Evaluation of tractogram filtering methods using human-like connectome phantoms.

Tractography algorithms are prone to reconstructing spurious connections. The set of streamlines generated with tractography can be post-processed to retain the streamlines that are most biologically plausible. Several microstructure-informed filtering algorithms are available for this purpose, however, the comparative performance of these methods has not been extensively evaluated. In this study, we aim to evaluate streamline filtering and post-processing algorithms using simulated connectome phantoms. We first establish a framework for generating connectome phantoms featuring brain-like white matter fiber architectures. We then use our phantoms to systematically evaluate the performance of a range of streamline filtering algorithms, including SIFT, COMMIT, and LiFE. We find that all filtering methods successfully improve connectome accuracy, although filter performance depends on the complexity of the underlying white matter fiber architecture. Filtering algorithms can markedly improve tractography accuracy for simple tubular fiber bundles (F-measure deterministic- unfiltered: 0.49 and best filter: 0.72; F-measure probabilistic- unfiltered: 0.37 and best filter: 0.81), but for more complex brain-like fiber architectures, the improvement is modest (F-measure deterministic- unfiltered: 0.53 and best filter: 0.54; F-measure probabilistic- unfiltered: 0.46 and best filter: 0.50). Overall, filtering algorithms have the potential to improve the accuracy of connectome mapping pipelines, particularly for weighted connectomes and pipelines using probabilistic tractography methods. Our results highlight the need for further advances tractography and streamline filtering to improve the accuracy of connectome mapping.

Connectomes for 40,000 UK Biobank participants: A multi-modal, multi-scale brain network resource.

We mapped functional and structural brain networks for more than 40,000 UK Biobank participants. Structural connectivity was estimated with tractography and diffusion MRI. Resting-state functional MRI was used to infer regional functional connectivity. We provide high-quality structural and functional connectomes for multiple parcellation granularities, several alternative measures of interregional connectivity, and a variety of common data pre-processing techniques, yielding more than one million connectomes in total and requiring more than 200,000 h of compute time. For a single subject, we provide 28 out-of-the-box versions of structural and functional brain networks, allowing users to select, e.g., the parcellation and connectivity measure that best suit their research goals. Furthermore, we provide code and intermediate data for the time-efficient reconstruction of more than 1000 different versions of a subject's connectome based on an array of methodological choices. All connectomes are available via the UK Biobank data-sharing platform and our connectome mapping pipelines are openly available. In this report, we describe our connectome resource in detail for users, outline key considerations in developing an efficient pipeline to map an unprecedented number of connectomes, and report on the quality control procedures that were completed to ensure connectome reliability and accuracy. We demonstrate that our structural and functional connectivity matrices meet a number of quality control checks and replicate previously established findings in network neuroscience. We envisage that our resource will enable new studies of the human connectome in health, disease, and aging at an unprecedented scale.

Notes on fiber length measurements: A case study in the underbelly of open source neuroscience.

Being on the bleeding edge of research requires the use of new and regularly updated software. The result is the occasional and inevitable occurrence of bugs. In the following work we present a case study where a feature request introduced a bug in a neuroimaging software package, which had consequences for the quality of results in a published article. We discuss the process of diagnosis, rectification and analysis replication.

Connectome spatial smoothing (CSS): Concepts, methods, and evaluation.

Structural connectomes are increasingly mapped at high spatial resolutions comprising many hundreds-if not thousands-of network nodes. However, high-resolution connectomes are particularly susceptible to image registration misalignment, tractography artifacts, and noise, all of which can lead to reductions in connectome accuracy and test-retest reliability. We investigate a network analogue of image smoothing to address these key challenges. Connectome Spatial Smoothing (CSS) involves jointly applying a carefully chosen smoothing kernel to the two endpoints of each tractography streamline, yielding a spatially smoothed connectivity matrix. We develop computationally efficient methods to perform CSS using a matrix congruence transformation and evaluate a range of different smoothing kernel choices on CSS performance. We find that smoothing substantially improves the identifiability, sensitivity, and test-retest reliability of high-resolution connectivity maps, though at a cost of increasing storage burden. For atlas-based connectomes (i.e. low-resolution connectivity maps), we show that CSS marginally improves the statistical power to detect associations between connectivity and cognitive performance, particularly for connectomes mapped using probabilistic tractography. CSS was also found to enable more reliable statistical inference compared to connectomes without any smoothing. We provide recommendations for optimal smoothing kernel parameters for connectomes mapped using both deterministic and probabilistic tractography. We conclude that spatial smoothing is particularly important for the reliability of high-resolution connectomes, but can also provide benefits at lower parcellation resolutions. We hope that our work enables computationally efficient integration of spatial smoothing into established structural connectome mapping pipelines.

