Determination of axonal and dendritic orientation distributions within the developing cerebral cortex by diffusion tensor imaging

LEARNING 3D WHITE MATTER MICROSTRUCTURE FROM 2D HISTOLOGY

Histological analysis is typically the gold standard for validating measures of tissue microstructure derived from magnetic resonance imaging (MRI) contrasts. However, most histological investigations are inherently 2-dimensional (2D), due to increased field-of-view, higher in-plane resolutions, ease of acquisition, decreased costs, and a large number of available contrasts compared to 3-dimensional (3D) analysis. Because of this, it would be of great interest to be able to learn the 3D tissue microstructure from 2D histology. In this study, we use diffusion MRI (dMRI) of a squirrel monkey brain and corresponding myelin stained sections in combination with a convolution neural network to learn the relationship between the 3D diffusion estimated axonal fiber orientation distributions and the 2D myelin stain. We find that we are able to estimate the 3D fiber distribution with moderate to high angular agreement with the ground truth (median angular correlation coefficients of 0.48 across the unseen slices). This network could be used to validate dMRI neuronal structural measurements in 3D, even if only 2D histology is available for validation. Generalization is possible to transfer this network to human stained sections to infer the 3D fiber distribution at resolutions currently unachievable with dMRI, which would allow diffusion fiber tractography at unprecedented resolutions. We envision the use of similar networks to learn other 3D microstructural measures from an array of potential common 2D histology contrasts.

Neurite orientation and dispersion density imaging (NODDI) detects cortical and corticospinal tract degeneration in ALS

BACKGROUND

Corticospinal tract (CST) degeneration and cortical atrophy are consistent features of amyotrophic lateral sclerosis (ALS). We hypothesised that neurite orientation dispersion and density imaging (NODDI), a multicompartment model of diffusion MRI, would reveal microstructural changes associated with ALS within the CST and precentral gyrus (PCG) 'in vivo'.

METHODS

23 participants with sporadic ALS and 23 healthy controls underwent diffusion MRI. Neurite density index (NDI), orientation dispersion index (ODI) and free water fraction (isotropic compartment (ISO)) were derived. Whole brain voxel-wise analysis was performed to assess for group differences. Standard diffusion tensor imaging (DTI) parameters were computed for comparison. Subgroup analysis was performed to investigate for NODDI parameter differences relating to bulbar involvement. Correlation of NODDI parameters with clinical variables were also explored. The results were accepted as significant where p<0.05 after family-wise error correction at the cluster level, clusters formed with p<0.001.

RESULTS

In the ALS group NDI was reduced in the extensive regions of the CST, the corpus callosum and the right PCG. ODI was reduced in the right anterior internal capsule and the right PCG. Significant differences in NDI were detected between subgroups stratified according to the presence or absence of bulbar involvement. ODI and ISO correlated with disease duration.

CONCLUSIONS

NODDI demonstrates that axonal loss within the CST is a core feature of degeneration in ALS. This is the main factor contributing to the altered diffusivity profile detected using DTI. NODDI also identified dendritic alterations within the PCG, suggesting microstructural cortical dendritic changes occur together with CST axonal damage.

Quantifying brain microstructure with diffusion MRI: Theory and parameter estimation

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

How Forces Fold the Cerebral Cortex

Improved understanding of the factors that govern folding of the cerebral cortex is desirable for many reasons. The existence of consistent patterns in folding within and between species suggests a fundamental role in brain function. Abnormal folding patterns found in individuals affected by a diverse array of neurodevelopmental disorders underline the clinical relevance of understanding the folding process. Recent experimental and computational efforts to elucidate the biomechanical forces involved in cerebral cortical folding have converged on a consistent approach. Brain growth is modeled with two components: an expanding outer zone, destined to become the cerebral cortex, is mechanically coupled to an inner zone, destined to become white matter, that grows at a slower rate, perhaps in response to stress induced by expansion from the outer layer. This framework is consistent with experimentally observed internal forces in developing brains, and with observations of the folding process in physical models. In addition, computational simulations based on this foundation can produce folding patterns that recapitulate the characteristics of folding patterns found in gyroencephalic brains. This perspective establishes the importance of mechanical forces in our current understanding of how brains fold, and identifies realistic ranges for specific parameters in biophysical models of developing brain tissue. However, further refinement of this approach is needed. An understanding of mechanical forces that arise during brain development and their cellular-level origins is necessary to interpret the consequences of abnormal brain folding and its role in functional deficits as well as neurodevelopmental disease.Dual Perspectives Companion Paper: How Cells Fold the Cerebral Cortex, by Víctor Borrell.

Magnetic resonance imaging correlates of neuro-axonal pathology in the MS spinal cord

In people with multiple sclerosis (MS), the spinal cord is the structure most commonly affected by clinically detectable pathology at presentation, and a key part of the central nervous system involved in chronic disease deterioration. Indices, such as the spinal cord cross-sectional area at the level C₂ have been developed as tools to predict future disability, and-by inference-axonal loss. However, this and other histo-pathological correlates of spinal cord magnetic resonance imaging (MRI) changes in MS remain incompletely understood. In recent years, there has been a surge of interest in developing quantitative MRI tools to measure specific tissue features, including axonal density, myelin content, neurite density, and orientation, among others, with an emphasis on the spinal cord. Quantitative MRI techniques including T₁ and T₂ , magnetization transfer and a number of diffusion-derived indices have all been applied to MS spinal cord. Particularly diffusion-based MRI techniques combined with microscopic resolution achievable using high magnetic field scanners enable a new level of anatomical detail and quantification of indices that are clinically meaningful.

Learning Compact q -Space Representations for Multi-Shell Diffusion-Weighted MRI

Diffusion-weighted MRI measures the direction and scale of the local diffusion process in every voxel through its spectrum in q -space, typically acquired in one or more shells. Recent developments in microstructure imaging and multi-tissue decomposition have sparked renewed attention in the radial b -value dependence of the signal. Applications in motion correction and outlier rejection, therefore, require a compact linear signal representation that extends over the radial as well as angular domain. Here, we introduce SHARD, a data-driven representation of the q$ -space signal based on spherical harmonics and a radial decomposition into orthonormal components. This representation provides a complete, orthogonal signal basis, tailored to the spherical geometry of q -space, and calibrated to the data at hand. We demonstrate that the rank-reduced decomposition outperforms model-based alternatives in human brain data, while faithfully capturing the micro- and meso-structural information in the signal. Furthermore, we validate the potential of joint radial-spherical as compared with single-shell representations. As such, SHARD is optimally suited for applications that require low-rank signal predictions, such as motion correction and outlier rejection. Finally, we illustrate its application for the latter using outlier robust regression.

Application of neurite orientation dispersion and density imaging to characterize brain microstructural abnormalities in type-2 diabetics with mild cognitive impairment

BACKGROUND

Diffusion-tensor-imaging (DTI) is sensitive in detecting white matter changes in type-2 diabetes mellitus (T2DM). However, DTI indices can be affected by either neurite density or spatial variation. A novel diffusion MRI technique, termed neurite orientation dispersion and density imaging (NODDI), can provide distinct indices of fiber density and dispersion.

