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Traumatic brain injury (TBI) is a risk factor for the later development of neurodegenerative diseases that may have various underlying pathologies. Chronic traumatic encephalopathy (CTE) in particular is associated with repetitive mild TBI (mTBI) and is characterized pathologically by aggregation of hyperphosphorylated tau into neurofibrillary tangles (NFTs). CTE may be suspected when behavior, cognition, and/or memory deteriorate following repetitive mTBI. Exposure to blast overpressure from improvised explosive devices (IEDs) has been implicated as a potential antecedent for CTE amongst Iraq and Afghanistan Warfighters. In this study, we identified biomarker signatures in rats exposed to repetitive low-level blast that develop chronic anxiety-related traits and in human veterans exposed to IED blasts in theater with behavioral, cognitive, and/or memory complaints. Rats exposed to repetitive low-level blasts accumulated abnormal hyperphosphorylated tau in neuronal perikarya and perivascular astroglial processes. Using positron emission tomography (PET) and the [ 18 F]AV1451 (flortaucipir) tau ligand, we found that five of 10 veterans exhibited excessive retention of [ 18 F]AV1451 at the white/gray matter junction in frontal, parietal, and temporal brain regions, a typical localization of CTE tauopathy. We also observed elevated levels of neurofilament light (NfL) chain protein in the plasma of veterans displaying excess [ 18 F]AV1451 retention. These findings suggest an association linking blast injury, tauopathy, and neuronal injury. Further study is required to determine whether clinical, neuroimaging, and/or fluid biomarker signatures can improve the diagnosis of long-term neuropsychiatric sequelae of mTBI., (© 2020. The Author(s).)

Computational models of the brain and its biomechanical response to skull accelerations are important tools for understanding and predicting traumatic brain injuries (TBIs). However, most models have been developed using experimental data collected on animal models and cadaveric specimens, both of which differ from the living human brain. Here we describe efforts to noninvasively measure the biomechanical response of the human brain with MRI-at non-injurious strain levels-and generate data that can be used to develop, calibrate, and evaluate computational brain biomechanics models. Specifically, this paper reports on a project supported by the National Institute of Neurological Disorders and Stroke to comprehensively image brain anatomy and geometry, mechanical properties, and brain deformations that arise from impulsive and harmonic skull loadings. The outcome of this work will be a publicly available dataset ( http://www.nitrc.org/projects/bbir ) that includes measurements on both males and females across an age range from adolescence to older adulthood. This article describes the rationale and approach for this study, the data available, and how these data may be used to develop new computational models and augment existing approaches; it will serve as a reference to researchers interested in using these data., (© 2021. Biomedical Engineering Society.)

We propose a method to jointly super-resolve an anisotropic image volume along with its corresponding voxel labels without external training data. Our method is inspired by internally trained superresolution, or self-super-resolution (SSR) techniques that target anisotropic, low-resolution (LR) magnetic resonance (MR) images. While resulting images from such methods are quite useful, their corresponding LR labels-derived from either automatic algorithms or human raters-are no longer in correspondence with the super-resolved volume. To address this, we develop an SSR deep network that takes both an anisotropic LR MR image and its corresponding LR labels as input and produces both a super-resolved MR image and its super-resolved labels as output. We evaluated our method with 50 T 1 -weighted brain MR images 4× down-sampled with 10 automatically generated labels. In comparison to other methods, our method had superior Dice across all labels and competitive metrics on the MR image. Our approach is the first reported method for SSR of paired anisotropic image and label volumes.

Brain movement during an impact can elicit a traumatic brain injury, but tissue kinematics vary from person to person and knowledge regarding this variability is limited. This study examines spatio-temporal brain-skull displacement and brain tissue deformation across groups of subjects during a mild impact in vivo . The heads of two groups of participants were imaged while subjected to a mild (less than 350 rad s -2 ) impact during neck extension (NE, n = 10) and neck rotation (NR, n = 9). A kinematic atlas of displacement and strain fields averaged across all participants was constructed and compared against individual participant data. The atlas-derived mean displacement magnitude was 0.26 ± 0.13 mm for NE and 0.40 ± 0.26 mm for NR, which is comparable to the displacement magnitudes from individual participants. The strain tensor from the atlas displacement field exhibited maximum shear strain (MSS) of 0.011 ± 0.006 for NE and 0.017 ± 0.009 for NR and was lower than the individual MSS averaged across participants. The atlas illustrates common patterns, containing some blurring but visible relationships between anatomy and kinematics. Conversely, the direction of the impact, brain size, and fluid motion appear to underlie kinematic variability. These findings demonstrate the biomechanical roles of key anatomical features and illustrate common features of brain response for model evaluation.

Fluorescence lifetime imaging microscopy (FLIM) is a powerful technique, capable of label-free assessment of the metabolic state and function within single cells. The FLIM measurements of autofluorescence were recently shown to be sensitive to the functional state and subtype of T cells. Therefore, autofluorescence FLIM could improve cell manufacturing technologies for adoptive immunotherapy, which currently require a time-intensive process of cell labeling with fluorescent antibodies. However, current autofluorescence FLIM implementations are typically too slow, bulky, and prohibitively expensive for use in cell manufacturing pipelines. Here we report a single photon-excited confocal whole-cell autofluorescence system that uses fast field-programmable gate array-based time tagging electronics to achieve time-correlated single photon counting (TCSPC) of single-cell autofluorescence. The system includes simultaneous near-infrared bright-field imaging and is sensitive to variations in the fluorescence decay profile of the metabolic coenzyme NAD(P)H in human T cells due to the activation state. The classification of activated and quiescent T cells achieved high accuracy and precision (area under the receiver operating characteristic curve, AUC = 0.92). The lower-cost, higher acquisition speed, and resistance to pile-up effects at high photon flux compared to traditional multiphoton-excited FLIM and TCSPC implementations with similar SNR make this system attractive for integration into flow cytometry, sorting, and quality control in cell manufacturing.

Natural modes and frequencies of three-dimensional (3D) deformation of the human brain were identified from in vivo tagged magnetic resonance images (MRI) acquired dynamically during transient mild acceleration of the head. Twenty 3D strain fields, estimated from tagged MRI image volumes in 19 adult subjects, were analyzed using dynamic mode decomposition (DMD). These strain fields represented dynamic, 3D brain deformations during constrained head accelerations, either involving rotation about the vertical axis of the neck or neck extension. DMD results reveal fundamental oscillatory modes of deformation at damped frequencies near 7 Hz (in neck rotation) and 11 Hz (in neck extension). Modes at these frequencies were found consistently among all subjects. These characteristic features of 3D human brain deformation are important for understanding the response of the brain in head impacts and provide valuable quantitative criteria for the evaluation and use of computer models of brain mechanics., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

High resolution magnetic resonance (MR) images are desired in many clinical and research applications. Acquiring such images with high signal-to-noise (SNR), however, can require a long scan duration, which is difficult for patient comfort, is more costly, and makes the images susceptible to motion artifacts. A very common practical compromise for both 2D and 3D MR imaging protocols is to acquire volumetric MR images with high in-plane resolution, but lower through-plane resolution. In addition to having poor resolution in one orientation, 2D MRI acquisitions will also have aliasing artifacts, which further degrade the appearance of these images. This paper presents an approach SMORE1 based on convolutional neural networks (CNNs) that restores image quality by improving resolution and reducing aliasing in MR images.2 This approach is self-supervised, which requires no external training data because the high-resolution and low-resolution data that are present in the image itself are used for training. For 3D MRI, the method consists of only one self-supervised super-resolution (SSR) deep CNN that is trained from the volumetric image data. For 2D MRI, there is a self-supervised anti-aliasing (SAA) deep CNN that precedes the SSR CNN, also trained from the volumetric image data. Both methods were evaluated on a broad collection of MR data, including filtered and downsampled images so that quantitative metrics could be computed and compared, and actual acquired low resolution images for which visual and sharpness measures could be computed and compared. The super-resolution method is shown to be visually and quantitatively superior to previously reported methods.

