Application and evaluation of NODDI in the cervical spinal cord of multiple sclerosis patients

Reproducibility of Neurite Orientation Dispersion and Density Imaging (NODDI) in rats at 9.4 Tesla

PURPOSE

Neurite Orientation Dispersion and Density Imaging (NODDI) is a diffusion MRI (dMRI) technique used to characterize tissue microstructure by compartmental modelling of neural water fractions. Intra-neurite, extra-neurite, and cerebral spinal fluid volume fractions are measured. The purpose of this study was to determine the reproducibility of NODDI in the rat brain at 9.4 Tesla.

METHODS

Eight data sets were successfully acquired on adult male Sprague Dawley rats. Each rat was scanned twice on a 9.4T Agilent MRI with a 7 ± 1 day separation between scans. A multi-shell diffusion protocol was implemented consisting of 108 total directions varied over two shells (b-values of 1000 s/mm2 and 2000 s/mm2). Three techniques were used to analyze the NODDI scalar maps: mean region of interest (ROI) analysis, whole brain voxel-wise analysis, and targeted ROI analyses (voxel-wise within a given ROI). The coefficient of variation (CV) was used to assess the reproducibility of NODDI and provide insight into necessary sample sizes and minimum detectable effect size.

RESULTS

CV maps for orientation dispersion index (ODI) and neurite density index (NDI) showed high reproducibility both between and within subjects. Furthermore, it was found that small biological changes (<5%) may be detected with feasible sample sizes (n < 6-10). In contrast, isotropic volume fraction (IsoVF) was found to have low reproducibility, requiring very large sample sizes (n > 50) for biological changes to be detected.

CONCLUSIONS

The ODI and NDI measured by NODDI in the rat brain at 9.4T are highly reproducible and may be sensitive to subtle changes in tissue microstructure.

Advances in spinal cord imaging in multiple sclerosis

The spinal cord is frequently affected in multiple sclerosis (MS), causing motor, sensory and autonomic dysfunction. A number of pathological abnormalities, including demyelination and neuroaxonal loss, occur in the MS spinal cord and are studied in vivo with magnetic resonance imaging (MRI). The aim of this review is to summarise and discuss recent advances in spinal cord MRI. Advances in conventional spinal cord MRI include improved identification of MS lesions, recommended spinal cord MRI protocols, enhanced recognition of MRI lesion characteristics that allow MS to be distinguished from other myelopathies, evidence for the role of spinal cord lesions in predicting prognosis and monitoring disease course, and novel post-processing methods to obtain lesion probability maps. The rate of spinal cord atrophy is greater than that of brain atrophy (-1.78% versus -0.5% per year), and reflects neuroaxonal loss in an eloquent site of the central nervous system, suggesting that it can become an important outcome measure in clinical trials, especially in progressive MS. Recent developments allow the calculation of spinal cord atrophy from brain volumetric scans and evaluation of its progression over time with registration-based techniques. Fully automated analysis methods, including segmentation of grey matter and intramedullary lesions, will facilitate the use of spinal cord atrophy in trial designs and observational studies. Advances in quantitative imaging techniques to evaluate neuroaxonal integrity, myelin content, metabolic changes, and functional connectivity, have provided new insights into the mechanisms of damage in MS. Future directions of research and the possible impact of 7T scanners on spinal cord imaging will be discussed.

