ANTsX neuroimaging-derived structural phenotypes of UK Biobank

UK Biobank is a large-scale epidemiological resource for investigating prospective correlations between various lifestyle, environmental, and genetic factors with health and disease progression. In addition to individual subject information obtained through surveys and physical examinations, a comprehensive neuroimaging battery consisting of multiple modalities provides imaging-derived phenotypes (IDPs) that can serve as biomarkers in neuroscience research. In this study, we augment the existing set of UK Biobank neuroimaging structural IDPs, obtained from well-established software libraries such as FSL and FreeSurfer, with related measurements acquired through the Advanced Normalization Tools Ecosystem. This includes previously established cortical and subcortical measurements defined, in part, based on the Desikan-Killiany-Tourville atlas. Also included are morphological measurements from two recent developments: medial temporal lobe parcellation of hippocampal and extra-hippocampal regions in addition to cerebellum parcellation and thickness based on the Schmahmann anatomical labeling. Through predictive modeling, we assess the clinical utility of these IDP measurements, individually and in combination, using commonly studied phenotypic correlates including age, fluid intelligence, numeric memory, and several other sociodemographic variables. The predictive accuracy of these IDP-based models, in terms of root-mean-squared-error or area-under-the-curve for continuous and categorical variables, respectively, provides comparative insights between software libraries as well as potential clinical interpretability. Results demonstrate varied performance between package-based IDP sets and their combination, emphasizing the need for careful consideration in their selection and utilization.

Neurological Effects of Repeated Blast Exposure in Special Operations Personnel

Exposure to blast overpressure has been a pervasive feature of combat-related injuries. Studies exploring the neurological correlates of repeated low-level blast exposure in career "breachers" demonstrated higher levels of tumor necrosis factor alpha (TNFα) and interleukin (IL)-6 and decreases in IL-10 within brain-derived extracellular vesicles (BDEVs). The current pilot study was initiated in partnership with the U.S. Special Operations Command (USSOCOM) to explore whether neuroinflammation is seen within special operators with prior blast exposure. Data were analyzed from 18 service members (SMs), inclusive of 9 blast-exposed special operators with an extensive career history of repeated blast exposures and 9 controls matched by age and duration of service. Neuroinflammation was assessed utilizing positron emission tomography (PET) imaging with [18F]DPA-714. Serum was acquired to assess inflammatory biomarkers within whole serum and BDEVs. The Blast Exposure Threshold Survey (BETS) was acquired to determine blast history. Both self-report and neurocognitive measures were acquired to assess cognition. Similarity-driven Multi-view Linear Reconstruction (SiMLR) was used for joint analysis of acquired data. Analysis of BDEVs indicated significant positive associations with a generalized blast exposure value (GBEV) derived from the BETS. SiMLR-based analyses of neuroimaging demonstrated exposure-related relationships between GBEV, PET-neuroinflammation, cortical thickness, and volume loss within special operators. Affected brain networks included regions associated with memory retrieval and executive functioning, as well as visual and heteromodal processing. Post hoc assessments of cognitive measures failed to demonstrate significant associations with GBEV. This emerging evidence suggests neuroinflammation may be a key feature of the brain response to blast exposure over a career in operational personnel. The common thread of neuroinflammation observed in blast-exposed populations requires further study.

Pulmonary MRI with hyperpolarized xenon-129 demonstrates novel alterations in gas transfer across the air-blood barrier in asthma

BACKGROUND

Individuals with asthma can vary widely in clinical presentation, severity, and pathobiology. Hyperpolarized xenon-129 (Xe129) MRI is a novel imaging method to provide 3-D mapping of both ventilation and gas exchange in the human lung.

PURPOSE

To evaluate the functional changes in adults with asthma as compared to healthy controls using Xe129 MRI.

METHODS

All subjects (20 controls and 20 asthmatics) underwent lung function measurements and Xe129 MRI on the same day. Outcome measures included the pulmonary ventilation defect and transfer of inspired Xe129 into two soluble compartments: tissue and blood. Ten asthmatics underwent Xe129 MRI before and after bronchodilator to test whether gas transfer measures change with bronchodilator effects.

