXI) as an extension of the SM of diffusion, which builds on the anisotropic Kärger model of two exchanging compartments. Using datasets acquired at multiple diffusion weightings (b) and diffusion times (t) in the rat brain in vivo, we investigate the suitability of NEXI to describe the diffusion signal in the gray matter, compared to the other two possible contributions. Our results for the diffusion time window 20-45 ms show minimal diffusivity time-dependence and more pronounced kurtosis decay with time, which is well fit by the exchange model. Moreover, we observe lower signal for longer diffusion times at high b. In light of these observations, we identify exchange as the mechanism that best explains these signal signatures in both low-b and high-b regime, and thereby propose NEXI as the minimal model for gray matter microstructure mapping. We finally highlight multi-b multi-t acquisition protocols as being best suited to estimate NEXI model parameters reliably. Using this approach, we estimate the inter-compartment water exchange time to be 15 - 60 ms in the rat cortex and hippocampus in vivo, which is of the same order or shorter than the diffusion time in typical diffusion MRI acquisitions. This suggests water exchange as an essential component for interpreting diffusion MRI measurements in gray matter.

Post mortem mapping of connectional anatomy for the validation of diffusion MRI

Diffusion MRI (dMRI) is a unique tool for the study of brain circuitry, as it allows us to image both the macroscopic trajectories and the microstructural properties of axon bundles in vivo. The Human Connectome Project ushered in an era of impressive advances in dMRI acquisition and analysis. As a result of these efforts, the quality of dMRI data that could be acquired in vivo improved substantially, and large collections of such data became widely available. Despite this progress, the main limitation of dMRI remains: it does not image axons directly, but only provides indirect measurements based on the diffusion of water molecules. Thus, it must be validated by methods that allow direct visualization of axons but that can only be performed in post mortem brain tissue. In this review, we discuss methods for validating the various features of connectional anatomy that are extracted from dMRI, both at the macro-scale (trajectories of axon bundles), and at micro-scale (axonal orientations and other microstructural properties). We present a range of validation tools, including anatomic tracer studies, Klingler's dissection, myelin stains, label-free optical imaging techniques, and others. We provide an overview of the basic principles of each technique, its limitations, and what it has taught us so far about the accuracy of different dMRI acquisition and analysis approaches.

Mapping the human connectome using diffusion MRI at 300 mT/m gradient strength: Methodological advances and scientific impact

Tremendous efforts have been made in the last decade to advance cutting-edge MRI technology in pursuit of mapping structural connectivity in the living human brain with unprecedented sensitivity and speed. The first Connectom 3T MRI scanner equipped with a 300 mT/m whole-body gradient system was installed at the Massachusetts General Hospital in 2011 and was specifically constructed as part of the Human Connectome Project. Since that time, numerous technological advances have been made to enable the broader use of the Connectom high gradient system for diffusion tractography and tissue microstructure studies and leverage its unique advantages and sensitivity to resolving macroscopic and microscopic structural information in neural tissue for clinical and neuroscientific studies. The goal of this review article is to summarize the technical developments that have emerged in the last decade to support and promote large-scale and scientific studies of the human brain using the Connectom scanner. We provide a brief historical perspective on the development of Connectom gradient technology and the efforts that led to the installation of three other Connectom 3T MRI scanners worldwide - one in the United Kingdom in Cardiff, Wales, another in continental Europe in Leipzig, Germany, and the latest in Asia in Shanghai, China. We summarize the key developments in gradient hardware and image acquisition technology that have formed the backbone of Connectom-related research efforts, including the rich array of high-sensitivity receiver coils, pulse sequences, image artifact correction strategies and data preprocessing methods needed to optimize the quality of high-gradient strength diffusion MRI data for subsequent analyses. Finally, we review the scientific impact of the Connectom MRI scanner, including advances in diffusion tractography, tissue microstructural imaging, ex vivo validation, and clinical investigations that have been enabled by Connectom technology. We conclude with brief insights into the unique value of strong gradients for diffusion MRI and where the field is headed in the coming years.

