Cortical depth dependence of the diffusion anisotropy in the human cortical gray matter in vivo

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

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

Column-based cortical depth analysis of the diffusion anisotropy and radiality in submillimeter whole-brain diffusion tensor imaging of the human cortical gray matter in vivo

High-resolution diffusion tensor imaging (DTI) can noninvasively probe the microstructure of cortical gray matter in vivo. In this study, 0.9-mm isotropic whole-brain DTI data were acquired in healthy subjects with an efficient multi-band multi-shot echo-planar imaging sequence. A column-based analysis that samples the fractional anisotropy (FA) and radiality index (RI) along radially oriented cortical columns was then performed to quantitatively analyze the FA and RI dependence on the cortical depth, cortical region, cortical curvature, and cortical thickness across the whole brain, which has not been simultaneously and systematically investigated in previous studies. The results showed characteristic FA and RI vs. cortical depth profiles, with an FA local maximum and minimum (or two inflection points) and a single RI maximum at intermediate cortical depths in most cortical regions, except for the postcentral gyrus where no FA peaks and a lower RI were observed. These results were consistent between repeated scans from the same subjects and across different subjects. They were also dependent on the cortical curvature and cortical thickness in that the characteristic FA and RI peaks were more pronounced i) at the banks than at the crown of gyri or at the fundus of sulci and ii) as the cortical thickness increases. This methodology can help characterize variations in microstructure along the cortical depth and across the whole brain in vivo, potentially providing quantitative biomarkers for neurological disorders.

High-resolution cortical MAP-MRI reveals areal borders and laminar substructures observed with histological staining

The variations in cellular composition and tissue architecture measured with histology provide the biological basis for partitioning the brain into distinct cytoarchitectonic areas and for characterizing neuropathological tissue alterations. Clearly, there is an urgent need to develop whole-brain neuroradiological methods that can assess cortical cyto- and myeloarchitectonic features non-invasively. Mean apparent propagator (MAP) MRI is a clinically feasible diffusion MRI method that quantifies efficiently and comprehensively the net microscopic displacements of water molecules diffusing in tissues. We investigate the sensitivity of high-resolution MAP-MRI to detecting areal and laminar variations in cortical cytoarchitecture and compare our results with observations from corresponding histological sections in the entire brain of a rhesus macaque monkey. High-resolution images of MAP-derived parameters, in particular the propagator anisotropy (PA), non-gaussianity (NG), and the return-to-axis probability (RTAP) reveal cortical area-specific lamination patterns in good agreement with the corresponding histological stained sections. In a few regions, the MAP parameters provide superior contrast to the five histological stains used in this study, delineating more clearly boundaries and transition regions between cortical areas and laminar substructures. Throughout the cortex, various MAP parameters can be used to delineate transition regions between specific cortical areas observed with histology and to refine areal boundaries estimated using atlas registration-based cortical parcellation. Using surface-based analysis of MAP parameters we quantify the cortical depth dependence of diffusion propagators in multiple regions-of-interest in a consistent and rigorous manner that is largely independent of the cortical folding geometry. The ability to assess cortical cytoarchitectonic features efficiently and non-invasively, its clinical feasibility, and translatability make high-resolution MAP-MRI a promising 3D imaging tool for studying whole-brain cortical organization, characterizing abnormal cortical development, improving early diagnosis of neurodegenerative diseases, identifying targets for biopsies, and complementing neuropathological investigations.

Diffusion MRI anisotropy in the cerebral cortex is determined by unmyelinated tissue features

Diffusion magnetic resonance imaging (dMRI) is commonly used to assess the tissue and cellular substructure of the human brain. In the white matter, myelinated axons are the principal neural elements that shape dMRI through the restriction of water diffusion; however, in the gray matter the relative contributions of myelinated axons and other tissue features to dMRI are poorly understood. Here we investigate the determinants of diffusion in the cerebral cortex. Specifically, we ask whether myelinated axons significantly shape dMRI fractional anisotropy (dMRI-FA), a measure commonly used to characterize tissue properties in humans. We compared ultra-high resolution ex vivo dMRI data from the brain of a marmoset monkey with both myelin- and Nissl-stained histological sections obtained from the same brain after scanning. We found that the dMRI-FA did not match the spatial distribution of myelin in the gray matter. Instead dMRI-FA was more closely related to the anisotropy of stained tissue features, most prominently those revealed by Nissl staining and to a lesser extent those revealed by myelin staining. Our results suggest that unmyelinated neurites such as large caliber apical dendrites are the primary features shaping dMRI measures in the cerebral cortex.

