Can increased spatial resolution solve the crossing fiber problem for diffusion MRI?

Considerations and recommendations from the ISMRM diffusion study group for preclinical diffusion MRI: Part 1: In vivo small-animal imaging

Small-animal diffusion MRI (dMRI) has been used for methodological development and validation, characterizing the biological basis of diffusion phenomena, and comparative anatomy. The steps from animal setup and monitoring, to acquisition, analysis, and interpretation are complex, with many decisions that may ultimately affect what questions can be answered using the resultant data. This work aims to present selected considerations and recommendations from the diffusion community on best practices for preclinical dMRI of in vivo animals. We describe the general considerations and foundational knowledge that must be considered when designing experiments. We briefly describe differences in animal species and disease models and discuss why some may be more or less appropriate for different studies. We, then, give recommendations for in vivo acquisition protocols, including decisions on hardware, animal preparation, and imaging sequences, followed by advice for data processing including preprocessing, model-fitting, and tractography. Finally, we provide an online resource that lists publicly available preclinical dMRI datasets and software packages to promote responsible and reproducible research. In each section, we attempt to provide guides and recommendations, but also highlight areas for which no guidelines exist (and why), and where future work should focus. Although we mainly cover the central nervous system (on which most preclinical dMRI studies are focused), we also provide, where possible and applicable, recommendations for other organs of interest. An overarching goal is to enhance the rigor and reproducibility of small animal dMRI acquisitions and analyses, and thereby advance biomedical knowledge.

Considerations and recommendations from the ISMRM Diffusion Study Group for preclinical diffusion MRI: Part 3-Ex vivo imaging: Data processing, comparisons with microscopy, and tractography

Preclinical diffusion MRI (dMRI) has proven value in methods development and validation, characterizing the biological basis of diffusion phenomena, and comparative anatomy. While dMRI enables in vivo non-invasive characterization of tissue, ex vivo dMRI is increasingly being used to probe tissue microstructure and brain connectivity. Ex vivo dMRI has several experimental advantages that facilitate high spatial resolution and high SNR images, cutting-edge diffusion contrasts, and direct comparison with histological data as a methodological validation. However, there are a number of considerations that must be made when performing ex vivo experiments. The steps from tissue preparation, image acquisition and processing, and interpretation of results are complex, with many decisions that not only differ dramatically from in vivo imaging of small animals, but ultimately affect what questions can be answered using the data. This work concludes a three-part series of recommendations and considerations for preclinical dMRI. Herein, we describe best practices for dMRI of ex vivo tissue, with a focus on image pre-processing, data processing, and comparisons with microscopy. In each section, we attempt to provide guidelines and recommendations but also highlight areas for which no guidelines exist (and why), and where future work should lie. We end by providing guidelines on code sharing and data sharing and point toward open-source software and databases specific to small animal and ex vivo imaging.

Considerations and recommendations from the ISMRM diffusion study group for preclinical diffusion MRI: Part 2-Ex vivo imaging: Added value and acquisition

The value of preclinical diffusion MRI (dMRI) is substantial. While dMRI enables in vivo non-invasive characterization of tissue, ex vivo dMRI is increasingly being used to probe tissue microstructure and brain connectivity. Ex vivo dMRI has several experimental advantages including higher SNR and spatial resolution compared to in vivo studies, and enabling more advanced diffusion contrasts for improved microstructure and connectivity characterization. Another major advantage of ex vivo dMRI is the direct comparison with histological data, as a crucial methodological validation. However, there are a number of considerations that must be made when performing ex vivo experiments. The steps from tissue preparation, image acquisition and processing, and interpretation of results are complex, with many decisions that not only differ dramatically from in vivo imaging of small animals, but ultimately affect what questions can be answered using the data. This work represents "Part 2" of a three-part series of recommendations and considerations for preclinical dMRI. We describe best practices for dMRI of ex vivo tissue, with a focus on the value that ex vivo imaging adds to the field of dMRI and considerations in ex vivo image acquisition. We first give general considerations and foundational knowledge that must be considered when designing experiments. We briefly describe differences in specimens and models and discuss why some may be more or less appropriate for different studies. We then give guidelines for ex vivo protocols, including tissue fixation, sample preparation, and MR scanning. In each section, we attempt to provide guidelines and recommendations, but also highlight areas for which no guidelines exist (and why), and where future work should lie. An overarching goal herein is to enhance the rigor and reproducibility of ex vivo dMRI acquisitions and analyses, and thereby advance biomedical knowledge.

Bridging the 3D geometrical organisation of white matter pathways across anatomical length scales and species

We used diffusion MRI and x-ray synchrotron imaging on monkey and mice brains to examine the organisation of fibre pathways in white matter across anatomical scales. We compared the structure in the corpus callosum and crossing fibre regions and investigated the differences in cuprizone-induced demyelination in mouse brains versus healthy controls. Our findings revealed common principles of fibre organisation that apply despite the varying patterns observed across species; small axonal fasciculi and major bundles formed laminar structures with varying angles, according to the characteristics of major pathways. Fasciculi exhibited non-straight paths around obstacles like blood vessels, comparable across the samples of varying fibre complexity and demyelination. Quantifications of fibre orientation distributions were consistent across anatomical length scales and modalities, whereas tissue anisotropy had a more complex relationship, both dependent on the field-of-view. Our study emphasises the need to balance field-of-view and voxel size when characterising white matter features across length scales.

Discussion on the comparison of Raman spectroscopy and cardiovascular disease-related imaging techniques and the future applications of Raman technology: a systematic review

Cardiovascular disease (CVD) is a major cause of unnatural death worldwide, so timely diagnosis of CVD is crucial for improving patient outcomes. Although the traditional diagnostic tools can locate plaque and observe inner wall of blood vessel structure, they commonly have radioactivity and cannot detect the chemical composition of the plaque accurately. Recently emerging Raman techniques can detect the plaque composition precisely, and have the advantages of being fast, high-resolution and marker-free. This makes Raman have great potential for detecting blood samples, understanding disease conditions, and real-time monitoring. This review summarizes the origin and state-of-art of Raman techniques, including the following aspects: (a) the principle and technical classification of Raman techniques; (b) the applicability of Raman techniques and its comparison with traditional diagnostic tools at different diagnosis targets; (c) the applicability of Raman spectroscopy in advanced CVD. Lastly, we highlight the possible future applications of Raman techniques in CVD diagnosis.

Super-resolution mapping of anisotropic tissue structure with diffusion MRI and deep learning

Diffusion magnetic resonance imaging (diffusion MRI) is widely employed to probe the diffusive motion of water molecules within the tissue. Numerous diseases and processes affecting the central nervous system can be detected and monitored via diffusion MRI thanks to its sensitivity to microstructural alterations in tissue. The latter has prompted interest in quantitative mapping of the microstructural parameters, such as the fiber orientation distribution function (fODF), which is instrumental for noninvasively mapping the underlying axonal fiber tracts in white matter through a procedure known as tractography. However, such applications demand repeated acquisitions of MRI volumes with varied experimental parameters demanding long acquisition times and/or limited spatial resolution. In this work, we present a deep-learning-based approach for increasing the spatial resolution of diffusion MRI data in the form of fODFs obtained through constrained spherical deconvolution. The proposed approach is evaluated on high quality data from the Human Connectome Project, and is shown to generate upsampled results with a greater correspondence to ground truth high-resolution data than can be achieved with ordinary spline interpolation methods. Furthermore, we employ a measure based on the earth mover's distance to assess the accuracy of the upsampled fODFs. At low signal-to-noise ratios, our super-resolution method provides more accurate estimates of the fODF compared to data collected with 8 times smaller voxel volume.

Multi-tensor fixel-based metrics in tractometry: application to multiple sclerosis

Traditional Diffusion Tensor Imaging (DTI) metrics are affected by crossing fibers and lesions. Most of the previous tractometry works use the single diffusion tensor, which leads to limited sensitivity and challenging interpretation of the results in crossing fiber regions. In this work, we propose a tractometry pipeline that combines white matter tractography with multi-tensor fixel-based metrics. These multi-tensors are estimated using the stable, accurate and robust to noise Multi-Resolution Discrete Search method (MRDS). The spatial coherence of the multi-tensor field estimated with MRDS, which includes up to three anisotropic and one isotropic tensors, is tractography-regularized using the Track Orientation Density Imaging method. Our end-to-end tractometry pipeline goes from raw data to track-specific multi-tensor-metrics tract profiles that are robust to noise and crossing fibers. A comprehensive evaluation conducted in a phantom simulating healthy and damaged tissue with the standard model, as well as in a healthy cohort of 20 individuals scanned along 5 time points, demonstrates the advantages of using multi-tensor metrics over traditional single-tensor metrics in tractometry. Qualitative assessment in a cohort of patients with relapsing-remitting multiple sclerosis reveals that the pipeline effectively detects white matter anomalies in the presence of crossing fibers and lesions.