RESULTS

Initial analysis of the results revealed striking differences in gas transfer measures based on age, hence we compared outcomes in younger (n = 24, ≤ 35 years) versus older (n = 16, > 45 years) asthmatics and controls. The younger asthmatics exhibited significantly lower Xe129 gas uptake by lung tissue (Asthmatic: 0.98% ± 0.24%, Control: 1.17% ± 0.12%, P = 0.035), and higher Xe129 gas transfer from tissue to the blood (Asthmatic: 0.40 ± 0.10, Control: 0.31% ± 0.03%, P = 0.035) than the younger controls. No significant difference in Xe129 gas transfer was observed in the older group between asthmatics and controls (P > 0.05). No significant change in Xe129 transfer was observed before and after bronchodilator treatment.

CONCLUSIONS

By using Xe129 MRI, we discovered heterogeneous alterations of gas transfer that have associations with age. This finding suggests a heretofore unrecognized physiological derangement in the gas/tissue/blood interface in young adults with asthma that deserves further study.

ANTsX neuroimaging-derived structural phenotypes of UK Biobank

UK Biobank is a large-scale epidemiological resource for investigating prospective correlations between various lifestyle, environmental, and genetic factors with health and disease progression. In addition to individual subject information obtained through surveys and physical examinations, a comprehensive neuroimaging battery consisting of multiple modalities provides imaging-derived phenotypes (IDPs) that can serve as biomarkers in neuroscience research. In this study, we augment the existing set of UK Biobank neuroimaging structural IDPs, obtained from well-established software libraries such as FSL and FreeSurfer, with related measurements acquired through the Advanced Normalization Tools Ecosystem. This includes previously established cortical and subcortical measurements defined, in part, based on the Desikan-Killiany-Tourville atlas. Also included are morphological measurements from two recent developments: medial temporal lobe parcellation of hippocampal and extra-hippocampal regions in addition to cerebellum parcellation and thickness based on the Schmahmann anatomical labeling. Through predictive modeling, we assess the clinical utility of these IDP measurements, individually and in combination, using commonly studied phenotypic correlates including age, fluid intelligence, numeric memory, and several other sociodemographic variables. The predictive accuracy of these IDP-based models, in terms of root-mean-squared-error or area-under-the-curve for continuous and categorical variables, respectively, provides comparative insights between software libraries as well as potential clinical interpretability. Results demonstrate varied performance between package-based IDP sets and their combination, emphasizing the need for careful consideration in their selection and utilization.

Developmental Mouse Brain Common Coordinate Framework

3D standard reference brains serve as key resources to understand the spatial organization of the brain and promote interoperability across different studies. However, unlike the adult mouse brain, the lack of standard 3D reference atlases for developing mouse brains has hindered advancement of our understanding of brain development. Here, we present a multimodal 3D developmental common coordinate framework (DevCCF) spanning mouse embryonic day (E) 11.5, E13.5, E15.5, E18.5, and postnatal day (P) 4, P14, and P56 with anatomical segmentations defined by a developmental ontology. At each age, the DevCCF features undistorted morphologically averaged atlas templates created from Magnetic Resonance Imaging and co-registered high-resolution templates from light sheet fluorescence microscopy. Expert-curated 3D anatomical segmentations at each age adhere to an updated prosomeric model and can be explored via an interactive 3D web-visualizer. As a use case, we employed the DevCCF to unveil the emergence of GABAergic neurons in embryonic brains. Moreover, we integrated the Allen CCFv3 into the P56 template with stereotaxic coordinates and mapped spatial transcriptome cell-type data with the developmental ontology. In summary, the DevCCF is an openly accessible resource that can be used for large-scale data integration to gain a comprehensive understanding of brain development.