Performance of orientation distribution function-fingerprinting with a biophysical multicompartment diffusion model

PURPOSE

Orientation Distribution Function (ODF) peak finding methods typically fail to reconstruct fibers crossing at shallow angles below 40°, leading to errors in tractography. ODF-Fingerprinting (ODF-FP) with the biophysical multicompartment diffusion model allows for breaking this barrier.

METHODS

A randomized mechanism to generate a multidimensional ODF-dictionary that covers biologically plausible ranges of intra- and extra-axonal diffusivities and fraction volumes is introduced. This enables ODF-FP to address the high variability of brain tissue. The performance of the proposed approach is evaluated on both numerical simulations and a reconstruction of major fascicles from high- and low-resolution in vivo diffusion images.

RESULTS

ODF-FP with the suggested modifications correctly identifies fibers crossing at angles as shallow as 10 degrees in the simulated data. In vivo, our approach reaches 56% of true positives in determining fiber directions, resulting in visibly more accurate reconstruction of pyramidal tracts, arcuate fasciculus, and optic radiations than the state-of-the-art techniques. Moreover, the estimated diffusivity values and fraction volumes in corpus callosum conform with the values reported in the literature.

CONCLUSION

The modified ODF-FP outperforms commonly used fiber reconstruction methods at shallow angles, which improves deterministic tractography outcomes of major fascicles. In addition, the proposed approach allows for linearization of the microstructure parameters fitting problem.

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.

The Use of 18F-FET-PET-MRI in Neuro-Oncology: The Best of Both Worlds—A Narrative Review

Gliomas are the most frequent primary tumors of the brain. They can be divided into grade II-IV astrocytomas and grade II-III oligodendrogliomas, based on their histomolecular profile. The prognosis and treatment is highly dependent on grade and well-identified prognostic and/or predictive molecular markers. Multi-parametric MRI, including diffusion weighted imaging, perfusion, and MR spectroscopy, showed increasing value in the non-invasive characterization of specific molecular subsets of gliomas. Radiolabeled amino-acid analogues, such as 18F-FET, have also been proven valuable in glioma imaging. These tracers not only contribute in the diagnostic process by detecting areas of dedifferentiation in diffuse gliomas, but this technique is also valuable in the follow-up of gliomas, as it can differentiate pseudo-progression from real tumor progression. Since multi-parametric MRI and 18F-FET PET are complementary imaging techniques, there may be a synergistic role for PET-MRI imaging in the neuro-oncological imaging of primary brain tumors. This could be of value for both primary staging, as well as during treatment and follow-up.

Diffusion time dependence, power-law scaling, and exchange in gray matter

Characterizing neural tissue microstructure is a critical goal for future neuroimaging. Diffusion MRI (dMRI) provides contrasts that reflect diffusing spins' interactions with myriad microstructural features of biological systems. However, the specificity of dMRI remains limited due to the ambiguity of its signals vis-à-vis the underlying microstructure. To improve specificity, biophysical models of white matter (WM) typically express dMRI signals according to the Standard Model (SM) and have more recently in gray matter (GM) taken spherical compartments into account (the SANDI model) in attempts to represent cell soma. The validity of the assumptions underlying these models, however, remains largely undetermined, especially in GM. To validate these assumptions experimentally, observing their unique, functional properties, such as the b-1/2 power-law associated with one-dimensional diffusion, has emerged as a fruitful strategy. The absence of this signature in GM, in turn, has been explained by neurite water exchange, non-linear morphology, and/or by obscuring soma signal contributions. Here, we present diffusion simulations in realistic neurons demonstrating that curvature and branching does not destroy the stick power-law behavior in impermeable neurites, but also that their signal is drowned by the soma signal under typical experimental conditions. Nevertheless, by studying the GM dMRI signal's behavior as a function of diffusion weighting as well as time, we identify an attainable experimental regime in which the neurite signal dominates. Furthermore, we find that exchange-driven time dependence produces a signal behavior opposite to that which would be expected from restricted diffusion, thereby providing a functional signature that disambiguates the two effects. We present data from dMRI experiments in ex vivo rat brain at ultrahigh field of 16.4T and observe a time dependence that is consistent with substantial exchange but also with a GM stick power-law. The first finding suggests significant water exchange between neurites and the extracellular space while the second suggests a small sub-population of impermeable neurites. To quantify these observations, we harness the Kärger exchange model and incorporate the corresponding signal time dependence in the SM and SANDI models.

