Novel white matter tract integrity metrics sensitive to Alzheimer disease progression

Age-related alterations in human cortical microstructure across the lifespan: Insights from high-gradient diffusion MRI

The human brain undergoes age-related microstructural alterations across the lifespan. Soma and Neurite Density Imaging (SANDI), a novel biophysical model of diffusion MRI, provides estimates of cell body (soma) radius and density, and neurite density in gray matter. The goal of this cross-sectional study was to assess the sensitivity of high-gradient diffusion MRI toward age-related alterations in cortical microstructure across the adult lifespan using SANDI. Seventy-two cognitively unimpaired healthy subjects (ages 19-85 years; 40 females) were scanned on the 3T Connectome MRI scanner with a maximum gradient strength of 300mT/m using a multi-shell diffusion MRI protocol incorporating 8 b-values and diffusion time of 19 ms. Intra-soma signal fraction obtained from SANDI model-fitting to the data was strongly correlated with age in all major cortical lobes (r = -0.69 to -0.60, FDR-p < 0.001). Intra-soma signal fraction (r = 0.48-0.63, FDR-p < 0.001) and soma radius (r = 0.28-0.40, FDR-p < 0.04) were significantly correlated with cortical volume in the prefrontal cortex, frontal, parietal, and temporal lobes. The strength of the relationship between SANDI metrics and age was greater than or comparable to the relationship between cortical volume and age across the cortical regions, particularly in the occipital lobe and anterior cingulate gyrus. In contrast to the SANDI metrics, all associations between diffusion tensor imaging (DTI) and diffusion kurtosis imaging metrics and age were low to moderate. These results suggest that high-gradient diffusion MRI may be more sensitive to underlying substrates of neurodegeneration in the aging brain than DTI and traditional macroscopic measures of neurodegeneration such as cortical volume and thickness.

Quantitative Analysis of Early White Matter Damage in Cuprizone Mouse Model of Demyelination Using 7.0 T MRI Multiparametric Approach

Magnetic Resonance Imaging (MRI) is commonly used to follow the progression of neurodegenerative conditions, including multiple sclerosis (MS). MRI is limited by a lack of correlation between imaging results and clinical presentations, referred to as the clinico-radiological paradox. Animal models are commonly used to mimic the progression of human neurodegeneration and as a tool to help resolve the paradox. Most studies focus on later stages of white matter (WM) damage whereas few focus on early stages when oligodendrocyte apoptosis has just begun. The current project focused on these time points, namely weeks 2 and 3 of cuprizone (CPZ) administration, a toxin which induces pathophysiology similar to MS. In vivo T2-weighted (T2W) and Magnetization Transfer Ratio (MTR) maps and ex vivo Diffusion Tensor Imaging (DTI), Magnetization Transfer Imaging (MTI), and relaxometry (T1 and T2) values were obtained at 7 T. Significant changes in T2W signal intensity and non-significant changes in MTR were observed to correspond to early WM damage, whereas significant changes in both corresponded with full demyelination. Some DTI metrics decrease with simultaneous increase in others, indicating acute demyelination. MTI metrics T2A, T2B, f and R were observed to have contradictory changes across CPZ administration. T1 relaxation times were observed to have stronger correlations to disease states during later stages of CPZ treatment, whereas T2 had weak correlations to early WM damage. These results all suggest the need for multiple metrics and further studies at early and late time points of demyelination. Further research is required to continue investigating the interplay between various MR metrics during all weeks of CPZ administration.

Comprehensive Analysis of the 5xFAD Mouse Model of Alzheimer's Disease Using dMRI, Immunohistochemistry, and Neuronal and Glial Functional Metabolic Mapping

Alzheimer's disease (AD) is characterized by complex interactions between neuropathological markers, metabolic dysregulation, and structural brain changes. In this study, we utilized a multimodal approach, combining immunohistochemistry, functional metabolic mapping, and microstructure sensitive diffusion MRI (dMRI) to progressively investigate these interactions in the 5xFAD mouse model of AD. Our analysis revealed age-dependent and region-specific accumulation of key AD markers, including amyloid-beta (Aβ), GFAP, and IBA1, with significant differences observed between the hippocampal formation and upper and lower regions of the cortex by 6 months of age. Functional metabolic mapping validated localized disruptions in energy metabolism, with glucose hypometabolism in the hippocampus and impaired astrocytic metabolism in the cortex. Notably, increased cortical glutaminolysis suggested a shift in microglial metabolism, reflecting an adaptive response to neuroinflammatory processes. While dMRI showed no significant microstructural differences between 5xFAD and wild-type controls, the study highlights the importance of metabolic alterations as critical events in AD pathology. These findings emphasize the need for targeted therapeutic strategies addressing specific metabolic disturbances and underscore the potential of integrating advanced imaging with metabolic and molecular analyses to advance our understanding of AD progression.

Cognitive Impairment in Cerebral Small Vessel Disease Is Associated with Corpus Callosum Microstructure Changes Based on Diffusion MRI

The cerebral small vessel disease (cSVD) is one of the main causes of vascular and mixed cognitive impairment (CI), and it is associated, in particular, with brain ageing. An understanding of structural tissue changes in an intact cerebral white matter in cSVD might allow one to develop the sensitive biomarkers for early diagnosis and monitoring of disease progression.

PURPOSE OF THE STUDY

to evaluate microstructural changes in the corpus callosum (CC) using diffusion MRI (D-MRI) approaches in cSVD patients with different severity of CI and reveal the most sensitive correlations of diffusion metrics with CI.

METHODS

the study included 166 cSVD patients (51.8% women; 60.4 ± 7.6 years) and 44 healthy volunteers (65.9% women; 59.6 ± 6.8 years). All subjects underwent D-MRI (3T) with signal (diffusion tensor and kurtosis) and biophysical (neurite orientation dispersion and density imaging, NODDI, white matter tract integrity, WMTI, multicompartment spherical mean technique, MC-SMT) modeling in three CC segments as well as a neuropsychological assessment.

RESULTS

in cSVD patients, microstructural changes were found in all CC segments already at the subjective CI stage, which was found to worsen into mild CI and dementia. More pronounced changes were observed in the forceps minor. Among the signal models FA, MD, MK, RD, and RK, as well as among the biophysical models, MC-SMT (EMD, ETR) and WMTI (AWF) metrics exhibited the largest area under the curve (>0.85), characterizing the loss of microstructural integrity, the severity of potential demyelination, and the proportion of intra-axonal water, respectively. Conclusion: the study reveals the relevance of advanced D-MRI approaches for the assessment of brain tissue changes in cSVD. The identified diffusion biomarkers could be used for the clarification and observation of CI progression.

Contributions of Brain Microstructures and Metabolism to Visual Field Loss Patterns in Glaucoma Using Archetypal and Information Gain Analyses

Purpose

To investigate the contributions of the microstructural and metabolic brain environment to glaucoma and their association with visual field (VF) loss patterns by using advanced diffusion magnetic resonance imaging (dMRI), proton magnetic resonance spectroscopy (MRS), and clinical ophthalmic measures.

Methods

Sixty-nine glaucoma and healthy subjects underwent dMRI and/or MRS at 3 Tesla. Ophthalmic data were collected from VF perimetry and optical coherence tomography. dMRI parameters of microstructural integrity in the optic radiation and MRS-derived neurochemical levels in the visual cortex were compared among early glaucoma, advanced glaucoma, and healthy controls. Multivariate regression was used to correlate neuroimaging metrics with 16 archetypal VF loss patterns. We also ranked neuroimaging, ophthalmic, and demographic attributes in terms of their information gain to determine their importance to glaucoma.

Results

In dMRI, decreasing fractional anisotropy, radial kurtosis, and tortuosity and increasing radial diffusivity correlated with greater overall VF loss bilaterally. Regionally, decreasing intra-axonal space and extra-axonal space diffusivities correlated with greater VF loss in the superior-altitudinal area of the right eye and the inferior-altitudinal area of the left eye. In MRS, both early and advanced glaucoma patients had lower gamma-aminobutyric acid (GABA), glutamate, and choline levels than healthy controls. GABA appeared to associate more with superonasal VF loss, and glutamate and choline more with inferior VF loss. Choline ranked third for importance to early glaucoma, whereas radial kurtosis and GABA ranked fourth and fifth for advanced glaucoma.