Advances in brain imaging and computational methods have facilitated the creation of subject-specific computational brain models that aid researchers in investigating brain trauma using simulated impacts. The emergence of magnetic resonance elastography (MRE) as a non-invasive mechanical neuroimaging tool has enabled in vivo estimation of material properties at low-strain, harmonic loading. An open question in the field has been how this data can be integrated into computational models. The goals of this study were to use a novel MRI dataset acquired in human volunteers to generate models with subject-specific anatomy and material properties, and then to compare simulated brain deformations to subject-specific brain deformation data under non-injurious loading. Models of five subjects were simulated with linear viscoelastic (LVE) material properties estimated directly from MRE data. Model predictions were compared to experimental brain deformation acquired in the same subjects using tagged MRI. Outcomes from the models matched the spatial distribution and magnitude of the measured peak strain components as well as the 95 th percentile in-plane peak strains within 0.005 mm/mm and maximum principal strain within 0.012 mm/mm. Sensitivity to material heterogeneity was also investigated. Simulated brain deformations from a model with homogenous brain properties and a model with brain properties discretized with up to ten regions were very similar (a mean absolute difference less than 0.0015 mm/mm in peak strains). Incorporating material properties directly from MRE into a biofidelic subject-specific model is an important step toward future investigations of higher-order model features and simulations under more severe loading conditions., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

The rapid deformation of brain tissue in response to head impact can lead to traumatic brain injury. In vivo measurements of brain deformation during non-injurious head impacts are necessary to understand the underlying mechanisms of traumatic brain injury and compare to computational models of brain biomechanics. Using tagged magnetic resonance imaging (MRI), we obtained measurements of three-dimensional strain tensors that resulted from a mild head impact after neck rotation or neck extension. Measurements of maximum principal strain (MPS) peaked shortly after impact, with maximal values of 0.019-0.053 that correlated strongly with peak angular velocity. Subject-specific patterns of MPS were spatially heterogeneous and consistent across subjects for the same motion, though regions of high deformation differed between motions. The largest MPS values were seen in the cortical gray matter and cerebral white matter for neck rotation and the brainstem and cerebellum for neck extension. Axonal fiber strain (Ef) was estimated by combining the strain tensor with diffusion tensor imaging data. As with MPS, patterns of Ef varied spatially within subjects, were similar across subjects within each motion, and showed group differences between motions. Values were highest and most strongly correlated with peak angular velocity in the corpus callosum for neck rotation and in the brainstem for neck extension. The different patterns of brain deformation between head motions highlight potential areas of greater risk of injury between motions at higher loading conditions. Additionally, these experimental measurements can be directly compared to predictions of generic or subject-specific computational models of traumatic brain injury.

Multi-site training methods for artificial neural networks are of particular interest to the medical machine learning community primarily due to the difficulty of data sharing between institutions. However, contemporary multi-site techniques such as weight averaging and cyclic weight transfer make theoretical sacrifices to simplify implementation. In this paper, we implement federated gradient averaging (FGA), a variant of federated learning without data transfer that is mathematically equivalent to single site training with centralized data. We evaluate two scenarios: a simulated multi-site dataset for handwritten digit classification with MNIST and a real multi-site dataset with head CT hemorrhage segmentation. We compare federated gradient averaging to single site training, federated weight averaging (FWA), and cyclic weight transfer. In the MNIST task, we show that training with FGA results in a weight set equivalent to centralized single site training. In the hemorrhage segmentation task, we show that FGA achieves on average superior results to both FWA and cyclic weight transfer due to its ability to leverage momentum-based optimization.

The meninges serve as a functional barrier surrounding the brain, critical to the immune response, and can be compromised following head trauma. Meningeal enhancement can be detected on contrast-enhanced MRI in patients presenting with acute traumatic brain injury, even when head CT is negative. Following head trauma, gadolinium-based contrast appears to extravasate from the vasculature, enhancing the dura within minutes, and later permeates the subarachnoid space. The aims of this study were to characterize the initial kinetics of the uptake of contrast agent after injury and the delayed redistribution of contrast enhancement in the subarachnoid space in hyperacute patients. Neuroimaging was obtained prospectively in two large ongoing observational studies of patients aged 18 years or older presenting to the emergency department with suspected acute head injury. Dynamic contrast-enhanced MRI studies in a cohort of consecutively enrolling patients with mild traumatic brain injury ( n  = 36) determined that the kinetic half-life of dural-related meningeal enhancement was 1.3 ± 0.6 min (95% enhancement within 6 min). The extravasation of contrast into the subarachnoid space was investigated in a cohort of CT negative mild traumatic brain injury patients initially imaged within 6 h of injury (hyperacute) who subsequently underwent a delayed MRI, with no additional contrast administration, several hours after the initial MRI. Of the 32 patients with delayed post-contrast imaging, 18 (56%) had conspicuous expansion of the contrast enhancement into the subarachnoid space, predominantly along the falx and superior sagittal sinus. Patients negative for traumatic meningeal enhancement on initial hyperacute MRI continued to have no evidence of meningeal enhancement on the delayed MRI. These studies demonstrate that (i) the initial enhancement of the traumatically injured meninges occurs within minutes of contrast injection, suggesting highly permeable meningeal vasculature, and that (ii) contrast in the meninges redistributes within the subarachnoid space over the period of hours, suggesting a compromise in the blood-brain and/or blood-cerebrospinal barriers. Data from the parent study indicate that up to one in two patients with mild traumatic brain injury have traumatic brain injury on acute (<48 h) MRI, with a higher prevalence seen in patients with moderate or severe traumatic brain injury. The current study's findings of traumatic meningeal enhancement and the subsequent delayed extravasation of contrast into the subarachnoid spaces indicate that a substantial percentage of patients with even mild traumatic brain injury may have a transient disruption in barriers separating the vasculature from the brain., (Published by Oxford University Press on behalf of the Guarantors of Brain 2020.)

Introduction: Diabetic retinopathy (DR) is the leading cause of blindness among the working population in the USA. Current therapies, including anti-vascular endothelial growth factor treatments, cannot completely reverse the visual defects induced by DR. MicroRNA-150 (miR-150) is a regulator that suppresses inflammation and pathological angiogenesis. In patients with diabetes, miR-150 is downregulated. As chronic inflammation is a major contributor to the pathogenesis of DR, whether diabetes-associated decrease of miR-150 is merely associated with the disease progression or decreased miR-150 causes retinal inflammation and pathological angiogenesis is still unknown., Research Design and Methods: We used high-fat diet (HFD)-induced type 2 diabetes (T2D) in wild type (WT) and miR-150 knockout (miR-150 -/- ) mice for this study and compared retinal function and microvasculature morphology., Results: We found that WT mice fed with an HFD for only 1 month had a significant decrease of miR-150 in the blood and retina, and retinal light sensitivity also decreased. The miR-150 -/- mice on the HFD developed diabetes similar to that of the WT. At 7-8 months old, miR-150 -/- mice under normal diet had increased degeneration of retinal capillaries compared with WT mice, indicating that miR-150 is important in maintaining the structural integrity of retinal microvasculature. Deletion of miR-150 worsened HFD-induced retinal dysfunction as early as 1 month after the diet regimen, and it exacerbated HFD-induced T2DR by further increasing retinal inflammation and microvascular degeneration., Conclusion: These data suggest that decreased miR-150 caused by obesity or diabetic insults is not merely correlated to the disease progression, but it contributes to the retinal dysfunction and inflammation, as well as the development of DR., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2020. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Objective: To determine whether neurofilament light (NfL), glial fibrillary acidic protein (GFAP), tau, and ubiquitin C-terminal hydrolase-L1 (UCH-L1) measured in serum relate to traumatic brain injury (TBI) diagnosis, injury severity, brain volume, and diffusion tensor imaging (DTI) measures of traumatic axonal injury (TAI) in patients with TBI., Methods: Patients with TBI (n = 162) and controls (n = 68) were prospectively enrolled between 2011 and 2019. Patients with TBI also underwent serum, functional outcome, and imaging assessments at 30 (n = 30), 90 (n = 48), and 180 (n = 59) days, and 1 (n = 84), 2 (n = 57), 3 (n = 46), 4 (n = 38), and 5 (n = 29) years after injury., Results: At enrollment, patients with TBI had increased serum NfL compared to controls ( p < 0.0001). Serum NfL decreased over the course of 5 years but remained significantly elevated compared to controls. Serum NfL at 30 days distinguished patients with mild, moderate, and severe TBI from controls with an area under the receiver-operating characteristic curve (AUROC) of 0.84, 0.92, and 0.92, respectively. At enrollment, serum GFAP was elevated in patients with TBI compared to controls ( p < 0.001). GFAP showed a biphasic release in serum, with levels decreasing during the first 6 months of injury but increasing over the subsequent study visits. The highest AUROC for GFAP was measured at 30 days, distinguishing patients with moderate and severe TBI from controls (both 0.89). Serum tau and UCH-L1 showed weak associations with TBI severity and neuroimaging measures. Longitudinally, serum NfL was the only biomarker that was associated with the likely rate of MRI brain atrophy and DTI measures of progression of TAI., Conclusions: Serum NfL shows greater diagnostic and prognostic utility than GFAP, tau, and UCH-L1 for subacute and chronic TBI., Classification of Evidence: This study provides Class III evidence that serum NfL distinguishes patients with mild TBI from healthy controls., (© 2020 American Academy of Neurology.)