Quantitative mapping of the brain's structural connectivity using diffusion MRI tractography: A review.

Diffusion magnetic resonance imaging (dMRI) tractography is an advanced imaging technique that enables in vivo reconstruction of the brain's white matter connections at macro scale. It provides an important tool for quantitative mapping of the brain's structural connectivity using measures of connectivity or tissue microstructure. Over the last two decades, the study of brain connectivity using dMRI tractography has played a prominent role in the neuroimaging research landscape. In this paper, we provide a high-level overview of how tractography is used to enable quantitative analysis of the brain's structural connectivity in health and disease. We focus on two types of quantitative analyses of tractography, including: 1) tract-specific analysis that refers to research that is typically hypothesis-driven and studies particular anatomical fiber tracts, and 2) connectome-based analysis that refers to research that is more data-driven and generally studies the structural connectivity of the entire brain. We first provide a review of methodology involved in three main processing steps that are common across most approaches for quantitative analysis of tractography, including methods for tractography correction, segmentation and quantification. For each step, we aim to describe methodological choices, their popularity, and potential pros and cons. We then review studies that have used quantitative tractography approaches to study the brain's white matter, focusing on applications in neurodevelopment, aging, neurological disorders, mental disorders, and neurosurgery. We conclude that, while there have been considerable advancements in methodological technologies and breadth of applications, there nevertheless remains no consensus about the "best" methodology in quantitative analysis of tractography, and researchers should remain cautious when interpreting results in research and clinical applications.

Daratumumab utilization and cost analysis among patients with multiple myeloma in a US community oncology setting.

Background: The introduction of daratumumab into the treatment of multiple myeloma has improved outcomes in patients; however, community oncologists often dose more frequently than the US FDA-approved label. Materials and methods: Integra analyzed its database to elucidate daratumumab treatment patterns and the impact of increased utilization on the cost of care for multiple myeloma. Results: Following week 24, 671 (65%) of 1037 patients remained on daratumumab-containing regimens, with 330 patients continuing more frequent treatments than the expected once-every-4-weeks dosing described in the standard dosing schedule. Patients received an average of 14% more daratumumab doses than the FDA-approved label indicates, increasing the 1-year daratumumab costs by an estimated US$31,353. Conclusion: Daratumumab is utilized more frequently than the FDA-recommended dosing, leading to higher multiple myeloma treatment costs.

Lay abstract Since its first approval in 2015, daratumumab has become the backbone of many multiple myeloma treatment regimens. While its approval has improved outcomes in many patients who undergo treatment, it is expensive and has largely contributed to the increasing costs of care in multiple myeloma. In its most common treatment schedule, patients should transition from weekly and biweekly dosing to treatment once every 4 weeks. However, many providers maintain their patients on a more frequent dosing schedule, which increases Medicare 1-year costs by an estimated US$31,353 and may have unforeseen impacts on adverse events and patient outcomes.

Real-world estimate of the value of one metabolic equivalent in a population of patients planning major surgery.