Conclusions

Our findings highlight the importance of non-invasive neuroimaging biomarkers and analytical modeling for unveiling glaucomatous neurodegeneration and how they reflect complementary VF loss patterns.

Callosal Interhemispheric Communication in Mild Traumatic Brain Injury: A Mediation Analysis on WM Microstructure Effects

BACKGROUND AND PURPOSE

Because the corpus callosum connects the left and right hemispheres and a variety of WM bundles across the brain in complex ways, damage to the neighboring WM microstructure may specifically disrupt interhemispheric communication through the corpus callosum following mild traumatic brain injury. Here we use a mediation framework to investigate how callosal interhemispheric communication is affected by WM microstructure in mild traumatic brain injury.

MATERIALS AND METHODS

Multishell diffusion MR imaging was performed on 23 patients with mild traumatic brain injury within 1 month of injury and 17 healthy controls, deriving 11 diffusion metrics, including DTI, diffusional kurtosis imaging, and compartment-specific standard model parameters. Interhemispheric processing speed was assessed using the interhemispheric speed of processing task (IHSPT) by measuring the latency between word presentation to the 2 hemivisual fields and oral word articulation. Mediation analysis was performed to assess the indirect effect of neighboring WM microstructures on the relationship between the corpus callosum and IHSPT performance. In addition, we conducted a univariate correlation analysis to investigate the direct association between callosal microstructures and IHSPT performance as well as a multivariate regression analysis to jointly evaluate both callosal and neighboring WM microstructures in association with IHSPT scores for each group.

RESULTS

Several significant mediators in the relationships between callosal microstructure and IHSPT performance were found in healthy controls. However, patients with mild traumatic brain injury appeared to lose such normal associations when microstructural changes occurred compared with healthy controls.

CONCLUSIONS

This study investigates the effects of neighboring WM microstructure on callosal interhemispheric communication in healthy controls and patients with mild traumatic brain injury, highlighting that neighboring noncallosal WM microstructures are involved in callosal interhemispheric communication and information transfer. Further longitudinal studies may provide insight into the temporal dynamics of interhemispheric recovery following mild traumatic brain injury.

Functional connectivity in autism spectrum disorder evaluated using rs-fMRI and DKI

We evaluated functional connectivity (FC) in patients with adult autism spectrum disorder (ASD) using resting-state functional MRI (rs-fMRI) and diffusion kurtosis imaging (DKI). We acquired rs-fMRI data from 33 individuals with ASD and 33 healthy controls (HC) and DKI data from 18 individuals with ASD and 17 HC. ASD showed attenuated FC between the right frontal pole (FP) and the bilateral temporal fusiform cortex (TFusC) and enhanced FC between the right thalamus and the bilateral inferior division of lateral occipital cortex, and between the cerebellar vermis and the right occipital fusiform gyrus (OFusG) and the right lingual gyrus, compared with HC. ASD demonstrated increased axial kurtosis (AK) and mean kurtosis (MK) in white matter (WM) tracts, including the right anterior corona radiata (ACR), forceps minor (FM), and right superior longitudinal fasciculus (SLF). In ASD, there was also a significant negative correlation between MK and FC between the cerebellar vermis and the right OFusG in the corpus callosum, FM, right SLF and right ACR. Increased DKI metrics might represent neuroinflammation, increased complexity, or disrupted WM tissue integrity that alters long-distance connectivity. Nonetheless, protective or compensating adaptations of inflammation might lead to more abundant glial cells and cytokine activation effectively alleviating the degeneration of neurons, resulting in increased complexity. FC abnormality in ASD observed in rs-fMRI may be attributed to microstructural alterations of the commissural and long-range association tracts in WM as indicated by DKI.

Purpose in life as a resilience factor for brain health: diffusion MRI findings from the Midlife in the U.S. study

Introduction

A greater sense of purpose in life is associated with several health benefits relevant for active aging, but the mechanisms remain unclear. We evaluated if purpose in life was associated with indices of brain health.

Methods

We examined data from the Midlife in the United States (MIDUS) Neuroscience Project. Diffusion weighted magnetic resonance imaging data (n=138; mean age 65.2 years, age range 48-95; 80 females; 37 black, indigenous, and people of color) were used to estimate microstructural indices of brain health such as axonal density, and axonal orientation. The seven-item purpose in life scale was used. Permutation analysis of linear models was used to examine associations between purpose in life scores and the diffusion metrics in white matter and in the bilateral hippocampus, adjusting for age, sex, education, and race.

Results and discussion

Greater sense of purpose in life was associated with brain microstructural features consistent with better brain health. Positive associations were found in both white matter and the right hippocampus, where multiple convergent associations were detected. The hippocampus is a brain structure involved in learning and memory that is vulnerable to stress but retains the capacity to grow and adapt through old age. Our findings suggest pathways through which an enhanced sense of purpose in life may contribute to better brain health and promote healthy aging. Since purpose in life is known to decline with age, interventions and policy changes that facilitate a greater sense of purpose may extend and improve the brain health of individuals and thus improve public health.

Splenium tract projections of the corpus callosum to the parietal cortex classifies Alzheimer's disease and mild cognitive impairment

The corpus callosum (CC) is the largest bundle of white matter tracts in the brain connecting the left and right cerebral hemispheres. The posterior region of the CC, known as the splenium, seems to be relatively preserved throughout the lifespan and is regularly examined for indications of various pathologies, including Alzheimer's disease (AD) and Mild Cognitive Impairment (MCI). However, the splenium has rarely been investigated in terms of its distinct inter-hemispheric tract bundles that project to bilateral occipital, parietal and temporal areas of the cortex. The aim of the present study was to determine if any of these sub-splenium tract bundles are specifically affected by individuals with AD and MCI compared to normal controls. Diffusion Tensor Imaging was used to directly examine the integrity of these distinct tract bundles and their diffusion metrics were compared between groups of MCI, AD, and control individuals. Results revealed that differences between MCI, AD, and controls were particularly evident at parietal tracts of the CC splenium and were consistent with an interpretation of compromised white matter integrity. Combined parietal tract diffusivity and density information strongly discriminated between AD patients and controls with an accuracy (AUC) of 97.19%. Combined parietal tract diffusivity parameters correctly classified MCI subjects against controls with an accuracy of 74.97%. These findings demonstrated the potential of examining the CC splenium in terms of its distinct inter-hemispheric tract bundles for the diagnosis of AD and MCI.

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 Use of Diffusion Kurtosis Imaging for the Differential Diagnosis of Alzheimer's Disease Spectrum

Structural and diffusion kurtosis imaging (DKI) can be used to assess hippocampal macrostructural and microstructural alterations respectively, in Alzheimer's disease (AD) spectrum, spanning from subjective cognitive decline (SCD) to mild cognitive impairment (MCI) and AD. In this study, we explored the diagnostic performance of structural imaging and DKI of the hippocampus in the AD spectrum. Eleven SCD, thirty-seven MCI, sixteen AD, and nineteen age- and sex-matched normal controls (NCs) were included. Bilateral hippocampal volume, mean diffusivity (MD), and mean kurtosis (MK) were obtained. We detected that in AD vs. NCs, the right hippocampal volume showed the most prominent AUC value (AUC = 0.977); in MCI vs. NCs, the right hippocampal MD was the most sensitive discriminator (AUC = 0.819); in SCD vs. NCs, the left hippocampal MK was the most sensitive biomarker (AUC = 0.775). These findings suggest that, in the predementia stage (SCD and MCI), hippocampal microstructural changes are predominant, and the best discriminators are microstructural measurements (left hippocampal MK for SCD and right hippocampal MD for MCI); while in the dementia stage (AD), hippocampal macrostructural alterations are superior, and the best indicator is the macrostructural index (right hippocampal volume).

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.

Topology of diffusion changes in corpus callosum in Alzheimer's disease: An exploratory case-control study

Aim

This study aims to assess the integrity of white matter in various segments of the corpus callosum in Alzheimer's disease (AD) by using metrics derived from diffusion tensor imaging (DTI), diffusion kurtosis imaging (DKI) and white matter tract integrity model (WMTI) and compare these findings to healthy controls (HC).