Objective: To determine whether serum neurofilament light (NfL) correlates with CSF NfL, traumatic brain injury (TBI) diagnosis, injury severity, brain volume, and diffusion tensor imaging (DTI) estimates of traumatic axonal injury (TAI)., Methods: Participants were prospectively enrolled in Sweden and the United States between 2011 and 2019. The Swedish cohort included 45 hockey players with acute concussion sampled at 6 days, 31 with repetitive concussion with persistent postconcussive symptoms (PCS) assessed with paired CSF and serum (median 1.3 years after concussion), 28 preseason controls, and 14 nonathletic controls. Our second cohort included 230 clinic-based participants (162 with TBI and 68 controls). Patients with TBI also underwent serum, functional outcome, and imaging assessments at 30 (n = 30), 90 (n = 48), and 180 (n = 59) days and 1 (n = 84), 2 (n = 57), 3 (n = 46), 4 (n = 38), and 5 (n = 29) years after injury., Results: In athletes with paired specimens, CSF NfL and serum NfL were correlated ( r = 0.71, p < 0.0001). CSF and serum NfL distinguished players with PCS >1 year from PCS ≤1 year (area under the receiver operating characteristic curve [AUROC] 0.81 and 0.80). The AUROC for PCS >1 year vs preseason controls was 0.97. In the clinic-based cohort, NfL at enrollment distinguished patients with mild from those with moderate and severe TBI ( p < 0.001 and p = 0.048). Serum NfL decreased over the course of 5 years (ß = -0.09 log pg/mL, p < 0.0001) but remained significantly elevated compared to controls. Serum NfL correlated with measures of functional outcome, MRI brain atrophy, and DTI estimates of TAI., Conclusions: Serum NfL shows promise as a biomarker for acute and repetitive sports-related concussion and patients with subacute and chronic TBI., Classification of Evidence: This study provides Class III evidence that increased concentrations of NfL distinguish patients with TBI from controls., (© 2020 American Academy of Neurology.)

The Sørensen-Dice index (SDI) is a widely used measure for evaluating medical image segmentation algorithms. It offers a standardized measure of segmentation accuracy which has proven useful. However, it offers diminishing insight when the number of objects is unknown, such as in white matter lesion segmentation of multiple sclerosis (MS) patients. We present a refinement for finer grained parsing of SDI results in situations where the number of objects is unknown. We explore these ideas with two case studies showing what can be learned from our two presented studies. Our first study explores an inter-rater comparison, showing that smaller lesions cannot be reliably identified. In our second case study, we demonstrate fusing multiple MS lesion segmentation algorithms based on the insights into the algorithms provided by our analysis to generate a segmentation that exhibits improved performance. This work demonstrates the wealth of information that can be learned from refined analysis of medical image segmentations.

New tools are needed to match cancer patients with effective treatments. Patient-derived organoids offer a high-throughput platform to personalize treatments and discover novel therapies. Currently, methods to evaluate drug response in organoids are limited because they overlook cellular heterogeneity. In this study, non-invasive optical metabolic imaging (OMI) of cellular heterogeneity was characterized in breast cancer (BC) and pancreatic cancer (PC) patient-derived organoids. Baseline heterogeneity was analyzed for each patient, demonstrating that single-cell techniques, such as OMI, are required to capture the complete picture of heterogeneity present in a sample. Treatment-induced changes in heterogeneity were also analyzed, further demonstrating that these measurements greatly complement current techniques that only gauge average cellular response. Finally, OMI of cellular heterogeneity in organoids was evaluated as a predictor of clinical treatment response for the first time. Organoids were treated with the same drugs as the patient's prescribed regimen, and OMI measurements of heterogeneity were compared to patient outcome. OMI distinguished subpopulations of cells with divergent and dynamic responses to treatment in living organoids without the use of labels or dyes. OMI of organoids agreed with long-term therapeutic response in patients. With these capabilities, OMI could serve as a sensitive high-throughput tool to identify optimal therapies for individual patients, and to develop new effective therapies that address cellular heterogeneity in cancer., (Copyright © 2020 Sharick, Walsh, Sprackling, Pasch, Pham, Esbona, Choudhary, Garcia-Valera, Burkard, McGregor, Matkowskyj, Parikh, Meszoely, Kelley, Tsai, Deming and Skala.)

Background: The effects of disease-modifying therapies (DMTs) on region-specific brain atrophy in multiple sclerosis (MS) are unclear., Objective: To determine the effects of higher versus lower efficacy DMTs on rates of brain substructure atrophy in MS., Methods: A non-randomized, observational cohort of people with MS followed with annual brain magnetic resonance imaging (MRI) was evaluated retrospectively. Whole brain, subcortical gray matter (GM), cortical GM, and cerebral white matter (WM) volume fractions were obtained. DMTs were categorized as higher (DMT-H: natalizumab and rituximab) or lower (DMT-L: interferon-beta and glatiramer acetate) efficacy. Follow-up epochs were analyzed if participants had been on a DMT for ⩾6 months prior to baseline and had at least one follow-up MRI while on DMTs in the same category., Results: A total of 86 DMT epochs (DMT-H: n  = 32; DMT-L: n  = 54) from 78 participants fulfilled the study inclusion criteria. Mean follow-up was 2.4 years. Annualized rates of thalamic (-0.15% vs -0.81%; p  = 0.001) and putaminal (-0.27% vs -0.73%; p  = 0.001) atrophy were slower during DMT-H compared to DMT-L epochs. These results remained significant in multivariate analyses including demographics, clinical characteristics, and T2 lesion volume., Conclusion: DMT-H treatment may be associated with slower rates of subcortical GM atrophy, especially of the thalamus and putamen. Thalamic and putaminal volumes are promising imaging biomarkers in MS.

Purpose: As deep neural networks achieve more success in the wide field of computer vision, greater emphasis is being placed on the generalizations of these models for production deployment. With sufficiently large training datasets, models can typically avoid overfitting their data; however, for medical imaging it is often difficult to obtain enough data from a single site. Sharing data between institutions is also frequently nonviable or prohibited due to security measures and research compliance constraints, enforced to guard protected health information (PHI) and patient anonymity., Methods: In this paper, we implement cyclic weight transfer with independent datasets from multiple geographically disparate sites without compromising PHI. We compare results between single-site learning (SSL) and multisite learning (MSL) models on testing data drawn from each of the training sites as well as two other institutions., Results: The MSL model attains an average dice similarity coefficient (DSC) of 0.690 on the holdout institution datasets with a volume correlation of 0.914, respectively corresponding to a 7% and 5% statistically significant improvement over the average of both SSL models, which attained an average DSC of 0.646 and average correlation of 0.871., Conclusions: We show that a neural network can be efficiently trained on data from two physically remote sites without consolidating patient data to a single location. The resulting network improves model generalization and achieves higher average DSCs on external datasets than neural networks trained on data from a single source., (© 2019 American Association of Physicists in Medicine.)

Progressive multifocal leukoencephalopathy (PML) is a rare opportunistic brain infection caused by the JC virus and associated with substantial morbidity and mortality. Accurate MRI assessment of PML lesion burden and brain parenchymal atrophy is of decisive value in monitoring the disease course and response to therapy. However, there are currently no validated automatic methods for quantification of PML lesion burden or associated parenchymal volume loss. Furthermore, manual brain or lesion delineations can be tedious, require the use of valuable time resources by radiologists or trained experts, and are often subjective. In this work, we introduce JCnet (named after the causative viral agent), an end-to-end, fully automated method for brain parenchymal and lesion segmentation in PML using consecutive 3D patch-based convolutional neural networks. The network architecture consists of multi-view feature pyramid networks with hierarchical residual learning blocks containing embedded batch normalization and nonlinear activation functions. The feature maps across the bottom-up and top-down pathways of the feature pyramids are merged, and an output probability membership generated through convolutional pathways, thus rendering the method fully convolutional. Our results show that this approach outperforms and improves longitudinal consistency compared to conventional, state-of-the-art methods of healthy brain and multiple sclerosis lesion segmentation, utilized here as comparators given the lack of available methods validated for use in PML. The ability to produce robust and accurate automated measures of brain atrophy and lesion segmentation in PML is not only valuable clinically but holds promise toward including standardized quantitative MRI measures in clinical trials of targeted therapies. Code is available at: https://github.com/omarallouz/JCnet., (Published by Elsevier Inc.)