A deep network for tissue microstructure estimation using modified LSTM units

Diffusion magnetic resonance imaging (dMRI) offers a unique tool for noninvasively assessing tissue microstructure. However, accurate estimation of tissue microstructure described by complicated signal models can be challenging when a reduced number of diffusion gradients are used. Deep learning based microstructure estimation has recently been developed and achieved promising results. In particular, optimization-based learning, where deep network structures are constructed by unfolding the iterative processes performed for solving optimization problems, has demonstrated great potential in accurate microstructure estimation with a reduced number of diffusion gradients. In this work, using the optimization-based learning strategy, we propose a deep network structure that is motivated by the use of historical information in iterative optimization for tissue microstructure estimation, and such incorporation of historical information has not been previously explored in the design of deep networks for microstructure estimation. We assume that (1) diffusion signals can be sparsely represented by a dictionary and its coefficients jointly in the spatial and angular domain, and (2) tissue microstructure can be computed from the sparse representation. Following these assumptions, our network comprises two cascaded stages. The first stage takes image patches as input and computes the spatial-angular sparse representation of the input with learned weights. Specifically, the network structure in the first stage is constructed by unfolding an iterative process for solving sparse reconstruction problems, where historical information is incorporated. The components in this network can be shown to correspond to modified long short-term memory (LSTM) units. In the second stage, fully connected layers are added to compute the mapping from the sparse representation to tissue microstructure. The weights in the two stages are learned jointly by minimizing the mean squared error of microstructure estimation. Experiments were performed on dMRI scans with a reduced number of diffusion gradients. For demonstration, we evaluated the estimation of tissue microstructure described by three signal models: the neurite orientation dispersion and density imaging (NODDI) model, the spherical mean technique (SMT) model, and the ensemble average propagator (EAP) model. The results indicate that the proposed approach outperforms competing methods.

Spinal Cord Imaging in Amyotrophic Lateral Sclerosis: Historical Concepts-Novel Techniques

Amyotrophic lateral sclerosis (ALS) is the most common adult onset motor neuron disease with no effective disease modifying therapies at present. Spinal cord degeneration is a hallmark feature of ALS, highlighted in the earliest descriptions of the disease by Lockhart Clarke and Jean-Martin Charcot. The anterior horns and corticospinal tracts are invariably affected in ALS, but up to recently it has been notoriously challenging to detect and characterize spinal pathology in vivo. With recent technological advances, spinal imaging now offers unique opportunities to appraise lower motor neuron degeneration, sensory involvement, metabolic alterations, and interneuron pathology in ALS. Quantitative spinal imaging in ALS has now been used in cross-sectional and longitudinal study designs, applied to presymptomatic mutation carriers, and utilized in machine learning applications. Despite its enormous clinical and academic potential, a number of physiological, technological, and methodological challenges limit the routine use of computational spinal imaging in ALS. In this review, we provide a comprehensive overview of emerging spinal cord imaging methods and discuss their advantages, drawbacks, and biomarker potential in clinical applications, clinical trial settings, monitoring, and prognostic roles.

Comparison of NODDI and spherical mean signal for measuring intra-neurite volume fraction

PURPOSE

Neurite orientation dispersion and density imaging (NODDI) is a clinically feasible approach to measure intra-neurite volume fraction (fin). However, the sophisticated fitting procedure takes several hours. And the NODDI model relied on several questionable assumptions. Recent analytical work demonstrated that fin could be simply calculated from the spherical mean signal (MEANS) averaged over all gradient directions with a more solid theoretical foundation. The current study aims to compare NODDI and MEANS for measuring fin in human brain and investigate the potential of MEANS as a fast approach in clinics.

METHODS

NODDI fin and MEANS fin were measured and compared on the same dataset. NODDI fin was obtained using the NODDI MATLAB Toolbox. MEANS fin is the product of the spherical mean signal and 2bD/π, where D is the intra-neurite intrinsic diffusivity.

RESULTS

NODDI fin and MEANS fin maps are similar. The voxel-by-voxel correlation suggests that NODDI fin and MEANS fin are approximately equivalent to each other.

CONCLUSION

MEANS may have potential to serve a fast and simple approach to estimate fin in clinics.

MRI in multiple sclerosis: what is changing?

PURPOSE OF REVIEW

To summarize recent findings from the application of MRI in the diagnostic work-up of patients with suspected multiple sclerosis (MS), and to review the insights into disease pathophysiology and the utility of MRI for monitoring treatment response.

RECENT FINDINGS

New evidence from the application of MRI in patients with clinically isolated syndromes has guided the 2017 revision of the McDonald criteria for MS diagnosis, which has simplified their clinical use while preserving accuracy. Other MRI measures (e.g., cortical lesions and central vein signs) may improve diagnostic specificity, but their assessment still needs to be standardized, and their reliability confirmed. Novel MRI techniques are providing fundamental insights into the pathological substrates of the disease and are helping to give a better understanding of its clinical manifestations. Combined clinical-MRI measures of disease activity and progression, together with the use of clinically relevant MRI measures (e.g., brain atrophy) might improve treatment monitoring, but these are still not ready for the clinical setting.