Methods

The study was approved by the institutional ethics board. 12 AD patients and 12 HC formed the study population. All AD patients were recruited from a tertiary neurology memory clinic. A standardized battery of neuropsychological assessments was administered to the study participants by a trained rater. MRI scans were performed with a Philips Ingenia 3.0T scanner equipped with a 32-channel head coil. The protocol included a T1-weighted sequence, FLAIR and a dMRI acquisition. The dMRI scan included a total of 71 volumes, 8 at b = 0 s/mm2, 15 at b = 1,000 s/mm2 and 48 at b = 2,000 s/mm2. Diffusion data fit was performed using DKI REKINDLE and WMTI models.

Results and discussion

We detected changes suggesting demyelination and axonal degeneration throughout the corpus callosum of patients with AD, most prominent in the mid-anterior and mid-posterior segments of CC. Axial kurtosis was the most significantly altered metric, being reduced in AD patients in almost all segments of corpus callosum. Reduced axial kurtosis in the CC segments correlated with poor cognition scores in AD patients in the visuospatial, language and attention domains.

Transdiagnostic In Vivo Magnetic Resonance Imaging Markers of Neuroinflammation

Accumulating evidence suggests that inflammation is not limited to archetypal inflammatory diseases such as multiple sclerosis, but instead represents an intrinsic feature of many psychiatric and neurological disorders not typically classified as neuroinflammatory. A growing body of research suggests that neuroinflammation can be observed in early and prodromal stages of these disorders and, under certain circumstances, may lead to tissue damage. Traditional methods to assess neuroinflammation include serum or cerebrospinal fluid markers and positron emission tomography. These methods require invasive procedures or radiation exposure and lack the exquisite spatial resolution of magnetic resonance imaging (MRI). There is, therefore, an increasing interest in noninvasive neuroimaging tools to evaluate neuroinflammation reliably and with high specificity. While MRI does not provide information at a cellular level, it facilitates the characterization of several biophysical tissue properties that are closely linked to neuroinflammatory processes. The purpose of this review is to evaluate the potential of MRI as a noninvasive, accessible, and cost-effective technology to image neuroinflammation across neurological and psychiatric disorders. We provide an overview of current and developing MRI methods used to study different aspects of neuroinflammation and weigh their strengths and shortcomings. Novel MRI contrast agents are increasingly able to target inflammatory processes directly, therefore offering a high degree of specificity, particularly if used in conjunction with multitissue, biophysical diffusion MRI compartment models. The capability of these methods to characterize several aspects of the neuroinflammatory milieu will likely push MRI to the forefront of neuroimaging modalities used to characterize neuroinflammation transdiagnostically.

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.

Investigating Brain White Matter in Football Players with and without Concussion Using a Biophysical Model from Multishell Diffusion MRI

BACKGROUND AND PURPOSE

There have been growing concerns around potential risks related to sports-related concussion and contact sport exposure to repetitive head impacts in young athletes. Here we investigate WM microstructural differences between collegiate football players with and without sports-related concussion.

MATERIALS AND METHODS

The study included 78 collegiate athletes (24 football players with sports-related concussion, 26 football players with repetitive head impacts, and 28 non-contact-sport control athletes), available through the Federal Interagency Traumatic Brain Injury Research registry. Diffusion metrics of diffusion tensor/kurtosis imaging and WM tract integrity were calculated. Tract-Based Spatial Statistics and post hoc ROI analyses were performed to test group differences.

RESULTS

Significantly increased axial kurtosis in those with sports-related concussion compared with controls was observed diffusely across the whole-brain WM, and some focal areas demonstrated significantly higher mean kurtosis and extra-axonal axial diffusivity in sports-related concussion. The extent of significantly different WM regions decreased across time points and remained present primarily in the corpus callosum. Similar differences in axial kurtosis were found between the repetitive head impact and control groups. Other significant differences were seen at unrestricted return-to-play with lower radial kurtosis and intra-axonal diffusivity in those with sports-related concussion compared with the controls, mainly restricted to the posterior callosum.

CONCLUSIONS

This study highlights the fact that there are differences in diffusion microstructure measures that are present not only between football players with sports-related injuries and controls, but that there are also measurable differences between football players with repetitive head impacts and controls. This work reinforces previous work showing that the corpus callosum is specifically implicated in sports-related concussion and also suggests this to be true for repetitive head impacts.

In vivo Correlation Tensor MRI reveals microscopic kurtosis in the human brain on a clinical 3T scanner

Diffusion MRI (dMRI) has become one of the most important imaging modalities for noninvasively probing tissue microstructure. Diffusional Kurtosis MRI (DKI) quantifies the degree of non-gaussian diffusion, which in turn has been shown to increase sensitivity towards, e.g., disease and orientation mapping in neural tissue. However, the specificity of DKI is limited as different sources can contribute to the total intravoxel diffusional kurtosis, including: variance in diffusion tensor magnitudes (K_iso), variance due to diffusion anisotropy (K_aniso), and microscopic kurtosis (μK) related to restricted diffusion, microstructural disorder, and/or exchange. Interestingly, μK is typically ignored in diffusion MRI signal modeling as it is assumed to be negligible in neural tissues. However, recently, Correlation Tensor MRI (CTI) based on Double-Diffusion-Encoding (DDE) was introduced for kurtosis source separation, revealing non negligible μK in preclinical imaging. Here, we implemented CTI for the first time on a clinical 3T scanner and investigated the sources of total kurtosis in healthy subjects. A robust framework for kurtosis source separation in humans is introduced, followed by estimation of μK (and the other kurtosis sources) in the healthy brain. Using this clinical CTI approach, we find that μK significantly contributes to total diffusional kurtosis both in gray and white matter tissue but, as expected, not in the ventricles. The first μK maps of the human brain are presented, revealing that the spatial distribution of μK provides a unique source of contrast, appearing different from isotropic and anisotropic kurtosis counterparts. Moreover, group average templates of these kurtosis sources have been generated for the first time, which corroborated our findings at the underlying individual-level maps. We further show that the common practice of ignoring μK and assuming the multiple gaussian component approximation for kurtosis source estimation introduces significant bias in the estimation of other kurtosis sources and, perhaps even worse, compromises their interpretation. Finally, a twofold acceleration of CTI is discussed in the context of potential future clinical applications. We conclude that CTI has much potential for future in vivo microstructural characterizations in healthy and pathological tissue.

Advanced Diffusion MRI of the Visual System in Glaucoma: From Experimental Animal Models to Humans

Glaucoma is a group of ophthalmologic conditions characterized by progressive retinal ganglion cell death, optic nerve degeneration, and irreversible vision loss. While intraocular pressure is the only clinically modifiable risk factor, glaucoma may continue to progress at controlled intraocular pressure, indicating other major factors in contributing to the disease mechanisms. Recent studies demonstrated the feasibility of advanced diffusion magnetic resonance imaging (dMRI) in visualizing the microstructural integrity of the visual system, opening new possibilities for non-invasive characterization of glaucomatous brain changes for guiding earlier and targeted intervention besides intraocular pressure lowering. In this review, we discuss dMRI methods currently used in visual system investigations, focusing on the eye, optic nerve, optic tract, subcortical visual brain nuclei, optic radiations, and visual cortex. We evaluate how conventional diffusion tensor imaging, higher-order diffusion kurtosis imaging, and other extended dMRI techniques can assess the neuronal and glial integrity of the visual system in both humans and experimental animal models of glaucoma, among other optic neuropathies or neurodegenerative diseases. We also compare the pros and cons of these methods against other imaging modalities. A growing body of dMRI research indicates that this modality holds promise in characterizing early glaucomatous changes in the visual system, determining the disease severity, and identifying potential neurotherapeutic targets, offering more options to slow glaucoma progression and to reduce the prevalence of this world's leading cause of irreversible but preventable blindness.

Greater diffusion restriction in white matter in Preclinical Alzheimer's disease

OBJECTIVE

The Alzheimer's Continuum is biologically defined by beta-amyloid deposition which, at the earliest stages, is superimposed upon white matter degeneration in aging. However, the extent to which these co-occurring changes are characterized is relatively under-explored. The goal of this study was to use Diffusional Kurtosis Imaging (DKI) and biophysical modeling to detect and describe amyloid-related white matter changes in preclinical Alzheimer's disease (AD).