Head impact can cause traumatic brain injury (TBI) through axonal overstretch or subsequent inflammation and understanding the biomechanics of the impact event is useful for TBI prevention research. Tagged magnetic resonance imaging (MRI) acquired during a mild-acceleration impact has enabled measurement and visualization of brain deformation in vivo. However, measurements using MRI are subject to error, and having independent validation while imaging in vivo is very difficult. Thus, characterizing the accuracy of these measurements needs to be done in a separate experiment using a phantom where a gold standard is available. This study describes a method for error quantification using a calibration phantom compatible with MRI and high-speed video (the gold standard). During linear acceleration, the maximum shear strain (MSS) in the phantom ranged from 0 to 12%, which is similar to in vivo brain deformation at a similar acceleration. The mean displacement error against video was 0.3±0.3 mm, and the MSS error was 1.4±0.3%. To match resolutions, video data was filtered temporally using an averaging filter. Compared to the unfiltered results, resolution matching improved the agreement between MRI and video results by 15%. In conclusion, tagged MRI analysis compares well to video data provided that resolutions are matched-a finding that is also applicable when using MRI to validate simulations.

Total brain white matter lesion (WML) volume is the most widely established magnetic resonance imaging (MRI) outcome measure in studies of multiple sclerosis (MS). To estimate WML volume, there are a number of automatic segmentation methods available, yet manual delineation remains the gold standard approach. Automatic approaches often yield a probability map to which a threshold is applied to create lesion segmentation masks. Unfortunately, few approaches systematically determine the threshold employed; many methods use a manually selected threshold, thus introducing human error and bias into the automated procedure. In this study, we propose and validate an automatic thresholding algorithm, Thresholding Approach for Probability Map Automatic Segmentation in Multiple Sclerosis (TAPAS), to obtain subject-specific threshold estimates for probability map automatic segmentation of T2-weighted (T2) hyperintense WMLs. Using multimodal MRI, the proposed method applies an automatic segmentation algorithm to obtain probability maps. We obtain the true subject-specific threshold that maximizes the Sørensen-Dice similarity coefficient (DSC). Then the subject-specific thresholds are modeled on a naive estimate of volume using a generalized additive model. Applying this model, we predict a subject-specific threshold in data not used for training. We ran a Monte Carlo-resampled split-sample cross-validation (100 validation sets) using two data sets: the first obtained from the Johns Hopkins Hospital (JHH) on a Philips 3 Tesla (3T) scanner (n = 94) and a second collected at the Brigham and Women's Hospital (BWH) using a Siemens 3T scanner (n = 40). By means of the proposed automated technique, in the JHH data we found an average reduction in subject-level absolute error of 0.1 mL per one mL increase in manual volume. Using Bland-Altman analysis, we found that volumetric bias associated with group-level thresholding was mitigated when applying TAPAS. The BWH data showed similar absolute error estimates using group-level thresholding or TAPAS likely since Bland-Altman analyses indicated no systematic biases associated with group or TAPAS volume estimates. The current study presents the first validated fully automated method for subject-specific threshold prediction to segment brain lesions., Competing Interests: Declaration of competing interest Ms. Alessandra Valcarcel has nothing to disclose. Dr. John Muschelli has nothing to disclose. Dr. Dzung Pham has nothing to disclose. Ms. Melissa Martin has nothing to disclose. Dr. Paul Yushkevich has nothing to disclose. Dr. Kristina Patterson has served on the advisory board for Alexion. Dr. Peter Calabresi has received personal consulting fees for serving on SABs for Biogen and Disarm Therapeutics. He is PI on grants to JHU from Biogen, Novartis, Sanofi, Annexon and MedImmune. Dr. Rohit Bakshi has received consulting fees from Bayer, Biogen, Celgene, EMD Serono, Genentech, Guerbet, Sanofi-Genzyme, and Shire and research support from EMD Serono and Sanofi-Genzyme. Dr. Russell (Taki) Shinohara has received consulting fees from Genentech and Roche., (Published by Elsevier Inc.)

Background: Activated eosinophils release extracellular traps (EETs), which contribute to airway inflammation in severe asthma (SA). However, the role of EETs in innate immunity has not yet been completely determined. The present study aimed to demonstrate the mechanism of airway inflammation in SA mediated by EETs., Methods: Peripheral counts of EET + eosinophils and type 2 innate lymphoid cells (ILC2s) were evaluated in patients with SA (n = 13), nonsevere asthma (NSA, n = 17), and healthy control subjects (HC, n = 8). To confirm the effect of EETs, airway hyperresponsiveness (AHR) and adapted/innate immune responses were assessed in mice. Furthermore, the effects of anti-IL-33/TSLP antibody were tested., Results: The numbers of EET + eosinophils and ILC2s were significantly elevated in SA, with a positive correlation between these two cells (r = .539, P < .001). When mice were injected with EETs, we observed significant increases in epithelium-derived cytokines (IL-1α, IL-1β, CXCL-1, CCL24, IL-33, and TSLP) and eosinophil/neutrophil count in bronchoalveolar lavage fluid (BALF) as well as an increased proportion of IL-5- or IL-13-producing ILC2s in the lungs. When Rag1 -/- mice receiving ILC2s were treated with EETs, increased AHR and IL-5/IL-13 levels in BALF were noted, which were effectively suppressed by anti-IL-33 or anti-TSLP antibody., Conclusion: EETs could enhance innate and type 2 immune responses in SA, in which epithelium-targeting biologics (anti-IL-33/TSLP antibody) may have a potential benefit., (© 2019 EAACI and John Wiley and Sons A/S. Published by John Wiley and Sons Ltd.)

Multiple instance learning (MIL) is a supervised learning methodology that aims to allow models to learn instance class labels from bag class labels, where a bag is defined to contain multiple instances. MIL is gaining traction for learning from weak labels but has not been widely applied to 3D medical imaging. MIL is well-suited to clinical CT acquisitions since (1) the highly anisotropic voxels hinder application of traditional 3D networks and (2) patch-based networks have limited ability to learn whole volume labels. In this work, we apply MIL with a deep convolutional neural network to identify whether clinical CT head image volumes possess one or more large hemorrhages (> 20cm 3 ), resulting in a learned 2D model without the need for 2D slice annotations. Individual image volumes are considered separate bags, and the slices in each volume are instances. Such a framework sets the stage for incorporating information obtained in clinical reports to help train a 2D segmentation approach. Within this context, we evaluate the data requirements to enable generalization of MIL by varying the amount of training data. Our results show that a training size of at least 400 patient image volumes was needed to achieve accurate per-slice hemorrhage detection. Over a five-fold cross-validation, the leading model, which made use of the maximum number of training volumes, had an average true positive rate of 98.10%, an average true negative rate of 99.36%, and an average precision of 0.9698. The models have been made available along with source code 1 to enabled continued exploration and adaption of MIL in CT neuroimaging.

Magnetic resonance imaging (MRI) is a flexible medical imaging modality that often lacks reproducibility between protocols and scanners. It has been shown that even when care is taken to standardize acquisitions, any changes in hardware, software, or protocol design can lead to differences in quantitative results. This greatly impacts the quantitative utility of MRI in multi-site or long-term studies, where consistency is often valued over image quality. We propose a method of contrast harmonization, called DeepHarmony, which uses a U-Net-based deep learning architecture to produce images with consistent contrast. To provide training data, a small overlap cohort (n = 8) was scanned using two different protocols. Images harmonized with DeepHarmony showed significant improvement in consistency of volume quantification between scanning protocols. A longitudinal MRI dataset of patients with multiple sclerosis was also used to evaluate the effect of a protocol change on atrophy calculations in a clinical research setting. The results show that atrophy calculations were substantially and significantly affected by protocol change, whereas such changes have a less significant effect and substantially reduced overall difference when using DeepHarmony. This establishes that DeepHarmony can be used with an overlap cohort to reduce inconsistencies in segmentation caused by changes in scanner protocol, allowing for modernization of hardware and protocol design in long-term studies without invalidating previously acquired data., (Copyright © 2019 Elsevier Inc. All rights reserved.)