Inter- and intra-individual variation in brain structural-cognition relationships in aging

The sources of inter- and intra-individual variability in age-related cognitive decline remain poorly understood. We examined the association between 20-year trajectories of cognitive decline and multimodal brain structure and morphology in older age. We used the Whitehall II Study, an extensively characterised cohort with 3T brain magnetic resonance images acquired at older age (mean age = 69.52 ± 4.9) and 5 repeated cognitive performance assessments between mid-life (mean age = 53.2 ±4.9 years) and late-life (mean age = 67.7 ± 4.9). Using non-negative matrix factorization, we identified 10 brain components integrating cortical thickness, surface area, fractional anisotropy, and mean and radial diffusivities. We observed two latent variables describing distinct brain-cognition associations. The first describes variations in 5 structural components associated with low mid-life performance across multiple cognitive domains, decline in reasoning, but maintenance of fluency abilities. The second describes variations in 6 structural components associated with low mid-life performance in fluency and memory, but retention of multiple abilities. Expression of latent variables predicts future cognition 3.2 years later (mean age = 70.87 ± 4.9). This data-driven approach highlights brain-cognition relationships wherein individuals degrees of cognitive decline and maintenance across diverse cognitive functions are both positively and negatively associated with markers of cortical structure.

Ultra-high field (10.5T) diffusion-weighted MRI of the macaque brain

Diffu0sion-weighted magnetic resonance imaging (dMRI) is a non-invasive imaging technique that provides information about the barriers to the diffusion of water molecules in tissue. In the brain, this information can be used in several important ways, including to examine tissue abnormalities associated with brain disorders and to infer anatomical connectivity and the organization of white matter bundles through the use of tractography algorithms. However, dMRI also presents certain challenges. For example, historically, the biological validation of tractography models has shown only moderate correlations with anatomical connectivity as determined through invasive tract-tracing studies. Some of the factors contributing to such issues are low spatial resolution, low signal-to-noise ratios, and long scan times required for high-quality data, along with modeling challenges like complex fiber crossing patterns. Leveraging the capabilities provided by an ultra-high field scanner combined with denoising, we have acquired whole-brain, 0.58 mm isotropic resolution dMRI with a 2D-single shot echo planar imaging sequence on a 10.5 Tesla scanner in anesthetized macaques. These data produced high-quality tractograms and maps of scalar diffusion metrics in white matter. This work demonstrates the feasibility and motivation for in-vivo dMRI studies seeking to benefit from ultra-high fields.

Cerebral cortex layer segmentation using diffusion magnetic resonance imaging in vivo with applications to laminar connections and working memory analysis

Understanding the laminar brain structure is of great help in further developing our knowledge of the functions of the brain. However, since most layer segmentation methods are invasive, it is difficult to apply them to the human brain in vivo. To systematically explore the human brain's laminar structure noninvasively, the K-means clustering algorithm was used to automatically segment the left hemisphere into two layers, the superficial and deep layers, using a 7 Tesla (T) diffusion magnetic resonance imaging (dMRI)open dataset. The obtained layer thickness was then compared with the layer thickness of the BigBrain reference dataset, which segmented the neocortex into six layers based on the von Economo atlas. The results show a significant correlation not only between our automatically segmented superficial layer thickness and the thickness of layers 1-3 from the reference histological data, but also between our automatically segmented deep layer thickness and the thickness of layers 4-6 from the reference histological data. Second, we constructed the laminar connections between two pairs of unidirectional connected regions, which is consistent with prior research. Finally, we conducted the laminar analysis of the working memory, which was challenging to do in the past, and explained the conclusions of the functional analysis. Our work successfully demonstrates that it is possible to segment the human cortex noninvasively into layers using dMRI data and further explores the mechanisms of the human brain.

Higher-order multi-shell diffusion measures complement tensor metrics and volume in gray matter when predicting age and cognition

Recent advances in diffusion-weighted imaging have enabled us to probe the microstructure of even gray matter non-invasively. However, these advanced multi-shell protocols are often not included in large-scale studies as they significantly increase scan time. In this study, we investigated whether one set of multi-shell diffusion metrics commonly used in gray matter (as derived from Neurite Orientation Dispersion and Density Imaging, NODDI) provide enough additional information over typical tensor and volume metrics to justify the increased acquisition time, using the cognitive aging framework in the human hippocampus as a testbed. We first demonstrated that NODDI metrics are robust and reliable by replicating previous findings from our lab in a larger population of 79 younger (20.41 ± 1.89 years, 46 females) and 75 older (73.56 ± 6.26 years, 45 females) adults, showing that these metrics in the hippocampal subfields are sensitive to age and memory performance. We then asked how these subfield specific hippocampal NODDI metrics compared with standard tensor metrics and volume in predicting age and memory ability. We discovered that both NODDI and tensor measures separately predicted age and cognition in comparable capacities. However, integrating these modalities together considerably increased the predictive power of our logistic models, indicating that NODDI and tensor measures may be capturing independent microstructural information. We use these findings to encourage neuroimaging data collection consortiums to include a multi-shell diffusion sequence in their protocols since existing NODDI measures (and potential future multi-shell measures) may be able to capture microstructural variance that is missed by traditional approaches, even in studies exclusively examining gray matter.