Automated analysis of low-field brain MRI in cerebral malaria

A central challenge of medical imaging studies is to extract biomarkers that characterize disease pathology or outcomes. Modern automated approaches have found tremendous success in high-resolution, high-quality magnetic resonance images. These methods, however, may not translate to low-resolution images acquired on magnetic resonance imaging (MRI) scanners with lower magnetic field strength. In low-resource settings where low-field scanners are more common and there is a shortage of radiologists to manually interpret MRI scans, it is critical to develop automated methods that can augment or replace manual interpretation, while accommodating reduced image quality. We present a fully automated framework for translating radiological diagnostic criteria into image-based biomarkers, inspired by a project in which children with cerebral malaria (CM) were imaged using low-field 0.35 Tesla MRI. We integrate multiatlas label fusion, which leverages high-resolution images from another sample as prior spatial information, with parametric Gaussian hidden Markov models based on image intensities, to create a robust method for determining ventricular cerebrospinal fluid volume. We also propose normalized image intensity and texture measurements to determine the loss of gray-to-white matter tissue differentiation and sulcal effacement. These integrated biomarkers have excellent classification performance for determining severe brain swelling due to CM.

ReMiND: Recovery of Missing Neuroimaging using Diffusion Models with Application to Alzheimer's Disease

Objective

Missing data is a significant challenge in medical research. In longitudinal studies of Alzheimer's disease (AD) where structural magnetic resonance imaging (MRI) is collected from individuals at multiple time points, participants may miss a study visit or drop out. Additionally, technical issues such as participant motion in the scanner may result in unusable imaging data at designated visits. Such missing data may hinder the development of high-quality imaging-based biomarkers. Furthermore, when imaging data are unavailable in clinical practice, patients may not benefit from effective application of biomarkers for disease diagnosis and monitoring.

Methods

To address the problem of missing MRI data in studies of AD, we introduced a novel 3D diffusion model specifically designed for imputing missing structural MRI (Recovery of Missing Neuroimaging using Diffusion models (ReMiND)). The model generates a whole-brain image conditional on a single structural MRI observed at a past visit or conditional on one past and one future observed structural MRI relative to the missing observation.

Results

Experimental results show that our method can generate high-quality individual 3D structural MRI with high similarity to ground truth, observed images. Additionally, images generated using ReMiND exhibit relatively lower error rates and more accurately estimated rates of atrophy over time in important anatomical brain regions compared with two alternative imputation approaches: forward filling and image generation using variational autoencoders.

Conclusion

Our 3D diffusion model can impute missing structural MRI data at a single designated visit and outperforms alternative methods for imputing whole-brain images that are missing from longitudinal trajectories.

An Anatomical Template for the Normalization of Medical Images of Adult Human Hands

During medical image analysis, it is often useful to align (or 'normalize') a given image of a given body part to a representative standard (or 'template') of that body part. The impact that brain templates have had on the analysis of brain images highlights the importance of templates in general. However, templates for human hands do not exist. Image normalization is especially important for hand images because hands, by design, readily change shape during various tasks. Here we report the construction of an anatomical template for healthy adult human hands. To do this, we used 27 anatomically representative T1-weighted magnetic resonance (MR) images of either hand from 21 demographically representative healthy adult subjects (13 females and 8 males). We used the open-source, cross-platform ANTs (Advanced Normalization Tools) medical image analysis software framework, to preprocess the MR images. The template was constructed using the ANTs standard multivariate template construction workflow. The resulting template image preserved all the essential anatomical features of the hand, including all the individual bones, muscles, tendons, ligaments, as well as the main branches of the median nerve and radial, ulnar, and palmar metacarpal arteries. Furthermore, the image quality of the template was significantly higher than that of the underlying individual hand images as measured by two independent canonical metrics of image quality.

Lung Volume Dependence and Repeatability of Hyperpolarized 129Xe MRI Gas Uptake Metrics in Healthy Volunteers and Participants with COPD

Purpose

To assess the effect of lung volume on measured values and repeatability of xenon 129 (129Xe) gas uptake metrics in healthy volunteers and participants with chronic obstructive pulmonary disease (COPD).