Recent studies have shown that re-expression of stem cell factors contribute to pathogenesis, therapy resistance, and recurrent disease in ovarian carcinomas. In this study, we compare the expression and co-expression of stem cell markers ALDH1 and SOX2 in different types of serous ovarian tumors. A total of 215 serous ovarian tumors (161 high-grade serous carcinomas (HGSC), 17 low-grade serous carcinomas (LGSC), 37 atypical proliferative serous tumors (APST)), and 10 cases of serous tubal intraepithelial carcinoma (STIC) were analyzed. Double immunostaining experiments addressed the association of cell proliferation (Ki67) with ALDH1 and the potential co-expression of SOX2 and ALDH1. The prognostic effect was analyzed in the cohort of HGSC. Expression of ALDH1and/or SOX2 was detected with increased frequency in HGSC (88.8%), compared with LGSC (70.5%) and APST (36.4%), while ALDH1 alone was significantly more frequently expressed in LGSC. The majority of all tumor types showed expression of ALDH1 and SOX2 in different cells. Only a minority of HGSC (4.6%) and STIC (20%) showed SOX2/ALDH1 co-expression in > 10% of tumor cells. Double staining also revealed that ALDH1 is associated with the non-proliferating Ki67-negative fraction consistent with a stem cell phenotype. Co-expression of ALDH1 and SOX2 or ALDH1 and Ki67 has no effect on survival. Expression of stem cell factors ALDH1 and/or SOX2 shows increased frequency in high-grade serous ovarian carcinomas compared to low-grade carcinomas and borderline tumors, supporting the concept that stem cell markers play different biological roles in low-grade versus high-grade serous neoplasia of the ovary.

We employ an advanced 3D computational model of the head with high anatomical fidelity, together with measured tissue properties, to assess the consequences of dynamic loading to the head in two distinct modes: head rotation and head extension. We use a subject-specific computational head model, using the material point method, built from T1 magnetic resonance images, and considering the anisotropic properties of the white matter which can predict strains in the brain under large rotational accelerations. The material model now includes the shear anisotropy of the white matter. We validate the model under head rotation and head extension motions using live human data, and advance a prior version of the model to include biofidelic falx and tentorium. We then examine the consequences of incorporating the falx and tentorium in terms of the predictions from the computational head model.

Objective: To obtain dense spatiotemporal measurements of brain deformation from two distinct but complementary head motion experiments: linear and rotational accelerations., Methods: This study introduces a strategy for integrating harmonic phase analysis of tagged magnetic resonance imaging (MRI) and finite-element models to extract mechanically representative deformation measurements. The method was calibrated using simulated as well as experimental data, demonstrated in a phantom including data with image artifacts, and used to measure brain deformation in human volunteers undergoing rotational and linear acceleration., Results: Evaluation methods yielded a displacement error of 1.1 mm compared to human observers and strain errors between [Formula: see text] for linear acceleration and [Formula: see text] for rotational acceleration. This study also demonstrates an approach that can reduce error by 86% in the presence of corrupted data. Analysis of results shows consistency with 2-D motion estimation, agreement with external sensors, and the expected physical behavior of the brain., Conclusion: Mechanical regularization is useful for obtaining dense spatiotemporal measurements of in vivo brain deformation under different loading regimes., Significance: The measurements suggest that the brain's 3-D response to mild accelerations includes distinct patterns observable using practical MRI resolutions. This type of measurement can provide validation data for computer models for the study of traumatic brain injury.

Purpose: Neutrophils are considered key effector cells in the pathogenic mechanisms of airway inflammation in asthma. This study assessed the activation status of neutrophils in adult asthmatics, and the therapeutic potential of FTY720, a synthetic sphingosine-1-phosphate analog, on activated neutrophils using an in vitro stimulation model., Methods: We isolated peripheral blood neutrophils (PBNs) from 59 asthmatic patients (including 20 aspirin-exacerbated respiratory disease [AERD] and 39 aspirin-tolerant asthma [ATA] groups). PBNs were stimulated with N -formyl-methionyl-leucyl-phenylalanine (fMLP) or lipopolysaccharide (LPS) and their activation status was determined based on reactive oxygen species (ROS) production, cell surface expression of CD11b, interleukin (IL)-8 and matrix metallopeptidase (MMP)-9 release. PBNs were primed with FTY720 to evaluate its anti-inflammatory action., Results: In vitro PBN stimulation with fMLP or LPS induced a significant increase in ROS/CD11b/IL-8/MMP-9 levels ( P < 0.05 for all). In asthmatics, fMLP-induced ROS level was significantly correlated with values of forced expiratory volume in 1 second/forced vital capacity ( r = -0.278; P = 0.036), maximal mid-expiratory flow ( r = -0.309; P = 0.019) and PC20 methacholine ( r = -0.302; P = 0.029). In addition, ROS levels were significantly higher in patients with AERD and in those with severe asthma than in those with ATA or non-severe asthma ( P < 0.05 for all). FTY720 treatment could suppress ROS/CD11b levels, and LPS-induced IL-8 and MMP-9 levels ( P < 0.05 for all). Responders to FTY720 treatment had significantly higher neutrophil counts in sputum ( P = 0.004)., Conclusions: Our findings suggest a useful in vitro PBN stimulation model for evaluating the neutrophil functional status and the therapeutic potentials of neutrophil-targeting candidates in asthmatics., Competing Interests: There are no financial or other issues that might lead to conflict of interest., (Copyright © 2019 The Korean Academy of Asthma, Allergy and Clinical Immunology · The Korean Academy of Pediatric Allergy and Respiratory Disease.)

Machine learning models are becoming commonplace in the domain of medical imaging, and with these methods comes an ever-increasing need for more data. However, to preserve patient anonymity it is frequently impractical or prohibited to transfer protected health information (PHI) between institutions. Additionally, due to the nature of some studies, there may not be a large public dataset available on which to train models. To address this conundrum, we analyze the efficacy of transferring the model itself in lieu of data between different sites. By doing so we accomplish two goals: 1) the model gains access to training on a larger dataset that it could not normally obtain and 2) the model better generalizes, having trained on data from separate locations. In this paper, we implement multi-site learning with disparate datasets from the National Institutes of Health (NIH) and Vanderbilt University Medical Center (VUMC) without compromising PHI. Three neural networks are trained to convergence on a computed tomography (CT) brain hematoma segmentation task: one only with NIH data, one only with VUMC data, and one multi-site model alternating between NIH and VUMC data. Resultant lesion masks with the multi-site model attain an average Dice similarity coefficient of 0.64 and the automatically segmented hematoma volumes correlate to those done manually with a Pearson correlation coefficient of 0.87, corresponding to an 8% and 5% improvement, respectively, over the single-site model counterparts.

On average, African Americans with multiple sclerosis demonstrate higher inflammatory disease activity, faster disability accumulation, greater visual dysfunction, more pronounced brain tissue damage and higher lesion volume loads compared to Caucasian Americans with multiple sclerosis. Neurodegeneration is an important component of multiple sclerosis, which in part accounts for the clinical heterogeneity of the disease. Brain atrophy appears to be widespread, although it is becoming increasingly recognized that regional substructure atrophy may be of greater clinical relevance. Patient race (within the limitations of self-identified ancestry) is regarded as an important contributing factor. However, there is a paucity of studies examining differences in neurodegeneration and brain substructure volumes over time in African Americans relative to Caucasian American patients. Optical coherence tomography is a non-invasive and reliable tool for measuring structural retinal changes. Recent studies support its utility for tracking neurodegeneration and disease progression in vivo in multiple sclerosis. Relative to Caucasian Americans, African American patients have been found to have greater retinal structural injury in the inner retinal layers. Increased thickness of the inner nuclear layer and the presence of microcystoid macular pathology at baseline predict clinical and radiological inflammatory activity, although whether race plays a role in these changes has not been investigated. Similarly, assessment of outer retinal changes according to race in multiple sclerosis remains incompletely characterized. Twenty-two African Americans and 60 matched Caucasian Americans with multiple sclerosis were evaluated with brain MRI, and 116 African Americans and 116 matched Caucasian Americans with multiple sclerosis were monitored with optical coherence tomography over a mean duration of 4.5 years. Mixed-effects linear regression models were used in statistical analyses. Grey matter (-0.9%/year versus -0.5%: P =0.02), white matter (-0.7%/year versus -0.3%: P =0.04) and nuclear thalamic (-1.5%/year versus -0.7%/year: P =0.02) atrophy rates were approximately twice as fast in African Americans. African Americans also exhibited higher proportions of microcystoid macular pathology (12.1% versus 0.9%, P =0.001). Retinal nerve fibre layer (-1.1% versus -0.8%: P =0.02) and ganglion cell+ inner plexiform layer (-0.7%/year versus -0.4%/year: P =0.01) atrophy rates were faster in African versus Caucasian Americans. African Americans on average exhibited more rapid neurodegeneration than Caucasian Americans and had significantly faster brain and retinal tissue loss. These results corroborate the more rapid clinical progression reported to occur, in general, in African Americans with multiple sclerosis and support the need for future studies involving African Americans in order to identify individual differences in treatment responses in multiple sclerosis.