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

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

Early life adversity in male mice sculpts reward circuits

Early life adversity (ELA) comprises a wide variety of negative experiences during early life and has been linked to cognitive impairments, reduced experiences of pleasure (anhedonia), and other long-term consequences implying that ELA impacts the reward circuitry. In this study, we focused on the projections from the dorsal raphe (DR) to the ventral tegmental area (VTA) and on to the nucleus accumbens (NAcc), an important pathway within the reward circuit. We hypothesized that ELA alters connectivity within the DR-VTA-NAcc pathway, associated with deficient reward seeking behaviors in adulthood. We used the limited bedding and nesting model to induce ELA in mice and measured reward-related behaviors in adulthood using the three-chamber social interaction and sucrose preference tests. High resolution ex vivo diffusion tensor imaging (DTI) was acquired and processed for regional DTI metrics, including tractography to assess circuit organization. We found brain-wide changes in radial diffusivity (RD) and altered connectivity of the reward circuit in the ELA group. DR-VTA-NAcc circuit tractography and axial diffusivity (AD) along this tract exhibited dispersed organization where AD was increased in the VTA segment. Behaviorally, ELA elicited a social anhedonia-like phenotype in adulthood with decreased direct social approach and time spent with peers in the three-chamber task, and no overt differences in sucrose preference. Our findings suggest that reward circuits, assessed using DTI, are altered following ELA and that these changes may reflect enduring reward deficits.

Automated cerebral cortex segmentation based solely on diffusion tensor imaging for investigating cortical anisotropy

To extract Diffusion Tensor Imaging (DTI) parameters from the human cortex, the inner and outer boundaries of the cortex are usually defined on 3D-T1-weighted images and then applied to the co-registered DTI. However, this analysis requires the acquisition of an additional high-resolution structural image that may not be practical in various imaging studies. Here an automatic cortical boundary segmentation method was developed to work directly only on the native DTI images by using fractional anisotropy (FA) maps and mean diffusion weighted images (DWI), the latter with acceptable gray-white matter image contrast. This new method was compared to the conventional cortical segmentations generated from high-resolution T1 structural images in 5 participants. In addition, the proposed method was applied to 15 healthy young adults (10 cross-sectional, 5 test-retest) to measure FA, MD, and radiality of the primary eigenvector across the cortex on whole-brain 1.5 mm isotropic images acquired in 3.5 min at 3T. The proposed method generated reasonable segmentations of the cortical boundaries for all individuals and large proportions of the proposed method segmentations (more than 85%) were within ±1 mm from those generated with the conventional approach on higher resolution T1 structural images. Both FA (~0.15) and MD (~0.77 × 10^-3 mm²/s) extracted halfway between the cortical boundaries were relatively stable across the cortex, although focal regions such as the posterior bank of the central sulcus, anterior insula, and medial temporal lobe showed higher FA. The primary eigenvectors were primarily oriented radially to the middle cortical surface, but there were tangential orientations in the sulcal fundi as well as in the posterior bank of the central sulcus. The proposed method demonstrates the feasibility and accuracy of cortical analysis in native DTI space while avoiding the acquisition of other imaging contrasts like 3D T1-weighted scans.

Detection of gray matter microstructural changes in Alzheimer's disease continuum using fiber orientation

BACKGROUND

This study aimed to investigate feasible gray matter microstructural biomarkers with high sensitivity for early Alzheimer's disease (AD) detection. We propose a diffusion tensor imaging (DTI) measure, "radiality", as an early AD biomarker. It is the dot product of the normal vector of the cortical surface and primary diffusion direction, which reflects the fiber orientation within the cortical column.

METHODS

We analyzed neuroimages from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database, including images from 78 cognitively normal (CN), 50 early mild cognitive impairment (EMCI), 34 late mild cognitive impairment (LMCI), and 39 AD patients. We then evaluated the cortical thickness (CTh), mean diffusivity (MD), which are conventional AD magnetic resonance imaging (MRI) biomarkers, and the amount of accumulated amyloid and tau using positron emission tomography (PET). Radiality was projected on the gray matter surface to compare and validate the changes with different stages alongside other neuroimage biomarkers.

RESULTS

The results revealed decreased radiality primarily in the entorhinal, insula, frontal, and temporal cortex with further progression of disease. In particular, radiality could delineate the difference between the CN and EMCI groups, while the other biomarkers could not. We examined the relationship between radiality and other biomarkers to validate its pathological evidence in AD. Overall, radiality showed a high association with conventional biomarkers. Additional ROI analysis revealed the dynamics of AD-related changes as stages onward.