PURPOSE

To characterize brain microstructural alterations in T2DM patients with mild cognitive impairment (MCI) using the NODDI model.

STUDY TYPE

Cross-sectional.

SUBJECTS

Twenty T2DM patients with (DM-MCI group), 18 age- and gender-matched T2DM patients with normal cognition (DM-NC group), and 28 euglycemic healthy controls (HC).

FIELD STRENGTH/SEQUENCE

3T/NODDI.

ASSESSMENT

Diffusion data were analyzed using tract-based-spatial-statistics (TBSS) analysis in white matter and voxel-based analysis in both white and gray matter. NODDI indices, including intracellular volume fraction (Vic) and orientation dispersion index (ODI), were estimated from multiple regions and compared among these groups.

STATISTICAL TESTS

Differences between groups were compared by Student's t-test, Pearson chi-square test, or analysis of variance when appropriate. Correlation analyses were performed to investigate the relationship between NODDI variables and clinical measurements.

RESULTS

Whole-brain TBSS revealed that 2.29% and 2.02% of the white matter regions exhibited decreased fractional anisotropy and Vic, respectively, between the DM-NC and HC, while considerably larger white matter areas showed decreased fractional anisotropy (38.38%) and Vic (34.64%) between the DM-MCI and HC (Student's t-test, P < 0.05). However, the angular variation of neurites, characterized by ODI, exhibited very little (0.1%, P < 0.05) or no difference (P > 0.05) between either the DM-MCI or DM-NC groups and HC. Decreased Vic values in the genu of the corpus callosum (R = 0.580, 0.551 and 0.586, P < 0.01) and thalamus (R = 0.570, 0.616, and 0.595, P < 0.05) correlated with glycosylated hemoglobin A1c level, disease duration, and neuropsychological scores, respectively.

DATA CONCLUSION

T2DM patients with cognitive decline had reduced Vic, which indicated decreased density of axons and dendrites. NODDI might be able to help probe microstructural changes in white and gray matter and provide information on diabetic encephalopathy, including those with cognitive impairment.

LEVEL OF EVIDENCE

1 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;50:889-898.

Diffusion time dependence of microstructural parameters in fixed spinal cord

Biophysical modelling of diffusion MRI is necessary to provide specific microstructural tissue properties. However, estimating model parameters from data with limited diffusion gradient strength, such as clinical scanners, has proven unreliable due to a shallow optimization landscape. On the other hand, estimation of diffusion kurtosis (DKI) parameters is more robust, and its parameters may be connected to microstructural parameters, given an appropriate biophysical model. However, it was previously shown that this procedure still does not provide sufficient information to uniquely determine all model parameters. In particular, a parameter degeneracy related to the relative magnitude of intra-axonal and extra-axonal diffusivities remains. Here we develop a model of diffusion in white matter including axonal dispersion and demonstrate stable estimation of all model parameters from DKI in fixed pig spinal cord. By employing the recently developed fast axisymmetric DKI, we use stimulated echo acquisition mode to collect data over a two orders of magnitude diffusion time range with very narrow diffusion gradient pulses, enabling finely resolved measurements of diffusion time dependence of both net diffusion and kurtosis metrics, as well as model intra- and extra-axonal diffusivities, and axonal dispersion. Our results demonstrate substantial time dependence of all parameters except volume fractions, and the additional time dimension provides support for intra-axonal diffusivity to be larger than extra-axonal diffusivity in spinal cord white matter, although not unambiguously. We compare our findings for the time-dependent compartmental diffusivities to predictions from effective medium theory with reasonable agreement.

Disruption of neurite morphology parallels MS progression

Objectives

To apply advanced diffusion MRI methods to the study of normal-appearing brain tissue in MS and examine their correlation with measures of clinical disability.

Methods

A multi-compartment model of diffusion MRI called neurite orientation dispersion and density imaging (NODDI) was used to study 20 patients with relapsing-remitting MS (RRMS), 15 with secondary progressive MS (SPMS), and 20 healthy controls. Maps of NODDI were analyzed voxel-wise to assess the presence of abnormalities within the normal-appearing brain tissue and the association with disease severity. Standard diffusion tensor imaging (DTI) parameters were also computed for comparing the 2 techniques.

Results

Patients with MS showed reduced neurite density index (NDI) and increased orientation dispersion index (ODI) compared with controls in several brain areas (p < 0.05), with patients with SPMS having more widespread abnormalities. DTI indices were also sensitive to some changes. In addition, patients with SPMS showed reduced ODI in the thalamus and caudate nucleus. These abnormalities were associated with scores of disease severity (p < 0.05). The association with the MS functional composite score was higher in patients with SPMS compared with patients with RRMS.

Conclusions

NODDI and DTI findings are largely overlapping. Nevertheless, NODDI helps interpret previous findings of increased anisotropy in the thalamus of patients with MS and are consistent with the degeneration of selective axon populations.

Mechanics of cortical folding: stress, growth and stability

Cortical folding, or gyrification, coincides with several important developmental processes. The folded shape of the human brain allows the cerebral cortex, the thin outer layer of neurons and their associated projections, to attain a large surface area relative to brain volume. Abnormal cortical folding has been associated with severe neurological, cognitive and behavioural disorders, such as epilepsy, autism and schizophrenia. However, despite decades of study, the mechanical forces that lead to cortical folding remain incompletely understood. Leading hypotheses have focused on the roles of (i) tangential growth of the outer cortex, (ii) spatio-temporal patterns in the birth and migration of neurons, and (iii) internal tension in axons. Recent experimental studies have illuminated not only the fundamental cellular and molecular processes underlying cortical development, but also the stress state, mechanical properties and spatio-temporal patterns of growth in the developing brain. The combination of mathematical modelling and physical measurements has allowed researchers to evaluate hypothesized mechanisms of folding, to determine whether each is consistent with physical laws. This review summarizes what physical scientists have learned from models and recent experimental observations, in the context of recent neurobiological discoveries regarding cortical development. Here, we highlight evidence of a combined mechanism, in which spatio-temporal patterns bias the locations of primary folds (i), but tangential growth of the cortical plate induces mechanical instability (ii) to propagate primary and higher-order folds.This article is part of the Theo Murphy meeting issue 'Mechanics of development'.

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.

Effects of SYN1Q555X mutation on cortical gray matter microstructure

A new Q555X mutation on the SYN1 gene was recently found in several members of a family segregating dyslexia, epilepsy, and autism spectrum disorder. To describe the effects of this mutation on cortical gray matter microstructure, we performed a surface-based group study using novel diffusion and quantitative multiparametric imaging on 13 SYN1Q555X mutation carriers and 13 age- and sex-matched controls. Specifically, diffusion kurtosis imaging (DKI) and neurite orientation and dispersion and density imaging (NODDI) were used to analyze multi-shell diffusion data and obtain parametric maps sensitive to tissue structure, while quantitative metrics sensitive to tissue composition (T1, T2* and relative proton density [PD]) were obtained from a multi-echo variable flip angle FLASH acquisition. Results showed significant microstructural alterations in several regions usually involved in oral and written language as well as dyslexia. The most significant changes in these regions were lowered mean diffusivity and increased fractional anisotropy. This study is, to our knowledge, the first to successfully use diffusion imaging and multiparametric mapping to detect cortical anomalies in a group of subjects with a well-defined genotype linked to language impairments, epilepsy and autism spectrum disorder (ASD).