BACKGROUND: One metabolic equivalent (MET) is equal to resting oxygen consumption. The average value for one MET in humans is widely quoted as 3.5 mL/kg/min. However, this value was derived from a single male participant at the end of the 19th century and has become canonical. Several small studies have identified varied estimates of one MET from widely varying populations. The ability of a patient to complete 4 MET (or 14 mL/kg/min) is considered an indicator of their fitness to proceed to surgery. AIMS: To define a typical value of one MET from a real-world patient population, as well as determine factors that influenced the value. METHODS: A database of cardiopulmonary exercise testing (CPET) was interrogated to find a total of 1847 adult patients who had undergone CPET testing in the previous 10 years. From this database, estimates of oxygen consumption (VO2 ) at rest and at the anaerobic threshold and several other variables were obtained. The influence of age, body mass index (BMI), sex and the use of beta-blockers was tested. RESULTS: The median resting VO2 at rest was 3.6 mL/kg/min (interquartile range (IQR): 3.0-4.2). Neither sex, age >65 years or the use of beta-blockers produced a significant difference in resting VO2 , while those with a BMI >25 kg/m2 had a significantly lower VO2 at rest (3.4 mL/kg/min vs 4.0 mL/kg/min; P < 0.001). CONCLUSIONS: The estimate of 3.6 mL/kg/min for resting VO2 presented here is consistent with the previous literature, despite this being the first large study of its kind. This estimate can be safely used for pre-operative risk stratification.

Characterisation of white matter asymmetries in the healthy human brain using diffusion MRI fixel-based analysis.

The diffusion tensor model for diffusion MRI has been used extensively to study asymmetry in the human brain white matter. However, given the limitations of the tensor model, the nature of any underlying asymmetries remains uncertain, particularly in crossing fibre regions. Here, we provide a more robust characterisation of human brain white matter asymmetries based on fibre-specific diffusion MRI metrics and a whole-brain data-driven approach. We used high-quality diffusion MRI data (n = 100) from the Human Connectome Project, the spherical deconvolution model for fibre orientation distribution estimation, and the Fixel-Based Analysis framework to utilise crossing fibre information in registration, data smoothing and statistical inference. We found many significant asymmetries, widespread throughout the brain white matter, with both left>right and right>left dominances observed in different pathways. No influences of sex, age, or handedness on asymmetry were found. We also report on the relative contributions of microstructural and morphological white matter properties toward the asymmetry findings. Our findings should provide important information to future studies focussing on how these asymmetries are affected by disease, development/ageing, or how they correlate to functional/cognitive measures.

Decoration of the enterococcal polysaccharide antigen EPA is essential for virulence, cell surface charge and interaction with effectors of the innate immune system.

Enterococcus faecalis is an opportunistic pathogen with an intrinsically high resistance to lysozyme, a key effector of the innate immune system. This high level of resistance requires a complex network of transcriptional regulators and several genes (oatA, pgdA, dltA and sigV) acting synergistically to inhibit both the enzymatic and cationic antimicrobial peptide activities of lysozyme. We sought to identify novel genes modulating E. faecalis resistance to lysozyme. Random transposon mutagenesis carried out in the quadruple oatA/pgdA/dltA/sigV mutant led to the identification of several independent insertions clustered on the chromosome. These mutations were located in a locus referred to as the enterococcal polysaccharide antigen (EPA) variable region located downstream of the highly conserved epaA-epaR genes proposed to encode a core synthetic machinery. The epa variable region was previously proposed to be responsible for EPA decorations, but the role of this locus remains largely unknown. Here, we show that EPA decoration contributes to resistance towards charged antimicrobials and underpins virulence in the zebrafish model of infection by conferring resistance to phagocytosis. Collectively, our results indicate that the production of the EPA rhamnopolysaccharide backbone is not sufficient to promote E. faecalis infections and reveal an essential role of the modification of this surface polymer for enterococcal pathogenesis.

The efficacy of different preprocessing steps in reducing motion-related confounds in diffusion MRI connectomics.