T1 relaxation and axon fibre configuration in human white matter

Understanding the effects of white matter (WM) axon fibre microstructure on T1 relaxation is important for neuroimaging. Here, we have studied the interrelationship between T1 and axon fibre configurations at 3T and 7T. T1 and S0 (=signal intensity at zero TI) were computed from MP2RAGE images acquired with six inversion recovery times. Multishell diffusion MRI images were analysed for fractional anisotropy (FA); MD; V1; the volume fractions for the first (f1), second (f2) and third (f3) fibre configuration; and fibre density cross-section images for the first (fdc1), second (fdc2) and third (fdc3) fibres. T1 values were plotted as a function of FA, f1, f2, f3, fdc1, fdc2 and fdc3 to examine interrelationships between the longitudinal relaxation and the diffusion MRI microstructural measures. T1 values decreased with increasing FA, f1 and f2 in a nonlinear fashion. At low FA values (from 0.2 to 0.4), a steep shortening of T1 was followed by a shallow shortening by 6%-10% at both fields. The steep shortening was associated with decreasing S0 and MD. T1 also decreased with increasing fdc1 values in a nonlinear fashion. Instead, only a small T1 change as a function of either f3 or fdc3 was observed. In WM areas selected by fdc1 only masks, T1 was shorter than in those with fdc2/fdc3. In WM areas with high single fibre populations, as delineated by f1/fdc1 masks, T1 was shorter than in tissue with high complex fibre configurations, as segmented by f2/fdc2 or f3/fdc3 masks. T1 differences between these WM areas are attributable to combined effects by T1 anisotropy and lowered FA. The current data show strong interrelationships between T1, axon fibre configuration and orientation in healthy WM. It is concluded that diffusion MRI microstructural measures are essential in the effort to interpret quantitative T1 images in terms of tissue state in health and disease.

Diffusion tensor imaging along the perivascular space: the bias from crossing fibres

Non-invasive evaluation of glymphatic function has emerged as a crucial goal in neuroimaging, and diffusion tensor imaging along the perivascular space (DTI-ALPS) has emerged as a candidate method for this purpose. Reduced ALPS index has been suggested to indicate impaired glymphatic function. However, the potential impact of crossing fibres on the ALPS index has not been assessed, which was the aim of this cross-sectional study. For this purpose, we used DTI-ALPS in a cohort with three groups: Parkinson's disease (PD) (n = 60, mean age 63.3 ± 1.5, 33 males), progressive supranuclear palsy (PSP) (n = 17, mean age 70.9 ± 1.5, 9 males) and healthy controls (n = 41, mean age 64.5 ± 8.4, 15 males). The ALPS index was calculated blinded to diagnosis, by manually placing two sets of regions of interest (ROI) on the projection and association fibres of each hemisphere. Annotation was performed twice: once on conventional diffusion-encoded colour maps weighted by fractional anisotropy and once on maps with weights adjusted for high incidence of crossing fibres. PSP patients had significantly lower conventional ALPS indices compared with both healthy controls (right hemisphere: P = 0.009; left hemisphere: P < 0.001) and PD patients (right hemisphere: P = 0.024; left hemisphere: P < 0.001). There were no differences between healthy controls and PD patients. After adjusting the ROI to avoid regions of crossing fibres, the ALPS index significantly decreased in healthy controls (right hemisphere: P < 0.001; left hemisphere: P < 0.001) and PD (right hemisphere: P < 0.001; left hemisphere: P < 0.001). In PSP, the adjusted ALPS index was lower compared with the conventional one only in the right hemisphere (P = 0.047). Overall, this adjustment led to less significant differences among diagnostic groups. Specifically, with the adjusted ALPS index, PSP patients showed significantly lower ALPS index compared with healthy controls (right hemisphere: P = 0.044; left hemisphere: P = 0.029) and PD patients (P = 0.003 for the left hemisphere only). Our results suggest that crossing fibres significantly inflate the ALPS index and should be considered a critical pitfall of this method. This factor could partly explain the variability observed in previous studies. Unlike previous research, we observed no differences between PD and healthy controls, likely because most patients in our cohort were in the early phase of the disease. Thus, the ALPS index may not be a sensitive indicator of glymphatic function at least in the initial stages of neurodegeneration in PD.

Modeling the impact of MRI acquisition bias on structural connectomes: Harmonizing structural connectomes

One way to increase the statistical power and generalizability of neuroimaging studies is to collect data at multiple sites or merge multiple cohorts. However, this usually comes with site-related biases due to the heterogeneity of scanners and acquisition parameters, negatively impacting sensitivity. Brain structural connectomes are not an exception: Being derived from T1-weighted and diffusion-weighted magnetic resonance images, structural connectivity is impacted by differences in imaging protocol. Beyond minimizing acquisition parameter differences, removing bias with postprocessing is essential. In this work we create, from the exhaustive Human Connectome Project Young Adult dataset, a resampled dataset of different b-values and spatial resolutions, modeling a cohort scanned across multiple sites. After demonstrating the statistical impact of acquisition parameters on connectivity, we propose a linear regression with explicit modeling of b-value and spatial resolution, and validate its performance on separate datasets. We show that b-value and spatial resolution affect connectivity in different ways and that acquisition bias can be reduced using a linear regression informed by the acquisition parameters while retaining interindividual differences and hence boosting fingerprinting performance. We also demonstrate the generative potential of our model, and its generalization capability in an independent dataset reflective of typical acquisition practices in clinical settings.

Exploring White Matter Microstructure with Symptom Severity and Outcomes Following Deep Brain Stimulation in Tremor Syndromes

Background

Essential tremor (ET) and dystonic tremor (DT) are movement disorders that cause debilitating symptoms, significantly impacting daily activities and quality of life. A poor understanding of their pathophysiology, as well as the mediators of clinical outcomes following deep brain stimulation (DBS), highlights the need for biomarkers to accurately characterise and optimally treat patients.

Objectives

We assessed the white matter microstructure of pathways implicated in the pathophysiology and therapeutic intervention in a retrospective cohort of patients with DT (n = 17) and ET (n = 19). We aimed to identity associations between white matter microstructure, upper limb tremor severity, and tremor improvement following DBS.

Methods

A fixel-based analysis pipeline was implemented to investigate white matter microstructural metrics in the whole brain, cerebello-thalamic pathways and tracts connected to stimulation volumes following DBS. Associations with preoperative and postoperative severity were analysed within each disorder group and across combined disorder groups.

Results

DBS led to significant improvements in both groups. No group differences in stimulation positions were identified. When white matter microstructural data was aligned according to the maximally affected upper limb, increased fiber density, and combined fiber density & cross-section of fixels in the left cerebellum were associated with greater tremor severity across DT and ET patients. White matter microstructure did not show associations with postoperative changes in cerebello-thalamic pathways, or tracts connected to stimulation volumes.

Discussion

Diffusion changes of the cerebellum are associated with the severity of upper limb tremor and appear to overlap in essential or dystonic tremor disorders.

Abnormally glymphatic system functional in patients with migraine: a diffusion kurtosis imaging study

BACKGROUND

The diffusion tensor imaging analysis along the perivascular space (DTI-ALPS) method has been used to evaluate glymphatic system function in patients with migraine. However, since the diffusion tensor model cannot accurately describe the diffusion coefficient of the nerve fibre crossing region, we proposed a diffusion kurtosis imaging ALPS (DKI-ALPS) method to evaluate glymphatic system function in patients with migraine.

METHODS

The study included 29 healthy controls and 37 patients with migraine. We used diffusion imaging data from a 3T MRI scanner to calculate DTI-ALPS and DKI-ALPS indices of the two groups. We compared the DTI-ALPS and DKI-ALPS indices between the two groups using a two-sample t-test and performed correlation analyses with clinical variables.

RESULTS

There was no significant difference in DTI-ALPS index between the two groups. Patients with migraine showed a significantly increased right DKI-ALPS index compared to healthy controls (1.6858 vs. 1.5729; p = 0.0301). There was no significant correlation between ALPS indices and clinical variables.

CONCLUSIONS

DKI-ALPS is a potential method to assess glymphatic system function and patients with migraine do not have impaired glymphatic system function.