SUMMARY

Advances in MRI technology are improving the diagnostic work-up and monitoring of MS, even in the earliest phases of the disease, and are providing MRI measures that are more specific and sensitive to disease pathological substrates.

Fornix white matter glia damage causes hippocampal gray matter damage during age-dependent limbic decline

Aging leads to gray and white matter decline but their causation remains unclear. We explored two classes of models of age and dementia risk related brain changes. The first class of models emphasises the importance of gray matter: age and risk-related processes cause neurodegeneration and this causes damage in associated white matter tracts. The second class of models reverses the direction of causation: aging and risk factors cause white matter damage and this leads to gray matter damage. We compared these models with linear mediation analysis and quantitative MRI indices (from diffusion, quantitative magnetization transfer and relaxometry imaging) of tissue properties in two limbic structures implicated in age-related memory decline: the hippocampus and the fornix in 166 asymptomatic individuals (aged 38–71 years). Aging was associated with apparent glia but not neurite density damage in the fornix and the hippocampus. Mediation analysis supported white matter damage causing gray matter decline; controlling for fornix glia damage, the correlations between age and hippocampal damage disappear, but not vice versa. Fornix and hippocampal differences were both associated with reductions in episodic memory performance. These results suggest that fornix white matter glia damage may cause hippocampal gray matter damage during age-dependent limbic decline.

Evidence of early microstructural white matter abnormalities in multiple sclerosis from multi-shell diffusion MRI

Irreversible white matter (WM) damage, including severe demyelination and axonal loss, is a main determinant of long-term disability in multiple sclerosis (MS). Non-invasive detection of changes in microstructural WM integrity in the disease is challenging since commonly used imaging metrics lack the necessary sensitivity, especially in the early phase of the disease. This study aims at assessing microstructural WM abnormalities in early-stage MS by using ultra-high gradient strength multi-shell diffusion MRI and the restricted signal fraction (FR) from the Composite Hindered and Restricted Model of Diffusion (CHARMED), a metric sensitive to the volume fraction of axons. In 22 early MS subjects (disease duration ≤5 years) and 15 age-matched healthy controls, restricted fraction estimates were obtained through the CHARMED model along with conventional Diffusion Tensor Imaging (DTI) metrics. All imaging parameters were compared cross-sectionally between the MS subjects and controls both in WM lesions and normal-appearing white matter (NAWM). We found a significant reduction in FR focally in WM lesions and widespread in the NAWM in MS patients relative to controls (corrected p < .05). Signal fraction changes in NAWM were not driven by perilesional tissue, nor were they influenced by proximity to the ventricles, challenging the hypothesis of an outside-in pathological process driven by CSF-mediated immune cytotoxic factors. No significant differences were found in conventional DTI parameters. In a cross-validated classification task, FR showed the largest effect size and outperformed all other diffusion imaging metrics in discerning lesions from contralateral NAWM. Taken together, our data provide evidence for the presence of widespread microstructural changes in the NAWM in early MS stages that are, at least in part, unrelated to focal demyelinating lesions. Interestingly, these pathological changes were not yet detectable by conventional diffusion imaging at this early disease stage, highlighting the sensitivity and value of multi-shell diffusion imaging for better characterizing axonal microstructure in MS.