Materials and Methods

This Health Insurance Portability and Accountability Act-compliant prospective study included data (March 2014-December 2015) from 49 participants (19 with COPD [mean age, 67 years ± 9 (SD)]; nine women]; 25 older healthy volunteers [mean age, 59 years ± 10; 20 women]; and five young healthy women [mean age, 23 years ± 3]). Thirty-two participants underwent repeated 129Xe and same-breath-hold proton MRI at residual volume plus one-third forced vital capacity (RV+FVC/3), with 29 also undergoing one examination at total lung capacity (TLC). The remaining 17 participants underwent imaging at TLC, RV+FVC/3, and residual volume (RV). Signal ratios between membrane, red blood cell (RBC), and gas-phase compartments were calculated using hierarchical iterative decomposition of water and fat with echo asymmetry and least-squares estimation (ie, IDEAL). Repeatability was assessed using coefficient of variation and intraclass correlation coefficient, and volume relationships were assessed using Spearman correlation and Wilcoxon rank sum tests.

Results

Gas uptake metrics were repeatable at RV+FVC/3 (intraclass correlation coefficient = 0.88 for membrane/gas; 0.71 for RBC/gas, and 0.88 for RBC/membrane). Relative ratio changes were highly correlated with relative volume changes for membrane/gas (r = -0.97) and RBC/gas (r = -0.93). Membrane/gas and RBC/gas measured at RV+FVC/3 were significantly lower in the COPD group than the corresponding healthy group (P ≤ .001). However, these differences lessened upon correction for individual volume differences (P = .23 for membrane/gas; P = .09 for RBC/gas).

Conclusion

Dissolved-phase 129Xe MRI-derived gas uptake metrics were repeatable but highly dependent on lung volume during measurement.Keywords: Blood-Air Barrier, MRI, Chronic Obstructive Pulmonary Disease, Pulmonary Gas Exchange, Xenon Supplemental material is available for this article © RSNA, 2023.

Hyperpolarized Xenon-129: A New Tool to Assess Pulmonary Physiology in Patients with Pulmonary Fibrosis

PURPOSE

The existing tools to quantify lung function in interstitial lung diseases have significant limitations. Lung MRI imaging using inhaled hyperpolarized xenon-129 gas (129Xe) as a contrast agent is a new technology for measuring regional lung physiology. We sought to assess the utility of the 129Xe MRI in detecting impaired lung physiology in usual interstitial pneumonia (UIP).

MATERIALS AND METHODS

After institutional review board approval and informed consent and in compliance with HIPAA regulations, we performed chest CT, pulmonary function tests (PFTs), and 129Xe MRI in 10 UIP subjects and 10 healthy controls.

RESULTS

The 129Xe MRI detected highly heterogeneous abnormalities within individual UIP subjects as compared to controls. Subjects with UIP had markedly impaired ventilation (ventilation defect fraction: UIP: 30 ± 9%; healthy: 21 ± 9%; p = 0.026), a greater amount of 129Xe dissolved in the lung interstitium (tissue-to-gas ratio: UIP: 1.45 ± 0.35%; healthy: 1.10 ± 0.17%; p = 0.014), and impaired 129Xe diffusion into the blood (RBC-to-tissue ratio: UIP: 0.20 ± 0.06; healthy: 0.28 ± 0.05; p = 0.004). Most MRI variables had no correlation with the CT and PFT measurements. The elevated level of 129Xe dissolved in the lung interstitium, in particular, was detectable even in subjects with normal or mildly impaired PFTs, suggesting that this measurement may represent a new method for detecting early fibrosis.

CONCLUSION

The hyperpolarized 129Xe MRI was highly sensitive to regional functional changes in subjects with UIP and may represent a new tool for understanding the pathophysiology, monitoring the progression, and assessing the effectiveness of treatment in UIP.