Structural connectivity at term equivalent age and language in preterm children at 2 years corrected

We previously reported interhemispheric structural hyperconnectivity bypassing the corpus callosum in children born extremely preterm (<28 weeks) versus term children. This increased connectivity was positively associated with language performance at 4-6 years of age in our prior work. In the present study, we aim to investigate whether this extracallosal connectivity develops in extremely preterm infants at term equivalent age by leveraging a prospective cohort study of 350 very and extremely preterm infants followed longitudinally in the Cincinnati Infant Neurodevelopment Early Prediction Study. For this secondary analysis, we included only children born extremely preterm and without significant brain injury (n = 95). We use higher-order diffusion modelling to assess the degree to which extracallosal pathways are present in extremely preterm infants and predictive of later language scores at 22-26 months corrected age. We compare results obtained from two higher-order diffusion models: generalized q-sampling imaging and constrained spherical deconvolution. Advanced MRI was obtained at term equivalent age (39-44 weeks post-menstrual age). For structural connectometry analysis, we assessed the level of correlation between white matter connectivity at the whole-brain level at term equivalent age and language scores at 2 years corrected age, controlling for post-menstrual age, sex, brain abnormality score and social risk. For our constrained spherical deconvolution analyses, we performed connectivity-based fixel enhancement, using probabilistic tractography to inform statistical testing of the hypothesis that fibre metrics at term equivalent age relate to language scores at 2 years corrected age after adjusting for covariates. Ninety-five infants were extremely preterm with no significant brain injury. Of these, 53 had complete neurodevelopmental and imaging data sets that passed quality control. In the connectometry analyses adjusted for covariates and multiple comparisons (P < 0.05), the following tracks were inversely correlated with language: bilateral cerebellar white matter and middle cerebellar peduncles, bilateral corticospinal tracks, posterior commissure and the posterior inferior fronto-occipital fasciculus. No tracks from the constrained spherical deconvolution/connectivity-based fixel enhancement analyses remained significant after correction for multiple comparisons. Our findings provide critical information about the ontogeny of structural brain networks supporting language in extremely preterm children. Greater connectivity in more posterior tracks that include the cerebellum and connections to the regions of the temporal lobes at term equivalent age appears to be disadvantageous for language development.

Identifying potential imaging markers for diffusion property changes in a mouse model of amyotrophic lateral sclerosis: Application of the continuous time random walk model to ultrahigh b-value diffusion-weighted MR images of spinal cord tissue

Diffusion MRI (dMRI) explores tissue microstructures by analyzing diffusion-weighted signal decay measured at different b-values. While relatively low b-values are used for most dMRI models, high b-value diffusion-weighted imaging (DWI) techniques have gained interest given that the non-Gaussian water diffusion behavior observed at high b-values can yield potentially valuable information. In this study, we investigated anomalous diffusion behaviors associated with degeneration of spinal cord tissue using a continuous time random walk (CTRW) model for DWI data acquired across an extensive range of ultrahigh b-values. The diffusion data were acquired in situ from the lumbar level of spinal cords of wild-type and age-matched transgenic SOD1G93A mice, a well-established animal model of amyotrophic lateral sclerosis (ALS) featuring progressive degeneration of axonal tracts in this tissue. Based on the diffusion decay behaviors at low and ultrahigh b-values, we applied the CTRW model using various combinations of b-values and compared diffusion metrics calculated from the CTRW model between the experimental groups. We found that diffusion-weighted signal decay curves measured with ultrahigh b-values (up to 858,022 s/mm2 in this study) were well represented by the CTRW model. The anomalous diffusion coefficient obtained from lumbar spinal cords was significantly higher in SOD1G93A mice compared with control mice (14.7 × 10-5  ± 5.54 × 10-5  vs. 7.87 × 10-5  ± 2.48 × 10-5  mm2 /s, p = 0.01). We believe this is the first study to illustrate the efficacy of the CTRW model for analyzing anomalous diffusion regimes at ultrahigh b-values. The CTRW modeling of ultrahigh b-value dMRI can potentially present a novel approach for noninvasively evaluating alterations in spinal cord tissue associated with ALS pathology.

CACTUS: a computational framework for generating realistic white matter microstructure substrates

Monte-Carlo diffusion simulations are a powerful tool for validating tissue microstructure models by generating synthetic diffusion-weighted magnetic resonance images (DW-MRI) in controlled environments. This is fundamental for understanding the link between micrometre-scale tissue properties and DW-MRI signals measured at the millimetre-scale, optimizing acquisition protocols to target microstructure properties of interest, and exploring the robustness and accuracy of estimation methods. However, accurate simulations require substrates that reflect the main microstructural features of the studied tissue. To address this challenge, we introduce a novel computational workflow, CACTUS (Computational Axonal Configurator for Tailored and Ultradense Substrates), for generating synthetic white matter substrates. Our approach allows constructing substrates with higher packing density than existing methods, up to 95% intra-axonal volume fraction, and larger voxel sizes of up to 500μm3 with rich fibre complexity. CACTUS generates bundles with angular dispersion, bundle crossings, and variations along the fibres of their inner and outer radii and g-ratio. We achieve this by introducing a novel global cost function and a fibre radial growth approach that allows substrates to match predefined targeted characteristics and mirror those reported in histological studies. CACTUS improves the development of complex synthetic substrates, paving the way for future applications in microstructure imaging.

Unraveling multi-fixel microstructure with tractography and angular weighting

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

A structural magnetic resonance imaging review of clinical motor outcomes from deep brain stimulation in movement disorders

Patients with movement disorders treated by deep brain stimulation do not always achieve successful therapeutic alleviation of motor symptoms, even in cases where surgery is without complications. Magnetic resonance imaging (MRI) offers methods to investigate structural brain-related factors that may be predictive of clinical motor outcomes. This review aimed to identify features which have been associated with variability in clinical post-operative motor outcomes in patients with Parkinson's disease, dystonia, and essential tremor from structural MRI modalities. We performed a literature search for articles published between 1 January 2000 and 1 April 2022 and identified 5197 articles. Following screening through our inclusion criteria, we identified 60 total studies (39 = Parkinson's disease, 11 = dystonia syndromes and 10 = essential tremor). The review captured a range of structural MRI methods and analysis techniques used to identify factors related to clinical post-operative motor outcomes from deep brain stimulation. Morphometric markers, including volume and cortical thickness were commonly identified in studies focused on patients with Parkinson's disease and dystonia syndromes. Reduced metrics in basal ganglia, sensorimotor and frontal regions showed frequent associations with reduced motor outcomes. Increased structural connectivity to subcortical nuclei, sensorimotor and frontal regions was also associated with greater motor outcomes. In patients with tremor, increased structural connectivity to the cerebellum and cortical motor regions showed high prevalence across studies for greater clinical motor outcomes. In addition, we highlight conceptual issues for studies assessing clinical response with structural MRI and discuss future approaches towards optimizing individualized therapeutic benefits. Although quantitative MRI markers are in their infancy for clinical purposes in movement disorder treatments, structural features obtained from MRI offer the powerful potential to identify candidates who are more likely to benefit from deep brain stimulation and provide insight into the complexity of disorder pathophysiology.

Superficial white matter across development, young adulthood, and aging: volume, thickness, and relationship with cortical features

Superficial white matter (SWM) represents a significantly understudied part of the human brain, despite comprising a large portion of brain volume and making up a majority of cortico-cortical white matter connections. Using multiple, high-quality datasets with large sample sizes (N = 2421, age range 5-100) in combination with methodological advances in tractography, we quantified features of SWM volume and thickness across the brain and across development, young adulthood, and aging. We had four primary aims: (1) characterize SWM thickness across brain regions (2) describe associations between SWM volume and age (3) describe associations between SWM thickness and age, and (4) quantify relationships between SWM thickness and cortical features. Our main findings are that (1) SWM thickness varies across the brain, with patterns robust across individuals and across the population at the region-level and vertex-level; (2) SWM volume shows unique volumetric trajectories with age that are distinct from gray matter and other white matter trajectories; (3) SWM thickness shows nonlinear cross-sectional changes across the lifespan that vary across regions; and (4) SWM thickness is associated with features of cortical thickness and curvature. For the first time, we show that SWM volume follows a similar trend as overall white matter volume, peaking at a similar time in adolescence, leveling off throughout adulthood, and decreasing with age thereafter. Notably, the relative fraction of total brain volume of SWM continuously increases with age, and consequently takes up a larger proportion of total white matter volume, unlike the other tissue types that decrease with respect to total brain volume. This study represents the first characterization of SWM features across the large portion of the lifespan and provides the background for characterizing normal aging and insight into the mechanisms associated with SWM development and decline.