Using diffusion anisotropy to study cerebral cortical gray matter development

Diffusion-weighted magnetic resonance imaging (diffusion MRI) is being used to characterize morphological development of cells within developing cerebral cortical gray matter. Abnormal morphology is a shared characteristic of cerebral cortical neurons for many neurodevelopmental disorders, and therefore diffusion MRI is potentially of high value for monitoring growth-related anatomical changes of relevance to brain function. Here, the theoretical framework for analyzing diffusion MRI data is summarized. An overview of quantitative methods for validating the interpretations of diffusion MRI data using light microscopy is then presented. These theoretical modeling and validation methods have been used to precisely characterize changes in water diffusion anisotropy with development in the context of several animal model systems. Further, in diffusion MRI studies of several preclinical models of neurodevelopmental disorders, the ability is demonstrated of diffusion MRI to detect abnormal morphological neural development. These animal model studies are reviewed along with recent initial efforts to translate the findings into an approach for studies of human subjects. This body of data indicates that diffusion MRI has the requisite sensitivity to detect abnormal cellular development in the context of several models of neurodevelopmental disorders, and therefore may provide a new strategy for detecting abnormalities in early stages of brain development in humans.

Neurite architecture of the planum temporale predicts neurophysiological processing of auditory speech

The left hemispheric advantage in speech perception is reflected in faster neurophysiological processing. On the basis of postmortem data, it has been suggested that asymmetries in the organization of the intrinsic microcircuitry of the posterior temporal lobe may produce this leftward timing advantage. However, whether this hypothetical structure-function relationship exists in vivo has never been empirically validated. To test this assumption, we used in vivo neurite orientation dispersion and density imaging to quantify microcircuitry in terms of axon and dendrite complexity of the left and right planum temporale in 98 individuals. We found that a higher density of dendrites and axons in the temporal speech area is associated with faster neurophysiological processing of auditory speech, as reflected by electroencephalography. Our results imply that a higher density and higher number of synaptic contacts in the left posterior temporal lobe increase temporal precision and decrease latency of neurophysiological processes in this brain region.

Empirical reproducibility, sensitivity, and optimization of acquisition protocol, for Neurite Orientation Dispersion and Density Imaging using AMICO

Neurite Orientation Dispersion and Density Imaging (NODDI) has been gaining prominence for estimating multiple diffusion compartments from MRI data acquired in a clinically feasible time. To establish a pathway for adoption of NODDI in clinical studies, it is important to understand the sensitivity and reproducibility of NODDI metrics on empirical data in the context of acquisition protocol and brain anatomy. Previous studies addressed reproducibility across the 3 T scanners and within session and between subject reproducibility at 1.5 T and 3 T. However, empirical reproducibility on the performance of NODDI metrics based on b-value and diffusion-sensitized directions has not yet been addressed. In this study, we investigate a high angular resolution dataset with 11 repeats of a study with five b-values shells (1000, 1500, 2000, 2500 and 3000 s/mm2) and 96 directions per shell on a single subject. We validated the findings with a dataset from second subject with 10 repeats and 3 b-value shells (1000, 2000, 3000 s/mm2). The NODDI model was estimated using Accelerated Microstructure Imaging via Convex Optimization (AMICO) for different b-values and gradient directions on two-shell High Angular Resolution Density Imaging (HARDI) data fixing the lower shell at b = 1000 s/mm2. NODDI model applied to all acquired imaging data was used as a baseline gold standard for comparison. Additionally, we characterize orientation dispersion index (ODI) reproducibility using single-shell data. The experimental findings confirmed the sensitivity of intracellular volume fraction (Vic) with the choice of outer shell b-value more than with the choice of gradient directions. On the other hand, ODI is more sensitive to the number of gradient directions compared to b-value selection. Single-shell results for ODI are more comparable to 2-shell data at lower b-values than higher b-values. Recommended settings by region of interest and acquisition time are reported for the researchers considering using NODDI in human studies and/or comparing results across acquisition protocols.

Validation strategies for the interpretation of microstructure imaging using diffusion MRI

Extracting microanatomical information beyond the image resolution of MRI would provide valuable tools for diagnostics and neuroscientific research. A number of mathematical models already suggest microstructural interpretations of diffusion MRI (dMRI) data. Examples of such microstructural features could be cell bodies and neurites, e.g. the axon's diameter or their orientational distribution for global connectivity analysis using tractography, and have previously only been possible to access through conventional histology of post mortem tissue or invasive biopsies. The prospect of gaining the same knowledge non-invasively from the whole living human brain could push the frontiers for the diagnosis of neurological and psychiatric diseases. It could also provide a general understanding of the development and natural variability in the healthy brain across a population. However, due to a limited image resolution, most of the dMRI measures are indirect estimations and may depend on the whole chain from experimental parameter settings to model assumptions and implementation. Here, we review current literature in this field and highlight the integrative work across anatomical length scales that is needed to validate and trust a new dMRI method. We encourage interdisciplinary collaborations and data sharing in regards to applying and developing new validation techniques to improve the specificity of future dMRI methods.

White matter biomarkers from diffusion MRI

As part of an issue celebrating 2 decades of Joseph Ackerman editing the Journal of Magnetic Resonance, this paper reviews recent progress in one of the many areas in which Ackerman and his lab has made significant contributions: NMR measurement of diffusion in biological media, specifically in brain tissue. NMR diffusion signals display exquisite sensitivity to tissue microstructure, and have the potential to offer quantitative and specific information on the cellular scale orders of magnitude below nominal image resolution when combined with biophysical modeling. Here, I offer a personal perspective on some recent advances in diffusion imaging, from diffusion kurtosis imaging to microstructural modeling, and the connection between the two. A new result on the estimation accuracy of axial and radial kurtosis with axially symmetric DKI is presented. I moreover touch upon recently suggested generalized diffusion sequences, promising to offer independent microstructural information. We discuss the need and some methods for validation, and end with an outlook on some promising future directions.

On modeling

Mapping tissue microstructure with MRI holds great promise as a noninvasive window into tissue organization at the cellular level. Having originated within the realm of diffusion NMR in the late 1970s, this field is experiencing an exponential growth in the number of publications. At the same time, model-based approaches are also increasingly incorporated into advanced MRI acquisition and reconstruction techniques. However, after about two decades of intellectual and financial investment, microstructural mapping has yet to find a single commonly accepted clinical application. Here, we suggest that slow progress in clinical translation may signify unresolved fundamental problems. We outline such problems and related practical pitfalls, as well as review strategies for developing and validating tissue microstructure models, to provoke a discussion on how to bridge the gap between our scientific aspirations and the clinical reality. We argue for recalibrating the efforts of our community toward a more systematic focus on fundamental research aimed at identifying relevant degrees of freedom affecting the measured MR signal. Such a focus is essential for realizing the truly revolutionary potential of noninvasive three-dimensional in vivo microstructural mapping.