Background: Clinical features of aspirin-exacerbated respiratory disease (AERD) are characterized by overproduction of cysteinyl leukotrienes (LT) and eosinophil activation, in which epithelial cells contribute to eosinophilic airway inflammation. Folliculin (FLCN) helps maintain the integrity of epithelial barrier, but little is known about FLCN in AERD., Objective: We investigated the role of FLCN in the pathogenic mechanisms of AERD., Methods: We recruited 178 subjects with AERD, 276 subjects with aspirin-tolerant asthma (ATA) and 71 normal healthy controls (NC) at Ajou Medical Center. Levels of FLCN and interleukin (IL)-8 in sera and supernatants were measured by ELISA. Peripheral blood eosinophils isolated from asthmatic patients were cocultured with human airway epithelial cells (HAECs) pretreated with LTE 4 , dexamethasone and montelukast. The intracellular expressions of FLCN, tight (TJ) (occludins, claudin-1) and adherens (AJ) junctions (E-cadherin) were analysed by Western blotting. shRNA was used to down-regulate FLCN (shFLCN) in HAECs., Results: Serum FLCN levels were significantly higher in AERD group than in ATA and NC groups (all P < 0.001). The cut-off value of 56.6 pg/mL was used to define the high FLCN phenotype (highFLCN). Asthmatic patients with highFLCN were associated with increased airway hyperresponsiveness to methacholine (P = 0.015). The serum FLCN level could discriminate AERD group from NC group with 82% sensitivity (AUC = 0.793, P < 0.001). When HAECs were exposed to LTE 4 , FLCN release was increased significantly (P < 0.05), which were amplified along with disruption of TJ and AJ expressions when HAECs were cocultured with eosinophils and LTE 4 (all P < 0.05); these effects were suppressed by dexamethasone and montelukast. FLCN knockdown reduced IL-8 release and occludin expression from shFLCN HAECs., Conclusions: Our findings suggest that high LT and airway eosinophilia increased FLCN release from HAECs, which enhance epithelial activation and disruption. Modulation of FLCN may be a potential target for AERD., (© 2018 John Wiley & Sons Ltd.)

Understanding of in vivo brain biomechanical behavior is critical in the study of traumatic brain injury (TBI) mechanisms and prevention. Using tagged magnetic resonance imaging, we measured spatiotemporal brain deformations in 34 healthy human volunteers under mild angular accelerations of the head. Two-dimensional (2D) Lagrangian strains were examined throughout the brain in each subject. Strain metrics peaked shortly after contact with a padded stop, corresponding to the inertial response of the brain after head deceleration. Maximum shear strain of at least 3% was experienced at peak deformation by an area fraction (median±standard error) of 23.5±1.8% of cortical gray matter, 15.9±1.4% of white matter, and 4.0±1.5% of deep gray matter. Cortical gray matter strains were greater in the temporal cortex on the side of the initial contact with the padded stop and also in the contralateral temporal, frontal, and parietal cortex. These tissue-level deformations from a population of healthy volunteers provide the first in vivo measurements of full-volume brain deformation in response to known kinematics. Although strains differed in different tissue type and cortical lobes, no significant differences between male and female head accelerations or strain metrics were found. These cumulative results highlight important kinematic features of the brain's mechanical response and can be used to facilitate the evaluation of computational simulations of TBI.

Dizziness, postural instability, and ataxia are among the most debilitating symptoms of multiple sclerosis (MS), reflecting, in large part, dysfunctional integration of visual, somatosensory, and vestibular sensory cues. However, the role of MS-related supratentorial lesions in producing such symptoms is poorly understood. In this study, motor control test (MCT) and dynamic sensory organization test (SOT) scores of 58 MS patients were compared to those of 72 healthy controls; correlations were determined between the MS scores of 49 patients and lesion volumes within 26 brain regions. Depending upon platform excursion direction and magnitude, MCT latencies, which were longer in MS patients than controls (p < 0.0001), were correlated with lesion volumes in the cortex, medial frontal lobes, temporal lobes, and parietal opercula (r's ranging from 0.20 to 0.39). SOT test scores were also impacted by MS and correlated with lesions in these same brain regions as well as within the superior frontal lobe (r's ranging from - 0.28 to - 0.40). The strongest and most consistent correlations occurred for the most challenging tasks in which incongruent visual and proprioceptive feedback were given. This study demonstrates that supratentorial lesion volumes are associated with quantitative balance measures in MS, in accord with the concept that balance relies upon highly convergent and multimodal neural pathways involving the skin, muscles, joints, eyes, and vestibular system.

Background and Purpose: The central vein sign is a promising MR imaging diagnostic biomarker for multiple sclerosis. Recent studies have demonstrated that patients with MS have higher proportions of white matter lesions with the central vein sign compared with those with diseases that mimic MS on MR imaging. However, the clinical application of the central vein sign as a biomarker is limited by interrater differences in the adjudication of the central vein sign as well as the time burden required for the determination of the central vein sign for each lesion in a patient's full MR imaging scan. In this study, we present an automated technique for the detection of the central vein sign in white matter lesions., Materials and Methods: Using multimodal MR imaging, the proposed method derives a central vein sign probability, π ij , for each lesion, as well as a patient-level central vein sign biomarker, ψ i . The method is probabilistic in nature, allows site-specific lesion segmentation methods, and is potentially robust to intersite variability. The proposed algorithm was tested on imaging acquired at the University of Vermont in 16 participants who have MS and 15 participants who do not., Results: By means of the proposed automated technique, participants with MS were found to have significantly higher values of ψ than those without MS (ψ MS = 0.55 ± 0.18; ψ non-MS = 0.31 ± 0.12; P < .001). The algorithm was also found to show strong discriminative ability between patients with and without MS, with an area under the curve of 0.88., Conclusions: The current study presents the first fully automated method for detecting the central vein sign in white matter lesions and demonstrates promising performance in a sample of patients with and without MS., (© 2018 by American Journal of Neuroradiology.)

Magnetic resonance imaging (MRI) is a powerful tool for visualizing traumatic brain injury(TBI)-related lesions. Trauma-induced encephalomalacia is frequently identified by its hyperintense appearance on fluid-attenuated inversion recovery (FLAIR) sequences. In addition to parenchymal lesions, TBI commonly results in cerebral microvascular injury, but its anatomical relationship to parenchymal encephalomalacia is not well characterized. The current study utilized a multi-modal MRI protocol to assess microstructural tissue integrity (by mean diffusivity [MD] and fractional aniosotropy [FA]) and altered vascular function (by cerebral blood flow [CBF] and cerebral vascular reactivity [CVR]) within regions of visible encephalomalacia and normal appearing tissue in 27 chronic TBI (minimum 6 months post-injury) subjects. Fifteen subjects had visible encephalomalacias whereas 12 did not have evident lesions on MRI. Imaging from 14 age-matched healthy volunteers were used as controls. CBF was assessed by arterial spin labeling (ASL) and CVR by measuring the change in blood-oxygen-level-dependent (BOLD) MRI during a hypercapnia challenge. There was a significant reduction in FA, CBF, and CVR with a complementary increase in MD within regions of FLAIR-visible encephalomalacia (p < 0.05 for all comparisons). In normal-appearing brain regions, only CVR was significantly reduced relative to controls (p < 0.05). These findings indicate that vascular dysfunction represents a TBI endophenotype that is distinct from structural injury detected using conventional MRI, may be present even in the absence of visible structural injury, and persists long after trauma. CVR may serve as a useful diagnostic and pharmacodynamic imaging biomarker of traumatic microvascular injury.