Transcranial Direct Current Stimulation for Chronic Stroke: Is Neuroimaging the Answer to the Next Leap Forward?

During rehabilitation, a large proportion of stroke patients either plateau or begin to lose motor skills. By priming the motor system, transcranial direct current stimulation (tDCS) is a promising clinical adjunct that could augment the gains acquired during therapy sessions. However, the extent to which patients show improvements following tDCS is highly variable. This variability may be due to heterogeneity in regions of cortical infarct, descending motor tract injury, and/or connectivity changes, all factors that require neuroimaging for precise quantification and that affect the actual amount and location of current delivery. If the relationship between these factors and tDCS efficacy were clarified, recovery from stroke using tDCS might be become more predictable. This review provides a comprehensive summary and timeline of the development of tDCS for stroke from the viewpoint of neuroimaging. Both animal and human studies that have explored detailed aspects of anatomy, connectivity, and brain activation dynamics relevant to tDCS are discussed. Selected computational works are also included to demonstrate how sophisticated strategies for reducing variable effects of tDCS, including electric field modeling, are moving the field ever closer towards the goal of personalizing tDCS for each individual. Finally, larger and more comprehensive randomized controlled trials involving tDCS for chronic stroke recovery are underway that likely will shed light on how specific tDCS parameters, such as dose, affect stroke outcomes. The success of these collective efforts will determine whether tDCS for chronic stroke gains regulatory approval and becomes clinical practice in the future.

Effect of sex on the APOE4-aging interaction in the white matter microstructure of cognitively normal older adults using diffusion-tensor MRI with orthogonal-tensor decomposition (DT-DOME)

The influence of the apolipoprotein E ε4 allele (APOE4) on brain microstructure of cognitively normal older adults remains incompletely understood, in part due to heterogeneity within study populations. In this study, we examined white-matter microstructural integrity in cognitively normal older adults as a function of APOE4 carrier status using conventional diffusion-tensor imaging (DTI) and the novel orthogonal-tensor decomposition (DT-DOME), accounting for the effects of age and sex. Age associations with white-matter microstructure did not significantly depend on APOE4 status, but did differ between sexes, emphasizing the importance of accounting for sex differences in APOE research. Moreover, we found the DT-DOME to be more sensitive than conventional DTI metrics to such age-related and sex effects, especially in crossing WM fiber regions, and suggest their use in further investigation of white matter microstructure across the life span in health and disease.

Graph Theory Measures and Their Application to Neurosurgical Eloquence

Improving patient safety and preserving eloquent brain are crucial in neurosurgery. Since there is significant clinical variability in post-operative lesions suffered by patients who undergo surgery in the same areas deemed compensable, there is an unknown degree of inter-individual variability in brain 'eloquence'. Advances in connectomic mapping efforts through diffusion tractography allow for utilization of non-invasive imaging and statistical modeling to graphically represent the brain. Extending the definition of brain eloquence to graph theory measures of hubness and centrality may help to improve our understanding of individual variability in brain eloquence and lesion responses. While functional deficits cannot be immediately determined intra-operatively, there has been potential shown by emerging technologies in mapping of hub nodes as an add-on to existing surgical navigation modalities to improve individual surgical outcomes. This review aims to outline and review current research surrounding novel graph theoretical concepts of hubness, centrality, and eloquence and specifically its relevance to brain mapping for pre-operative planning and intra-operative navigation in neurosurgery.

Anomalous and heterogeneous characteristics of the BOLD hemodynamic response function in white matter

Detailed knowledge of the BOLD hemodynamic response function (HRF) is crucial for accurate analyses and interpretation of functional MRI data. Considerable efforts have been made to characterize the HRF in gray matter (GM), but much less attention has been paid to BOLD effects in white matter (WM). However, several recent reports have demonstrated reliable detection and analyses of WM BOLD signals both after stimulation and in a resting state. WM and GM differ in composition, energy requirements, and blood flow, so their neurovascular couplings also may well be different. We aimed to derive a comprehensive characterization of the HRF in WM across a population, including accurate measurements of its shape and its variation along and between WM pathways, using resting-state fMRI acquisitions. Our results show that the HRF is significantly different between WM and GM. Features of the HRF, such as a prominent initial dip, show strong relationships with features of the tissue microstructure derived from diffusion imaging, and these relationships differ between WM and GM, consistent with BOLD signal fluctuations reflecting different energy demands and neurovascular couplings in tissues of different composition and function. We also show that the HRF varies in shape significantly along WM pathways and is different between different WM pathways, suggesting the temporal evolution of BOLD signals after an event vary in different parts of the WM. These features of the HRF in WM are especially relevant for interpretation of the biophysical basis of BOLD effects in WM.

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

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

Subiculum-BNST structural connectivity in humans and macaques

Invasive tract-tracing studies in rodents implicate a direct connection between the subiculum and bed nucleus of the stria terminalis (BNST) as a key component of neural pathways mediating hippocampal regulation of the Hypothalamic-Pituitary-Adrenal (HPA) axis. A clear characterisation of the connections linking the subiculum and BNST in humans and non-human primates is lacking. To address this, we first delineated the projections from the subiculum to the BNST using anterograde tracers injected into macaque monkeys, revealing evidence for a monosynaptic subiculum-BNST projection involving the fornix. Second, we used in vivo diffusion MRI tractography in macaques and humans to demonstrate substantial subiculum complex connectivity to the BNST in both species. This connection was primarily carried by the fornix, with additional connectivity via the amygdala, consistent with rodent anatomy. Third, utilising the twin-based nature of our human sample, we found that microstructural properties of these tracts were moderately heritable (h2 ∼ 0.5). In a final analysis, we found no evidence of any significant association between subiculum complex-BNST tract microstructure and indices of perceived stress/dispositional negativity and alcohol use, derived from principal component analysis decomposition of self-report data. Our findings address a key translational gap in our knowledge of the neurocircuitry regulating stress.

Resolution and b value dependent Structural Connectome in ex vivo Mouse Brain

Diffusion magnetic resonance imaging has been widely used in both clinical and preclinical studies to characterize tissue microstructure and structural connectivity. The diffusion MRI protocol for the Human Connectome Project (HCP) has been developed and optimized to obtain high-quality, high-resolution diffusion MRI (dMRI) datasets. However, such efforts have not been fully explored in preclinical studies, especially for rodents. In this study, high quality dMRI datasets of mouse brains were acquired at 9.4T system from two vendors. In particular, we acquired a high-spatial resolution dMRI dataset (25 μm isotropic with 126 diffusion encoding directions), which we believe to be the highest spatial resolution yet obtained; and a high-angular resolution dMRI dataset (50 μm isotropic with 384 diffusion encoding directions), which we believe to be the highest angular resolution compared to the dMRI datasets at the microscopic resolution. We systematically investigated the effects of three important parameters that affect the final outcome of the connectome: b value (1000s/mm² to 8000 s/mm²), angular resolution (10 to 126), and spatial resolution (25 μm to 200 μm). The stability of tractography and connectome increase with the angular resolution, where more than 50 angles is necessary to achieve consistent results. The connectome and quantitative parameters derived from graph theory exhibit a linear relationship to the b value (R² > 0.99); a single-shell acquisition with b value of 3000 s/mm² shows comparable results to the multi-shell high angular resolution dataset. The dice coefficient decreases and both false positive rate and false negative rate gradually increase with coarser spatial resolution. Our study provides guidelines and foundations for exploration of tradeoffs among acquisition parameters for the structural connectome in ex vivo mouse brain.

Abnormal White Matter Microstructure in the Limbic System Is Associated With Tuberous Sclerosis Complex-Associated Neuropsychiatric Disorders

Objective

Tuberous sclerosis complex (TSC) is a genetic disease that arises from TSC1 or TSC2 abnormalities and induces the overactivation of the mammalian/mechanistic target of rapamycin pathways. The neurological symptoms of TSC include epilepsy and tuberous sclerosis complex-associated neuropsychiatric disorders (TAND). Although TAND affects TSC patients' quality of life, the specific region in the brain associated with TAND remains unknown. We examined the association between white matter microstructural abnormalities and TAND, using diffusion tensor imaging (DTI).

