Recommended Implementation of Quantitative Susceptibility Mapping for Clinical Research in The Brain: A Consensus of the ISMRM Electro-Magnetic Tissue Properties Study Group

This article provides recommendations for implementing quantitative susceptibility mapping (QSM) for clinical brain research. It is a consensus of the ISMRM Electro-Magnetic Tissue Properties Study Group. While QSM technical development continues to advance rapidly, the current QSM methods have been demonstrated to be repeatable and reproducible for generating quantitative tissue magnetic susceptibility maps in the brain. However, the many QSM approaches available give rise to the need in the neuroimaging community for guidelines on implementation. This article describes relevant considerations and provides specific implementation recommendations for all steps in QSM data acquisition, processing, analysis, and presentation in scientific publications. We recommend that data be acquired using a monopolar 3D multi-echo GRE sequence, that phase images be saved and exported in DICOM format and unwrapped using an exact unwrapping approach. Multi-echo images should be combined before background removal, and a brain mask created using a brain extraction tool with the incorporation of phase-quality-based masking. Background fields should be removed within the brain mask using a technique based on SHARP or PDF, and the optimization approach to dipole inversion should be employed with a sparsity-based regularization. Susceptibility values should be measured relative to a specified reference, including the common reference region of whole brain as a region of interest in the analysis, and QSM results should be reported with - as a minimum - the acquisition and processing specifications listed in the last section of the article. These recommendations should facilitate clinical QSM research and lead to increased harmonization in data acquisition, analysis, and reporting

Nanostructure-specific X-ray tomography reveals myelin levels, integrity and axon orientations in mouse and human nervous tissue

Myelin insulates neuronal axons and enables fast signal transmission, constituting a key component of brain development, aging and disease. Yet, myelin-specific imaging of macroscopic samples remains a challenge. Here, we exploit myelin’s nanostructural periodicity, and use small-angle X-ray scattering tensor tomography (SAXS-TT) to simultaneously quantify myelin levels, nanostructural integrity and axon orientations in nervous tissue. Proof-of-principle is demonstrated in whole mouse brain, mouse spinal cord and human white and gray matter samples. Outcomes are validated by 2D/3D histology and compared to MRI measurements sensitive to myelin and axon orientations. Specificity to nanostructure is exemplified by concomitantly imaging different myelin types with distinct periodicities. Finally, we illustrate the method’s sensitivity towards myelin-related diseases by quantifying myelin alterations in dysmyelinated mouse brain. This non-destructive, stain-free molecular imaging approach enables quantitative studies of myelination within and across samples during development, aging, disease and treatment, and is applicable to other ordered biomolecules or nanostructures

Reduced structural connectivity in non-motor networks in children born preterm and the influence of early postnatal human cytomegalovirus infection.

INTRODUCTION Preterm birth is increasingly recognized to cause lifelong functional deficits, which often show no correlate in conventional MRI. In addition, early postnatal infection with human cytomegalovirus (hCMV) is being discussed as a possible cause for further impairments. In the present work, we used fixel-based analysis of diffusion-weighted MRI to assess long-term white matter alterations associated with preterm birth and/or early postnatal hCMV infection. MATERIALS AND METHODS 36 former preterms (PT, median age 14.8 years, median gestational age 28 weeks) and 18 healthy term-born controls (HC, median age 11.1 years) underwent high angular resolution DWI scans (1.5 T, b = 2 000 s/mm2, 60 directions) as well as clinical assessment. All subjects showed normal conventional MRI and normal motor function. Early postnatal hCMV infection status (CMV+ and CMV-) had been determined from repeated screening, ruling out congenital infections. Whole-brain analysis was performed, yielding fixel-wise metrics for fiber density (FD), fiber cross-section (FC), and fiber density and cross-section (FDC). Group differences were identified in a whole-brain analysis, followed by an analysis of tract-averaged metrics within a priori selected tracts associated with cognitive function. Both analyses were repeated while differentiating for postnatal hCMV infection status. RESULTS PT showed significant reductions of fixel metrics bilaterally in the cingulum, the genu corporis callosum and forceps minor, the capsula externa, and cerebellar and pontine structures. After including intracranial volume as a covariate, reductions remained significant in the cingulum. The tract-specific investigation revealed further reductions bilaterally in the superior longitudinal fasciculus and the uncinate fasciculus. When differentiating for hCMV infection status, no significant differences were found between CMV+ and CMV-. However, comparing CMV+ against HC, fixel metric reductions were of higher magnitude and of larger spatial extent than in CMV- against HC. CONCLUSION Preterm birth can lead to long-lasting alterations of WM micro- and macrostructure, not visible on conventional MRI. Alterations are located predominantly in WM structures associated with cognitive function, likely underlying the cognitive deficits observed in our cohort. These observed structural alterations were more pronounced in preterms who suffered from early postnatal hCMV infection, in line with previous studies suggesting an additive effect