Diffusion markers of dendritic density and arborization in gray matter predict differences in intelligence

Previous research has demonstrated that individuals with higher intelligence are more likely to have larger gray matter volume in brain areas predominantly located in parieto-frontal regions. These findings were usually interpreted to mean that individuals with more cortical brain volume possess more neurons and thus exhibit more computational capacity during reasoning. In addition, neuroimaging studies have shown that intelligent individuals, despite their larger brains, tend to exhibit lower rates of brain activity during reasoning. However, the microstructural architecture underlying both observations remains unclear. By combining advanced multi-shell diffusion tensor imaging with a culture-fair matrix-reasoning test, we found that higher intelligence in healthy individuals is related to lower values of dendritic density and arborization. These results suggest that the neuronal circuitry associated with higher intelligence is organized in a sparse and efficient manner, fostering more directed information processing and less cortical activity during reasoning.

Future Directions for Examination of Brain Networks in Neurodevelopmental Disorders

Neurodevelopmental disorders are associated with atypical development and maturation of brain networks. A recent focus on human connectomics research and the growing popularity of open science initiatives has created the ideal climate in which to make real progress toward understanding the neurobiology of disorders affecting youth. Here we outline future directions for neuroscience researchers examining brain networks in neurodevelopmental disorders, highlighting gaps in the current literature. We emphasize the importance of leveraging large neuroimaging and phenotypic data sets recently made available to the research community, and we suggest specific novel methodological approaches, including analysis of brain dynamics and structural connectivity, that have the potential to produce the greatest clinical insight. Transdiagnostic approaches will also become increasingly necessary as the Research Domain Criteria framework put forth by the National Institute of Mental Health permeates scientific discourse. During this exciting era of big data and increased computational sophistication of analytic tools, the possibilities for significant advancement in understanding neurodevelopmental disorders are limitless.

Regional microstructural organization of the cerebral cortex is affected by preterm birth

Objectives

To compare regional cerebral cortical microstructural organization between preterm infants at term-equivalent age (TEA) and healthy full-term newborns, and to examine the impact of clinical risk factors on cerebral cortical micro-organization in the preterm cohort.

Study design

We prospectively enrolled very preterm infants (gestational age (GA) at birth<32 weeks; birthweight<1500 g) and healthy full-term controls. Using non-invasive 3T diffusion tensor imaging (DTI) metrics, we quantified regional micro-organization in ten cerebral cortical areas: medial/dorsolateral prefrontal cortex, anterior/posterior cingulate cortex, insula, posterior parietal cortex, motor/somatosensory/auditory/visual cortex. ANCOVA analyses were performed controlling for sex and postmenstrual age at MRI.

Results

We studied 91 preterm infants at TEA and 69 full-term controls. Preterm infants demonstrated significantly higher diffusivity in the prefrontal, parietal, motor, somatosensory, and visual cortices suggesting delayed maturation of these cortical areas. Additionally, postnatal hydrocortisone treatment was related to accelerated microstructural organization in the prefrontal and somatosensory cortices.

Conclusions

Preterm birth alters regional microstructural organization of the cerebral cortex in both neurocognitive brain regions and areas with primary sensory/motor functions. We also report for the first time a potential protective effect of postnatal hydrocortisone administration on cerebral cortical development in preterm infants.

Influence of the size and curvedness of neural projections on the orientationally averaged diffusion MR signal

Neuronal and glial projections can be envisioned to be tubes of infinitesimal diameter as far as diffusion magnetic resonance (MR) measurements via clinical scanners are concerned. Recent experimental studies indicate that the decay of the orientationally-averaged signal in white-matter may be characterized by the power-law, Ē(q) ∝ q^-1, where q is the wavenumber determined by the parameters of the pulsed field gradient measurements. One particular study by McKinnon et al. [1] reports a distinctively faster decay in gray-matter. Here, we assess the role of the size and curvature of the neurites and glial arborizations in these experimental findings. To this end, we studied the signal decay for diffusion along general curves at all three temporal regimes of the traditional pulsed field gradient measurements. We show that for curvy projections, employment of longer pulse durations leads to a disappearance of the q^-1 decay, while such decay is robust when narrow gradient pulses are used. Thus, in clinical acquisitions, the lack of such a decay for a fibrous specimen can be seen as indicative of fibers that are curved. We note that the above discussion is valid for an intermediate range of q-values as the true asymptotic behavior of the signal decay is Ē(q) ∝ q^-4 for narrow pulses (through Debye-Porod law) or steeper for longer pulses. This study is expected to provide insights for interpreting the diffusion-weighted images of the central nervous system and aid in the design of acquisition strategies.

Magnetic resonance temporal diffusion tensor spectroscopy of disordered anisotropic tissue

Molecular diffusion measured with diffusion weighted MRI (DWI) offers a probe for tissue microstructure. However, inferring microstructural properties from conventional DWI data is a complex inverse problem and has to account for heterogeneity in sizes, shapes and orientations of the tissue compartments contained within an imaging voxel. Alternative experimental means for disentangling the signal signatures of such features could provide a stronger link between the data and its interpretation. Double diffusion encoding (DDE) offers the possibility to factor out variation in compartment shapes from orientational dispersion of anisotropic domains by measuring the correlation between diffusivity in multiple directions. Time dependence of the diffusion is another effect reflecting the dimensions and distributions of barriers. In this paper we extend on DDE with a modified version of the oscillating gradient spin echo (OGSE) experiment, giving a basic contrast mechanism closely linked to both the temporal diffusion spectrum and the compartment anisotropy. We demonstrate our new method on post mortem brain tissue and show that we retrieve the correct temporal diffusion tensor spectrum in synthetic data from Monte Carlo simulations of random walks in a range of disordered geometries of different sizes and shapes.

Differential microstructural alterations in rat cerebral cortex in a model of chronic mild stress depression

Chronic mild stress leads to depression in many cases and is linked to several debilitating diseases including mental disorders. Recently, neuronal tracing techniques, stereology, and immunohistochemistry have revealed persistent and significant microstructural alterations in the hippocampus, hypothalamus, prefrontal cortex, and amygdala, which form an interconnected system known as the stress circuit. Most studies have focused only on this circuit, however, some studies indicate that manipulation of sensory and motor systems may impact genesis and therapy of mood disorders and therefore these areas should not be neglected in the study of brain microstructure alterations in response to stress and depression. For this reason, we explore the microstructural alterations in different cortical regions in a chronic mild stress model of depression. The study employs ex-vivo diffusion MRI (d-MRI) to assess cortical microstructure in stressed (anhedonic and resilient) and control animals. MRI is followed by immunohistochemistry to substantiate the d-MRI findings. We find significantly lower extracellular diffusivity in auditory cortex (AC) of stress groups and a significantly higher fractional anisotropy in the resilient group. Neurite density was not found to be significantly higher in any cortical ROIs in the stress group compared to control, although axonal density is higher in the stress groups. We also report significant thinning of motor cortex (MC) in both stress groups. This is in agreement with recent clinical and preclinical studies on depression and similar disorders where significant microstructural and metabolic alterations were found in AC and MC. Our findings provide further evidence that the AC and MC are sensitive towards stress exposure and may extend our understanding of the microstructural effects of stress beyond the stress circuit of the brain. Progress in this field may provide new avenues of research to help in diagnosis and treatment intervention for depression and related disorders.