Evidence for Progressive Microstructural Damage in Early Multiple Sclerosis by Multi-Shell Diffusion Magnetic Resonance Imaging

In multiple sclerosis (MS), it would be of clinical value to be able to track the progression of axonal pathology, especially before the manifestation of clinical disability. However, non-invasive evaluation of short-term longitudinal progression of white matter integrity is challenging. This study aims at assessing longitudinal changes in the restricted (i.e. intracellular) diffusion signal fraction (FR) in early-stage MS by using ultra-high gradient strength multi-shell diffusion magnetic resonance imaging. In 11 early MS subjects (disease duration ≤5 years), FR was obtained at two timepoints (one year apart) through the Composite Hindered and Restricted Model of Diffusion, along with conventional Diffusion Tensor Imaging metrics. At follow-up, no statistically significant change was detected in clinical variables, while all imaging metrics showed statistically significant longitudinal changes (p < 0.01, corrected for multiple comparisons) in widespread regions in normal-appearing white matter (NAWM). The most extensive longitudinal changes were observed in FR, including areas known to include a large fraction of crossing fibers. Furthermore, FR was also the only metric showing significant longitudinal changes in lesions that were present at both time points (p = 0.007), with no significant differences found for conventional diffusion metrics. Finally, FR was the only diffusion metric (as compared to Diffusion Tensor Imaging) that revealed pre-lesional changes already present at baseline. Taken together, our data provide evidence for progressive microstructural damage in the NAWM of early MS cases detectable already at 1-year follow-up. Our study highlights the value of multi-shell diffusion imaging for sensitive tracking of disease evolution in MS before any clinical changes are observed. This article is part of a Special Issue entitled: SI: MRI and Neuroinflammation.

Noninvasive Quantification of Axonal Loss in the Presence of Tissue Swelling in Traumatic Spinal Cord Injury Mice

Neuroimaging plays an important role in assessing axonal pathology after traumatic spinal cord injury. However, coexisting inflammation confounds imaging assessment of the severity of axonal injury. Herein, we applied diffusion basis spectrum imaging (DBSI) to quantitatively differentiate and quantify underlying pathologies in traumatic spinal cord injury at 3 days post-injury. Results reveal that DBSI was capable of detecting and differentiating axonal injury, demyelination, and inflammation-associated edema and cell infiltration in contusion-injured spinal cords. DBSI was able to detect and quantify axonal loss in the presence of white matter tract swelling. The DBSI-defined apparent axonal volume correlated with the corresponding histological markers. DBSI-derived pathological metrics could serve as neuroimaging biomarkers to differentiate and quantify coexisting white matter pathologies in spinal cord injury, providing potential surrogate outcome measures to assess spinal cord injury progression and response to therapies.

Characterizing Structural Changes With Devolving Remyelination Following Experimental Demyelination Using High Angular Resolution Diffusion MRI and Texture Analysis

BACKGROUND

Changes in myelin integrity are associated with the pathophysiology of many neurological diseases, including multiple sclerosis. However, noninvasive measurement of myelin injury and repair remains challenging. Advanced MRI techniques including diffusion tensor imaging (DTI), neurite orientation dispersion and density index (NODDI), and texture analysis have shown promise in quantifying subtle abnormalities in white matter structure.

PURPOSE

To determine whether and how these advanced imaging methods help understand remyelination changes after demyelination using a mouse model.

STUDY TYPE

Prospective, longitudinal.

ANIMAL MODEL

Demyelination was induced in the thoracic spinal cord of 21 mice using the chemical toxin lysolecithin.

FIELD STRENGTH/SEQUENCES

9.4T ASSESSMENT: Imaging was done at day 7 (demyelination) and days 14 to 35 (ongoing remyelination) postsurgery, followed by histology. Image analysis focused on both lesions and peri-lesional areas where remyelination began. In histology, we quantified the complexity of tissue alignment using angular entropy, in addition to staining area.

STATISTICAL ANALYSIS

Two-way analysis of variance was performed for assessing differences between tissue types and across timepoints, followed by post-hoc analysis to correct for multiple comparisons (P < 0.05).

RESULTS

All diffusion and texture parameters were worse in lesions than the control tissue (P < 0.05) except orientation dispersion index (ODI) and neurite density index (NDI) over late remyelination. Longitudinally, ODI decreased and NDI increased persistently in both lesions and peri-lesion regions (P < 0.05). Fractional anisotropy showed a mild decrease at day 35 after increase, when lesion texture heterogeneity showed a trend to decrease (P > 0.05). Both lesion size and angular entropy decreased over time, and no change in any measure in the control tissue.