Head motion is a major confounding factor in neuroimaging studies. While numerous studies have investigated how motion impacts estimates of functional connectivity, the effects of motion on structural connectivity measured using diffusion MRI have not received the same level of attention, despite the fact that, like functional MRI, diffusion MRI relies on elaborate preprocessing pipelines that require multiple choices at each step. Here, we report a comprehensive analysis of how these choices influence motion-related contamination of structural connectivity estimates. Using a healthy adult sample (N = 294), we evaluated 240 different preprocessing pipelines, devised using plausible combinations of different choices related to explicit head motion correction, tractography propagation algorithms, track seeding methods, track termination constraints, quantitative metrics derived for each connectome edge, and parcellations. We found that an approach to motion correction that includes outlier replacement and within-slice volume correction led to a dramatic reduction in cross-subject correlations between head motion and structural connectivity strength, and that motion contamination is more severe when quantifying connectivity strength using mean tract fractional anisotropy rather than streamline count. We also show that the choice of preprocessing strategy can significantly influence subsequent inferences about network organization, with the location of network hubs varying considerably depending on the specific preprocessing steps applied. Our findings indicate that the impact of motion on structural connectivity can be successfully mitigated using recent motion-correction algorithms that include outlier replacement and within-slice motion correction.

Adrenarcheal hormone-related development of white matter during late childhood.

The aim of the current study was to longitudinally examine how adrenarcheal hormones influence the development of white matter structure from age 8.5 to 10 years. Participants were 120 children (66 female; mean age 8.45 years at Time 1 and 9.97 years at Time 2) who completed two diffusion-weighted imaging scans 1.5 years apart. Morning saliva samples were taken at both assessment time points to measure levels of dehydroepiandrosterone (DHEA), its sulphate (DHEAS), and testosterone. Fixel-based analysis was performed to examine how changes in white matter fibre density (FD) and cross-section (FC) over time were associated with initial levels of hormones, and changes in hormone levels over time. Both FD and FC increased over time in a wide range of white matter tracts. Increases in testosterone over time were related to relatively weaker increases in FC in the inferior fronto-occipital fasciculus. Levels and change in DHEA and DHEAS were not related to FD or FC changes. The results demonstrated development of white matter fibre density and cross-section from age 8.5 to 10 years. Changes in adrenarcheal hormone levels showed limited, localized associations with development of white matter FC. Future research should examine the relevance of adrenarcheal hormone-related white matter development for cognitive functioning; as well as directly compare analysis techniques of white matter structure.

Early childhood development of white matter fiber density and morphology.

Early childhood is an important period for cognitive and brain development, though white matter changes specific to this period remain understudied. Here we utilize a novel analytic approach to quantify and track developmental changes in white matter micro- and macro-structure, calculated from individually oriented fiber-bundle populations, termed "fixels". Fixel-based analysis and mixed-effects models were used to assess tract-wise changes in fiber density and bundle morphology in 73 girls scanned at baseline (ages 4.09-7.02, mean â€‹= â€‹5.47, SD â€‹= â€‹0.81), 6-month (N â€‹= â€‹7), and one-year follow-up (N â€‹= â€‹42). For comparison, we also assessed changes in commonly utilized diffusion tensor metrics: fractional anisotropy (FA), and mean, radial and axial diffusivity (MD, RD, AD). Maturational increases in fixel-metrics were seen in most major white matter tracts, with the most rapid increases in the corticospinal tract and slowest or non-significant increases in the genu of the corpus callosum and uncinate fasciculi. As expected, we observed developmental increases in FA and decreases in MD, RD and AD, though percent changes were smaller relative to fixel-metrics. The majority of tracts showed more substantial morphological than microstructural changes. These findings highlight early childhood as a period of dynamic white matter maturation, characterized by large increases in macroscopic fiber bundle size, mild changes in axonal density, and parallel, albeit less substantial, changes in diffusion tensor metrics.

Maturation and interhemispheric asymmetry in neurite density and orientation dispersion in early childhood.