Quantification of anisotropy and orientation in 3D electron microscopy and diffusion tensor imaging in injured rat brain

Diffusion tensor imaging (DTI) reveals microstructural features of grey and white matter non-invasively. The contrast produced by DTI, however, is not fully understood and requires further validation. We used serial block-face scanning electron microscopy (SBEM) to acquire tissue metrics, i.e., anisotropy and orientation, using three-dimensional Fourier transform-based (3D-FT) analysis, to correlate with fractional anisotropy and orientation in DTI. SBEM produces high-resolution 3D data at the mesoscopic scale with good contrast of cellular membranes. We analysed selected samples from cingulum, corpus callosum, and perilesional cortex of sham-operated and traumatic brain injury (TBI) rats. Principal orientations produced by DTI and 3D-FT in all samples were in good agreement. Anisotropy values showed similar patterns of change in corresponding DTI and 3D-FT parameters in sham-operated and TBI rats. While DTI and 3D-FT anisotropy values were similar in grey matter, 3D-FT anisotropy values were consistently lower than fractional anisotropy values from DTI in white matter. We also evaluated the effect of resolution in 3D-FT analysis. Despite small angular differences in grey matter samples, lower resolution datasets provided reliable results, allowing for analysis of larger fields of view. Overall, 3D SBEM allows for more sophisticated validation studies of diffusion imaging contrast from a tissue microstructural perspective.

Quantifying multiple sclerosis pathology in post mortem spinal cord using MRI

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

Binge drinking differentially affects cortical and subcortical microstructure

Young adult binge drinkers represent a model for endophenotypic risk factors for alcohol misuse and early exposure to repeated binge cycles. Chronic or harmful alcohol use leads to neurochemical, structural and morphological neuroplastic changes, particularly in regions associated with reward processing and motivation. We investigated neural microstructure in 28 binge drinkers compared with 38 matched healthy controls. We used a recently developed diffusion magnetic resonance imaging acquisition and analysis, which uses three‐compartment modelling (of intracellular, extracellular and cerebrospinal fluid) to determine brain tissue microstructure features including neurite density and orientation dispersion index (ODI). Binge drinkers had reduced ODI, a proxy of neurite complexity, in frontal cortical grey matter and increased ODI in parietal grey matter. Neurite density was higher in cortical white matter in adjacent regions of lower ODI in binge drinkers. Furthermore, binge drinkers had higher ventral striatal grey matter ODI that was positively correlated with binge score. Healthy volunteers showed no such relationships. We demonstrate disturbed dendritic complexity of higher‐order prefrontal and parietal regions, along with higher dendritic complexity of a subcortical region known to mediate reward‐related motivation. The findings illustrate novel microstructural abnormalities that may reflect an infnce of alcohol bingeing on critical neurodevelopmental processes in an at‐risk young adult group.

NODDI-DTI: Estimating Neurite Orientation and Dispersion Parameters from a Diffusion Tensor in Healthy White Matter

The NODDI-DTI signal model is a modification of the NODDI signal model that formally allows interpretation of standard single-shell DTI data in terms of biophysical parameters in healthy human white matter (WM). The NODDI-DTI signal model contains no CSF compartment, restricting application to voxels without CSF partial-volume contamination. This modification allowed derivation of analytical relations between parameters representing axon density and dispersion, and DTI invariants (MD and FA) from the NODDI-DTI signal model. These relations formally allow extraction of biophysical parameters from DTI data. NODDI-DTI parameters were estimated by applying the proposed analytical relations to DTI parameters estimated from the first shell of data, and compared to parameters estimated by fitting the NODDI-DTI model to both shells of data (reference dataset) in the WM of 14 in vivo diffusion datasets recorded with two different protocols, and in simulated data. The first two datasets were also fit to the NODDI-DTI model using only the first shell (as for DTI) of data. NODDI-DTI parameters estimated from DTI, and NODDI-DTI parameters estimated by fitting the model to the first shell of data gave similar errors compared to two-shell NODDI-DTI estimates. The simulations showed the NODDI-DTI method to be more noise-robust than the two-shell fitting procedure. The NODDI-DTI method gave unphysical parameter estimates in a small percentage of voxels, reflecting voxelwise DTI estimation error or NODDI-DTI model invalidity. In the course of evaluating the NODDI-DTI model, it was found that diffusional kurtosis strongly biased DTI-based MD values, and so, making assumptions based on healthy WM, a novel heuristic correction requiring only DTI data was derived and used to mitigate this bias. Since validations were only performed on healthy WM, application to grey matter or pathological WM would require further validation. Our results demonstrate NODDI-DTI to be a promising model and technique to interpret restricted datasets acquired for DTI analysis in healthy white matter with greater biophysical specificity, though its limitations must be borne in mind.

Confirmation of a gyral bias in diffusion MRI fiber tractography

Diffusion MRI fiber tractography has been increasingly used to map the structural connectivity of the human brain. However, this technique is not without limitations; for example, there is a growing concern over anatomically correlated bias in tractography findings. In this study, we demonstrate that there is a bias for fiber tracking algorithms to terminate preferentially on gyral crowns, rather than the banks of sulci. We investigate this issue by comparing diffusion MRI (dMRI) tractography with equivalent measures made on myelin-stained histological sections. We begin by investigating the orientation and trajectories of axons near the white matter/gray matter boundary, and the density of axons entering the cortex at different locations along gyral blades. These results are compared with dMRI orientations and tract densities at the same locations, where we find a significant gyral bias in many gyral blades across the brain. This effect is shown for a range of tracking algorithms, both deterministic and probabilistic, and multiple diffusion models, including the diffusion tensor and a high angular resolution diffusion imaging technique. Additionally, the gyral bias occurs for a range of diffusion weightings, and even for very high-resolution datasets. The bias could significantly affect connectivity results using the current generation of tracking algorithms.

