Estimating axon radius using diffusion-relaxation MRI: calibrating a surface-based relaxation model with histology

Axon radius is a potential biomarker for brain diseases and a crucial tissue microstructure parameter that determines the speed of action potentials. Diffusion MRI (dMRI) allows non-invasive estimation of axon radius, but accurately estimating the radius of axons in the human brain is challenging. Most axons in the brain have a radius below one micrometer, which falls below the sensitivity limit of dMRI signals even when using the most advanced human MRI scanners. Therefore, new MRI methods that are sensitive to small axon radii are needed. In this proof-of-concept investigation, we examine whether a surface-based axonal relaxation process could mediate a relationship between intra-axonal T2 and T1 times and inner axon radius, as measured using postmortem histology. A unique in vivo human diffusion-T1-T2 relaxation dataset was acquired on a 3T MRI scanner with ultra-strong diffusion gradients, using a strong diffusion-weighting (i.e., b = 6,000 s/mm2) and multiple inversion and echo times. A second reduced diffusion-T2 dataset was collected at various echo times to evaluate the model further. The intra-axonal relaxation times were estimated by fitting a diffusion-relaxation model to the orientation-averaged spherical mean signals. Our analysis revealed that the proposed surface-based relaxation model effectively explains the relationship between the estimated relaxation times and the histological axon radius measured in various corpus callosum regions. Using these histological values, we developed a novel calibration approach to predict axon radius in other areas of the corpus callosum. Notably, the predicted radii and those determined from histological measurements were in close agreement

Multiple sclerosis cortical and WM lesion segmentation at 3T MRI: a deep learning method based on FLAIR and MP2RAGE.

The presence of cortical lesions in multiple sclerosis patients has emerged as an important biomarker of the disease. They appear in the earliest stages of the illness and have been shown to correlate with the severity of clinical symptoms. However, cortical lesions are hardly visible in conventional magnetic resonance imaging (MRI) at 3T, and thus their automated detection has been so far little explored. In this study, we propose a fully-convolutional deep learning approach, based on the 3D U-Net, for the automated segmentation of cortical and white matter lesions at 3T. For this purpose, we consider a clinically plausible MRI setting consisting of two MRI contrasts only: one conventional T2-weighted sequence (FLAIR), and one specialized T1-weighted sequence (MP2RAGE). We include 90 patients from two different centers with a total of 728 and 3856 gray and white matter lesions, respectively. We show that two reference methods developed for white matter lesion segmentation are inadequate to detect small cortical lesions, whereas our proposed framework is able to achieve a detection rate of 76% for both cortical and white matter lesions with a false positive rate of 29% in comparison to manual segmentation. Further results suggest that our framework generalizes well for both types of lesion in subjects acquired in two hospitals with different scanners

Model-informed machine learning for multi-component T₂ relaxometry.

Recovering the T <sub>2</sub> distribution from multi-echo T <sub>2</sub> magnetic resonance (MR) signals is challenging but has high potential as it provides biomarkers characterizing the tissue micro-structure, such as the myelin water fraction (MWF). In this work, we propose to combine machine learning and aspects of parametric (fitting from the MRI signal using biophysical models) and non-parametric (model-free fitting of the T <sub>2</sub> distribution from the signal) approaches to T <sub>2</sub> relaxometry in brain tissue by using a multi-layer perceptron (MLP) for the distribution reconstruction. For training our network, we construct an extensive synthetic dataset derived from biophysical models in order to constrain the outputs with a priori knowledge of in vivo distributions. The proposed approach, called Model-Informed Machine Learning (MIML), takes as input the MR signal and directly outputs the associated T <sub>2</sub> distribution. We evaluate MIML in comparison to a Gaussian Mixture Fitting (parametric) and Regularized Non-Negative Least Squares algorithms (non-parametric) on synthetic data, an ex vivo scan, and high-resolution scans of healthy subjects and a subject with Multiple Sclerosis. In synthetic data, MIML provides more accurate and noise-robust distributions. In real data, MWF maps derived from MIML exhibit the greatest conformity to anatomical scans, have the highest correlation to a histological map of myelin volume, and the best unambiguous lesion visualization and localization, with superior contrast between lesions and normal appearing tissue. In whole-brain analysis, MIML is 22 to 4980 times faster than the non-parametric and parametric methods, respectively

Magnetic resonance imaging of T2 - and diffusion snisotropy using a tiltable receive coil