METHODS

Cognitively unimpaired participants ages 45-85 completed brain MRI, amyloid PET (florbetapir), neuropsychological testing, and other clinical measures at baseline in a cohort study. We tested whether beta amyloid-negative (AB-) and -positive (AB+) participants differed on DKI-based conventional (i.e. Fractional Anisotropy [FA], Mean Diffusivity [MD], Mean Kurtosis [MK]) and modeling (i.e. Axonal Water Fraction [AWF], extra-axonal radial diffusivity [D_e,⊥ ]) metrics, and whether these metrics were associated with other biomarkers.

RESULTS

We found significantly greater diffusion restriction (higher FA/AWF, lower MD/ D_e,⊥ ) in white matter in AB+ than AB- (partial η² = 0.08-0.19), more notably in the extra-axonal space within primarily late-myelinating tracts. Diffusion metrics predicted amyloid status incrementally over age (AUC=0.84) with modest yet selective associations, where AWF (a marker of axonal density) correlated with speed/executive functions and neurodegeneration, whereas D_e,⊥ (a marker of gliosis/myelin repair) correlated with amyloid deposition and white matter hyperintensity volume.

INTERPRETATION

These results support prior evidence of a non-monotonic change in diffusion behavior, where an early increase in diffusion restriction is hypothesized to reflect inflammation and myelin repair prior to an ensuing decrease in diffusion restriction, indicating glial and neuronal degeneration. This article is protected by copyright. All rights reserved.

SDnDTI: Self-supervised deep learning-based denoising for diffusion tensor MRI

Diffusion tensor magnetic resonance imaging (DTI) is a widely adopted neuroimaging method for the in vivo mapping of brain tissue microstructure and white matter tracts. Nonetheless, the noise in the diffusion-weighted images (DWIs) decreases the accuracy and precision of DTI derived microstructural parameters and leads to prolonged acquisition time for achieving improved signal-to-noise ratio (SNR). Deep learning-based image denoising using convolutional neural networks (CNNs) has superior performance but often requires additional high-SNR data for supervising the training of CNNs, which reduces the feasibility of supervised learning-based denoising in practice. In this work, we develop a self-supervised deep learning-based method entitled "SDnDTI" for denoising DTI data, which does not require additional high-SNR data for training. Specifically, SDnDTI divides multi-directional DTI data into many subsets of six DWI volumes and transforms DWIs from each subset to along the same diffusion-encoding directions through the diffusion tensor model, generating multiple repetitions of DWIs with identical image contrasts but different noise observations. SDnDTI removes noise by first denoising each repetition of DWIs using a deep 3-dimensional CNN with the average of all repetitions with higher SNR as the training target, following the same approach as normal supervised learning based denoising methods, and then averaging CNN-denoised images for achieving higher SNR. The denoising efficacy of SDnDTI is demonstrated in terms of the similarity of output images and resultant DTI metrics compared to the ground truth generated using substantially more DWI volumes on two datasets with different spatial resolutions, b-values and numbers of input DWI volumes provided by the Human Connectome Project (HCP) and the Lifespan HCP in Aging. The SDnDTI results preserve image sharpness and textural details and substantially improve upon those from the raw data. The results of SDnDTI are comparable to those from supervised learning-based denoising and outperform those from state-of-the-art conventional denoising algorithms including BM4D, AONLM and MPPCA. By leveraging domain knowledge of diffusion MRI physics, SDnDTI makes it easier to use CNN-based denoising methods in practice and has the potential to benefit a wider range of research and clinical applications that require accelerated DTI acquisition and high-quality DTI data for mapping of tissue microstructure, fiber tracts and structural connectivity in the living human brain.

White Matter Damage in Alzheimer's Disease: Contribution of Oligodendrocytes

Alzheimer's disease (AD) is an age-related neurodegenerative disease seriously influencing the quality of life and is a global health problem. Many factors affect the onset and development of AD, but specific mechanisms underlying the disease are unclear. Most studies investigating AD have focused on neurons and the gray matter in the central nervous system (CNS) but have not led to effective treatments. Recently, an increasing number of studies have focused on white matter (WM). Magnetic resonance imaging and pathology studies have shown different degrees of WM abnormality during the progression of AD. Myelin sheaths, the main component of WM in the CNS, wrap and insulate axons to ensure conduction of the rapid action potential and axonal integrity. WM damage is characterized by progressive degeneration of axons, oligodendrocytes (OLs), and myelin in one or more areas of the CNS. The contributions of OLs to AD progression have, until recently, been largely overlooked. OLs are integral to myelin production, and the proliferation and differentiation of OLs, an early characteristic of AD, provide a promising target for preclinical diagnosis and treatment. However, despite some progress, the key mechanisms underlying the contributions of OLs to AD remain unclear. Given the heavy burden of medical treatment, a better understanding of the pathophysiological mechanisms underlying AD is vital. This review comprehensively summarizes the results on WM abnormalities in AD and explores the relationship between OL progenitor cells and the pathogenesis of AD. Finally, the underlying molecular mechanisms and potential future research directions are discussed.

Incorporating outlier information into diffusion-weighted MRI modeling for robust microstructural imaging and structural brain connectivity analyses

The white matter structures of the human brain can be represented using diffusion-weighted MRI tractography. Unfortunately, tractography is prone to find false-positive streamlines causing a severe decline in its specificity and limiting its feasibility in accurate structural brain connectivity analyses. Filtering algorithms have been proposed to reduce the number of invalid streamlines but the currently available filtering algorithms are not suitable to process data that contains motion artefacts which are typical in clinical research. We augmented the Convex Optimization Modelling for Microstructure Informed Tractography (COMMIT) algorithm to adjust for these signals drop-out motion artefacts. We demonstrate with comprehensive Monte-Carlo whole brain simulations and in vivo infant data that our robust algorithm is capable of properly filtering tractography reconstructions despite these artefacts. We evaluated the results using parametric and non-parametric statistics and our results demonstrate that if not accounted for, motion artefacts can have severe adverse effects in human brain structural connectivity analyses as well as in microstructural property mappings. In conclusion, the usage of robust filtering methods to mitigate motion related errors in tractogram filtering is highly beneficial, especially in clinical studies with uncooperative patient groups such as infants. With our presented robust augmentation and open-source implementation, robust tractogram filtering is readily available.

On the generalizability of diffusion MRI signal representations across acquisition parameters, sequences and tissue types: Chronicles of the MEMENTO challenge

Diffusion MRI (dMRI) has become an invaluable tool to assess the microstructural organization of brain tissue. Depending on the specific acquisition settings, the dMRI signal encodes specific properties of the underlying diffusion process. In the last two decades, several signal representations have been proposed to fit the dMRI signal and decode such properties. Most methods, however, are tested and developed on a limited amount of data, and their applicability to other acquisition schemes remains unknown. With this work, we aimed to shed light on the generalizability of existing dMRI signal representations to different diffusion encoding parameters and brain tissue types. To this end, we organized a community challenge - named MEMENTO, making available the same datasets for fair comparisons across algorithms and techniques. We considered two state-of-the-art diffusion datasets, including single-diffusion-encoding (SDE) spin-echo data from a human brain with over 3820 unique diffusion weightings (the MASSIVE dataset), and double (oscillating) diffusion encoding data (DDE/DODE) of a mouse brain including over 2520 unique data points. A subset of the data sampled in 5 different voxels was openly distributed, and the challenge participants were asked to predict the remaining part of the data. After one year, eight participant teams submitted a total of 80 signal fits. For each submission, we evaluated the mean squared error, the variance of the prediction error and the Bayesian information criteria. The received submissions predicted either multi-shell SDE data (37%) or DODE data (22%), followed by cartesian SDE data (19%) and DDE (18%). Most submissions predicted the signals measured with SDE remarkably well, with the exception of low and very strong diffusion weightings. The prediction of DDE and DODE data seemed more challenging, likely because none of the submissions explicitly accounted for diffusion time and frequency. Next to the choice of the model, decisions on fit procedure and hyperparameters play a major role in the prediction performance, highlighting the importance of optimizing and reporting such choices. This work is a community effort to highlight strength and limitations of the field at representing dMRI acquired with trending encoding schemes, gaining insights into how different models generalize to different tissue types and fiber configurations over a large range of diffusion encodings.