CONCLUSION

Radiality in cortical gray matter showed AD-specific changes and relevance with other conventional AD biomarkers with high sensitivity. Moreover, radiality could identify the group differences seen in EMCI, representative of changes in early AD, which supports its superiority in early diagnosis compared to that possible with conventional biomarkers. We provide evidence of structural changes with cognitive impairment and suggest radiality as a sensitive biomarker for identifying early AD.

Volumetric and Diffusion Abnormalities in Subcortical Nuclei of Older Adults With Cognitive Frailty

Background: Cognitive frailty (CF) is defined as the simultaneous presence of physical frailty and cognitive impairment among older adults without dementia. Previous studies have revealed that neuropathological changes may contribute to the degeneration of subcortical nuclei in the process of cognitive impairment. However, it is unclear in CF. The aim of this study is to investigate the changes in subcortical nuclei in older adults with CF and their relationship with cognitive decline and physical frailty.

Methods: A total of 26 older adults with CF and 26 matched healthy subjects were enrolled. Cognitive function and physical frailty were assessed with the Montreal Cognitive Assessment (MoCA) scale (Fuzhou version) and the Chinese version of the Edmonton Frailty Scale (EFS). Volumetric and diffusion tensor imaging (DTI) parameters of subcortical nuclei were measured with structural and DTI brain magnetic resonance imaging (MRI) and compared between groups. Partial correlation analysis was conducted between subcortical nuclei volumes, MoCA scores, and physical frailty indexes.

Results: Significant volume reductions were found in five subcortical nuclei, including the bilateral thalami, left caudate, right pallidum, and accumbens area, in older adults with CF (P < 0.05), and the bilateral thalami was most obvious. Decreased fractional anisotropy and relative anisotropy values were observed only in the left thalamus in the CF group (P < 0.05). No group differences were found in apparent diffusion coefficient (ADC) values. The MoCA scores were positively correlated with the volumes of the bilateral thalami, right pallidum, and accumbens area (P < 0.05). Negative correlations were found between the physical frailty index and the volumes of the bilateral thalami, caudate, pallidum, and right accumbens area (P < 0.05).

Conclusion: Microstructural changes occur in the subcortical nuclei of older adults with CF, and these changes are correlated with cognitive decline and physical frailty. Therefore, microstructural atrophy of the subcortical nuclei may be involved in the pathological progression of CF.

Mapping fine-scale anatomy of gray matter, white matter, and trigeminal-root region applying spherical deconvolution to high-resolution 7-T diffusion MRI

OBJECTIVES

We assessed the use of high-resolution ultra-high-field diffusion magnetic resonance imaging (dMRI) to determine neuronal fiber orientation density functions (fODFs) throughout the human brain, including gray matter (GM), white matter (WM), and small intertwined structures in the cerebellopontine region.

MATERIALS AND METHODS

We acquired 7-T whole-brain dMRI data of 23 volunteers with 1.4-mm isotropic resolution; fODFs were estimated using constrained spherical deconvolution.

RESULTS

High-resolution fODFs enabled a detailed view of the intravoxel distributions of fiber populations in the whole brain. In the brainstem region, the fODF of the extra- and intrapontine parts of the trigeminus could be resolved. Intrapontine trigeminal fiber populations were crossed in a network-like fashion by fiber populations of the surrounding cerebellopontine tracts. In cortical GM, additional evidence was found that in parts of primary somatosensory cortex, fODFs seem to be oriented less perpendicular to the cortical surface than in GM of motor, premotor, and secondary somatosensory cortices.

CONCLUSION

With 7-T MRI being introduced into clinical routine, high-resolution dMRI and derived measures such as fODFs can serve to characterize fine-scale anatomic structures as a prerequisite to detecting pathologies in GM and small or intertwined WM tracts.

The effects of breastfeeding versus formula-feeding on cerebral cortex maturation in infant rhesus macaques