The anisotropic microstructure of white matter is reflected in various MRI contrasts. Transverse relaxation rates can be probed as a function of fibre-orientation with respect to the main magnetic field, while diffusion properties are probed as a function of fibre-orientation with respect to an encoding gradient. While the latter is easy to obtain by varying the orientation of the gradient, as the magnetic field is fixed, obtaining the former requires re-orienting the head. In this work we deployed a tiltable RF-coil to study T2 - and diffusional anisotropy of the brain white matter simultaneously in diffusion- T2 correlation experiments

Relax! Diffusion is not the only way to estimate axon radius in vivo

Axon radius is a potential biomarker for brain diseases and a crucial tissue microstructure parameter that determines the speed of action potentials. Diffusion MRI (dMRI) allows non-invasive estimation of axon radius, but accurately estimating the radius of axons in the human brain is challenging. Most axons in the brain have a radius below one micrometre, which falls below the sensitivity limit of dMRI signals even when using the most advanced human MRI scanners. Therefore, new MRI methods that are sensitive to small axon radii are needed. In this proof-of-concept investigation, we examine whether a surface-based axonal relaxation process could mediate a relationship between intra-axonal T2 and T1 times and inner axon radius, as measured using postmortem histology. A unique in vivo human diffusion-T1-T2 relaxation dataset was acquired on a 3T MRI scanner with ultra-strong diffusion gradients, using a strong diffusion-weighting (i.e., b=6000 s/mm2) and multiple inversion and echo times. A second reduced diffusion-T2 dataset was collected at various echo times to evaluate the model further. The intra-axonal relaxation times were estimated by fitting a diffusion-relaxation model to the orientation-averaged spherical mean signals. Our analysis revealed that the proposed surface-based relaxation model effectively explains the relationship between the estimated relaxation times and the histological axon radius measured in various corpus callosum regions. Using these histological values, we developed a novel calibration approach to predict axon radius in other areas of the corpus callosum. Notably, the predicted radii and those determined from histological measurements were in close agreement.Comment: 48 pages, 10 figure

Consequences of chronic diseases and other limitations associated with old age - A scoping review

Funding Information: This work supported in part by the LTC INTER COST, Evaluation of the Potential for Reducing Health and Social Expenses for Elderly People Using the Smart Environment, through the Ministry of Education, Youth and Sports, Czech Republic, under Project LTC18035; and in part by the project of Excellence, University of Hradec Kralove, FIM, Czech Republic (ID: 2205–2019). First author – Petra Maresova is principle investigator of LTC18035 INTER COST project, from which Petra Maresova, Ondrej Krejcar and Kamil Kuca are funded for all expenses including personal costs. Ehsan Javanmardi is funded from project of Excellence ID: 2205–2019 for personal costs. Sabina Barakovic, Jasmina Barakovic Husic and Signe Tomsone are members of COST ACTION 16226 of which also Petra Maresova and Ondrej Krejcar are paticipants, while this article also ACKnowledge this project CA16226. Funding Information: The authors would like to hereby acknowledge COST Action CA16226 for their networking support. The Indoor Living Space Improvement: Smart Habitat for the Elderly played a role of networking platform for knowledge sharing and interchanging ideas for joint research and publication, what was the base for creating this study. Based on CA16226 project LTC18035 INTER COST was proposed for national funding support of COST ACTION Framework. COST is a funding agency that helps innovation and research networks. Our Action was instrumental in connecting research programmes throughout the EU region. Their contribution has made it possible for scientists to connect with each other and share their ideas and findings. This allows for more research and better innovation. More information can be found at www.cost.eu. The authors would also like to acknowledge the Excellence 2019 internal research project, Faculty of Informatics and Management, University of Hradec Kralove, Czech Republic. Publisher Copyright: © 2019 The Author(s).Background: The phenomenon of the increasing number of ageing people in the world is arguably the most significant economic, health and social challenge that we face today. Additionally, one of the major epidemiologic trends of current times is the increase in chronic and degenerative diseases. This paper tries to deliver a more up to date overview of chronic diseases and other limitations associated with old age and provide a more detailed outlook on the research that has gone into this field. Methods: First, challenges for seniors, including chronic diseases and other limitations associated with old age, are specified. Second, a review of seniors' needs and concerns is performed. Finally, solutions that can improve seniors' quality of life are discussed. Publications obtained from the following databases are used in this scoping review: Web of Science, PubMed, and Science Direct. Four independent reviewers screened the identified records and selected relevant publications published from 2010 to 2017. A total of 1916 publications were selected. In all, 52 papers were selected based on abstract content. For further processing, 21 full papers were screened." Results: The results indicate disabilities as a major problem associated with seniors' activities of daily living dependence. We founded seven categories of different conditions - psychological problems, difficulties in mobility, poor cognitive function, falls and incidents, wounds and injuries, undernutrition, and communication problems. In order to minimize ageing consequences, some areas require more attention, such as education and training; technological tools; government support and welfare systems; early diagnosis of undernutrition, cognitive impairment, and other diseases; communication solutions; mobility solutions; and social contributions. Conclusions: This scoping review supports the view on chronic diseases in old age as a complex issue. To prevent the consequences of chronic diseases and other limitations associated with old age related problems demands multicomponent interventions. Early recognition of problems leading to disability and activities of daily living (ADL) dependence should be one of essential components of such interventions.publishersversionPeer reviewe