Diffusion-Weighted Imaging: Recent Advances and Applications

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

Toward more robust and reproducible diffusion kurtosis imaging

PURPOSE

The general utility of diffusion kurtosis imaging (DKI) is challenged by its poor robustness to imaging artifacts and thermal noise that often lead to implausible kurtosis values.

THEORY AND METHODS

A robust scalar kurtosis index can be estimated from powder-averaged diffusion-weighted data. We introduce a novel DKI estimator that uses this scalar kurtosis index as a proxy for the mean kurtosis to regularize the fit.

RESULTS

The regularized DKI estimator improves the robustness and reproducibility of the kurtosis metrics and results in parameter maps with enhanced quality and contrast.

CONCLUSION

Our novel DKI estimator promotes the wider use of DKI in clinical research and potentially diagnostics by improving the reproducibility and precision of DKI fitting and, as such, enabling enhanced visual, quantitative, and statistical analyses of DKI parameters.

Neurobiological underpinnings of rapid white matter plasticity during intensive reading instruction

Diffusion MRI is a powerful tool for imaging brain structure, but it is challenging to discern the biological underpinnings of plasticity inferred from these and other non-invasive MR measurements. Biophysical modeling of the diffusion signal aims to render a more biologically rich image of tissue microstructure, but the application of these models comes with important caveats. A separate approach for gaining biological specificity has been to seek converging evidence from multi-modal datasets. Here we use metrics derived from diffusion kurtosis imaging (DKI) and the white matter tract integrity (WMTI) model along with quantitative MRI measurements of T1 relaxation to characterize changes throughout the white matter during an 8-week, intensive reading intervention (160 total hours of instruction). Behavioral measures, multi-shell diffusion MRI data, and quantitative T1 data were collected at regular intervals during the intervention in a group of 33 children with reading difficulties (7-12 years old), and over the same period in an age-matched non-intervention control group. Throughout the white matter, mean 'extra-axonal' diffusivity was inversely related to intervention time. In contrast, model estimated axonal water fraction (AWF), overall diffusion kurtosis, and T1 relaxation time showed no significant change over the intervention period. Both diffusion and quantitative T1 based metrics were correlated with pre-intervention reading performance, albeit with distinct anatomical distributions. These results are consistent with the view that rapid changes in diffusion properties reflect phenomena other than widespread changes in myelin density. We discuss this result in light of recent work highlighting non-axonal factors in experience-dependent plasticity and learning.

Evidence for microscopic kurtosis in neural tissue revealed by correlation tensor MRI

PURPOSE

The impact of microscopic diffusional kurtosis (µK), arising from restricted diffusion and/or structural disorder, remains a controversial issue in contemporary diffusion MRI (dMRI). Recently, correlation tensor imaging (CTI) was introduced to disentangle the sources contributing to diffusional kurtosis, without relying on a-priori multi-gaussian component (MGC) or other microstructural assumptions. Here, we investigated µK in in vivo rat brains and assessed its impact on state-of-the-art methods ignoring µK.

THEORY AND METHODS

CTI harnesses double diffusion encoding (DDE) experiments, which were here improved for speed and minimal bias using four different sets of acquisition parameters. The robustness of the improved CTI protocol was assessed via simulations. In vivo CTI acquisitions were performed in healthy rat brains using a 9.4T pre-clinical scanner equipped with a cryogenic coil, and targeted the estimation of µK, anisotropic kurtosis, and isotropic kurtosis.

RESULTS

The improved CTI acquisition scheme substantially reduces scan time and importantly, also minimizes higher-order-term biases, thus enabling robust µK estimation, alongside K_aniso and K_iso metrics. Our CTI experiments revealed positive µK both in white and gray matter of the rat brain in vivo; µK is the dominant kurtosis source in healthy gray matter tissue. The non-negligible µK substantially were found to bias prior MGC analyses of K_iso and K_aniso .

CONCLUSIONS

Correlation Tensor MRI offers a more accurate and robust characterization of kurtosis sources than its predecessors. µK is non-negligible in vivo in healthy white and gray matter tissues and could be an important biomarker for future studies. Our findings thus have both theoretical and practical implications for future dMRI research.

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

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

Diffusional Kurtosis Imaging in the Diffusion Imaging in Python Project

Diffusion-weighted magnetic resonance imaging (dMRI) measurements and models provide information about brain connectivity and are sensitive to the physical properties of tissue microstructure. Diffusional Kurtosis Imaging (DKI) quantifies the degree of non-Gaussian diffusion in biological tissue from dMRI. These estimates are of interest because they were shown to be more sensitive to microstructural alterations in health and diseases than measures based on the total anisotropy of diffusion which are highly confounded by tissue dispersion and fiber crossings. In this work, we implemented DKI in the Diffusion in Python (DIPY) project-a large collaborative open-source project which aims to provide well-tested, well-documented and comprehensive implementation of different dMRI techniques. We demonstrate the functionality of our methods in numerical simulations with known ground truth parameters and in openly available datasets. A particular strength of our DKI implementations is that it pursues several extensions of the model that connect it explicitly with microstructural models and the reconstruction of 3D white matter fiber bundles (tractography). For instance, our implementations include DKI-based microstructural models that allow the estimation of biophysical parameters, such as axonal water fraction. Moreover, we illustrate how DKI provides more general characterization of non-Gaussian diffusion compatible with complex white matter fiber architectures and gray matter, and we include a novel mean kurtosis index that is invariant to the confounding effects due to tissue dispersion. In summary, DKI in DIPY provides a well-tested, well-documented and comprehensive reference implementation for DKI. It provides a platform for wider use of DKI in research on brain disorders and in cognitive neuroscience.

MESMERISED: Super-accelerating T1 relaxometry and diffusion MRI with STEAM at 7 T for quantitative multi-contrast and diffusion imaging

There is an increasing interest in quantitative imaging of T₁, T₂ and diffusion contrast in the brain due to greater robustness against bias fields and artifacts, as well as better biophysical interpretability in terms of microstructure. However, acquisition time constraints are a challenge, particularly when multiple quantitative contrasts are desired and when extensive sampling of diffusion directions, high b-values or long diffusion times are needed for multi-compartment microstructure modeling. Although ultra-high fields of 7 T and above have desirable properties for many MR modalities, the shortening T₂ and the high specific absorption rate (SAR) of inversion and refocusing pulses bring great challenges to quantitative T₁, T₂ and diffusion imaging. Here, we present the MESMERISED sequence (Multiplexed Echo Shifted Multiband Excited and Recalled Imaging of STEAM Encoded Diffusion). MESMERISED removes the dead time in Stimulated Echo Acquisition Mode (STEAM) imaging by an echo-shifting mechanism. The echo-shift (ES) factor is independent of multiband (MB) acceleration and allows for very high multiplicative (ESxMB) acceleration factors, particularly under moderate and long mixing times. This results in super-acceleration and high time efficiency at 7 T for quantitative T₁ and diffusion imaging, while also retaining the capacity to perform quantitative T₂ and B₁ mapping. We demonstrate the super-acceleration of MESMERISED for whole-brain T₁ relaxometry with total acceleration factors up to 36 at 1.8 mm isotropic resolution, and up to 54 at 1.25 mm resolution qT₁ imaging, corresponding to a 6x and 9x speedup, respectively, compared to MB-only accelerated acquisitions. We then demonstrate highly efficient diffusion MRI with high b-values and long diffusion times in two separate cases. First, we show that super-accelerated multi-shell diffusion acquisitions with 370 whole-brain diffusion volumes over 8 b-value shells up to b = 7000 s/mm² can be generated at 2 mm isotropic in under 8 minutes, a data rate of almost a volume per second, or at 1.8 mm isotropic in under 11 minutes, achieving up to 3.4x speedup compared to MB-only. A comparison of b = 7000 s/mm² MESMERISED against standard MB pulsed gradient spin echo (PGSE) diffusion imaging shows 70% higher SNR efficiency and greater effectiveness in supporting complex diffusion signal modeling. Second, we demonstrate time-efficient sampling of different diffusion times with 1.8 mm isotropic diffusion data acquired at four diffusion times up to 290 ms, which supports both Diffusion Tensor Imaging (DTI) and Diffusion Kurtosis Imaging (DKI) at each diffusion time. Finally, we demonstrate how adding quantitative T₂ and B₁⁺ mapping to super-accelerated qT₁ and diffusion imaging enables efficient quantitative multi-contrast mapping with the same MESMERISED sequence and the same readout train. MESMERISED extends possibilities to efficiently probe T₁, T₂ and diffusion contrast for multi-component modeling of tissue microstructure.