Methods

A total of 19 subjects with TSC and 24 age-matched control subjects were enrolled. Tract-based spatial statistics (TBSS) were performed to assess group differences in fractional anisotropy (FA) between the TSC and control groups. Atlas-based association analysis was performed to reveal TAND-related white matter in subjects with TSC. Multiple linear regression was performed to evaluate the association between TAND and the DTI parameters; FA and mean diffusivity in seven target regions and projection fibers.

Results

The TBSS showed significantly reduced FA in the right hemisphere and particularly in the inferior frontal occipital fasciculus (IFOF), inferior longitudinal fasciculus (ILF), superior longitudinal fasciculus (SLF), uncinate fasciculus (UF), and genu of corpus callosum (CC) in the TSC group relative to the control group. In the association analysis, intellectual disability was widely associated with all target regions. In contrast, behavioral problems and autistic features were associated with the limbic system white matter and anterior limb of the internal capsule (ALIC) and CC.

Conclusion

The disruption of white matter integrity may induce underconnectivity between cortical and subcortical regions. These findings suggest that TANDs are not the result of an abnormality in a specific brain region, but rather caused by connectivity dysfunction as a network disorder. This study indicates that abnormal white matter connectivity including the limbic system is relevant to TAND. The analysis of brain and behavior relationship is a feasible approach to reveal TAND related white matter and neural networks. TAND should be carefully assessed and treated at an early stage.

Uncovering Cortical Units of Processing From Multi-Layered Connectomes

Modern diffusion and functional magnetic resonance imaging (dMRI/fMRI) provide non-invasive high-resolution images from which multi-layered networks of whole-brain structural and functional connectivity can be derived. Unfortunately, the lack of observed correspondence between the connectivity profiles of the two modalities challenges the understanding of the relationship between the functional and structural connectome. Rather than focusing on correspondence at the level of connections we presently investigate correspondence in terms of modular organization according to shared canonical processing units. We use a stochastic block-model (SBM) as a data-driven approach for clustering high-resolution multi-layer whole-brain connectivity networks and use prediction to quantify the extent to which a given clustering accounts for the connectome within a modality. The employed SBM assumes a single underlying parcellation exists across modalities whilst permitting each modality to possess an independent connectivity structure between parcels thereby imposing concurrent functional and structural units but different structural and functional connectivity profiles. We contrast the joint processing units to their modality specific counterparts and find that even though data-driven structural and functional parcellations exhibit substantial differences, attributed to modality specific biases, the joint model is able to achieve a consensus representation that well accounts for both the functional and structural connectome providing improved representations of functional connectivity compared to using functional data alone. This implies that a representation persists in the consensus model that is shared by the individual modalities. We find additional support for this viewpoint when the anatomical correspondence between modalities is removed from the joint modeling. The resultant drop in predictive performance is in general substantial, confirming that the anatomical correspondence of processing units is indeed present between the two modalities. Our findings illustrate how multi-modal integration admits consensus representations well-characterizing each individual modality despite their biases and points to the importance of multi-layered connectomes as providing supplementary information regarding the brain's canonical processing units.

Prevalence of white matter pathways coming into a single white matter voxel orientation: The bottleneck issue in tractography

Characterizing and understanding the limitations of diffusion MRI fiber tractography is a prerequisite for methodological advances and innovations which will allow these techniques to accurately map the connections of the human brain. The so-called "crossing fiber problem" has received tremendous attention and has continuously triggered the community to develop novel approaches for disentangling distinctly oriented fiber populations. Perhaps an even greater challenge occurs when multiple white matter bundles converge within a single voxel, or throughout a single brain region, and share the same parallel orientation, before diverging and continuing towards their final cortical or sub-cortical terminations. These so-called "bottleneck" regions contribute to the ill-posed nature of the tractography process, and lead to both false positive and false negative estimated connections. Yet, as opposed to the extent of crossing fibers, a thorough characterization of bottleneck regions has not been performed. The aim of this study is to quantify the prevalence of bottleneck regions. To do this, we use diffusion tractography to segment known white matter bundles of the brain, and assign each bundle to voxels they pass through and to specific orientations within those voxels (i.e. fixels). We demonstrate that bottlenecks occur in greater than 50-70% of fixels in the white matter of the human brain. We find that all projection, association, and commissural fibers contribute to, and are affected by, this phenomenon, and show that even regions traditionally considered "single fiber voxels" often contain multiple fiber populations. Together, this study shows that a majority of white matter presents bottlenecks for tractography which may lead to incorrect or erroneous estimates of brain connectivity or quantitative tractography (i.e., tractometry), and underscores the need for a paradigm shift in the process of tractography and bundle segmentation for studying the fiber pathways of the human brain.

Potential Pitfalls of Using Fractional Anisotropy, Axial Diffusivity, and Radial Diffusivity as Biomarkers of Cerebral White Matter Microstructure

Fractional anisotropy (FA), axial diffusivity (AD), and radial diffusivity (RD) are commonly used as MRI biomarkers of white matter microstructure in diffusion MRI studies of neurodevelopment, brain aging, and neurologic injury/disease. Some of the more frequent practices include performing voxel-wise or region-based analyses of these measures to cross-sectionally compare individuals or groups, longitudinally assess individuals or groups, and/or correlate with demographic, behavioral or clinical variables. However, it is now widely recognized that the majority of cerebral white matter voxels contain multiple fiber populations with different trajectories, which renders these metrics highly sensitive to the relative volume fractions of the various fiber populations, the microstructural integrity of each constituent fiber population, and the interaction between these factors. Many diffusion imaging experts are aware of these limitations and now generally avoid using FA, AD or RD (at least in isolation) to draw strong reverse inferences about white matter microstructure, but based on the continued application and interpretation of these metrics in the broader biomedical/neuroscience literature, it appears that this has perhaps not yet become common knowledge among diffusion imaging end-users. Therefore, this paper will briefly discuss the complex biophysical underpinnings of these measures in the context of crossing fibers, provide some intuitive “thought experiments” to highlight how conventional interpretations can lead to incorrect conclusions, and suggest that future studies refrain from using (over-interpreting) FA, AD, and RD values as standalone biomarkers of cerebral white matter microstructure.

A critical review of connectome validation studies

Diffusion MRI tractography is the most widely used macroscale method for mapping connectomes in vivo. However, tractography is prone to various errors and biases, and thus tractography-derived connectomes require careful validation. Here, we critically review studies that have developed or utilized phantoms and tracer maps to validate tractography-derived connectomes, either quantitatively or qualitatively. We identify key factors impacting connectome reconstruction accuracy, including streamline seeding, propagation and filtering methods, and consider the strengths and limitations of state-of-the-art connectome phantoms and associated validation studies. These studies demonstrate the inherent limitations of current fiber orientation models and tractography algorithms and their impact on connectome reconstruction accuracy. Reconstructing connectomes with both high sensitivity and high specificity is challenging, given that some tractography methods can generate an abundance of spurious connections, while others can overlook genuine fiber bundles. We argue that streamline filtering can minimize spurious connections and potentially improve the biological plausibility of connectomes derived from tractography. We find that algorithmic choices such as the tractography seeding methodology, angular threshold, and streamline propagation method can substantially impact connectome reconstruction accuracy. Hence, careful application of tractography is necessary to reconstruct accurate connectomes. Improvements in diffusion MRI acquisition techniques will not necessarily overcome current tractography limitations without accompanying modeling and algorithmic advances.

High-fidelity approximation of grid- and shell-based sampling schemes from undersampled DSI using compressed sensing: Post mortem validation

While many useful microstructural indices, as well as orientation distribution functions, can be obtained from multi-shell dMRI data, there is growing interest in exploring the richer set of microstructural features that can be extracted from the full ensemble average propagator (EAP). The EAP can be readily computed from diffusion spectrum imaging (DSI) data, at the cost of a very lengthy acquisition. Compressed sensing (CS) has been used to make DSI more practical by reducing its acquisition time. CS applied to DSI (CS-DSI) attempts to reconstruct the EAP from significantly undersampled q-space data. We present a post mortem validation study where we evaluate the ability of CS-DSI to approximate not only fully sampled DSI but also multi-shell acquisitions with high fidelity. Human brain samples are imaged with high-resolution DSI at 9.4T and with polarization-sensitive optical coherence tomography (PSOCT). The latter provides direct measurements of axonal orientations at microscopic resolutions, allowing us to evaluate the mesoscopic orientation estimates obtained from diffusion MRI, in terms of their angular error and the presence of spurious peaks. We test two fast, dictionary-based, L2-regularized algorithms for CS-DSI reconstruction. We find that, for a CS acceleration factor of R=3, i.e., an acquisition with 171 gradient directions, one of these methods is able to achieve both low angular error and low number of spurious peaks. With a scan length similar to that of high angular resolution multi-shell acquisition schemes, this CS-DSI approach is able to approximate both fully sampled DSI and multi-shell data with high accuracy. Thus it is suitable for orientation reconstruction and microstructural modeling techniques that require either grid- or shell-based acquisitions. We find that the signal-to-noise ratio (SNR) of the training data used to construct the dictionary can have an impact on the accuracy of CS-DSI, but that there is substantial robustness to loss of SNR in the test data. Finally, we show that, as the CS acceleration factor increases beyond R=3, the accuracy of these reconstruction methods degrade, either in terms of the angular error, or in terms of the number of spurious peaks. Our results provide useful benchmarks for the future development of even more efficient q-space acceleration techniques.