Optical coherence tomography reflects clinically relevant gray matter damage in patients with multiple sclerosis

BACKGROUND: Retinal degeneration leading to optical coherence tomography (OCT) changes is frequent in patients with multiple sclerosis (PwMS). OBJECTIVE: To investigate associations among OCT changes, MRI measurements of global and regional brain volume loss, and physical and cognitive impairment in PwMS. METHODS: 95 PwMS and 52 healthy controls underwent OCT and MRI examinations. Mean peripapillary retinal nerve fiber layer (pRNFL) thickness and ganglion cell/inner plexiform layer (GCIPL) volume were measured. In PwMS disability was quantified with the Expanded Disability Status Scale (EDSS) and Symbol Digit Modalities Test (SDMT). Associations between OCT, MRI, and clinical measures were investigated with multivariable regression models. RESULTS: In PwMS, pRNFL and GCIPL were associated with the volume of whole brain (p < 0.04), total gray matter (p < 0.002), thalamus (p ≤ 0.04), and cerebral cortex (p ≤ 0.003) -both globally and regionally-, but not white matter. pRNFL and GCIPL were also inversely associated with T2-lesion volume (T2LV), especially in the optic radiations (p < 0.0001). The brain volumes associated with EDSS and SDMT significantly overlapped with those correlating with pRNFL and GCIPL. CONCLUSIONS: In PwMS, pRNFL and GCIPL reflect the integrity of clinically-relevant gray matter structures, underling the value of OCT measures as markers of neurodegeneration and disability in multiple sclerosis

Association of Spinal Cord Atrophy and Brain Paramagnetic Rim Lesions With Progression Independent of Relapse Activity in People With MS.

Progression independent of relapse activity (PIRA) is a crucial determinant of overall disability accumulation in multiple sclerosis (MS). Accelerated brain atrophy has been shown in patients experiencing PIRA. In this study, we assessed the relation between PIRA and neurodegenerative processes reflected by (1) longitudinal spinal cord atrophy and (2) brain paramagnetic rim lesions (PRLs). Besides, the same relationship was investigated in progressive MS (PMS). Last, we explored the value of cross-sectional brain and spinal cord volumetric measurements in predicting PIRA. From an ongoing multicentric cohort study, we selected patients with MS with (1) availability of a susceptibility-based MRI scan and (2) regular clinical and conventional MRI follow-up in the 4 years before the susceptibility-based MRI. Comparisons in spinal cord atrophy rates (explored with linear mixed-effect models) and PRL count (explored with negative binomial regression models) were performed between: (1) relapsing-remitting (RRMS) and PMS phenotypes and (2) patients experiencing PIRA and patients without confirmed disability accumulation (CDA) during follow-up (both considering the entire cohort and the subgroup of patients with RRMS). Associations between baseline MRI volumetric measurements and time to PIRA were explored with multivariable Cox regression analyses. In total, 445 patients with MS (64.9% female; mean [SD] age at baseline 45.0 [11.4] years; 11.2% with PMS) were enrolled. Compared with patients with RRMS, those with PMS had accelerated cervical cord atrophy (mean difference in annual percentage volume change [MD-APC] -1.41; p = 0.004) and higher PRL load (incidence rate ratio [IRR] 1.93; p = 0.005). Increased spinal cord atrophy (MD-APC -1.39; p = 0.0008) and PRL burden (IRR 1.95; p = 0.0008) were measured in patients with PIRA compared with patients without CDA; such differences were also confirmed when restricting the analysis to patients with RRMS. Baseline volumetric measurements of the cervical cord, whole brain, and cerebral cortex significantly predicted time to PIRA (all p ≤ 0.002). Our results show that PIRA is associated with both increased spinal cord atrophy and PRL burden, and this association is evident also in patients with RRMS. These findings further point to the need to develop targeted treatment strategies for PIRA to prevent irreversible neuroaxonal loss and optimize long-term outcomes of patients with MS

Serum neurofilament light chain for individual prognostication of disease activity in people with multiple sclerosis: a retrospective modelling and validation study