Rapid microstructural plasticity in the cortical semantic network following a short language learning session

Despite the clear importance of language in our life, our vital ability to quickly and effectively learn new words and meanings is neurobiologically poorly understood. Conventional knowledge maintains that language learning—especially in adulthood—is slow and laborious. Furthermore, its structural basis remains unclear. Even though behavioural manifestations of learning are evident near instantly, previous neuroimaging work across a range of semantic categories has largely studied neural changes associated with months or years of practice. Here, we address rapid neuroanatomical plasticity accompanying new lexicon acquisition, specifically focussing on the learning of action-related language, which has been linked to the brain’s motor systems. Our results show that it is possible to measure and to externally modulate (using transcranial magnetic stimulation (TMS) of motor cortex) cortical microanatomic reorganisation after mere minutes of new word learning. Learning-induced microstructural changes, as measured by diffusion kurtosis imaging (DKI) and machine learning-based analysis, were evident in prefrontal, temporal, and parietal neocortical sites, likely reflecting integrative lexico-semantic processing and formation of new memory circuits immediately during the learning tasks. These results suggest a structural basis for the rapid neocortical word encoding mechanism and reveal the causally interactive relationship of modal and associative brain regions in supporting learning and word acquisition.

This combined neuroimaging and brain stimulation study reveals rapid and distributed microstructural plasticity after a single immersive language learning session, demonstrating the causal relevance of the motor cortex in encoding the meaning of novel action words.

Rapid microscopic fractional anisotropy imaging via an optimized linear regression formulation

Water diffusion anisotropy in the human brain is affected by disease, trauma, and development. Microscopic fractional anisotropy (μFA) is a diffusion MRI (dMRI) metric that can quantify water diffusion anisotropy independent of neuron fiber orientation dispersion. However, there are several different techniques to estimate μFA and few have demonstrated full brain imaging capabilities within clinically viable scan times and resolutions. Here, we present an optimized spherical tensor encoding (STE) technique to acquire μFA directly from the 2nd order cumulant expansion of the powder averaged dMRI signal obtained from direct linear regression (i.e. diffusion kurtosis) which requires fewer powder-averaged signals than other STE fitting techniques and can be rapidly computed. We found that the optimal dMRI parameters for white matter μFA imaging were a maximum b-value of 2000 s/mm² and a ratio of STE to LTE tensor encoded acquisitions of 1.7 for our system specifications. We then compared two implementations of the direct regression approach to the well-established gamma model in 4 healthy volunteers on a 3 Tesla system. One implementation used mean diffusivity (D) obtained from a 2nd order fit of the cumulant expansion, while the other used a linear estimation of D from the low b-values. Both implementations of the direct regression approach showed strong linear correlations with the gamma model (ρ = 0.97 and ρ = 0.90) but mean biases of -0.11 and - 0.02 relative to the gamma model were also observed, respectively. All three μFA measurements showed good test-retest reliability (ρ ≥ 0.79 and bias = 0). To demonstrate the potential scan time advantage of the direct approach, 2 mm isotropic resolution μFA was demonstrated over a 10 cm slab using a subsampled data set with fewer powder-averaged signals that would correspond to a 3.3-min scan. Accordingly, our results introduce an optimization procedure that has enabled nearly full brain μFA in only several minutes.

Double diffusion encoding and applications for biomedical imaging

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

Challenges for biophysical modeling of microstructure

The biophysical modeling efforts in diffusion MRI have grown considerably over the past 25 years. In this review, we dwell on the various challenges along the journey of bringing a biophysical model from initial design to clinical implementation, identifying both hurdles that have been already overcome and outstanding issues. First, we describe the critical initial task of selecting which features of tissue microstructure can be estimated using a model and which acquisition protocol needs to be implemented to make the estimation possible. The model performance should necessarily be tested in realistic numerical simulations and in experimental data - adapting the fitting strategy accordingly, and parameter estimates should be validated against complementary techniques, when/if available. Secondly, the model performance and validity should be explored in pathological conditions, and, if appropriate, dedicated models for pathology should be developed. We build on examples from tumors, ischemia and demyelinating diseases. We then discuss the challenges associated with clinical translation and added value. Finally, we single out four major unresolved challenges that are related to: the availability of a microstructural ground truth, the validation of model parameters which cannot be accessed with complementary techniques, the development of a generalized standard model for any brain region and pathology, and the seamless communication between different parties involved in the development and application of biophysical models of diffusion.

Evaluation of the diffusion MRI white matter tract integrity model using myelin histology and Monte-Carlo simulations

Quantitative evaluation of brain myelination has drawn considerable attention. Conventional diffusion-based magnetic resonance imaging models, including diffusion tensor imaging and diffusion kurtosis imaging (DKI),¹ have been used to infer the microstructure and its changes in neurological diseases. White matter tract integrity (WMTI) was proposed as a biophysical model to relate the DKI-derived metrics to the underlying microstructure. Although the model has been validated on ex vivo animal brains, it was not well evaluated with ex vivo human brains. In this study, histological samples (namely corpus callosum) from postmortem human brains have been investigated based on WMTI analyses on a clinical 3T scanner and comparisons with gold standard myelin staining in proteolipid protein and Luxol fast blue. In addition, Monte Carlo simulations were conducted to link changes from ex vivo to in vivo conditions based on the microscale parameters of water diffusivity and permeability. The results show that WMTI metrics, including axonal water fraction AWF, radial extra-axonal diffusivity D_e^⊥, and intra-axonal diffusivity D_awere needed to characterize myelin content alterations. Thus, WMTI model metrics are shown to be promising candidates as sensitive biomarkers of demyelination.

Axonal damage in the optic radiation assessed by white matter tract integrity metrics is associated with retinal thinning in multiple sclerosis

INTRODUCTION

White matter damage in the visual pathway is common in multiple sclerosis (MS) and is associated with retinal thinning, although the underlying mechanism of association remains unclear. The goal of this work was to evaluate the presence and extent of white matter tract integrity (WMTI) alterations in the optic radiation (OR) in people with MS and to investigate the association between WMTI metrics and retinal thinning in the eyes of MS patients without a history of optic neuritis (ON) as measured by optical coherence tomography (OCT). We hypothesized that WMTI metrics would reflect axonal damage that occurs in the OR in MS, and that axonal alterations revealed by WMTI would be associated with retinal thinning.

METHODS

Twenty-nine MS patients without previous ON in at least one eye and twenty-nine age-matched healthy controls (HC) were scanned on a dedicated high-gradient 3-Tesla MRI scanner with 300 mT/m maximum gradient strength using a multi-shell diffusion MRI protocol (b = 800, 1500, 2400 s/mm²). The patients were divided into two subgroups according to history without ON (N = 18) or with ON in one eye (N = 11). Diffusion tensor imaging (DTI) metrics and WMTI metrics derived from diffusion kurtosis imaging were assessed in normal-appearing white matter (NAWM) of the OR and in focal lesions. Retinal thickness in the eyes of MS patients was measured by OCT. Student's t-test was used to assess group differences between MRI metrics. Linear regression was used to study the relationship between OCT metrics, including retinal nerve fiber layer (RNFL) and combined ganglion cell and inner plexiform layer thickness (GCL/IPL), visual acuity measures and DTI and WMTI metrics.

RESULTS

OR NAWM in MS showed significantly decreased axonal water fraction (AWF) compared to HC (0.36 vs 0.39, p < 0.001), with similar trends observed in AWF of lesions compared to NAWM (0.27 vs 0.36, p < 0.001). Fractional anisotropy (FA) was lower in OR NAWM of MS patients compared to HC (0.49 vs 0.52, p < 0.001). In patients without ON, AWF was the only diffusion MRI metric that was significantly associated with average RNFL (r = 0.68, p = 0.005), adjusting for age, sex and disease duration and correcting for multiple comparisons. Of all the DTI and WMTI metrics, AWF was the strongest and most significant predictor of average RNFL thickness in MS patients without ON. There was no significant correlation between visual acuity scores and DTI or WMTI metrics after correction for multiple comparisons.