Precision and accuracy of diffusion kurtosis estimation and the influence of b-value selection

Diffusion kurtosis imaging (DKI) is an extension of diffusion tensor imaging that accounts for leading non-Gaussian diffusion effects. In DKI studies, a wide range of different gradient strengths (b-values) is used, which is known to affect the estimated diffusivity and kurtosis parameters. Hence there is a need to assess the accuracy and precision of the estimated parameters as a function of b-value. This work examines the error in the estimation of mean of the kurtosis tensor (MKT) with respect to the ground truth, using simulations based on a biophysical model for both gray (GM) and white (WM) matter. Model parameters are derived from densely sampled experimental data acquired in ex vivo rat brain and in vivo human brain. Additionally, the variability of MKT is studied using the experimental data. Prevalent fitting protocols are implemented and investigated. The results show strong dependence on the maximum b-value of both net relative error and standard deviation of error for all of the employed fitting protocols. The choice of b-values with minimum MKT estimation error and standard deviation of error was found to depend on the protocol type and the tissue. Protocols that utilize two terms of the cumulant expansion (DKI) were found to achieve minimum error in GM at b-values less than 1 ms/μm² , whereas maximal b-values of about 2.5 ms/μm² were found to be optimal in WM. Protocols including additional higher order terms of the cumulant expansion were found to provide higher accuracy for the more commonly used b-value regime in GM, but were associated with higher error in WM. Averaged over multiple voxels, a net average error of around 15% for both WM and GM was observed for the optimal b-value choice. These results suggest caution when using DKI generated metrics for microstructural modeling and when comparing results obtained using different fitting techniques and b-values.

Design and validation of diffusion MRI models of white matter

Diffusion MRI is arguably the method of choice for characterizing white matter microstructure in vivo. Over the typical duration of diffusion encoding, the displacement of water molecules is conveniently on a length scale similar to that of the underlying cellular structures. Moreover, water molecules in white matter are largely compartmentalized which enables biologically-inspired compartmental diffusion models to characterize and quantify the true biological microstructure. A plethora of white matter models have been proposed. However, overparameterization and mathematical fitting complications encourage the introduction of simplifying assumptions that vary between different approaches. These choices impact the quantitative estimation of model parameters with potential detriments to their biological accuracy and promised specificity. First, we review biophysical white matter models in use and recapitulate their underlying assumptions and realms of applicability. Second, we present up-to-date efforts to validate parameters estimated from biophysical models. Simulations and dedicated phantoms are useful in assessing the performance of models when the ground truth is known. However, the biggest challenge remains the validation of the "biological accuracy" of estimated parameters. Complementary techniques such as microscopy of fixed tissue specimens have facilitated direct comparisons of estimates of white matter fiber orientation and densities. However, validation of compartmental diffusivities remains challenging, and complementary MRI-based techniques such as alternative diffusion encodings, compartment-specific contrast agents and metabolites have been used to validate diffusion models. Finally, white matter injury and disease pose additional challenges to modeling, which are also discussed. This review aims to provide an overview of the current state of models and their validation and to stimulate further research in the field to solve the remaining open questions and converge towards consensus.

Histological validation of diffusion MRI fiber orientation distributions and dispersion

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

Cortical pathology in multiple sclerosis detected by the T1/T2-weighted ratio from routine magnetic resonance imaging

OBJECTIVE

In multiple sclerosis, neuropathological studies have shown widespread changes in the cerebral cortex. In vivo imaging is critical, because the histopathological substrate of most measurements is unknown.

METHODS

Using a novel magnetic resonance imaging analysis technique, based on the ratio of T1- and T2-weighted signal intensities, we studied the cerebral cortex of a large cohort of patients in early stages of multiple sclerosis. A total of 168 patients with clinically isolated syndrome or relapsing-remitting multiple sclerosis (Expanded Disability Status Scale: median = 1, range = 0-3.5) and 80 age- and sex-matched healthy controls were investigated. We also searched for the histopathological substrate of the T1/T2-weighted ratio by combining postmortem imaging and histopathology in 9 multiple sclerosis brain donors.

RESULTS

Patients showed lower T1/T2-weighted ratio values in parietal and occipital areas. The 4 most significant clusters appeared in the medial occipital and posterior cingulate cortex (each left and right). The decrease of the T1/T2-weighted ratio in the posterior cingulate was related to performance in attention. Analysis of the T1/T2-weighted ratio values of postmortem imaging yielded a strong correlation with dendrite density but none of the other parameters including myelin.

INTERPRETATION

The T1/T2-weighted ratio decreases in early stages of multiple sclerosis in a widespread manner, with a preponderance of posterior areas and with a contribution to attentional performance; it seems to reflect dendrite pathology. As the method is broadly available and applicable to available clinical scans, we believe that it is a promising candidate for studying and monitoring cortical pathology or therapeutic effects in multiple sclerosis. Ann Neurol 2017;82:519-529.

White matter biomarkers from fast protocols using axially symmetric diffusion kurtosis imaging

White matter tract integrity (WMTI) can characterize brain microstructure in areas with highly aligned fiber bundles. Several WMTI biomarkers have now been validated against microscopy and provided promising results in studies of brain development and aging, as well as in a number of brain disorders. Currently, WMTI is mostly used in dedicated animal studies and clinical studies of slowly progressing diseases, and has not yet emerged as a routine clinical tool. To this end, a less data intensive experimental method would be beneficial by enabling high resolution validation studies, and ease clinical applications by speeding up data acquisition compared with typical diffusion kurtosis imaging (DKI) protocols utilized as part of WMTI imaging. Here, we evaluate WMTI based on recently introduced axially symmetric DKI, which has lower data demand than conventional DKI. We compare WMTI parameters derived from conventional DKI with those calculated analytically from axially symmetric DKI. We employ numerical simulations, as well as data from fixed rat spinal cord (one sample) and in vivo human (three subjects) and rat brain (four animals). Our analysis shows that analytical WMTI based on axially symmetric DKI with sparse data sets (19 images) produces WMTI metrics that correlate strongly with estimates based on traditional DKI data sets (60 images or more). We demonstrate the preclinical potential of the proposed WMTI technique in in vivo rat brain (300 μm isotropic resolution with whole brain coverage in a 1 h acquisition). WMTI parameter estimates are subject to a duality leading to two solution branches dependent on a sign choice, which is currently debated. Results from both of these branches are presented and discussed throughout our analysis. The proposed fast WMTI approach may be useful for preclinical research and e.g. clinical evaluation of patients with traumatic white matter injuries or symptoms of neurovascular or neuroinflammatory disorders.

ApoE influences regional white-matter axonal density loss in Alzheimer's disease

Mechanisms underlying phenotypic heterogeneity in young onset Alzheimer disease (YOAD) are poorly understood. We used diffusion tensor imaging and neurite orientation dispersion and density imaging (NODDI) with tract-based spatial statistics to investigate apolipoprotein (APOE) ε4 modulation of white-matter damage in 37 patients with YOAD (22, 59% APOE ε4 positive) and 23 age-matched controls. Correlation between neurite density index (NDI) and neuropsychological performance was assessed in 4 white-matter regions of interest. White-matter disruption was more widespread in ε4+ individuals but more focal (posterior predominant) in the absence of an ε4 allele. NODDI metrics indicate fractional anisotropy changes are underpinned by combinations of axonal loss and morphological change. Regional NDI in parieto-occipital white matter correlated with visual object and spatial perception battery performance (right and left, both p = 0.02), and performance (nonverbal) intelligence (WASI matrices, right, p = 0.04). NODDI provides tissue-specific microstructural metrics of white-matter tract damage in YOAD, including NDI which correlates with focal cognitive deficits, and APOEε4 status is associated with different patterns of white-matter neurodegeneration.