High resolution magnetic resonance (MR) images are desired in many clinical applications, yet acquiring such data with an adequate signal-to-noise ratio requires a long time, making them costly and susceptible to motion artifacts. A common way to partly achieve this goal is to acquire MR images with good in-plane resolution and poor through-plane resolution (i.e., large slice thickness). For such 2D imaging protocols, aliasing is also introduced in the through-plane direction, and these high-frequency artifacts cannot be removed by conventional interpolation. Super-resolution (SR) algorithms which can reduce aliasing artifacts and improve spatial resolution have previously been reported. State-of-the-art SR methods are mostly learning-based and require external training data consisting of paired low resolution (LR) and high resolution (HR) MR images. However, due to scanner limitations, such training data are often unavailable. This paper presents an anti-aliasing (AA) and self super-resolution (SSR) algorithm that needs no external training data. It takes advantage of the fact that the in-plane slices of those MR images contain high frequency information. Our algorithm consists of three steps: 1) We build a self AA (SAA) deep network followed by 2) an SSR deep network, both of which can be applied along different orientations within the original images, and 3) recombine the multiple orientations output from Steps 1 and 2 using Fourier burst accumulation. We perform our SAA+SSR algorithm on a diverse collection of MR data without modification or preprocessing other than N4 inhomogeneity correction, and demonstrate significant improvement compared to competing SSR methods.

The ambrosia beetle, Platypus quercivorus (Murayama), is the vector of a fungal pathogen that causes mass mortality of Fagaceae trees (Japanese oak wilt). Therefore, knowing the dispersal capacity may help inform trapping/tree removal efforts to prevent this disease more effectively. In this study, we measured the flight velocity and duration and estimated the flight distance of the beetle using a newly developed flight mill. The flight mill is low cost, small, and constructed using commonly available items. Both the flight mill arm and its vertical axis comprise a thin needle. A beetle specimen is glued to one tip of the arm using instant glue. The other tip is thick due to being covered with plastic, thus it facilitates the detection of rotations of the arm. The revolution of the arm is detected by a photo sensor mounted on an infrared LED, and is indicated by a change in the output voltage when the arm passed above the LED. The photo sensor is connected to a personal computer and the output voltage data are stored at a sampling rate of 1 kHz. By conducting experiments using this flight mill, we found that P. quercivorus can fly at least 27 km. Because our flight mill comprises cheap and small ordinary items, many flight mills can be prepared and used simultaneously in a small laboratory space. This enables experimenters to obtain a sufficient amount of data within a short period.

Background and Purpose: Magnetic resonance imaging (MRI) is crucial for in vivo detection and characterization of white matter lesions (WMLs) in multiple sclerosis. While WMLs have been studied for over two decades using MRI, automated segmentation remains challenging. Although the majority of statistical techniques for the automated segmentation of WMLs are based on single imaging modalities, recent advances have used multimodal techniques for identifying WMLs. Complementary modalities emphasize different tissue properties, which help identify interrelated features of lesions., Methods: Method for Inter-Modal Segmentation Analysis (MIMoSA), a fully automatic lesion segmentation algorithm that utilizes novel covariance features from intermodal coupling regression in addition to mean structure to model the probability lesion is contained in each voxel, is proposed. MIMoSA was validated by comparison with both expert manual and other automated segmentation methods in two datasets. The first included 98 subjects imaged at Johns Hopkins Hospital in which bootstrap cross-validation was used to compare the performance of MIMoSA against OASIS and LesionTOADS, two popular automatic segmentation approaches. For a secondary validation, a publicly available data from a segmentation challenge were used for performance benchmarking., Results: In the Johns Hopkins study, MIMoSA yielded average Sørensen-Dice coefficient (DSC) of .57 and partial AUC of .68 calculated with false positive rates up to 1%. This was superior to performance using OASIS and LesionTOADS. The proposed method also performed competitively in the segmentation challenge dataset., Conclusion: MIMoSA resulted in statistically significant improvements in lesion segmentation performance compared with LesionTOADS and OASIS, and performed competitively in an additional validation study., (Copyright © 2018 by the American Society of Neuroimaging.)

Type 2-immunity represents the typical adaptive response to allergen exposure in atopic individuals. It mainly involves Th2 cells and immunoglobulin E, as the main orchestrators of type 2-inflammation. Recently, it has been highlighted that allergens may be responsible for a Th2 response beside specific IgE activation and that a number of other environmental stimuli, such as viruses and pollutants, can trigger the same pattern of inflammation beyond atopy. Emerging data sustain a substantial role of the so-called epithelial dysfunction in asthma pathogenesis, both from anatomic and functional point of view. Furthermore an increasing amount of evidence demonstrates the relevance of innate immunity in polarizing a Th2 impaired response in asthmatic patients. Under this perspective, the complex cross-talking between airway epithelium, innate and adaptive immunity is emerging as a major determinant of type 2-inflammation beyond allergens. This review will include an update on the relevance of dysregulation of innate and adaptive type 2-immunity in asthma pathogenesis, particularly severe asthma, and on the role of the allergens that are associated with severe asthma. Type 2-immunity also will be reviewed in the light of the current and upcoming targeted treatments for severe asthma., Competing Interests: Not applicable.The authors declare that they have no competing interests.Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

The antibiotic activity of erythromycin, which reversibly binds to a site within the bacterial ribosome exit tunnel, against many gram positive microorganisms indicates that it effectively inhibits the production of proteins. Similar to other macrolides, the activity of erythromycin is far from universal, as some peptides can bypass the macrolide-obstructed exit tunnel and become partially or fully synthesized. It is unclear why, at the molecular level, some proteins can be synthesized while others cannot. Here, we use steered molecular dynamics simulations to examine how erythromycin inhibits synthesis of the peptide ErmCL but not the peptide H-NS. By pulling these peptides through the exit tunnel of the E.coli ribosome with and without erythromycin present, we find that erythromycin directly interacts with both nascent peptides, but the force required for ErmCL to bypass erythromycin is greater than that of H-NS. The largest forces arise three to six residues from their N-terminus as they start to bypass Erythromycin. Decomposing the interaction energies between erythromycin and the peptides at this point, we find that there are stronger electrostatic and dispersion interactions with the more C-terminal residues of ErmCL than with H-NS. These results suggest that erythromycin slows or stalls synthesis of ErmCL compared to H-NS due to stronger interactions with particular residue positions along the nascent protein.

Purpose: To explore (i) the variability of upper cervical cord area (UCCA) measurements from volumetric brain 3D T 1 -weighted scans related to gradient nonlinearity (GNL) and subject positioning; (ii) the effect of vendor-implemented GNL corrections; and (iii) easily applicable methods that can be used to retrospectively correct data., Methods: A multiple sclerosis patient was scanned at seven sites using 3T MRI scanners with the same 3D T 1 -weighted protocol without GNL-distortion correction. Two healthy subjects and a phantom were additionally scanned at a single site with varying table positions. The 2D and 3D vendor-implemented GNL-correction algorithms and retrospective methods based on (i) phantom data fit, (ii) normalization with C2 vertebral body diameters, and (iii) the Jacobian determinant of nonlinear registrations to a template were tested., Results: Depending on the positioning of the subject, GNL introduced up to 15% variability in UCCA measurements from volumetric brain T 1 -weighted scans when no distortion corrections were used. The 3D vendor-implemented correction methods and the three proposed methods reduced this variability to less than 3%., Conclusions: Our results raise awareness of the significant impact that GNL can have on quantitative UCCA studies, and point the way to prospectively and retrospectively managing GNL distortions in a variety of settings, including clinical environments. Magn Reson Med 79:1595-1601, 2018. © 2017 International Society for Magnetic Resonance in Medicine., (© 2017 International Society for Magnetic Resonance in Medicine.)

Vietnam is one of the most important countries for pig domestication, and a total of 26 local breeds have been reported. In the present study, genetic relationships among the various pig breeds were investigated using 90 samples collected from local pigs (15 breeds) in 15 distantly separated, distinct areas of the country and six samples from Landrace pigs in Hanoi as an out-group of a common Western breed. All samples were genotyped using the Illumina Porcine SNP60 v2 Genotyping BeadChip. We used 15 160-15 217 SNPs that showed a high degree of polymorphism in the Vietnamese breeds for identifying genetic relationships among the Vietnamese breeds. Principal components analysis showed that most pigs indigenous to Vietnam formed clusters correlated with their original geographic locations. Some Vietnamese breeds formed a cluster that was genetically related to the Western breed Landrace, suggesting the possibility of crossbreeding. These findings will be useful for the conservation and management of Vietnamese local pig breeds., (© 2018 Stichting International Foundation for Animal Genetics.)

The Dice overlap ratio is commonly used to evaluate the performance of image segmentation algorithms. While Dice overlap is very useful as a standardized quantitative measure of segmentation accuracy in many applications, it offers a very limited picture of segmentation quality in complex segmentation tasks where the number of target objects is not known a priori , such as the segmentation of white matter lesions or lung nodules. While Dice overlap can still be used in these applications, segmentation algorithms may perform quite differently in ways not reflected by differences in their Dice score. Here we propose a new set of evaluation techniques that offer new insights into the behavior of segmentation algorithms. We illustrate these techniques with a case study comparing two popular multiple sclerosis (MS) lesion segmentation algorithms: OASIS and LesionTOADS.