DATA CONCLUSION

Diffusion and MRI texture metrics may provide compensatory information on myelin repair and ODI and NDI could be sensitive measures of evolving remyelination, deserving further validation.

LEVEL OF EVIDENCE

1 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2019;49:1750-1759.

Usefulness of conventional magnetic resonance imaging, diffusion tensor imaging and neurite orientation dispersion and density imaging in evaluating postoperative function in patients with cervical spondylotic myelopathy

Objective

The objective of this study was to evaluate the usefulness of T2 high signal intensity (T2-HSI) and decreased anteroposterior diameter (APD), diffusion tensor imaging (DTI) and neurite orientation dispersion and density imaging (NODDI) in evaluating postoperative cervical cord function.

Methods

The study included 57 postoperative cervical spondylotic myelopathy patients. Clinical evaluation and functional recovery assessments were performed using the modified Japanese Orthopaedic Association (mJOA) score and recovery rate. The presence of T2-HSI and decreased APD was recorded for exploring the relevance. Spearman correlation was applied to investigate the relationships between DTI and NODDI metrics and mJOA score. Multiple comparisons of T2 signal intensity, APD and diffusion metrics were evaluated by using multiple linear regression.

Results

Only the recovery rate was significantly different between T2-HSI and non-T2-HSI (nT2-HSI) patients (χ² = 4.466, p = 0.045). Significant differences were not observed between cervical cords with and without decreased APD. Diffusion metrics, including fractional anisotropy (p = 0.0005), mean diffusivity (p = 0.0008), radial diffusivity (p = 0.0003) and intracellular volume fraction (p = 0.001), were significantly correlated with mJOA score. The ability of T2 signal intensity (p = 0.421) and APD (p = 0.420) to evaluate the postoperative function was inferior to that of fractional anisotropy (p = 0.002), mean diffusivity (p = 0.001), radial diffusivity (p = 0.001) and intracellular volume fraction (p = 0.004).

Conclusion

Conventional magnetic resonance imaging signs could be considered as a reference to make an approximate assessment, whereas DTI and NODDI could be better quantitative tools for evaluating the postoperative function and may help in interpreting residual symptoms.

The translational potential of this article

DTI and NODDI could provide reliable postoperative evaluation and analysis for cervical spondylotic myelopathy patients.

Relevance of time-dependence for clinically viable diffusion imaging of the spinal cord

Purpose

Time‐dependence is a key feature of the diffusion‐weighted (DW) signal, knowledge of which informs biophysical modelling. Here, we study time‐dependence in the human spinal cord, as its axonal structure is specific and different from the brain.

Methods

We run Monte Carlo simulations using a synthetic model of spinal cord white matter (WM) (large axons), and of brain WM (smaller axons). Furthermore, we study clinically feasible multi‐shell DW scans of the cervical spinal cord (b = 0; b = 711 s mm⁻²; b = 2855 s mm⁻²), obtained using three diffusion times (Δ of 29, 52 and 76 ms) from three volunteers.

Results

Both intra‐/extra‐axonal perpendicular diffusivities and kurtosis excess show time‐dependence in our synthetic spinal cord model. This time‐dependence is reflected mostly in the intra‐axonal perpendicular DW signal, which also exhibits strong decay, unlike our brain model. Time‐dependence of the total DW signal appears detectable in the presence of noise in our synthetic spinal cord model, but not in the brain. In WM in vivo, we observe time‐dependent macroscopic and microscopic diffusivities and diffusion kurtosis, NODDI and two‐compartment SMT metrics. Accounting for large axon calibers improves fitting of multi‐compartment models to a minor extent.

Conclusions

Time‐dependence of clinically viable DW MRI metrics can be detected in vivo in spinal cord WM, thus providing new opportunities for the non‐invasive estimation of microstructural properties. The time‐dependence of the perpendicular DW signal may feature strong intra‐axonal contributions due to large spinal axon caliber. Hence, a popular model known as “stick” (zero‐radius cylinder) may be sub‐optimal to describe signals from the largest spinal axons.