Population based MRI and DTI templates of the adult ferret brain and tools for voxelwise analysis

Non-invasive imaging has the potential to play a crucial role in the characterization and translation of experimental animal models to investigate human brain development and disorders, especially when employed to study animal models that more accurately represent features of human neuroanatomy. The purpose of this study was to build and make available MRI and DTI templates and analysis tools for the ferret brain as the ferret is a well-suited species for pre-clinical MRI studies with folded cortical surface, relatively high white matter volume and body dimensions that allow imaging with pre-clinical MRI scanners. Four ferret brain templates were built in this study – in-vivo MRI and DTI and ex-vivo MRI and DTI – using brain images across many ferrets and region of interest (ROI) masks corresponding to established ferret neuroanatomy were generated by semi-automatic and manual segmentation. The templates and ROI masks were used to create a web-based ferret brain viewing software for browsing the MRI and DTI volumes with annotations based on the ROI masks. A second objective of this study was to provide a careful description of the imaging methods used for acquisition, processing, registration and template building and to demonstrate several voxelwise analysis methods including Jacobian analysis of morphometry differences between the female and male brain and bias-free identification of DTI abnormalities in an injured ferret brain. The templates, tools and methodological optimization presented in this study are intended to advance non-invasive imaging approaches for human-similar animal species that will enable the use of pre-clinical MRI studies for understanding and treating brain disorders.

Early development of structural networks and the impact of prematurity on brain connectivity

Preterm infants are at high risk of neurodevelopmental impairment, which may be due to altered development of brain connectivity. We aimed to (i) assess structural brain development from 25 to 45 weeks gestational age (GA) using graph theoretical approaches and (ii) test the hypothesis that preterm birth results in altered white matter network topology. Sixty-five infants underwent MRI between 25⁺³ and 45⁺⁶ weeks GA. Structural networks were constructed using constrained spherical deconvolution tractography and were weighted by measures of white matter microstructure (fractional anisotropy, neurite density and orientation dispersion index). We observed regional differences in brain maturation, with connections to and from deep grey matter showing most rapid developmental changes during this period. Intra-frontal, frontal to cingulate, frontal to caudate and inter-hemispheric connections matured more slowly. We demonstrated a core of key connections that was not affected by GA at birth. However, local connectivity involving thalamus, cerebellum, superior frontal lobe, cingulate gyrus and short range cortico-cortical connections was related to the degree of prematurity and contributed to altered global topology of the structural brain network. The relative preservation of core connections at the expense of local connections may support more effective use of impaired white matter reserve following preterm birth.

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First characterisation of preterm brain networks weighted by microstructural features.

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Preterm brain is resistant to disruptions in development of core connections.

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Peripheral connections associated with cognition and behaviour are more vulnerable.

Folding, But Not Surface Area Expansion, Is Associated with Cellular Morphological Maturation in the Fetal Cerebral Cortex

Altered macroscopic anatomical characteristics of the cerebral cortex have been identified in individuals affected by various neurodevelopmental disorders. However, the cellular developmental mechanisms that give rise to these abnormalities are not understood. Previously, advances in image reconstruction of diffusion magnetic resonance imaging (MRI) have made possible high-resolution in utero measurements of water diffusion anisotropy in the fetal brain. Here, diffusion anisotropy within the developing fetal cerebral cortex is longitudinally characterized in the rhesus macaque, focusing on gestation day (G85) through G135 of the 165 d term. Additionally, for subsets of animals characterized at G90 and G135, immunohistochemical staining was performed, and 3D structure tensor analyses were used to identify the cellular processes that most closely parallel changes in water diffusion anisotropy with cerebral cortical maturation. Strong correlations were found between maturation of dendritic arbors on the cellular level and the loss of diffusion anisotropy with cortical development. In turn, diffusion anisotropy changes were strongly associated both regionally and temporally with cortical folding. Notably, the regional and temporal dependence of diffusion anisotropy and folding were distinct from the patterns observed for cerebral cortical surface area expansion. These findings strengthen the link proposed in previous studies between cellular-level changes in dendrite morphology and noninvasive diffusion MRI measurements of the developing cerebral cortex and support the possibility that, in gyroencephalic species, structural differentiation within the cortex is coupled to the formation of gyri and sulci.SIGNIFICANCE STATEMENT Abnormal brain morphology has been found in populations with neurodevelopmental disorders. However, the mechanisms linking cellular level and macroscopic maturation are poorly understood, even in normal brains. This study contributes new understanding to this subject using serial in utero MRI measurements of rhesus macaque fetuses, from which macroscopic and cellular information can be derived. We found that morphological differentiation of dendrites was strongly associated both regionally and temporally with folding of the cerebral cortex. Interestingly, parallel associations were not observed with cortical surface area expansion. These findings support the possibility that perturbed morphological differentiation of cells within the cortex may underlie abnormal macroscopic characteristics of individuals affected by neurodevelopmental disorders.

Assessing Microstructural Substrates of White Matter Abnormalities: A Comparative Study Using DTI and NODDI

Neurite orientation dispersion and density imaging (NODDI) enables more specific characterization of tissue microstructure by estimating neurite density (NDI) and orientation dispersion (ODI), two key contributors to fractional anisotropy (FA). The present work compared NODDI- with diffusion tensor imaging (DTI)-derived indices for investigating white matter abnormalities in a clinical sample. We assessed the added value of NODDI parameters over FA, by contrasting group differences identified by both models. Diffusion-weighted images with multiple shells were acquired in a group of 8 healthy controls and 8 patients with an inherited metabolic disease. Both standard DTI and NODDI analyses were performed. Tract based spatial statistics (TBSS) was used for group inferences, after which overlap and unique contributions across different parameters were evaluated. Results showed that group differences in NDI and ODI were complementary, and together could explain much of the FA results. Further, compared to FA analysis, NDI and ODI gave a pattern of results that was more regionally specific and were able to capture additional discriminative voxels that FA failed to identify. Finally, ODI from single-shell NODDI analysis, but not NDI, was found to reproduce the group differences from the multi-shell analysis. To conclude, by using a clinically feasible acquisition and analysis protocol, we demonstrated that NODDI is of added value to standard DTI, by revealing specific microstructural substrates to white matter changes detected with FA. As the (simpler) DTI model was more sensitive in identifying group differences, NODDI is recommended to be used complementary to DTI, thereby adding greater specificity regarding microstructural underpinnings of the differences. The finding that ODI abnormalities can be identified reliably using single-shell data may allow the retrospective analysis of standard DTI with NODDI.