The role of spatial embedding in mouse brain networks constructed from diffusion tractography and tracer injections

Diffusion MRI tractography is the only noninvasive method to measure the structural connectome in humans. However, recent validation studies have revealed limitations of modern tractography approaches, which lead to significant mistracking caused in part by local uncertainties in fiber orientations that accumulate to produce larger errors for longer streamlines. Characterizing the role of this length bias in tractography is complicated by the true underlying contribution of spatial embedding to brain topology. In this work, we compare graphs constructed with ex vivo tractography data in mice and neural tracer data from the Allen Mouse Brain Connectivity Atlas to random geometric surrogate graphs which preserve the low-order distance effects from each modality in order to quantify the role of geometry in various network properties. We find that geometry plays a substantially larger role in determining the topology of graphs produced by tractography than graphs produced by tracers. Tractography underestimates weights at long distances compared to neural tracers, which leads tractography to place network hubs close to the geometric center of the brain, as do corresponding tractography-derived random geometric surrogates, while tracer graphs place hubs further into peripheral areas of the cortex. We also explore the role of spatial embedding in modular structure, network efficiency and other topological measures in both modalities. Throughout, we compare the use of two different tractography streamline node assignment strategies and find that the overall differences between tractography approaches are small relative to the differences between tractography- and tracer-derived graphs. These analyses help quantify geometric biases inherent to tractography and promote the use of geometric benchmarking in future tractography validation efforts.

Fiber tractography bundle segmentation depends on scanner effects, vendor effects, acquisition resolution, diffusion sampling scheme, diffusion sensitization, and bundle segmentation workflow

When investigating connectivity and microstructure of white matter pathways of the brain using diffusion tractography bundle segmentation, it is important to understand potential confounds and sources of variation in the process. While cross-scanner and cross-protocol effects on diffusion microstructure measures are well described (in particular fractional anisotropy and mean diffusivity), it is unknown how potential sources of variation effect bundle segmentation results, which features of the bundle are most affected, where variability occurs, nor how these sources of variation depend upon the method used to reconstruct and segment bundles. In this study, we investigate six potential sources of variation, or confounds, for bundle segmentation: variation (1) across scan repeats, (2) across scanners, (3) across vendors (4) across acquisition resolution, (5) across diffusion schemes, and (6) across diffusion sensitization. We employ four different bundle segmentation workflows on two benchmark multi-subject cross-scanner and cross-protocol databases, and investigate reproducibility and biases in volume overlap, shape geometry features of fiber pathways, and microstructure features within the pathways. We find that the effects of acquisition protocol, in particular acquisition resolution, result in the lowest reproducibility of tractography and largest variation of features, followed by vendor-effects, scanner-effects, and finally diffusion scheme and b-value effects which had similar reproducibility as scan-rescan variation. However, confounds varied both across pathways and across segmentation workflows, with some bundle segmentation workflows more (or less) robust to sources of variation. Despite variability, bundle dissection is consistently able to recover the same location of pathways in the deep white matter, with variation at the gray matter/ white matter interface. Next, we show that differences due to the choice of bundle segmentation workflows are larger than any other studied confound, with low-to-moderate overlap of the same intended pathway when segmented using different methods. Finally, quantifying microstructure features within a pathway, we show that tractography adds variability over-and-above that which exists due to noise, scanner effects, and acquisition effects. Overall, these confounds need to be considered when harmonizing diffusion datasets, interpreting or combining data across sites, and when attempting to understand the successes and limitations of different methodologies in the design and development of new tractography or bundle segmentation methods.

The glial framework reveals white matter fiber architecture in human and primate brains

[Figure: see text].

Early micro- and macrostructure of sensorimotor tracts and development of cerebral palsy in high risk infants

Infants born very preterm (VPT) are at high risk of motor impairments such as cerebral palsy (CP), and diagnosis can take 2 years. Identifying in vivo determinants of CP could facilitate presymptomatic detection and targeted intervention. Our objectives were to derive micro‐ and macrostructural measures of sensorimotor white matter tract integrity from diffusion MRI at term‐equivalent age, and determine their association with early diagnosis of CP. We enrolled 263 VPT infants (≤32 weeks gestational age) as part of a large prospective cohort study. Diffusion and structural MRI were acquired at term. Following consensus guidelines, we defined early diagnosis of CP based on abnormal structural MRI at term and abnormal neuromotor exam at 3–4 months corrected age. Using Constrained Spherical Deconvolution, we derived a white matter fiber orientation distribution (fOD) for subjects, performed probabilistic whole‐brain tractography, and segmented nine sensorimotor tracts of interest. We used the recently developed fixel‐based (FB) analysis to compute fiber density (FD), fiber‐bundle cross‐section (FC), and combined fiber density and cross‐section (FDC) for each tract. Of 223 VPT infants with high‐quality diffusion MRI data, 14 (6.3%) received an early diagnosis of CP. The cohort's mean (SD) gestational age was 29.4 (2.4) weeks and postmenstrual age at MRI scan was 42.8 (1.3) weeks. FD, FC, and FDC for each sensorimotor tract were significantly associated with early CP diagnosis, with and without adjustment for confounders. Measures of sensorimotor tract integrity enhance our understanding of white matter changes that antecede and potentially contribute to the development of CP in VPT infants.

Diffusion MRI tractography for neurosurgery: the basics, current state, technical reliability and challenges

Diffusion magnetic resonance imaging (dMRI) tractography is currently the only imaging technique that allows for non-invasive delineation and visualisation of white matter (WM) tractsin vivo,prompting rapid advances in related fields of brain MRI research in recent years. One of its major clinical applications is for pre-surgical planning and intraoperative image guidance in neurosurgery, where knowledge about the location of WM tracts nearby the surgical target can be helpful to guide surgical resection and optimise post-surgical outcomes. Surgical injuries to these WM tracts can lead to permanent neurological and functional deficits, making the accuracy of tractography reconstructions paramount. The quality of dMRI tractography is influenced by many modifiable factors, ranging from MRI data acquisition through to the post-processing of tractography output, with the potential of error propagation based on decisions made at each and subsequent processing steps. Research over the last 25 years has significantly improved the anatomical accuracy of tractography. An updated review about tractography methodology in the context of neurosurgery is now timely given the thriving research activities in dMRI, to ensure more appropriate applications in the clinical neurosurgical realm. This article aims to review the dMRI physics, and tractography methodologies, highlighting recent advances to provide the key concepts of tractography-informed neurosurgery, with a focus on the general considerations, the current state of practice, technical challenges, potential advances, and future demands to this field.

Interpolation and Averaging of Diffusion MRI Multi-Compartment Models

Multi-compartment models (MCM) are increasingly used to characterize the brain white matter microstructure from diffusion-weighted imaging (DWI). Their use in clinical studies is however limited by the inability to resample an MCM image towards a common reference frame, or to construct atlases from such brain microstructure models. We propose to solve this problem by first identifying that these two tasks amount to the same problem. We propose to tackle it by viewing it as a simplification problem, solved thanks to spectral clustering and the definition of semi-metrics between several usual compartments encountered in the MCM literature. This generic framework is evaluated for two models: the multi-tensor model where individual fibers are modeled as individual tensors and the diffusion direction imaging (DDI) model that differentiates intra- and extra-axonal components of each fiber. Results on simulated data, simulated transformations and real data show the ability of our method to well interpolate MCM images of these types. We finally present as an application an MCM template of normal controls constructed using our approach.