In vivo Estimation of Axonal Morphology From Magnetic Resonance Imaging and Electroencephalography Data

Purpose

We present a novel approach that allows the estimation of morphological features of axonal fibers from data acquired in vivo in humans. This approach allows the assessment of white matter microscopic properties non-invasively with improved specificity.

Theory

The proposed approach is based on a biophysical model of Magnetic Resonance Imaging (MRI) data and of axonal conduction velocity estimates obtained with Electroencephalography (EEG). In a white matter tract of interest, these data depend on (1) the distribution of axonal radius [P(r)] and (2) the g-ratio of the individual axons that compose this tract [g(r)]. P(r) is assumed to follow a Gamma distribution with mode and scale parameters, M and θ, and g(r) is described by a power law with parameters α and β.

Methods

MRI and EEG data were recorded from 14 healthy volunteers. MRI data were collected with a 3T scanner. MRI-measured g-ratio maps were computed and sampled along the visual transcallosal tract. EEG data were recorded using a 128-lead system with a visual Poffenberg paradigm. The interhemispheric transfer time and axonal conduction velocity were computed from the EEG current density at the group level. Using the MRI and EEG measures and the proposed model, we estimated morphological properties of axons in the visual transcallosal tract.

Results

The estimated interhemispheric transfer time was 11.72 ± 2.87 ms, leading to an average conduction velocity across subjects of 13.22 ± 1.18 m/s. Out of the 4 free parameters of the proposed model, we estimated θ – the width of the right tail of the axonal radius distribution – and β – the scaling factor of the axonal g-ratio, a measure of fiber myelination. Across subjects, the parameter θ was 0.40 ± 0.07 μm and the parameter β was 0.67 ± 0.02 μm^−α.

Conclusion

The estimates of axonal radius and myelination are consistent with histological findings, illustrating the feasibility of this approach. The proposed method allows the measurement of the distribution of axonal radius and myelination within a white matter tract, opening new avenues for the combined study of brain structure and function, and for in vivo histological studies of the human brain.

Generalisation of continuous time random walk to anomalous diffusion MRI models with an age-related evaluation of human corpus callosum

Diffusion MRI measures of the human brain provide key insight into microstructural variations across individuals and into the impact of central nervous system diseases and disorders. One approach to extract information from diffusion signals has been to use biologically relevant analytical models to link millimetre scale diffusion MRI measures with microscale influences. The other approach has been to represent diffusion as an anomalous transport process and infer microstructural information from the different anomalous diffusion equation parameters. In this study, we investigated how parameters of various anomalous diffusion models vary with age in the human brain white matter, particularly focusing on the corpus callosum. We first unified several established anomalous diffusion models (the super-diffusion, sub-diffusion, quasi-diffusion and fractional Bloch-Torrey models) under the continuous time random walk modelling framework. This unification allows a consistent parameter fitting strategy to be applied from which meaningful model parameter comparisons can be made. We then provided a novel way to derive the diffusional kurtosis imaging (DKI) model, which is shown to be a degree two approximation of the sub-diffusion model. This link between the DKI and sub-diffusion models led to a new robust technique for generating maps of kurtosis and diffusivity using the sub-diffusion parameters β_SUB and D_SUB. Superior tissue contrast is achieved in kurtosis maps based on the sub-diffusion model. 7T diffusion weighted MRI data for 65 healthy participants in the age range 19-78 years was used in this study. Results revealed that anomalous diffusion model parameters α and β have shown consistent positive correlation with age in the corpus callosum, indicating α and β are sensitive to tissue microstructural changes in ageing.