BACKGROUND: The brain's white matter undergoes profound changes during early childhood, which are believed to underlie the rapid development of cognitive and behavioral skills during this period. Neurite density, and complexity of axonal projections, have been shown to change across the life span, though changes during early childhood are poorly characterized. Here, we utilize neurite orientation dispersion and density imaging (NODDI) to investigate maturational changes in tract-wise neurite density index (NDI) and orientation dispersion index (ODI) during early childhood. Additionally, we assess hemispheric asymmetry of tract-wise NDI and ODI values, and longitudinal changes. METHODS: Two sets of diffusion weighted images with different diffusion-weighting were collected from 125 typically developing children scanned at baseline (N = 125; age range = 4.14-7.29; F/M = 73/52), 6-month (N = 8; F/M = 8/0), and 12-month (N = 52; F/M = 39/13) timepoints. NODDI and template-based tractography using constrained spherical deconvolution were utilized to calculate NDI and ODI values for major white matter tracts. Mixed-effects models controlling for sex, handedness, and in-scanner head motion were utilized to assess developmental changes in tract-wise NDI and ODI. Additional mixed-effects models were used to assess interhemispheric differences in tract-wise NDI and ODI values and hemispheric asymmetries in tract-wise development. RESULTS: Maturational increases in NDI were seen in all major white matter tracts, though we did not observe the expected tract-wise pattern of maturational rates (e.g. fast commissural/projection and slow frontal/temporal tract change). ODI did not change significantly with age in any tract. We observed greater NDI and ODI values in the right as compared to the left hemisphere for most tracts, but no hemispheric asymmetry for rate of change with age. CONCLUSIONS: These findings suggest that neurite density, but not orientation dispersion, increases with age during early childhood. In relation to NDI growth trends reported in infancy and late-childhood, our results suggest that early childhood may be a transitional period for neurite density maturation wherein commissural/projection fibers are approaching maturity, maturation in long range association fibers is increasing, and changes in limbic/frontal fibers remain modest. Rightward asymmetry in NDI and ODI values, but no asymmetry in developmental changes, suggests that rightward asymmetry of neurite density and orientation dispersion is established prior to age 4.

Reduced White Matter Fiber Density in Autism Spectrum Disorder.

Differences in brain networks and underlying white matter abnormalities have been suggested to underlie symptoms of autism spectrum disorder (ASD). However, robustly characterizing microstructural white matter differences has been challenging. In the present study, we applied an analytic technique that calculates structural metrics specific to differently-oriented fiber bundles within a voxel, termed "fixels". Fixel-based analyses were used to compare diffusion-weighted magnetic resonance imaging data from 25 individuals with ASD (mean age = 16.8 years) and 27 typically developing age-matched controls (mean age = 16.9 years). Group comparisons of fiber density (FD) and bundle morphology were run on a fixel-wise, tract-wise, and global white matter (GWM) basis. We found that individuals with ASD had reduced FD, suggestive of decreased axonal count, in several major white matter tracts, including the corpus callosum (CC), bilateral inferior frontal-occipital fasciculus, right arcuate fasciculus, and right uncinate fasciculus, as well as a GWM reduction. Secondary analyses assessed associations with social impairment in participants with ASD, and showed that lower FD in the splenium of the CC was associated with greater social impairment. Our findings suggest that reduced FD could be the primary microstructural white matter abnormality in ASD.

Connectomes from streamlines tractography: Assigning streamlines to brain parcellations is not trivial but highly consequential.

When using diffusion MRI streamlines tractograms to construct structural connectomes, ideally, each streamline should connect exactly 2 regions-of-interest (i.e. network nodes) as defined by a given brain parcellation scheme. However, the ill-posed nature of termination criteria in many tractography algorithms can cause streamlines apparently being associated with zero, one, or more than two grey matter (GM) nodes; streamlines that terminate in white matter or cerebrospinal fluid may even end up being assigned to nodes if the definitions of these nodes are not strictly constrained to genuine GM areas, resulting in a misleading connectome in non-trivial ways. Based on both in-house MRI data and state-of-the-art data provided by the Human Connectome Project, this study investigates the actual influence of streamline-to-node assignment methods, and their interactions with fibre-tracking terminations and brain parcellations, on the construction of pairwise regional connectivity and subsequent connectomic measures. Our results show that the frequency of generating successful pairwise connectivity is heavily affected by the convoluted interactions between the applied strategies for connectome construction, and that minor changes in the mechanism can cause significant variations in the within- and between-module connectivity strengths as well as in the commonly-used graph theory metrics. Our data suggest that these fundamental processes should not be overlooked in structural connectomics research, and that improved data quality is not in itself sufficient to solve the underlying problems associated with assigning streamlines to brain nodes. We demonstrate that the application of advanced fibre-tracking techniques that are designed to correct for inaccuracies of track terminations with respect to anatomical information at the fibre-tracking stage is advantageous to the subsequent connectome construction process, in which pairs of parcellation nodes can be more robustly identified from streamline terminations via a suitable assignment mechanism.