Speech rate association with cerebellar white-matter diffusivity in adults with persistent developmental stuttering

Speech rate is a basic characteristic of language production, which affects the speaker's intelligibility and communication efficiency. Various speech disorders, including persistent developmental stuttering, present altered speech rate. Specifically, adults who stutter (AWS) typically exhibit a slower speech rate compared to fluent speakers. Evidence from imaging studies suggests that the cerebellum contributes to the paced production of speech. People who stutter show structural and functional abnormalities in the cerebellum. However, the involvement of the cerebellar pathways in controlling speech rate remains unexplored. Here, we assess the association of the cerebellar peduncles with speech rate in AWS and control speakers. Diffusion MRI and speech-rate data were collected in 42 participants (23 AWS, 19 controls). We used deterministic tractography with Automatic Fiber segmentation and Quantification (AFQ) to identify the superior, middle, and inferior cerebellar peduncles (SCP, MCP, ICP) bilaterally, and quantified fractional anisotropy (FA) and mean diffusivity (MD) along each tract. No significant differences were observed between AWS and controls in the diffusivity values of the cerebellar peduncles. However, AWS demonstrated a significant negative association between speech rate and FA within the left ICP, a major cerebellar pathway that transmits sensory feedback signals from the olivary nucleus into the cerebellum. The involvement of the ICP in controlling speech production in AWS is compatible with the view that stuttering stems from hyperactive speech monitoring, where even minor deviations from the speech plan are considered as errors. In conclusion, our findings suggest a plausible neural mechanism for speech rate reduction observed in AWS.

Optimization and validation of diffusion MRI-based fiber tracking with neural tracer data as a reference

Diffusion-weighted magnetic resonance imaging (dMRI) allows non-invasive investigation of whole-brain connectivity, which can reveal the brain’s global network architecture and also abnormalities involved in neurological and mental disorders. However, the reliability of connection inferences from dMRI-based fiber tracking is still debated, due to low sensitivity, dominance of false positives, and inaccurate and incomplete reconstruction of long-range connections. Furthermore, parameters of tracking algorithms are typically tuned in a heuristic way, which leaves room for manipulation of an intended result. Here we propose a general data-driven framework to optimize and validate parameters of dMRI-based fiber tracking algorithms using neural tracer data as a reference. Japan’s Brain/MINDS Project provides invaluable datasets containing both dMRI and neural tracer data from the same primates. A fundamental difference when comparing dMRI-based tractography and neural tracer data is that the former cannot specify the direction of connectivity; therefore, evaluating the fitting of dMRI-based tractography becomes challenging. The framework implements multi-objective optimization based on the non-dominated sorting genetic algorithm II. Its performance is examined in two experiments using data from ten subjects for optimization and six for testing generalization. The first uses a seed-based tracking algorithm, iFOD2, and objectives for sensitivity and specificity of region-level connectivity. The second uses a global tracking algorithm and a more refined set of objectives: distance-weighted coverage, true/false positive ratio, projection coincidence, and commissural passage. In both experiments, with optimized parameters compared to default parameters, fiber tracking performance was significantly improved in coverage and fiber length. Improvements were more prominent using global tracking with refined objectives, achieving an average fiber length from 10 to 17 mm, voxel-wise coverage of axonal tracts from 0.9 to 15%, and the correlation of target areas from 40 to 68%, while minimizing false positives and impossible cross-hemisphere connections. Optimized parameters showed good generalization capability for test brain samples in both experiments, demonstrating the flexible applicability of our framework to different tracking algorithms and objectives. These results indicate the importance of data-driven adjustment of fiber tracking algorithms and support the validity of dMRI-based tractography, if appropriate adjustments are employed.

Evolution of Brain Connections: Integrating Diffusion MR Tractography With Gene Expression Highlights Increased Corticocortical Projections in Primates

Diffusion MR tractography permits investigating the 3D structure of cortical pathways as interwoven paths across the entire brain. We use high-resolution scans from diffusion spectrum imaging and high angular resolution diffusion imaging to investigate the evolution of cortical pathways within the euarchontoglire (i.e., primates, rodents) lineage. More specifically, we compare cortical fiber pathways between macaques (Macaca mulatta), marmosets (Callithrix jachus), and rodents (mice, Mus musculus). We integrate these observations with comparative analyses of Neurofilament heavy polypeptide (NEFH) expression across the cortex of mice and primates. We chose these species because their phylogenetic position serves to trace the early evolutionary history of the human brain. Our comparative analysis from diffusion MR tractography, cortical white matter scaling, and NEFH expression demonstrates that the examined primates deviate from mice in possessing increased long-range cross-cortical projections, many of which course across the anterior to posterior axis of the cortex. Our study shows that integrating gene expression data with diffusion MR data is an effective approach in identifying variation in connectivity patterns between species. The expansion of corticocortical pathways and increased anterior to posterior cortical integration can be traced back to an extension of neurogenetic schedules during development in primates.

Assessment of the structural complexity of diffusion MRI voxels using 3D electron microscopy in the rat brain

Validation and interpretation of diffusion magnetic resonance imaging (dMRI) requires detailed understanding of the actual microstructure restricting the diffusion of water molecules. In this study, we used serial block-face scanning electron microscopy (SBEM), a three-dimensional electron microscopy (3D-EM) technique, to image seven white and grey matter volumes in the rat brain. SBEM shows excellent contrast of cellular membranes, which are the major components restricting the diffusion of water in tissue. Additionally, we performed 3D structure tensor (3D-ST) analysis on the SBEM volumes and parameterised the resulting orientation distributions using Watson and angular central Gaussian (ACG) probability distributions as well as spherical harmonic (SH) decomposition. We analysed how these parameterisations described the underlying orientation distributions and compared their orientation and dispersion with corresponding parameters from two dMRI methods, neurite orientation dispersion and density imaging (NODDI) and constrained spherical deconvolution (CSD). Watson and ACG parameterisations and SH decomposition captured well the 3D-ST orientation distributions, but ACG and SH better represented the distributions due to its ability to model asymmetric dispersion. The dMRI parameters corresponded well with the 3D-ST parameters in the white matter volumes, but the correspondence was less evident in the more complex grey matter. SBEM imaging and 3D-ST analysis also revealed that the orientation distributions were often not axially symmetric, a property neatly captured by the ACG distribution. Overall, the ability of SBEM to image diffusion barriers in intricate detail, combined with 3D-ST analysis and parameterisation, provides a step forward toward interpreting and validating the dMRI signals in complex brain tissue microstructure.

Challenges for biophysical modeling of microstructure

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

On the cortical connectivity in the macaque brain: A comparison of diffusion tractography and histological tracing data

Diffusion-weighted magnetic resonance imaging (DW-MRI) tractography is a non-invasive tool to probe neural connections and the structure of the white matter. It has been applied successfully in studies of neurological disorders and normal connectivity. Recent work has revealed that tractography produces a high incidence of false-positive connections, often from "bottleneck" white matter configurations. The rich literature in histological connectivity analysis studies in the macaque monkey enables quantitative evaluation of the performance of tractography algorithms. In this study, we use the intricate connections of frontal, cingulate, and parietal areas, well established by the anatomical literature, to derive a symmetrical histological connectivity matrix composed of 59 cortical areas. We evaluate the performance of fifteen diffusion tractography algorithms, including global, deterministic, and probabilistic state-of-the-art methods for the connectivity predictions of 1711 distinct pairs of areas, among which 680 are reported connected by the literature. The diffusion connectivity analysis was performed on a different ex-vivo macaque brain, acquired using multi-shell DW-MRI protocol, at high spatial and angular resolutions. Across all tested algorithms, the true-positive and true-negative connections were dominant over false-positive and false-negative connections, respectively. Moreover, three-quarters of streamlines had endpoints location in agreement with histological data, on average. Furthermore, probabilistic streamline tractography algorithms show the best performances in predicting which areas are connected. Altogether, we propose a method for quantitative evaluation of tractography algorithms, which aims at improving the sensitivity and the specificity of diffusion-based connectivity analysis. Overall, those results confirm the usefulness of tractography in predicting connectivity, although errors are produced. Many of the errors result from bottleneck white matter configurations near the cortical grey matter and should be the target of future implementation of methods.