Evaluation of neuregulin-1's neuroprotection against ischemic injury in rats using diffusion tensor imaging

Stroke is a devastating neurovascular disorder that results in damage to neurons and white matter tracts. It has been previously demonstrated that neuregulin-1 (NRG-1) protects neurons from ischemic injury following stroke. Here, diffusion tensor imaging (DTI) was utilized to characterize the effects of NRG-1 treatment on cererbral infarction and integrity of white matter after ischemic insult using a permanent middle celebral artery occlusion (pMCAo) rat model. In the present study, sixteen Sprague-Dawley rats underwent pMCAo surgery and received either a single intra-arterial bolus (20 μg/kg) dose of NRG-1 or saline immediately prior to pMCAo. MRI including T2-weighted imaging and DTI was performed in the first 3 h post stroke, and repeated 48 h later. It is found that the stroke infarction was significantly reduced in the NRG-1 treated group. Also, NRG-1 prevented the reduction of fractional anisotropy (FA) in white matter tracts of fornix and corpus callosum (CC), indicating its protection of CC and fornix white matter bundles from ischemia insult. As a conclusion, the present DTI results demonstrate that NRG-1 has significantly neuroprotective effects in both cerebral cortex and white matter including corpus callosum and fornix during acute stroke. In particular, NRG-1 is more effective on stroke lesion with mild ischemia. As CC and fornix white matter bundles play critical roles in transcallosal connectivity and hippocampal projections respectively in the central nervous system, the findings could provide complementary information for better understanding the biological mechanism of NRG-1's neuroprotection in ischemic tissues and neurobehavioral effects.

Microstructural imaging in the spinal cord and validation strategies

In vivo histology using magnetic resonance imaging (MRI) is a newly emerging research field that aims to non-invasively characterize tissue microstructure. The implications of in vivo histology are many, from discovering novel biomarkers to studying human development, to providing tools for disease diagnosis and monitoring the effects of novel treatments on tissue. This review focuses on quantitative MRI (qMRI) techniques that are used to map spinal cord microstructure. Opening with a rationale for non-invasive imaging of the spinal cord, this article continues with a brief overview of the existing MRI techniques for axon and myelin imaging, followed by the specific challenges and potential solutions for acquiring and processing such data. The final part of this review focuses on histological validation, with suggested tissue preparation, acquisition and processing protocols for large-scale microscopy.

Multi-compartmental diffusion characterization of the human cervical spinal cord in vivo using the spherical mean technique

The purpose of this work was to evaluate the feasibility and reproducibility of the spherical mean technique (SMT), a multi-compartmental diffusion model, in the spinal cord of healthy controls, and to assess its ability to improve spinal cord characterization in multiple sclerosis (MS) patients at 3 T. SMT was applied in the cervical spinal cord of eight controls and six relapsing-remitting MS patients. SMT provides an elegant framework to model the apparent axonal volume fraction vax , intrinsic diffusivity Dax , and extra-axonal transverse diffusivity Dex_perp (which is estimated as a function of vax and Dax ) without confounds related to complex fiber orientation distribution that reside in diffusion MRI modeling. SMT's reproducibility was assessed with two different scans within a month, and SMT-derived indices in healthy and MS cohorts were compared. The influence of acquisition scheme on SMT was also evaluated. SMT's vax , Dax , and Dex_perp measurements all showed high reproducibility. A decrease in vax was observed at the site of lesions and normal appearing white matter (p < 0.05), and trends towards a decreased Dax and increased Dex_perp were seen. Importantly, a twofold reduction in acquisition yielded similarly high accuracy with SMT. SMT provides a fast, reproducible, and accurate method to improve characterization of the cervical spinal cord, and may have clinical potential for MS patients.