Development of white matter fibre density and morphology over childhood: A longitudinal fixel-based analysis.

PURPOSE: White matter fibre development in childhood involves dynamic changes to microstructural organisation driven by increasing axon diameter, density, and myelination. However, there is a lack of longitudinal studies that have quantified advanced diffusion metrics to identify regions of accelerated fibre maturation, particularly across the early pubertal period. We applied a novel longitudinal fixel-based analysis (FBA) framework, in order to estimate microscopic and macroscopic white matter changes over time. METHODS: Diffusion-weighted imaging (DWI) data were acquired for 59 typically developing children (27 female) aged 9-13 years  at two time-points approximately 16 months apart (time-point 1: 10.4 ±â€¯0.4 years, time-point 2: 11.7 ±â€¯0.5 years). Whole brain FBA was performed using the connectivity-based fixel enhancement method, to assess longitudinal changes in fibre microscopic density and macroscopic morphological measures, and how these changes are related to sex, pubertal stage, and pubertal progression. Follow-up analyses were performed in sub-regions of the corpus callosum to confirm the main findings using a Bayesian repeated measures approach. RESULTS: There was a statistically significant increase in fibre density over time localised to medial and posterior commissural and association fibres, including the forceps major and bilateral superior longitudinal fasciculus. Increases in fibre cross-section were substantially more widespread. The rate of fibre development was not associated with age or sex. In addition, there was no significant relationship between pubertal stage or progression and longitudinal fibre development over time. Follow-up Bayesian analyses were performed to confirm the findings, which supported the null effect of the longitudinal pubertal comparison. CONCLUSION: Using a novel longitudinal fixel-based analysis framework, we demonstrate that white matter fibre density and fibre cross-section increased within a 16-month scan rescan period in specific regions. The observed increases might reflect increasing axonal diameter or axon count. Pubertal stage or progression did not influence the rate of fibre development in the early stages of puberty. Future work should focus on quantifying these measures across a wider age range to capture the full spectrum of fibre development across the pubertal period.

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

PURPOSE: To investigate whether diffusion MRI can be used to study cortical segregation based on a contrast related to neurite density, thus providing a complementary tool to myelin-based MRI techniques used for myeloarchitecture. METHODS: Several myelin-sensitive MRI methods (e.g., based on T1 , T2 , and T2*) have been proposed to parcellate cortical areas based on their myeloarchitecture. Recent improvements in hardware, acquisition, and analysis methods have opened the possibility of achieving a more robust characterization of cortical microstructure using diffusion MRI. High-quality diffusion MRI data from the Human Connectome Project was combined with recent advances in fiber orientation modeling. The orientational average of the fiber orientation distribution was used as a summary parameter, which was displayed as inflated brain surface views. RESULTS: Diffusion MRI identifies cortical patterns consistent with those previously seen by MRI methods used for studying myeloarchitecture, which have shown patterns of high myelination in the sensorimotor strip, visual cortex, and auditory areas and low myelination in frontal and anterior temporal areas. CONCLUSION: In vivo human diffusion MRI provides a useful complementary noninvasive approach to myelin-based methods used to study whole-brain cortical parcellation, by exploiting a contrast based on tissue microstructure related to neurite density, rather than myelin itself. Magn Reson Med 79:2738-2744, 2018. © 2017 International Society for Magnetic Resonance in Medicine.

BIDS apps: Improving ease of use, accessibility, and reproducibility of neuroimaging data analysis methods.