Breastfeeding is positively associated with several outcomes reflecting early brain development and cognitive functioning. Brain neuroimaging studies have shown that exclusively breastfed children have increased white matter and subcortical gray matter volume compared to formula-fed children. However, it is difficult to disentangle the effects of nutrition in breast milk from other confounding factors that affect brain development, particularly in studies of human subjects. Among the nutrients provided by human breast milk are the carotenoid lutein and the natural form of tocopherol, both of which are selectively deposited in brain. Lutein is the predominant carotenoid in breast milk but not in most infant formulas, whereas infant formulas are supplemented with the synthetic form of tocopherol. In this study, a non-human primate model was used to investigate the effects of breastfeeding versus formula-feeding, as well as lutein and natural RRR-α-tocopherol supplementation of infant formula, on brain maturation under controlled experimental conditions. Infant rhesus macaques (Macaca mulatta) were exclusively breastfed, or were fed infant formulas with different levels and sources of lutein and α-tocopherol. Of note, the breastfed group were mother-reared whereas the formula-fed infants were nursery-reared. Brain structural and diffusion MR images were collected, and brain T₂ was measured, at two, four and six months of age. The mother-reared breastfed group was observed to differ from the formula-fed groups by possessing higher diffusion fractional anisotropy (FA) in the corpus callosum, and lower FA in the cerebral cortex at four and six months of age. Cortical regions exhibiting the largest differences include primary motor, premotor, lateral prefrontal, and inferior temporal cortices. No differences were found between the formula groups. Although this study did not identify a nutritional component of breast milk that could be provided to infant formula to facilitate brain maturation consistent with that observed in breastfed animals, our findings indicate that breastfeeding promoted maturation of the corpus callosum and cerebral cortical gray matter in the absence of several confounding factors that affect studies in human infants. However, differences in rearing experience remain as a potential contributor to brain structural differences between breastfed and formula fed infants.

A comparison of three fiber tract delineation methods and their impact on white matter analysis

Diffusion magnetic resonance imaging (dMRI) is an important method for studying white matter connectivity in the brain in vivo in both healthy and clinical populations. Improvements in dMRI tractography algorithms, which reconstruct macroscopic three-dimensional white matter fiber pathways, have allowed for methodological advances in the study of white matter; however, insufficient attention has been paid to comparing post-tractography methods that extract white matter fiber tracts of interest from whole-brain tractography. Here we conduct a comparison of three representative and conceptually distinct approaches to fiber tract delineation: 1) a manual multiple region of interest-based approach, 2) an atlas-based approach, and 3) a groupwise fiber clustering approach, by employing methods that exemplify these approaches to delineate the arcuate fasciculus, the middle longitudinal fasciculus, and the uncinate fasciculus in 10 healthy male subjects. We enable qualitative comparisons across methods, conduct quantitative evaluations of tract volume, tract length, mean fractional anisotropy, and true positive and true negative rates, and report measures of intra-method and inter-method agreement. We discuss methodological similarities and differences between the three approaches and the major advantages and drawbacks of each, and review research and clinical contexts for which each method may be most apposite. Emphasis is given to the means by which different white matter fiber tract delineation approaches may systematically produce variable results, despite utilizing the same input tractography and reliance on similar anatomical knowledge.

Cortical fibers orientation mapping using in-vivo whole brain 7 T diffusion MRI

Diffusion MRI of the cortical gray matter is challenging because the micro-environment probed by water molecules is much more complex than within the white matter. High spatial and angular resolutions are therefore necessary to uncover anisotropic diffusion patterns and laminar structures, which provide complementary (e.g. to anatomical and functional MRI) microstructural information about the cortex architectonic. Several ex-vivo and in-vivo MRI studies have recently addressed this question, however predominantly with an emphasis on specific cortical areas. There is currently no whole brain in-vivo data leveraging multi-shell diffusion MRI acquisition at high spatial resolution, and depth dependent analysis, to characterize the complex organization of cortical fibers. Here, we present unique in-vivo human 7T diffusion MRI data, and a dedicated cortical depth dependent analysis pipeline. We leverage the high spatial (1.05 mm isotropic) and angular (198 diffusion gradient directions) resolution of this whole brain dataset to improve cortical fiber orientations mapping, and study neurites (axons and/or dendrites) trajectories across cortical depths. Tangential fibers in superficial cortical depths and crossing fiber configurations in deep cortical depths are identified. Fibers gradually inserting into the gyral walls are visualized, which contributes to mitigating the gyral bias effect. Quantitative radiality maps and histograms in individual subjects and cortex-based aligned datasets further support our results.

A gyral coordinate system predictive of fibre orientations

When axonal fibres approach or leave the cortex, their trajectories tend to closely follow the cortical convolutions. To quantify this tendency, we propose a three-dimensional coordinate system based on the gyral geometry. For every voxel in the brain, we define a "radial" axis orthogonal to nearby surfaces, a "sulcal" axis along the sulcal depth gradient that preferentially points from deep white matter to the gyral crown, and a "gyral" axis aligned with the long axis of the gyrus. When compared with high-resolution, in-vivo diffusion MRI data from the Human Connectome Project, we find that in superficial white matter the apparent diffusion coefficient (at b = 1000) along the sulcal axis is on average 16% larger than along the gyral axis and twice as large as along the radial axis. This is reflected in the vast majority of observed fibre orientations lying close to the tangential plane (median angular offset < 7°), with the dominant fibre orientation typically aligning with the sulcal axis. In cortical grey matter, fibre orientations transition to a predominantly radial orientation. We quantify the width and location of this transition and find strong reproducibility in test-retest data, but also a clear dependence on the resolution of the diffusion data. The ratio of radial to tangential diffusion is fairly constant throughout most of the cortex, except for a decrease of the diffusivitiy ratio in the sulcal fundi and the primary somatosensory cortex (Brodmann area 3) and an increase in the primary motor cortex (Brodmann area 4). Although only constrained by cortical folds, the proposed gyral coordinate system provides a simple and intuitive representation of white and grey matter fibre orientations near the cortex, and may be useful for future studies of white matter development and organisation.