Quantifying multiple sclerosis pathology in post mortem spinal cord using MRI

Multiple sclerosis (MS) is a common inflammatory, demyelinating and degenerative disease of the central nervous system. The majority of people with MS present with symptoms due to spinal cord damage, and in more advanced MS a clinical syndrome resembling that of progressive myelopathy is not uncommon. Significant efforts have been undertaken to predict MS-related disability based on short-term observations, for example, the spinal cord cross-sectional area measured using MRI. The histo-pathological correlates of spinal cord MRI changes in MS are incompletely understood, however a surge of interest in tissue microstructure has recently led to new approaches to improve the precision with which MRI indices relate to underlying tissue features, such as myelin content, neurite density and orientation, among others. Quantitative MRI techniques including T₁ and T₂, magnetisation transfer (MT) and a number of diffusion-derived indices have all been successfully applied to post mortem MS spinal cord. Combining advanced quantification of histological features with quantitative - particularly diffusion-based - MRI techniques provide a new platform for high-quality MR/pathology data generation. To more accurately quantify grey matter pathology in the MS spinal cord, a key driver of physical disability in advanced MS, remains an important challenge of microstructural imaging.

Neurite orientation dispersion and density imaging can detect presymptomatic axonal degeneration in the spinal cord of ALS mice

Neurite orientation dispersion and density imaging (NODDI), a MRI multi-shell diffusion technique, has offered new insights for the study of microstructural changes in neurodegenerative diseases. Mainly, the present study aimed to determine the connection between NODDI-derived parameters and changes in white matter (WM) abnormalities at early stages of amyotrophic lateral sclerosis (ALS). Spinal cords from ALS mice (G93A-SOD1 mice) were scanned in a Bruker Avance III HD 17.6T magnet. Fluorescent axonal-tagged mice (YFP, G93A-SOD1 mice) were used for quantitative histological analysis. NODDI showed a decrease in intra-cellular volume fraction (-24%) and increases in orientation dispersion index (+35%) and isotropic volume fraction (+33%). In addition, histoathological results demonstrated a reductions in axonal area (-11%) and myelin content (-29%). A histological decrease in WM intra-axonal space (-71%) and an increase in the extra-axonal compartment (+22%) were also detected. Our studies demonstrate that NODDI may be a suitable technique for detecting presymptomatic spinal cord WM microstructural degeneration in ALS.

Histological validation of diffusion MRI fiber orientation distributions and dispersion

Diffusion magnetic resonance imaging (dMRI) is widely used to probe tissue microstructure, and is currently the only non-invasive way to measure the brain's fiber architecture. While a large number of approaches to recover the intra-voxel fiber structure have been utilized in the scientific community, a direct, 3D, quantitative validation of these methods against relevant histological fiber geometries is lacking. In this study, we investigate how well different high angular resolution diffusion imaging (HARDI) models and reconstruction methods predict the ground-truth histologically defined fiber orientation distribution (FOD), as well as investigate their behavior over a range of physical and experimental conditions. The dMRI methods tested include constrained spherical deconvolution (CSD), Q-ball imaging (QBI), diffusion orientation transform (DOT), persistent angular structure (PAS), and neurite orientation dispersion and density imaging (NODDI) methods. Evaluation criteria focus on overall agreement in FOD shape, correct assessment of the number of fiber populations, and angular accuracy in orientation. In addition, we make comparisons of the histological orientation dispersion with the fiber spread determined from the dMRI methods. As a general result, no HARDI method outperformed others in all quality criteria, with many showing tradeoffs in reconstruction accuracy. All reconstruction techniques describe the overall continuous angular structure of the histological FOD quite well, with good to moderate correlation (median angular correlation coefficient > 0.70) in both single- and multiple-fiber voxels. However, no method is consistently successful at extracting discrete measures of the number and orientations of FOD peaks. The major inaccuracies of all techniques tend to be in extracting local maxima of the FOD, resulting in either false positive or false negative peaks. Median angular errors are ∼10° for the primary fiber direction and ∼20° for the secondary fiber, if present. For most methods, these results did not vary strongly over a wide range of acquisition parameters (number of diffusion weighting directions and b value). Regardless of acquisition parameters, all methods show improved successes at resolving multiple fiber compartments in a voxel when fiber populations cross at near-orthogonal angles, with no method adequately capturing low to moderate angle (<60°) crossing fibers. Finally, most methods are limited in their ability to capture orientation dispersion, resulting in low to moderate, yet statistically significant, correlation with histologically-derived dispersion with both HARDI and NODDI methodologies. Together, these results provide quantitative measures of the reliability and limitations of dMRI reconstruction methods and can be used to identify relative advantages of competing approaches as well as potential strategies for improving accuracy.