Machine learning based computational models with permeability for white matter microstructure imaging

Characterising tissue microstructure is of paramount importance for understanding neurological conditions such as Multiple Sclerosis. Therefore, there is a growing interest in imaging tissue microstructure non-invasively. One way to achieve this is by developing tissue models and fitting them to the diffusion-MRI signal. Nevertheless, some microstructure parameters, such as permeability, remain elusive because analytical models that incorporate them are intractable. Machine learning based computational models offer a promising alternative as they bypass the need for analytical expressions. The aim of this thesis is to develop the first machine learning based computational model for white matter microstructure imaging using two promising approaches: random forests and neural networks. To test the feasibility of this new approach, we provide for the first time a direct comparison of machine learning parameter estimates with histology. In this thesis, we demonstrate the idea by estimating permeability via the intra-axonal exchange time τ_i, a potential imaging biomarker for demyelinating pathologies. We use simulations of the diffusion-MRI signal to construct a mapping between signals and microstructure parameters including τ_i. We show for the first time that clinically viable diffusion-weighted sequences can probe exchange times up to approximately 1000 ms. Using healthy in-vivo human and mouse data, we show that our model's estimates are within the plausible range for white matter tissue and display well known trends such as the high-low-high intra-axonal volume fraction f across the corpus callosum. Using human and mouse data from demyelinated tissue, we show that our model detects trends in line with the expected MS pathology: a significant decrease in f and τ_i. Moreover, we show that our random forest estimates of f and τ_i correlate very strongly with histological measurements of f and myelin thickness. This thesis demonstrates that machine learning based computational models are a feasible approach for white matter microstructure imaging. The continually improving SNR in the clinical scanners and the availability of more realistic simulations open up possibilities of using such models as imaging biomarkers for demyelinating diseases such as Multiple Sclerosis

Interindividual differences in intergenerational sustainable behavior are associated with cortical thickness of the dorsomedial and dorsolateral prefrontal cortex.

Intergenerational sustainability requires people of the present generation to make sacrifices today to benefit others of future generations (e.g. mitigating climate change, reducing public debt). Individuals vary greatly in their intergenerational sustainability, and the cognitive and neural sources of these interindividual differences are not yet well understood. We here combined neuroscientific and behavioral methods by assessing interindividual differences in cortical thickness and by using a common-pool resource paradigm with intergenerational contingencies. This enabled us to look for objective, stable, and trait-like neural markers of interindividual differences in consequential intergenerational behavior. We found that individuals behaving sustainably (vs. unsustainably) were marked by greater cortical thickness of the dorsomedial and dorsolateral prefrontal cortex. Given that these brain areas are involved in perspective-taking and self-control and supported by mediation analyses, we speculate that greater cortical thickness of these brain areas better enable individuals to take the perspective of future generations and to resist temptations to maximize personal benefits that incur costs for future generations. By meeting recent calls for the contribution of neuroscience to sustainability research, it is our hope that the present study advances the transdisciplinary understanding of interindividual differences in intergenerational sustainability

Diffusion tensor imaging and resting state functional connectivity as advanced imaging biomarkers of outcome in infants with hypoxic-ischaemic encephalopathy treated with hypothermia

Therapeutic hypothermia confers significant benefit in term neonates with hypoxic-ischaemic encephalopathy (HIE). However, despite the treatment nearly half of the infants develop an unfavourable outcome. Intensive bench-based and early phase clinical research is focused on identifying treatments that augment hypothermic neuroprotection. Qualified biomarkers are required to test these promising therapies efficiently. This thesis aims to assess advanced magnetic resonance imaging (MRI) techniques, including diffusion tensor imaging (DTI) and resting state functional MRI (fMRI) as imaging biomarkers of outcome in infants with HIE who underwent hypothermic neuroprotection. FA values in the white matter (WM), obtained in the neonatal period and assessed by tract-based spatial statistics (TBSS), correlated with subsequent developmental quotient (DQ). However, TBSS is not suitable to study grey matter (GM), which is the primary site of injury following an acute hypoxic-ischaemic event. Therefore, a neonatal atlas-based automated tissue labelling approach was applied to segment central and cortical grey and whole brain WM. Mean diffusivity (MD) in GM structures, obtained in the neonatal period correlated with subsequent DQ. Although the central GM is the primary site of injury on conventional MRI following HIE; FA within WM tissue labels also correlated to neurodevelopmental performance scores. As DTI does not provide information on functional consequences of brain injury functional sequel of HIE was studied with resting state fMRI. Diminished functional connectivity was demonstrated in infants who suffered HIE, which associated with an unfavourable outcome. The results of this thesis suggest that MD in GM tissue labels and FA either determined within WM tissue labels or analysed with TBSS correlate to subsequent neurodevelopmental performance scores in infants who suffered HIE treated with hypothermia and may be applied as imaging biomarkers of outcome in this population. Although functional connectivity was diminished in infants with HIE, resting state fMRI needs further study to assess its utility as an imaging biomarker following a hypoxic-ischaemic brain injury.Open Acces