CONCLUSION

Axonal damage may be the substrate of previously observed DTI alterations in the OR, as supported by the significant reduction in AWF within both NAWM and lesions of the OR in MS. Our results support the concept that axonal damage is widespread throughout the visual pathway in MS and may be mediated through trans-synaptic degeneration.

Diffusion MRI biomarkers of white matter microstructure vary nonmonotonically with increasing cerebral amyloid deposition

Beta amyloid (Aβ) accumulation is the earliest pathological marker of Alzheimer's disease (AD), but early AD pathology also affects white matter (WM) integrity. We performed a cross-sectional study including 44 subjects (23 healthy controls and 21 mild cognitive impairment or early AD patients) who underwent simultaneous PET-MR using 18F-Florbetapir, and were categorized into 3 groups based on Aβ burden: Aβ- [mean mSUVr ≤1.00], Aβi [1.00 < mSUVr <1.17], Aβ+ [mSUVr ≥1.17]. Intergroup comparisons of diffusion MRI metrics revealed significant differences across multiple WM tracts. Aβi group displayed more restricted diffusion (higher fractional anisotropy, radial kurtosis, axonal water fraction, and lower radial diffusivity) than both Aβ- and Aβ+ groups. This nonmonotonic trend was confirmed by significant continuous correlations between mSUVr and diffusion metrics going in opposite direction for 2 cohorts: pooled Aβ-/Aβi and pooled Aβi/Aβ+. The transient period of increased diffusion restriction may be due to inflammation that accompanies rising Aβ burden. In the later stages of Aβ accumulation, neurodegeneration is the predominant factor affecting diffusion.

Advanced Imaging of Traumatic Brain Injury

Traumatic brain injury (TBI) is a major health and socio-economic problem worldwide that mainly affects young adults. Neuroimaging plays a critical role in the diagnosis and evaluation of patients with TBI. Some patients with mild TBI have variable neurological symptoms. In such patients, computed tomography and magnetic resonance imaging (MRI) can present normal findings. Advanced imaging techniques, such as diffusion tensor imaging, magnetic resonance spectroscopy, perfusion weighted imaging, or functional MRI, can reveal abnormalities that are not detected using conventional imaging methods. Here, I briefly review current neuroimaging for TBI and survey advanced imaging techniques in terms of structural and functional aspects, which include a few promising areas of TBI research.

Tractography of the external capsule and cognition: A diffusion MRI study of cholinergic fibers

INTRODUCTION

White matter changes (WMC) in the cholinergic tracts contribute to executive dysfunction in the context of cognitive aging. WMC in the external capsule have been associated with executive dysfunction. The objectives of this study were to: 1) Characterize the lateral cholinergic tracts (LCT) and the superior longitudinal fasciculus (SLF). 2) Evaluate the association between diffusion measures within those tracts and cognitive performance.

METHODS

Neuropsychological testing and high angular resolution diffusion imaging (HARDI) of 34 healthy elderly participants was done, followed by anatomically constrained probabilistic tractography reconstruction robust to crossing fibers. The external capsule was manually segmented on a mean T1 image then merged with an atlas, allowing extraction of the LCT. Diffusion tensor imaging (DTI) and HARDI-based measures were obtained.

RESULTS

Correlations between diffusion measures in the LCT and the time of completion of Stroop (left LCT radial and medial diffusivity), the Symbol Search score (right LCT apparent fiber density) and the motor part of Trail-B (left LCT axial and radial diffusivity) were observed. Correlations were also found with diffusion measures in the SLF. WMC burden was low, and no correlation was found with diffusion measures or cognitive performance.

DISCUSSION

DTI and HARDI, with isolation of strategic white matter tracts for cognitive functions, represent complimentary tools to better understand the complex process of brain aging.

Computer-assisted cognitive remediation therapy for patients with schizophrenia induces microstructural changes in cerebellar regions involved in cognitive functions

Previous studies have reported that cognitive remediation therapy (CRT) improves cognitive deficits in patents with schizophrenia. However, few studies have focused on the underlying structural alterations in the brain following Vocational Cognitive Ability Training by the Japanese Cognitive Rehabilitation Program for Schizophrenia (VCAT-J). In this study, we analyzed changes in diffusion tensor imaging parameters in 31 patients with schizophrenia after 12 weeks of intervention consisting of standard treatment alone or standard treatment plus VCAT-J, in order to determine the effect of the latter on white matter microstructural plasticity. Cognitive function was evaluated using the Japanese version of the Brief Assessment of Cognition in Schizophrenia (BACS-J) scale. The CRT group exhibited significant improvements in verbal fluency and composite BACS-J scores, relative to the treatment-as-usual (TAU) group. In addition, the CRT group exhibited significantly increased fractional anisotropy (FA) values, along with significantly decreased radial (RD) and mean diffusivity (MD) values, in the posterior lobe of the left cerebellum. Changes in RD and MD values were negatively correlated with changes in BACS-J composite scores. These suggest that VCAT-J might mediate improvements in myelin sheath composition in the posterior lobe of the left cerebellum, which may have been associated with improvements in cognitive function.

[Diffusion kurtosis imaging in diffuse axonal injury]

Diffuse axonal injury (DAI) is one of the most severe traumatic brain injuries. The availability of neuroimaging biomarkers for monitoring expansion of traumatic brain injury in vivo is a topical issue.

PURPOSE

To evaluate novel neuroimaging biomarkers for monitoring brain injury using diffusion kurtosis imaging (DKI) in patients with severe diffuse axonal injury.

MATERIAL AND METHODS

DKI data of 12 patients with severe DAI (11 patients with a Glasgow Coma Scale (GCS) score of ≤ 8 and 1 patient with a GCS score of 9) and 8 healthy volunteers (control group) were compared. MRI examination was performed 5 to 19 days after injury; 7 of the 12 patients underwent repeated MRI examinations. We assessed the following parameters: mean, axial, and radial kurtosis (MK, AK, RK, respectively) and kurtosis anisotropy (KA) of the white and gray matter; fractional anisotropy (FA), axonal water fraction (AWF), axial and radial extra-axonal diffusion (AxEAD and RadEAD, respectively), and tortuosity (TORT) of the extra-axonal space) of the white matter. Regions of interest (ROIs) were set bilaterally in the centrum semiovale, genu and splenium of the corpus callosum, anterior and posterior limbs of the internal capsule, putamen, thalamus, midbrain, and pons.

RESULTS

A significant reduction in KA (p<0.05) in most of ROIs set on the white matter was revealed. AK was increased (p<0.05) not only in the white matter but also in the putamen and thalamus. A significant reduction in MK with time was observed when the first and second DKI data were compared. AWF was reduced in the centrum semiovale and peduncles. The TORT parameter was decreased (p<0.05) in the majority of ROIs in the white matter, with the most pronounced changes occurring in the genu and splenium of the corpus callosum.

CONCLUSION

DKI provides novel data about microstructural injury in DAI and improves our knowledge of brain trauma pathophysiology. DKI parameters should be considered as potential biomarkers of brain injury and potential predictors of the outcome.

White Matter Tract Integrity: An Indicator of Axonal Pathology after Mild Traumatic Brain Injury

We seek to elucidate the underlying pathophysiology of injury sustained after mild traumatic brain injury (mTBI) using multi-shell diffusion magnetic resonance imaging, deriving compartment-specific white matter tract integrity (WMTI) metrics. WMTI allows a more biophysical interpretation of white matter (WM) changes by describing microstructural characteristics in both intra- and extra-axonal environments. Thirty-two patients with mTBI within 30 days of injury and 21 age- and sex-matched controls were imaged on a 3 Tesla magnetic resonance scanner. Multi-shell diffusion acquisition was performed with five b-values (250-2500 sec/mm²) along 6-60 diffusion encoding directions. Tract-based spatial statistics (TBSS) was used with family-wise error (FWE) correction for multiple comparisons. TBSS results demonstrated focally lower intra-axonal diffusivity (D_axon) in mTBI patients in the splenium of the corpus callosum (sCC; p < 0.05, FWE-corrected). The area under the curve value for D_axon was 0.76 with a low sensitivity of 46.9% but 100% specificity. These results indicate that D_axon may be a useful imaging biomarker highly specific for mTBI-related WM injury. The observed decrease in D_axon suggests restriction of the diffusion along the axons occurring shortly after injury.