Investigating the maturation of microstructure and radial orientation in the preterm human cortex with diffusion MRI

Preterm birth disrupts and alters the complex developmental processes in the cerebral cortex. This disruption may be a contributing factor to widespread delay and cognitive difficulties in the preterm population. Diffusion-weighted magnetic resonance imaging (DW MRI) is a noninvasive imaging technique that makes inferences about cellular structures, at scales smaller than the imaging resolution. One established finding is that DW MRI shows a transient radial alignment in the preterm cortex. In this study, we quantify this maturational process with the "radiality index", a parameter that measures directional coherence, which we expect to change rapidly in the perinatal period. To measure this index, we used structural T²-weighted MRI to segment the cortex and generate cortical meshes. We obtained normal vectors for each face of the mesh and compared them to the principal diffusion direction, calculated by both the DTI and DIAMOND models, to generate the radiality index. The subjects included in this study were 89 infants born at fewer than 34 weeks completed gestation, each imaged at up to four timepoints between 27 and 42 weeks gestational age. In this manuscript, we quantify the longitudinal trajectory of radiality, fractional anisotropy and mean diffusivity from the DTI and DIAMOND models. For the radiality index and fractional anisotropy, the DIAMOND model offers improved sensitivity over the DTI model. The radiality index has a consistent progression across time, with the rate of change depending on the cortical lobe. The occipital lobe changes most rapidly, and the frontal and temporal least: this is commensurate with known developmental anatomy. Analysing the radiality index offers information complementary to other diffusion parameters.

The role of whole-brain diffusion MRI as a tool for studying human in vivo cortical segregation based on a measure of neurite density

PURPOSE

To investigate whether diffusion MRI can be used to study cortical segregation based on a contrast related to neurite density, thus providing a complementary tool to myelin-based MRI techniques used for myeloarchitecture.

METHODS

Several myelin-sensitive MRI methods (e.g., based on T₁ , T₂ , and T2*) have been proposed to parcellate cortical areas based on their myeloarchitecture. Recent improvements in hardware, acquisition, and analysis methods have opened the possibility of achieving a more robust characterization of cortical microstructure using diffusion MRI. High-quality diffusion MRI data from the Human Connectome Project was combined with recent advances in fiber orientation modeling. The orientational average of the fiber orientation distribution was used as a summary parameter, which was displayed as inflated brain surface views.

RESULTS

Diffusion MRI identifies cortical patterns consistent with those previously seen by MRI methods used for studying myeloarchitecture, which have shown patterns of high myelination in the sensorimotor strip, visual cortex, and auditory areas and low myelination in frontal and anterior temporal areas.

CONCLUSION

In vivo human diffusion MRI provides a useful complementary noninvasive approach to myelin-based methods used to study whole-brain cortical parcellation, by exploiting a contrast based on tissue microstructure related to neurite density, rather than myelin itself. Magn Reson Med 79:2738-2744, 2018. © 2017 International Society for Magnetic Resonance in Medicine.

High resolution anatomical and quantitative MRI of the entire human occipital lobe ex vivo at 9.4T

Several magnetic resonance imaging (MRI) contrasts are sensitive to myelin content in gray matter in vivo which has ignited ambitions of MRI-based in vivo cortical histology. Ultra-high field (UHF) MRI, at fields of 7 T and beyond, is crucial to provide the resolution and contrast needed to sample contrasts over the depth of the cortex and get closer to layer resolved imaging. Ex vivo MRI of human post mortem samples is an important stepping stone to investigate MRI contrast in the cortex, validate it against histology techniques applied in situ to the same tissue, and investigate the resolutions needed to translate ex vivo findings to in vivo UHF MRI. Here, we investigate key technology to extend such UHF studies to large human brain samples while maintaining high resolution, which allows investigation of the layered architecture of several cortical areas over their entire 3D extent and their complete borders where architecture changes. A 16 channel cylindrical phased array radiofrequency (RF) receive coil was constructed to image a large post mortem occipital lobe sample (~80×80×80 mm³) in a wide-bore 9.4 T human scanner with the aim of achieving high-resolution anatomical and quantitative MR images. Compared with a human head coil at 9.4 T, the maximum Signal-to-Noise ratio (SNR) was increased by a factor of about five in the peripheral cortex. Although the transmit profile with a circularly polarized transmit mode at 9.4 T is relatively inhomogeneous over the large sample, this challenge was successfully resolved with parallel transmit using the kT-points method. Using this setup, we achieved 60μm anatomical images for the entire occipital lobe showing increased spatial definition of cortical details compared to lower resolutions. In addition, we were able to achieve sufficient control over SNR, B₀ and B₁ homogeneity and multi-contrast sampling to perform quantitative T₂* mapping over the same volume at 200 μm. Markov Chain Monte Carlo sampling provided maximum posterior estimates of quantitative T₂* and their uncertainty, allowing delineation of the stria of Gennari over the entire length and width of the calcarine sulcus. We discuss how custom RF receive coil arrays built to specific large post mortem sample sizes can provide a platform for UHF cortical layer-specific quantitative MRI over large fields of view.