Tissue microstructure estimation using a deep network inspired by a dictionary-based framework

Diffusion magnetic resonance imaging (dMRI) captures the anisotropic pattern of water displacement in the neuronal tissue and allows noninvasive investigation of the complex tissue microstructure. A number of biophysical models have been proposed to relate the tissue organization with the observed diffusion signals, so that the tissue microstructure can be inferred. The Neurite Orientation Dispersion and Density Imaging (NODDI) model has been a popular choice and has been widely used for many neuroscientific studies. It models the diffusion signal with three compartments that are characterized by distinct diffusion properties, and the parameters in the model describe tissue microstructure. In NODDI, these parameters are estimated in a maximum likelihood framework, where the nonlinear model fitting is computationally intensive. Therefore, efforts have been made to develop efficient and accurate algorithms for NODDI microstructure estimation, which is still an open problem. In this work, we propose a deep network based approach that performs end-to-end estimation of NODDI microstructure, which is named Microstructure Estimation using a Deep Network (MEDN). MEDN comprises two cascaded stages and is motivated by the AMICO algorithm, where the NODDI microstructure estimation is formulated in a dictionary-based framework. The first stage computes the coefficients of the dictionary. It resembles the solution to a sparse reconstruction problem, where the iterative process in conventional estimation approaches is unfolded and truncated, and the weights are learned instead of predetermined by the dictionary. In the second stage, microstructure properties are computed from the output of the first stage, which resembles the weighted sum of normalized dictionary coefficients in AMICO, and the weights are also learned. Because spatial consistency of diffusion signals can be used to reduce the effect of noise, we also propose MEDN+, which is an extended version of MEDN. MEDN+ allows incorporation of neighborhood information by inserting a stage with learned weights before the MEDN structure, where the diffusion signals in the neighborhood of a voxel are processed. The weights in MEDN or MEDN+ are jointly learned from training samples that are acquired with diffusion gradients densely sampling the q-space. We performed MEDN and MEDN+ on brain dMRI scans, where two shells each with 30 gradient directions were used, and measured their accuracy with respect to the gold standard. Results demonstrate that the proposed networks outperform the competing methods.

Neurite dispersion: a new marker of multiple sclerosis spinal cord pathology?

Objective

Conventional magnetic resonance imaging (MRI) of the multiple sclerosis spinal cord is limited by low specificity regarding the underlying pathological processes, and new MRI metrics assessing microscopic damage are required. We aim to show for the first time that neurite orientation dispersion (i.e., variability in axon/dendrite orientations) is a new biomarker that uncovers previously undetected layers of complexity of multiple sclerosis spinal cord pathology. Also, we validate against histology a clinically viable MRI technique for dispersion measurement (neurite orientation dispersion and density imaging,NODDI), to demonstrate the strong potential of the new marker.

Methods

We related quantitative metrics from histology and MRI in four post mortem spinal cord specimens (two controls; two progressive multiple sclerosis cases). The samples were scanned at high field, obtaining maps of neurite density and orientation dispersion from NODDI and routine diffusion tensor imaging (DTI) indices. Histological procedures provided markers of astrocyte, microglia, myelin and neurofilament density, as well as neurite dispersion.

Results

We report from both NODDI and histology a trend toward lower neurite dispersion in demyelinated lesions, indicative of reduced neurite architecture complexity. Also, we provide unequivocal evidence that NODDI‐derived dispersion matches its histological counterpart (P < 0.001), while DTI metrics are less specific and influenced by several biophysical substrates.

Interpretation

Neurite orientation dispersion detects a previously undescribed and potentially relevant layer of microstructural complexity of multiple sclerosis spinal cord pathology. Clinically feasible techniques such as NODDI may play a key role in clinical trial and practice settings, as they provide histologically meaningful dispersion indices.