Disentangling white-matter damage from physiological fibre orientation dispersion in multiple sclerosis

Multiple sclerosis leads to diffuse damage of the central nervous system, affecting also the normal-appearing white matter. Demyelination and axonal degeneration reduce regional fractional anisotropy in normal-appearing white matter, which can be routinely mapped with diffusion tensor imaging. However, the standard fractional anisotropy metric is also sensitive to physiological variations in orientation dispersion of white matter fibres. This complicates the detection of disease-related damage in large parts of cerebral white matter where microstructure physiologically displays a high degree of fibre dispersion. To resolve this ambiguity, we employed a novel tensor-valued encoding method for diffusion MRI, which yields a microscopic fractional anisotropy metric that is unaffected by regional variations in orientation dispersion. In 26 patients with relapsing-remitting multiple sclerosis, 14 patients with primary-progressive multiple sclerosis and 27 age-matched healthy controls, we compared standard fractional anisotropy mapping with the novel microscopic fractional anisotropy mapping method, focusing on normal-appearing white matter. Mean microscopic fractional anisotropy and standard fractional anisotropy of normal-appearing white matter were significantly reduced in both patient groups relative to healthy controls, but microscopic fractional anisotropy yielded a better reflection of disease-related white-matter alterations. The reduction in mean microscopic fractional anisotropy showed a significant positive linear relationship with physical disability, as reflected by the expanded disability status scale. Mean reduction of microscopic fractional anisotropy in normal-appearing white matter also scaled positively with individual cognitive dysfunction, as measured with the symbol digit modality test. Mean microscopic fractional anisotropy reduction in normal-appearing white matter also showed a positive relationship with total white-matter lesion load as well as lesion load in specific tract systems. None of these relationships between normal-appearing white-matter microstructure and clinical, cognitive or structural measures emerged when using mean fractional anisotropy. Together, the results provide converging evidence that microscopic fractional anisotropy mapping substantially advances the assessment of cerebral white matter in multiple sclerosis by disentangling microstructure damage from variations in physiological fibre orientation dispersion at the stage of data acquisition. Since tensor-valued encoding can be implemented in routine diffusion MRI, microscopic fractional anisotropy mapping bears considerable potential for the future assessment of disease progression in normal-appearing white matter in both relapsing-remitting and progressive forms of multiple sclerosis as well as other white-matter-related brain diseases.

Visualization of live, mammalian neurons during Kainate-infusion using magnetic resonance microscopy

Since its first description and development in the late 20th century, diffusion magnetic resonance imaging (dMRI) has proven useful in describing the microstructural details of biological tissues. Signal generated from the protons of water molecules undergoing Brownian motion produces contrast based on the varied diffusivity of tissue types. Images employing diffusion contrast were first used to describe the diffusion characteristics of tissues, later used to describe the fiber orientations of white matter through tractography, and most recently proposed as a functional contrast method capable of delineating neuronal firing in the active brain. Thanks to the molecular origins of its signal source, diffusion contrast is inherently useful at describing features of the microenvironment; however, limitations in achievable resolution in magnetic resonance imaging (MRI) scans precluded direct visualization of tissue microstructure for decades following MRI's inception as an imaging modality. Even after advancements in MRI hardware had permitted the visualization of mammalian cells, these specialized systems could only accommodate fixed specimens that prohibited the observation and characterization of physiological processes. The goal of the current study was to visualize cellular structure and investigate the subcellular origins of the functional diffusion contrast mechanism (DfMRI) in living, mammalian tissue explants. Using a combination of ultra-high field spectrometers, micro radio frequency (RF) coils, and an MRI-compatible superfusion device, we are able to report the first live, mammalian cells-α-motor neurons-visualized with magnetic resonance microscopy (MRM). We are also able to report changes in the apparent diffusion of the stratum oriens within the hippocampus-a layer comprised primarily of pyramidal cell axons and basal dendrites-and the spinal cord's ventral horn following exposure to kainate.

Cytoarchitecture of the mouse brain by high resolution diffusion magnetic resonance imaging

MRI has been widely used to probe the neuroanatomy of the mouse brain, directly correlating MRI findings to histology is still challenging due to the limited spatial resolution and various image contrasts derived from water relaxation or diffusion properties. Magnetic resonance histology has the potential to become an indispensable research tool to mitigate such challenges. In the present study, we acquired high spatial resolution MRI datasets, including diffusion MRI (dMRI) at 25 ​μm isotropic resolution and quantitative susceptibility mapping (QSM) at 21.5 ​μm isotropic resolution to validate with conventional mouse brain histology. Diffusion weighted images (DWIs) show better delineation of cortical layers and glomeruli in the olfactory bulb than fractional anisotropy (FA) maps. However, among all the image contrasts, including quantitative susceptibility mapping (QSM), T1/T2∗ images and DTI metrics, FA maps highlight unique laminar architecture in sub-regions of the hippocampus, including the strata of the dentate gyrus and CA fields of the hippocampus. The mean diffusivity (MD) and axial diffusivity (AD) yield higher correlation with DAPI (0.62 and 0.71) and NeuN (0.78 and 0.74) than with NF-160 (-0.34 and -0.49). The correlations between FA and DAPI, NeuN, and NF-160 are 0.31, -0.01, and -0.49, respectively. Our findings demonstrate that MRI at microscopic resolution deliver a three-dimensional, non-invasive and non-destructive platform for characterization of fine structural detail in both gray matter and white matter of the mouse brain.

Improved fibre dispersion estimation using b-tensor encoding

Measuring fibre dispersion in white matter with diffusion magnetic resonance imaging (MRI) is limited by an inherent degeneracy between fibre dispersion and microscopic diffusion anisotropy (i.e., the diffusion anisotropy expected for a single fibre orientation). This means that estimates of fibre dispersion rely on strong assumptions, such as constant microscopic anisotropy throughout the white matter or specific biophysical models. Here we present a simple approach for resolving this degeneracy using measurements that combine linear (conventional) and spherical tensor diffusion encoding. To test the accuracy of the fibre dispersion when our microstructural model is only an approximation of the true tissue structure, we simulate multi-compartment data and fit this with a single-compartment model. For such overly simplistic tissue assumptions, we show that the bias in fibre dispersion is greatly reduced (~5x) for single-shell linear and spherical tensor encoding data compared with single-shell or multi-shell conventional data. In in-vivo data we find a consistent estimate of fibre dispersion as we reduce the b-value from 3 to 1.5 ms/μm², increase the repetition time, increase the echo time, or increase the diffusion time. We conclude that the addition of spherical tensor encoded data to conventional linear tensor encoding data greatly reduces the sensitivity of the estimated fibre dispersion to the model assumptions of the tissue microstructure.

Insight into the fundamental trade-offs of diffusion MRI from polarization-sensitive optical coherence tomography in ex vivo human brain

In the first study comparing high angular resolution diffusion MRI (dMRI) in the human brain to axonal orientation measurements from polarization-sensitive optical coherence tomography (PSOCT), we compare the accuracy of orientation estimates from various dMRI sampling schemes and reconstruction methods. We find that, if the reconstruction approach is chosen carefully, single-shell dMRI data can yield the same accuracy as multi-shell data, and only moderately lower accuracy than a full Cartesian-grid sampling scheme. Our results suggest that current dMRI reconstruction approaches do not benefit substantially from ultra-high b-values or from very large numbers of diffusion-encoding directions. We also show that accuracy remains stable across dMRI voxel sizes of 1 mm or smaller but degrades at 2 mm, particularly in areas of complex white-matter architecture. We also show that, as the spatial resolution is reduced, axonal configurations in a dMRI voxel can no longer be modeled as a small set of distinct axon populations, violating an assumption that is sometimes made by dMRI reconstruction techniques. Our findings have implications for in vivo studies and illustrate the value of PSOCT as a source of ground-truth measurements of white-matter organization that does not suffer from the distortions typical of histological techniques.

Validation of structural brain connectivity networks: The impact of scanning parameters

Evaluation of the structural connectivity (SC) of the brain based on tractography has mainly focused on the choice of diffusion model, tractography algorithm, and their respective parameter settings. Here, we systematically validate SC derived from a post mortem monkey brain, while varying key acquisition parameters such as the b-value, gradient angular resolution and image resolution. As gold standard we use the connectivity matrix obtained invasively with histological tracers by Markov et al. (2014). As performance metric, we use cross entropy as a measure that enables comparison of the relative tracer labeled neuron counts to the streamline counts from tractography. We find that high angular resolution and high signal-to-noise ratio are important to estimate SC, and that SC derived from low image resolution (1.0³ mm³) are in better agreement with the tracer network, than those derived from high image resolution (0.5³ mm³) or at an even lower image resolution (2.0³ mm³). In contradiction, sensitivity and specificity analyses suggest that if the angular resolution is sufficient, the balanced compromise in which sensitivity and specificity are identical remains 60-64% regardless of the other scanning parameters. Interestingly, the tracer graph is assumed to be the gold standard but by thresholding, the balanced compromise increases to 70-75%. Hence, by using performance metrics based on binarized tracer graphs, one risks losing important information, changing the performance of SC graphs derived by tractography and their dependence of different scanning parameters.