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Custom-built 16 channel 9.4 T RF-coil to image large post mortem samples at high resolution.

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Parallel transmit techniques allow homogenization of B₁⁺ for 3D GRE imaging at UHF.

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60 μm anatomical MRI of the entire human occipital lobe.

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200 μm isotropic quantitative T₂^* mapping of the entire human occipital lobe.

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A platform for future UHF cortical layer specific qMRI over large FoVs.

Automatic Segmentation of Human Cortical Layer-Complexes and Architectural Areas Using Ex vivo Diffusion MRI and Its Validation

Recently, several magnetic resonance imaging contrast mechanisms have been shown to distinguish cortical substructure corresponding to selected cortical layers. Here, we investigate cortical layer and area differentiation by automatized unsupervised clustering of high-resolution diffusion MRI data. Several groups of adjacent layers could be distinguished in human primary motor and premotor cortex. We then used the signature of diffusion MRI signals along cortical depth as a criterion to detect area boundaries and find borders at which the signature changes abruptly. We validate our clustering results by histological analysis of the same tissue. These results confirm earlier studies which show that diffusion MRI can probe layer-specific intracortical fiber organization and, moreover, suggests that it contains enough information to automatically classify architecturally distinct cortical areas. We discuss the strengths and weaknesses of the automatic clustering approach and its appeal for MR-based cortical histology.

Subcortical White Matter Changes with Normal Aging Detected by Multi-Shot High Resolution Diffusion Tensor Imaging

Subcortical white matter builds neural connections between cortical and subcortical regions and constitutes the basis of neural networks. It plays a very important role in normal brain function. Various studies have shown that white matter deteriorates with aging. However, due to the limited spatial resolution provided by traditional diffusion imaging techniques, microstructural information from subcortical white matter with normal aging has not been comprehensively assessed. This study aims to investigate the deterioration effect with aging in the subcortical white matter and provide a baseline standard for pathological disorder diagnosis. We apply our newly developed multi-shot high resolution diffusion tensor imaging, using self-feeding multiplexed sensitivity-encoding, to measure subcortical white matter changes in regions of interest of healthy persons with a wide age range. Results show significant fractional anisotropy decline and radial diffusivity increasing with age, especially in the anterior part of the brain. We also find that subcortical white matter has more prominent changes than white matter close to the central brain. The observed changes in the subcortical white matter may be indicative of a mild demyelination and a loss of myelinated axons, which may contribute to normal age-related functional decline.

Thalamic structures and associated cognitive functions: Relations with age and aging

The thalamus, with its cortical, subcortical, and cerebellar connections, is a critical node in networks supporting cognitive functions known to decline in normal aging, including component processes of memory and executive functions of attention and information processing. The macrostructure, microstructure, and neural connectivity of the thalamus changes across the adult lifespan. Structural and functional magnetic resonance imaging (MRI) and diffusion tensor imaging (DTI) have demonstrated, regional thalamic volume shrinkage and microstructural degradation, with anterior regions generally more compromised than posterior regions. The integrity of selective thalamic nuclei and projections decline with advancing age, particularly those in thalamofrontal, thalamoparietal, and thalamolimbic networks. This review presents studies that assess the relations between age and aging and the structure, function, and connectivity of the thalamus and associated neural networks and focuses on their relations with processes of attention, speed of information processing, and working and episodic memory.