Irritable Bowel Syndrome and Neuroimaging-based Biomarkers

Postponed access: the file will be accessible after 2021-06-15RAB395MAMD-HELS

Associations between Proprioceptive Neural Pathway Structural Connectivity and Balance in People with Multiple Sclerosis

Mobility and balance impairments are a hallmark of multiple sclerosis (MS), affecting nearly half of patients at presentation and resulting in decreased activity and participation, falls, injuries, and reduced quality of life. A growing body of work suggests that balance impairments in people with mild MS are primarily the result of deficits in proprioception, the ability to determine body position in space in the absence of vision. A better understanding of the pathophysiology of balance disturbances in MS is needed to develop evidence-based rehabilitation approaches. The purpose of the current study was to (1) map the cortical proprioceptive pathway in vivo using diffusion-weighted imaging and (2) assess associations between proprioceptive pathway white matter microstructural integrity and performance on clinical and behavioral balance tasks. We hypothesized that people with MS (PwMS) would have reduced integrity of cerebral proprioceptive pathways, and that reduced white matter microstructure within these tracts would be strongly related to proprioceptive-based balance deficits. We found poorer balance control on proprioceptive-based tasks and reduced white matter microstructural integrity of the cortical proprioceptive tracts in PwMS compared with age-matched healthy controls (HC). Microstructural integrity of this pathway in the right hemisphere was also strongly associated with proprioceptive-based balance control in PwMS and controls. Conversely, while white matter integrity of the right hemisphere’s proprioceptive pathway was significantly correlated with overall balance performance in HC, there was no such relationship in PwMS. These results augment existing literature suggesting that balance control in PwMS may become more dependent upon (1) cerebellar-regulated proprioceptive control, (2) the vestibular system, and/or (3) the visual system

brainlife.io: A decentralized and open source cloud platform to support neuroscience research

Neuroscience research has expanded dramatically over the past 30 years by advancing standardization and tool development to support rigor and transparency. Consequently, the complexity of the data pipeline has also increased, hindering access to FAIR data analysis to portions of the worldwide research community. brainlife.io was developed to reduce these burdens and democratize modern neuroscience research across institutions and career levels. Using community software and hardware infrastructure, the platform provides open-source data standardization, management, visualization, and processing and simplifies the data pipeline. brainlife.io automatically tracks the provenance history of thousands of data objects, supporting simplicity, efficiency, and transparency in neuroscience research. Here brainlife.io's technology and data services are described and evaluated for validity, reliability, reproducibility, replicability, and scientific utility. Using data from 4 modalities and 3,200 participants, we demonstrate that brainlife.io's services produce outputs that adhere to best practices in modern neuroscience research

Anisotropy Across Fields and Scales

This open access book focuses on processing, modeling, and visualization of anisotropy information, which are often addressed by employing sophisticated mathematical constructs such as tensors and other higher-order descriptors. It also discusses adaptations of such constructs to problems encountered in seemingly dissimilar areas of medical imaging, physical sciences, and engineering. Featuring original research contributions as well as insightful reviews for scientists interested in handling anisotropy information, it covers topics such as pertinent geometric and algebraic properties of tensors and tensor fields, challenges faced in processing and visualizing different types of data, statistical techniques for data processing, and specific applications like mapping white-matter fiber tracts in the brain. The book helps readers grasp the current challenges in the field and provides information on the techniques devised to address them. Further, it facilitates the transfer of knowledge between different disciplines in order to advance the research frontiers in these areas. This multidisciplinary book presents, in part, the outcomes of the seventh in a series of Dagstuhl seminars devoted to visualization and processing of tensor fields and higher-order descriptors, which was held in Dagstuhl, Germany, on October 28–November 2, 2018