Background: Serum neurofilament light chain (sNfL) is a biomarker of neuronal damage that is used not only to monitor disease activity and response to drugs and to prognosticate disease course in people with multiple sclerosis on the group level. The absence of representative reference values to correct for physiological age-dependent increases in sNfL has limited the diagnostic use of this biomarker at an individual level. We aimed to assess the applicability of sNfL for identification of people at risk for future disease activity by establishing a reference database to derive reference values corrected for age and body-mass index (BMI). Furthermore, we used the reference database to test the suitability of sNfL as an endpoint for group-level comparison of effectiveness across disease-modifying therapies. Methods: For derivation of a reference database of sNfL values, a control group was created, comprising participants with no evidence of CNS disease taking part in four cohort studies in Europe and North America. We modelled the distribution of sNfL concentrations in function of physiological age-related increase and BMI-dependent modulation, to derive percentile and Z score values from this reference database, via a generalised additive model for location, scale, and shape. We tested the reference database in participants with multiple sclerosis in the Swiss Multiple Sclerosis Cohort (SMSC). We compared the association of sNfL Z scores with clinical and MRI characteristics recorded longitudinally to ascertain their respective disease prognostic capacity. We validated these findings in an independent sample of individuals with multiple sclerosis who were followed up in the Swedish Multiple Sclerosis registry. Findings: We obtained 10 133 blood samples from 5390 people (median samples per patient 1 [IQR 1–2] in the control group). In the control group, sNfL concentrations rose exponentially with age and at a steeper increased rate after approximately 50 years of age. We obtained 7769 samples from 1313 people (median samples per person 6·0 [IQR 3·0–8·0]). In people with multiple sclerosis from the SMSC, sNfL percentiles and Z scores indicated a gradually increased risk for future acute (eg, relapse and lesion formation) and chronic (disability worsening) disease activity. A sNfL Z score above 1·5 was associated with an increased risk of future clinical or MRI disease activity in all people with multiple sclerosis (odds ratio 3·15, 95% CI 2·35–4·23; p<0·0001) and in people considered stable with no evidence of disease activity (2·66, 1·08–6·55; p=0·034). Increased Z scores outperformed absolute raw sNfL cutoff values for diagnostic accuracy. At the group level, the longitudinal course of sNfL Z score values in people with multiple sclerosis from the SMSC decreased to those seen in the control group with use of monoclonal antibodies (ie, alemtuzumab, natalizumab, ocrelizumab, and rituximab) and, to a lesser extent, oral therapies (ie, dimethyl fumarate, fingolimod, siponimod, and teriflunomide). However, longitudinal sNfL Z scores remained elevated with platform compounds (interferons and glatiramer acetate; p<0·0001 for the interaction term between treatment category and treatment duration). Results were fully supported in the validation cohort (n=4341) from the Swedish Multiple Sclerosis registry. Interpretation: The use of sNfL percentiles and Z scores allows for identification of individual people with multiple sclerosis at risk for a detrimental disease course and suboptimal therapy response beyond clinical and MRI measures, specifically in people with disease activity-free status. Additionally, sNfL might be used as an endpoint for comparing effectiveness across drug classes in pragmatic trials. Funding: Swiss National Science Foundation, Progressive Multiple Sclerosis Alliance, Biogen, Celgene, Novartis, Roche

Tractography dissection variability: What happens when 42 groups dissect 14 white matter bundles on the same dataset?

White matter bundle segmentation using diffusion MRI fiber tractography has become the method of choice to identify white matter fiber pathways in vivo in human brains. However, like other analyses of complex data, there is considerable variability in segmentation protocols and techniques. This can result in different reconstructions of the same intended white matter pathways, which directly affects tractography results, quantification, and interpretation. In this study, we aim to evaluate and quantify the variability that arises from different protocols for bundle segmentation. Through an open call to users of fiber tractography, including anatomists, clinicians, and algorithm developers, 42 independent teams were given processed sets of human whole-brain streamlines and asked to segment 14 white matter fascicles on six subjects. In total, we received 57 different bundle segmentation protocols, which enabled detailed volume-based and streamline-based analyses of agreement and disagreement among protocols for each fiber pathway. Results show that even when given the exact same sets of underlying streamlines, the variability across protocols for bundle segmentation is greater than all other sources of variability in the virtual dissection process, including variability within protocols and variability across subjects. In order to foster the use of tractography bundle dissection in routine clinical settings, and as a fundamental analytical tool, future endeavors must aim to resolve and reduce this heterogeneity. Although external validation is needed to verify the anatomical accuracy of bundle dissections, reducing heterogeneity is a step towards reproducible research and may be achieved through the use of standard nomenclature and definitions of white matter bundles and well-chosen constraints and decisions in the dissection process