Probing region-specific microstructure of human cortical areas using high angular and spatial resolution diffusion MRI

Regional heterogeneity in cortical cyto- and myeloarchitecture forms the structural basis of mapping of cortical areas in the human brain. In this study, we investigate the potential of diffusion MRI to probe the microstructure of cortical gray matter and its region-specific heterogeneity across cortical areas in the fixed human brain. High angular resolution diffusion imaging (HARDI) data at an isotropic resolution of 92-μm and 30 diffusion-encoding directions were acquired using a 3D diffusion-weighted gradient-and-spin-echo sequence, from prefrontal (Brodmann area 9), primary motor (area 4), primary somatosensory (area 3b), and primary visual (area 17) cortical specimens (n=3 each) from three human subjects. Further, the diffusion MR findings in these cortical areas were compared with histological silver impregnation of the same specimens, in order to investigate the underlying architectonic features that constitute the microstructural basis of diffusion-driven contrasts in cortical gray matter. Our data reveal distinct and region-specific diffusion MR contrasts across the studied areas, allowing delineation of intracortical bands of tangential fibers in specific layers-layer I, layer VI, and the inner and outer bands of Baillarger. The findings of this work demonstrate unique sensitivity of diffusion MRI to differentiate region-specific cortical microstructure in the human brain, and will be useful for myeloarchitectonic mapping of cortical areas as well as to achieve an understanding of the basis of diffusion NMR contrasts in cortical gray matter.

Improved delineation of short cortical association fibers and gray/white matter boundary using whole-brain three-dimensional diffusion tensor imaging at submillimeter spatial resolution

Recent emergence of human connectome imaging has led to a high demand on angular and spatial resolutions for diffusion magnetic resonance imaging (MRI). While there have been significant growths in high angular resolution diffusion imaging, the improvement in spatial resolution is still limited due to a number of technical challenges, such as the low signal-to-noise ratio and high motion artifacts. As a result, the benefit of a high spatial resolution in the whole-brain connectome imaging has not been fully evaluated in vivo. In this brief report, the impact of spatial resolution was assessed in a newly acquired whole-brain three-dimensional diffusion tensor imaging data set with an isotropic spatial resolution of 0.85 mm. It was found that the delineation of short cortical association fibers is drastically improved as well as the definition of fiber pathway endings into the gray/white matter boundary-both of which will help construct a more accurate structural map of the human brain connectome.

Self-feeding MUSE: a robust method for high resolution diffusion imaging using interleaved EPI

Single-shot echo planar imaging (EPI) with parallel imaging techniques has been well established as the most popular method for clinical diffusion imaging, due to its fast acquisition and motion insensitivity. However, this approach is limited by the relatively low spatial resolution and image distortion. Interleaved EPI is able to break the limitations but the phase variations among different shots must be considered for artifact suppression. The introduction of multiplexed sensitivity-encoding (MUSE) can address the phase issue using sensitivity encoding (SENSE) for self-navigation of each interleave. However, MUSE has suboptimal results when the number of shots is high. To achieve higher spatial resolution and lower geometric distortion, we introduce two new schemes into the MUSE framework: 1) a self-feeding mechanism is adopted by using prior information regularized SENSE in order to obtain reliable phase estimation; and 2) retrospective motion detection and data rejection strategies are performed to exclude unusable data corrupted by severe pulsatile motions. The proposed method is named self-feeding MUSE (SF-MUSE). Experiments on healthy volunteers demonstrate that this new SF-MUSE approach provides more accurate motion-induced phase estimation and fewer artifacts caused by data corruption when compared with the original MUSE method. SF-MUSE is a robust method for high resolution diffusion imaging and suitable for practical applications with reasonable scan time.

Quantification of microscopic diffusion anisotropy disentangles effects of orientation dispersion from microstructure: applications in healthy volunteers and in brain tumors

The anisotropy of water diffusion in brain tissue is affected by both disease and development. This change can be detected using diffusion MRI and is often quantified by the fractional anisotropy (FA) derived from diffusion tensor imaging (DTI). Although FA is sensitive to anisotropic cell structures, such as axons, it is also sensitive to their orientation dispersion. This is a major limitation to the use of FA as a biomarker for "tissue integrity", especially in regions of complex microarchitecture. In this work, we seek to circumvent this limitation by disentangling the effects of microscopic diffusion anisotropy from the orientation dispersion. The microscopic fractional anisotropy (μFA) and the order parameter (OP) were calculated from the contrast between signal prepared with directional and isotropic diffusion encoding, where the latter was achieved by magic angle spinning of the q-vector (qMAS). These parameters were quantified in healthy volunteers and in two patients; one patient with meningioma and one with glioblastoma. Finally, we used simulations to elucidate the relation between FA and μFA in various micro-architectures. Generally, μFA was high in the white matter and low in the gray matter. In the white matter, the largest differences between μFA and FA were found in crossing white matter and in interfaces between large white matter tracts, where μFA was high while FA was low. Both tumor types exhibited a low FA, in contrast to the μFA which was high in the meningioma and low in the glioblastoma, indicating that the meningioma contained disordered anisotropic structures, while the glioblastoma did not. This interpretation was confirmed by histological examination. We conclude that FA from DTI reflects both the amount of diffusion anisotropy and orientation dispersion. We suggest that the μFA and OP may complement FA by independently quantifying the microscopic anisotropy and the level of orientation coherence.