The rate of progress in human neurosciences is limited by the inability to easily apply a wide range of analysis methods to the plethora of different datasets acquired in labs around the world. In this work, we introduce a framework for creating, testing, versioning and archiving portable applications for analyzing neuroimaging data organized and described in compliance with the Brain Imaging Data Structure (BIDS). The portability of these applications (BIDS Apps) is achieved by using container technologies that encapsulate all binary and other dependencies in one convenient package. BIDS Apps run on all three major operating systems with no need for complex setup and configuration and thanks to the comprehensiveness of the BIDS standard they require little manual user input. Previous containerized data processing solutions were limited to single user environments and not compatible with most multi-tenant High Performance Computing systems. BIDS Apps overcome this limitation by taking advantage of the Singularity container technology. As a proof of concept, this work is accompanied by 22 ready to use BIDS Apps, packaging a diverse set of commonly used neuroimaging algorithms.

Investigating white matter fibre density and morphology using fixel-based analysis.

Voxel-based analysis of diffusion MRI data is increasingly popular. However, most white matter voxels contain contributions from multiple fibre populations (often referred to as crossing fibres), and therefore voxel-averaged quantitative measures (e.g. fractional anisotropy) are not fibre-specific and have poor interpretability. Using higher-order diffusion models, parameters related to fibre density can be extracted for individual fibre populations within each voxel ('fixels'), and recent advances in statistics enable the multi-subject analysis of such data. However, investigating within-voxel microscopic fibre density alone does not account for macroscopic differences in the white matter morphology (e.g. the calibre of a fibre bundle). In this work, we introduce a novel method to investigate the latter, which we call fixel-based morphometry (FBM). To obtain a more complete measure related to the total number of white matter axons, information from both within-voxel microscopic fibre density and macroscopic morphology must be combined. We therefore present the FBM method as an integral piece within a comprehensive fixel-based analysis framework to investigate measures of fibre density, fibre-bundle morphology (cross-section), and a combined measure of fibre density and cross-section. We performed simulations to demonstrate the proposed measures using various transformations of a numerical fibre bundle phantom. Finally, we provide an example of such an analysis by comparing a clinical patient group to a healthy control group, which demonstrates that all three measures provide distinct and complementary information. By capturing information from both sources, the combined fibre density and cross-section measure is likely to be more sensitive to certain pathologies and more directly interpretable.

All-Cause Mortality and Progression Risks to Hepatic Decompensation and Hepatocellular Carcinoma in Patients Infected With Hepatitis C Virus.

BACKGROUND: A key question in care of patients with chronic hepatitis C virus (HCV) infection is beginning treatment immediately vs delaying treatment. Risks of mortality and disease progression in "real world" settings are important to assess the implications of delaying HCV treatment. METHODS: This was a cohort study of HCV patients identified from 4 integrated health systems in the United States who had liver biopsies during 2001-2012. The probabilities of death and progression to hepatocellular carcinoma, hepatic decompensation (hepatic encephalopathy, esophageal varices, ascites, or portal hypertension) or liver transplant were estimated over 1, 2, or 5 years by fibrosis stage (Metavir F0-F4) determined by biopsy at beginning of observation. RESULTS: Among 2799 HCV-monoinfected patients who had a qualifying liver biopsy, the mean age at the time of biopsy was 50.7 years. The majority were male (58.9%) and non-Hispanic white (66.9%). Over a mean observation of 5.0 years, 261 (9.3%) patients died and 34 (1.2%) received liver transplants. At 5 years after biopsy, the estimated risk of progression to hepatic decompensation or hepatocellular carcinoma was 37.2% in stage F4, 19.6% in F3, 4.7% in F2, and 2.3% in F0-F1 patients. Baseline biopsy stage F3 or F4 and platelet count below normal were the strongest predictors of progression to hepatic decompensation or hepatocellular carcinoma. CONCLUSIONS: The risks of death and progression to liver failure varied greatly by fibrosis stage. Clinicians and policy makers could use these progression risk data in prioritization and in determining the timing of treatment for patients in early stages of liver disease.