White matter alterations in adult with autism spectrum disorder evaluated using diffusion kurtosis imaging

PURPOSE

Autism spectrum disorder (ASD) is related to impairment in various white matter (WM) pathways. Utility of the recently developed two-compartment model of diffusion kurtosis imaging (DKI) to analyse axial diffusivity of WM is restricted by several limitations. The present study aims to validate the utility of model-free DKI in the evaluation of WM alterations in ASD and analyse the potential relationship between DKI-evident WM alterations and personality scales.

METHODS

Overall, 15 participants with ASD and 15 neurotypical (NT) controls were scanned on a 3 T magnetic resonance (MR) scanner, and scores for autism quotient (AQ), systemising quotient (SQ) and empathising quotient (EQ) were obtained for both groups. Multishell diffusion-weighted MR data were acquired using two b-values (1000 and 2000 s/mm²). Differences in mean kurtosis (MK), radial kurtosis (RK) and axial kurtosis (AK) between the groups were evaluated using tract-based spatial statistics (TBSS). Finally, the relationships between the kurtosis indices and personality quotients were examined.

RESULTS

The ASD group demonstrated significantly lower AK in the body and splenium of corpus callosum than the NT group; however, no other significant differences were identified. Negative correlations were found between AK and AQ or SQ, predominantly in WM areas related to social-emotional processing such as uncinate fasciculus, inferior fronto-occipital fasciculus, and inferior and superior longitudinal fasciculi.

CONCLUSIONS

Model-free DKI and its indices may represent a novel, objective method for detecting the disease severity and WM alterations in patients with ASD.

Diffusion MRI detects longitudinal white matter changes in the 3xTg-AD mouse model of Alzheimer's disease

The sensitivity of multiple diffusion MRI (dMRI) parameters to longitudinal changes in white matter microstructure was investigated for the 3xTg-AD transgenic mouse model of Alzheimer's disease, which manifests both amyloid beta plaques and neurofibrillary tangles. By employing a specific dMRI method known as diffusional kurtosis imaging, eight different diffusion parameters were quantified to characterize distinct aspects of water diffusion. Four female 3xTg-AD mice were imaged at five time points, ranging from 4.5 to 18 months of age, and the diffusion parameters were investigated in four white matter regions (fimbria, external capsule, internal capsule and corpus callosum). Significant changes were observed in several diffusion parameters, particularly in the fimbria and in the external capsule, with a statistically significant decrease in diffusivity and a statistically significant increase in kurtosis. Our preliminary results demonstrate that dMRI can detect microstructural changes in white matter for the 3xTg-AD mouse model due to aging and/or progression of pathology, depending strongly on the diffusion parameter and anatomical region.

Imaging brain microstructure with diffusion MRI: practicality and applications

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

Axonal water fraction as marker of white matter injury in primary-progressive multiple sclerosis: a longitudinal study

BACKGROUND AND PURPOSE

Diffuse white matter (WM) injury is prominent in primary-progressive multiple sclerosis (PP-MS) pathology and is a potential biomarker of disease progression. Diffusion kurtosis imaging allows the quantification of non-Gaussian water diffusion, providing metrics with high WM pathological specificity. The aim of this study was to characterize the pathological changes occurring in the normal-appearing WM of patients with PP-MS at baseline and at 1-year follow-up and to assess their impact on disability and short-term disease progression.

METHODS

A total of 26 patients with PP-MS and 20 healthy controls were prospectively enrolled. Diffusion kurtosis imaging single-shot echo-planar imaging (EPI) was acquired on a 3-T scanner (Philips Achieva, Best, The Netherlands) (voxel size, 2 × 2 × 2 mm³ , 30 directions for each b-value = 1000, 2000 s/mm² and one b = 0 s/mm² ). A two-compartment biophysical model of WM tract integrity was used to derive spatial maps of axonal water fraction (AWF), intra-axonal diffusivity, extra-axonal axial and radial diffusivities (D_e,axial , D_e,radial ) and tortuosity from the following WM tracts: corpus callosum (CC), corticospinal tract (CST) and posterior thalamic radiation (PTR).

RESULTS

At baseline, patients with PP-MS showed a widespread decrease of AWF, tortuosity and D_e,axial and an increase of D_e,radial in CC, CST and PTR (P ranging from 0.001 to 0.036). At 1-year follow-up, a significant AWF decrease was detected in the body of CC (P = 0.048), PTR (P = 0.008) and CST (P = 0.044). Baseline AWF values in CST significantly discriminated progressed from non-progressed patients (P = 0.021; area under the curve, 0.854).

CONCLUSION

Based on its change over time and its relationship with disease progression, among the analyzed metrics, AWF seems the most sensitive metric of WM tissue damage in PP-MS and therefore it could be considered as a marker for monitoring disease progression.

Applying microstructural models to understand the role of white matter in cognitive development

Diffusion MRI (dMRI) holds great promise for illuminating the biological changes that underpin cognitive development. The diffusion of water molecules probes the cellular structure of brain tissue, and biophysical modeling of the diffusion signal can be used to make inferences about specific tissue properties that vary over development or predict cognitive performance. However, applying these models to study development requires that the parameters can be reliably estimated given the constraints of data collection with children. Here we collect repeated scans using a typical multi-shell diffusion MRI protocol in a group of children (ages 7-12) and use two popular modeling techniques to examine individual differences in white matter structure. We first assess scan-rescan reliability of model parameters and show that axon water faction can be reliably estimated from a relatively fast acquisition, without applying spatial smoothing or de-noising. We then investigate developmental changes in the white matter, and individual differences that correlate with reading skill. Specifically, we test the hypothesis that previously reported correlations between reading skill and diffusion anisotropy in the corpus callosum reflect increased axon water fraction in poor readers. Both models support this interpretation, highlighting the utility of these approaches for testing specific hypotheses about cognitive development.

Dynamic changes in hippocampal diffusion and kurtosis metrics following experimental mTBI correlate with glial reactivity

Diffusion magnetic resonance imaging biomarkers can provide quantifiable information of the brain tissue after a mild traumatic brain injury (mTBI). However, the commonly applied diffusion tensor imaging (DTI) model is not very specific to changes in the underlying cellular structures. To overcome these limitations, other diffusion models have recently emerged to provide a more complete view on the damage profile following TBI. In this study, we investigated longitudinal changes in advanced diffusion metrics following experimental mTBI, utilising three different diffusion models in a rat model of mTBI, including DTI, diffusion kurtosis imaging and a white matter model. Moreover, we investigated the association between the diffusion metrics with histological markers, including glial fibrillary acidic protein (GFAP), neurofilaments and synaptophysin in order to investigate specificity. Our results revealed significant decreases in mean diffusivity in the hippocampus and radial diffusivity and radial extra axonal diffusivity (RadEAD) in the cingulum one week post injury. Furthermore, correlation analysis showed that increased values of fractional anisotropy one day post injury in the hippocampus was highly correlated with GFAP reactivity three months post injury. Additionally, we observed a positive correlation between GFAP on one hand and the kurtosis parameters in the hippocampus on the other hand three months post injury. This result indicated that prolonged glial activation three months post injury is related to higher kurtosis values at later time points. In conclusion, our findings point out to the possible role of kurtosis metrics as well as metrics from the white matter model as prognostic biomarker to monitor prolonged glial reactivity and inflammatory responses after a mTBI not only at early timepoints but also several months after injury.

•

Advanced diffusion metrics show longitudinal changes following mTBI

•

Radial diffusivity (RD) and radial extra-axonal diffusivity ↓ in the cingulum

•

Mean diffusivity ↓ in the hippocampus

•

In the cingulum RD is continuously decreased until three months post injury

•

Glial activity correlates with fractional anisotropy in hippocampus