We propose a new approach to Multiple Sclerosis lesion segmentation that utilizes synthesized images. A new method of image synthesis is considered: joint intensity fusion (JIF). JIF synthesizes an image from a library of deformably registered and intensity normalized atlases. Each location in the synthesized image is a weighted average of the registered atlases; atlas weights vary spatially. The weights are determined using the joint label fusion (JLF) framework. The primary methodological contribution is the application of JLF to MRI signal directly rather than labels. Synthesized images are then used as additional features in a lesion segmentation task using the OASIS classifier, a logistic regression model on intensities from multiple modalities. The addition of JIF synthesized images improved the Dice-Sorensen coefficient (relative to manually drawn gold standards) of lesion segmentations over the standard model segmentations by 0.0462 ± 0.0050 (mean ± standard deviation) at optimal threshold over all subjects and 10 separate training/testing folds.

Toluene diisocyanate (TDI) exposure can directly activate and damage airway epithelium. Folliculin (FLCN) is a protein expressed by human airway epithelial cells (HAECs) to maintain airway epithelial integrity and survival. This study investigated the involvement of FLCN in the pathogenesis of TDI-induced occupational asthma (OA). Enzyme-linked immunosorbent assay was used to measure serum levels of FLCN in TDI-exposed subjects (93 TDI-OA patients and 119 asymptomatic exposed controls (AEC)), 200 non-occupational asthma (NOA) patients and 71 unexposed healthy normal controls (NCs). Significantly more subjects in the TDI-OA and AEC groups had high serum levels of FLCN compared to those in the NOA group (P=0.002 and P=0.001, respectively), all of which were higher than the NC group (all P<0.001). The serum level of FLCN was positively correlated with TDI exposure duration (r=0.251, P=0.027), but was negatively correlated with asthma duration of TDI-OA patients (r=-0.329, P=0.029). TDI-exposed subjects with high FLCN levels had higher serum levels of total IgE than those with lower levels. The effects of TDI exposure on FLCN production was investigated by treating HAECs (A549 cells) with TDI-human serum albumin conjugate, which showed increased expression and release of FLCN and interleukin-8 from HAECs. Co-culture with peripheral blood neutrophils also induced FLCN expression and release from HAECs. In conclusion, TDI exposure and TDI-induced neutrophil recruitment into the airways can activate and stimulate HAECs to produce FLCN, which could be involved in airway inflammation in workers exposed to TDI.

Being able to focus on a complex task and inhibit unwanted actions or interfering information (i.e., inhibitory control) are essential human cognitive abilities. However, it remains unknown the extent to which mild traumatic brain injury (mTBI) may impact these critical functions. In this study, seventeen patients and age-matched healthy controls (HC) performed a variant of the Stroop task and attention-demanding 4-choice response tasks (4CRT) with identical stimuli but two contexts: one required only routine responses and the other with occasional response conflicts. The results showed that mTBI patients performed equally well as the HC when the 4CRT required only routine responses. However, when the task conditions included occasional response conflicts, mTBI patients with even a single concussion showed a significant slow-down in all responses and higher error rates relative to the HC. Results from event-related functional magnetic resonance imaging (efMRI) revealed altered neural activity in the mTBI patients in the cerebellum-thalamo-cortical and the fronto-basal-ganglia networks regulating inhibitory control. These results suggest that even without apparent difficulties in performing complex attention-demanding but routine tasks, patients with mTBI may experience long-lasting deficits in regulating inhibitory control when situations call for rapid conflict resolutions.

Mild traumatic brain injury (mTBI) has been firmly associated with disrupted white matter integrity due to induced white matter damage and degeneration. However, comparatively less is known about the changes of the intrinsic functional connectivity mediated via neural synchronization in the brain after mTBI. Moreover, despite the presumed link between structural and functional connectivity, no existing studies in mTBI have demonstrated clear association between the structural abnormality of white matter axons and the disruption of neural synchronization. To investigate these questions, we recorded resting state EEG and diffusion tensor imaging (DTI) from a cohort of military service members. A newly developed synchronization measure, the weighted phase lag index was applied on the EEG data for estimating neural synchronization. Fractional anisotropy was computed from the DTI data for estimating white matter integrity. Fifteen service members with a history of mTBI within the past 3 years were compared to 22 demographically similar controls who reported no history of head injury. We observed that synchronization at low-gamma frequency band (25-40 Hz) across scalp regions was significantly decreased in mTBI cases compared with controls. The synchronization in theta (4-7 Hz), alpha (8-13 Hz), and beta (15-23 Hz) frequency bands were not significantly different between the two groups. In addition, we found that across mTBI cases, the disrupted synchronization at low-gamma frequency was significantly correlated with the white matter integrity of the inferior cerebellar peduncle, which was also significantly reduced in the mTBI group. These findings demonstrate an initial correlation between the impairment of white matter integrity and alterations in EEG synchronization in the brain after mTBI. The results also suggest that disruption of intrinsic neural synchronization at low-gamma frequency may be a characteristic functional pathology following mTBI and may prove useful for developing better methods of diagnosis and treatment.

Background: MicroRNAs (miRNAs) modulate expressions of inflammatory genes, thereby regulating inflammatory responses. Single nucleotide polymorphisms (SNPs) in miRNAs could affect their efficiency in binding to messenger RNAs (mRNAs)., Objective: We investigated the associations of miRNA SNPs with asthma phenotypes. miR-196a2 (rs11614913 T>C), miR-146a (rs2910164 C>G), and miR-499 (rs3746444 A>G) were genotyped in 347 asthma patients and 172 normal healthy controls (NCs)., Results: The CT/CC genotype of miR-196a2 rs11614913 was associated with eosinophilic asthma (p = 0.004) and a higher sputum eosinophil count compared with the TT genotype (p = 0.003). The CG/GG genotype of miR-146a rs2910164 tended to be associated with higher bronchial hyperresponsiveness to methacholine (PC 20 ) compared with the CC genotype. The AG/GG genotype of miR-499 rs3746444 was associated with higher predicted values of forced expiratory volume in 1 s (%FEV 1 ) compared with the AA genotype (p = 0.008)., Conclusions: Genetic polymorphisms in miR-196a2, miR-146a, and miR-499 could be potential biomarkers for asthma phenotypes and targets for asthma treatments in a Korean population.

Tagged magnetic resonance imaging has been used for decades to observe and quantify motion and strain of deforming tissue. It is challenging to obtain 3-D motion estimates due to a tradeoff between image slice density and acquisition time. Typically, interpolation methods are used either to combine 2-D motion extracted from sparse slice acquisitions into 3-D motion or to construct a dense volume from sparse acquisitions before image registration methods are applied. This paper proposes a new phase-based 3-D motion estimation technique that first computes harmonic phase volumes from interpolated tagged slices and then matches them using an image registration framework. The approach uses several concepts from diffeomorphic image registration with a key novelty that defines a symmetric similarity metric on harmonic phase volumes from multiple orientations. The material property of harmonic phase solves the aperture problem of optical flow and intensity-based methods and is robust to tag fading. A harmonic magnitude volume is used in enforcing incompressibility in the tissue regions. The estimated motion fields are dense, incompressible, diffeomorphic, and inverse-consistent at a 3-D voxel level. The method was evaluated using simulated phantoms, human brain data in mild head accelerations, human tongue data during speech, and an open cardiac data set. The method shows comparable accuracy to three existing methods while demonstrating low computation time and robustness to tag fading and noise.

Numerous brain disorders are associated with ventriculomegaly; normal pressure hydrocephalus (NPH) is one example. NPH presents with dementia-like symptoms and is often misdiagnosed as Alzheimer's due to its chronic nature and nonspecific presenting symptoms. However, unlike other forms of dementia NPH can be treated surgically with an over 80% success rate on appropriately selected patients. Accurate assessment of the ventricles, in particular its sub-compartments, is required to diagnose the condition. Existing segmentation algorithms fail to accurately identify the ventricles in patients with such extreme pathology. We present an improvement to a whole brain segmentation approach that accurately identifies the ventricles and parcellates them into four sub-compartments. Our work is a combination of patch-based tissue segmentation and multi-atlas registration-based labeling. We include a validation on NPH patients, demonstrating superior performance against state-of-the-art methods.