Bingham-NODDI: Mapping anisotropic orientation dispersion of neurites using diffusion MRI

This paper presents Bingham-NODDI, a clinically-feasible technique for estimating the anisotropic orientation dispersion of neurites. Direct quantification of neurite morphology on clinical scanners was recently realised by a diffusion MRI technique known as neurite orientation dispersion and density imaging (NODDI). However in its current form NODDI cannot estimate anisotropic orientation dispersion, which is widespread in the brain due to common fanning and bending of neurites. This work proposes Bingham-NODDI that extends the NODDI formalism to address this limitation. Bingham-NODDI characterises anisotropic orientation dispersion by utilising the Bingham distribution to model neurite orientation distribution. The new model estimates the extent of dispersion about the dominant orientation, separately along the primary and secondary dispersion orientations. These estimates are subsequently used to estimate the overall dispersion about the dominant orientation and the dispersion anisotropy. We systematically evaluate the ability of the new model to recover these key parameters of anisotropic orientation dispersion with standard NODDI protocol, both in silico and in vivo. The results demonstrate that the parameters of the proposed model can be estimated without additional acquisition requirements over the standard NODDI protocol. Thus anisotropic dispersion can be determined and has the potential to be used as a marker for normal brain development and ageing or in pathology. We additionally find that the original NODDI model is robust to the effects of anisotropic orientation dispersion, when the quantification of anisotropic dispersion is not of interest.

Comparison of 3D orientation distribution functions measured with confocal microscopy and diffusion MRI

The ability of diffusion MRI (dMRI) fiber tractography to non-invasively map three-dimensional (3D) anatomical networks in the human brain has made it a valuable tool in both clinical and research settings. However, there are many assumptions inherent to any tractography algorithm that can limit the accuracy of the reconstructed fiber tracts. Among them is the assumption that the diffusion-weighted images accurately reflect the underlying fiber orientation distribution (FOD) in the MRI voxel. Consequently, validating dMRI's ability to assess the underlying fiber orientation in each voxel is critical for its use as a biomedical tool. Here, using post-mortem histology and confocal microscopy, we present a method to perform histological validation of orientation functions in 3D, which has previously been limited to two-dimensional analysis of tissue sections. We demonstrate the ability to extract the 3D FOD from confocal z-stacks, and quantify the agreement between the MRI estimates of orientation information obtained using constrained spherical deconvolution (CSD) and the true geometry of the fibers. We find an orientation error of approximately 6° in voxels containing nearly parallel fibers, and 10-11° in crossing fiber regions, and note that CSD was unable to resolve fibers crossing at angles below 60° in our dataset. This is the first time that the 3D white matter orientation distribution is calculated from histology and compared to dMRI. Thus, this technique serves as a gold standard for dMRI validation studies - providing the ability to determine the extent to which the dMRI signal is consistent with the histological FOD, and to establish how well different dMRI models can predict the ground truth FOD.

Characterization of Laminar Zones in the Mid-Gestation Primate Brain with Magnetic Resonance Imaging and Histological Methods

Distinct populations of progenitor and postmitotic neural and glial cells are stratified in the fetal primate brain across developmentally transient tissue zones between the ventricular and pial surfaces. These zones were originally identified by light microscopy. However, it has subsequently been shown that various forms of magnetic resonance image (MRI) contrast can be used to distinguish layers of developing neural tissue in ex vivo, as well as in vivo (including in utero) conditions. Here we compare mid-gestation rhesus macaque tissue zones identified using histological techniques to ex vivo as well as in utero MRI performed on the same brains. These data are compared to mid-gestation fetal human brain MRI results, obtained in utero. We observe strong similarity between MRI contrast in vivo and post mortem, which facilitates interpretation of in utero images based on the histological characterization performed here. Additionally, we observe differential correspondence between the various forms of ex vivo MRI contrast and microscopy data, with maps of the water apparent diffusion coefficient providing the closest match to histologically-identified lamina of the nonhuman primate brain. Examination of histology and post mortem MRI helps to provide a better understanding of cytoarchitectrual characteristics that give rise to in utero MRI contrast.

Fronto-striatal organization: Defining functional and microstructural substrates of behavioural flexibility

Discrete yet overlapping frontal-striatal circuits mediate broadly dissociable cognitive and behavioural processes. Using a recently developed multi-echo resting-state functional MRI (magnetic resonance imaging) sequence with greatly enhanced signal compared to noise ratios, we map frontal cortical functional projections to the striatum and striatal projections through the direct and indirect basal ganglia circuit. We demonstrate distinct limbic (ventromedial prefrontal regions, ventral striatum – VS, ventral tegmental area – VTA), motor (supplementary motor areas – SMAs, putamen, substantia nigra) and cognitive (lateral prefrontal and caudate) functional connectivity. We confirm the functional nature of the cortico-striatal connections, demonstrating correlates of well-established goal-directed behaviour (involving medial orbitofrontal cortex – mOFC and VS), probabilistic reversal learning (lateral orbitofrontal cortex – lOFC and VS) and attentional shifting (dorsolateral prefrontal cortex – dlPFC and VS) while assessing habitual model-free (SMA and putamen) behaviours on an exploratory basis. We further use neurite orientation dispersion and density imaging (NODDI) to show that more goal-directed model-based learning (MB_c) is also associated with higher mOFC neurite density and habitual model-free learning (MF_c) implicates neurite complexity in the putamen. This data highlights similarities between a computational account of MF_c and conventional measures of habit learning. We highlight the intrinsic functional and structural architecture of parallel systems of behavioural control.

Application of neurite orientation dispersion and density imaging (NODDI) to a tau pathology model of Alzheimer's disease

Increased hyperphosphorylated tau and the formation of intracellular neurofibrillary tangles are associated with the loss of neurons and cognitive decline in Alzheimer's disease, and related neurodegenerative conditions. We applied two diffusion models, diffusion tensor imaging (DTI) and neurite orientation dispersion and density imaging (NODDI), to in vivo diffusion magnetic resonance images (dMRI) of a mouse model of human tauopathy (rTg4510) at 8.5 months of age. In grey matter regions with the highest degree of tau burden, microstructural indices provided by both NODDI and DTI discriminated the rTg4510 (TG) animals from wild type (WT) controls; however only the neurite density index (NDI) (the volume fraction that comprises axons or dendrites) from the NODDI model correlated with the histological measurements of the levels of hyperphosphorylated tau protein. Reductions in diffusion directionality were observed when implementing both models in the white matter region of the corpus callosum, with lower fractional anisotropy (DTI) and higher orientation dispersion (NODDI) observed in the TG animals. In comparison to DTI, histological measures of tau pathology were more closely correlated with NODDI parameters in this region. This in vivo dMRI study demonstrates that NODDI identifies potential tissue sources contributing to DTI indices and NODDI may provide greater specificity to pathology in Alzheimer's disease.

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We analyzed the microstructural changes in rTg4510 and wild type mice at 8.5 months.

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We correlated microstructural findings with histological measures of tau burden

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We compare two diffusion MR models: DTI and NODDI.

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Both models revealed changes in tissue microstructure due to tau pathology.

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The NODDI metrics demonstrated a good correlation with histological measures of tau burden.