DeepComBat: A Statistically Motivated, Hyperparameter-Robust, Deep Learning Approach to Harmonization of Neuroimaging Data

Neuroimaging data from multiple batches (i.e. acquisition sites, scanner manufacturer, datasets, etc.) are increasingly necessary to gain new insights into the human brain. However, multi-batch data, as well as extracted radiomic features, exhibit pronounced technical artifacts across batches. These batch effects introduce confounding into the data and can obscure biological effects of interest, decreasing the generalizability and reproducibility of findings. This is especially true when multi-batch data is used alongside complex downstream analysis models, such as machine learning methods. Image harmonization methods seeking to remove these batch effects are important for mitigating these issues; however, significant multivariate batch effects remain in the data following harmonization by current state-of-the-art statistical and deep learning methods. We present DeepCombat, a deep learning harmonization method based on a conditional variational autoencoder architecture and the ComBat harmonization model. DeepCombat learns and removes subject-level batch effects by accounting for the multivariate relationships between features. Additionally, DeepComBat relaxes a number of strong assumptions commonly made by previous deep learning harmonization methods and is empirically robust across a wide range of hyperparameter choices. We apply this method to neuroimaging data from a large cognitive-aging cohort and find that DeepCombat outperforms existing methods, as assessed by a battery of machine learning methods, in removing scanner effects from cortical thickness measurements while preserving biological heterogeneity. Additionally, DeepComBat provides a new perspective for statistically-motivated deep learning harmonization methods.

Differential effects of emotional valence on mnemonic performance with greater hippocampal maturity

The hippocampal formation (HF) facilitates declarative memory, with subfields providing unique contributions to memory performance. Maturational differences across subfields facilitate a shift toward increased memory specificity, with peripuberty sitting at the inflection point. Peripuberty is also a sensitive period in the development of anxiety disorders. We believe HF development during puberty is critical to negative overgeneralization, a common feature of anxiety disorders. To investigate this claim, we examined the relationship between mnemonic generalization and a cross-sectional pubertal maturity index (PMI) derived from partial least squares correlation (PLSC) analyses of subfield volumes and structural connectivity from T1-weighted and diffusion-weighted scans, respectively. Participants aged 9-14 yr, from clinical and community sources, performed a recognition task with emotionally valent (positive, negative, and neutral) images. HF volumetric PMI was positively associated with generalization for negative images. Hippocampal-medial prefrontal cortex connectivity PMI evidenced a behavioral relationship similar to that of the HF volumetric approach. These findings reflect a novel developmentally related balance between generalization behavior supported by the hippocampus and its connections with other regions, with maturational differences in this balance potentially contributing to negative overgeneralization during peripuberty.

Multivariate Residualization in Medical Imaging Analysis

Nuisance variables in medical imaging research are common, complicating association and prediction studies based on image data. Medical image data are typically high dimensional, often consisting of many highly correlated features. As a result, computationally efficient and robust methods to address nuisance variables are difficult to implement. By-region univariate residualization is commonly used to remove the influence of nuisance variables, as are various extensions. However, these methods neglect multivariate properties and may fail to fully remove influence related to the joint distribution of these regions. Some methods, such as functional regression and others, do consider multivariate properties when controlling for nuisance variables. However, the utility of these methods is limited for data with many image regions due to computational and model complexity. We develop a multivariate residualization method to estimate the association between the image and nuisance variable using a machine learning algorithm and then compute the orthogonal projection of each subject's image data onto this space. We illustrate this method's performance in a set of simulation studies and apply it to data from the Alzheimer's Disease Neuroimaging Initiative (ADNI).

Posterior white matter hyperintensities are associated with reduced medial temporal lobe subregional integrity and long-term memory in older adults

White matter hyperintensities are a marker of small vessel cerebrovascular disease that are strongly related to cognition in older adults. Similarly, medial temporal lobe atrophy is well-documented in aging and Alzheimer's disease and is associated with memory decline. Here, we assessed the relationship between lobar white matter hyperintensities, medial temporal lobe subregional volumes, and hippocampal memory in older adults. We collected MRI scans in a sample of 139 older adults without dementia (88 females, mean age (SD) = 76.95 (10.61)). Participants were administered the Rey Auditory Verbal Learning Test (RAVLT). Regression analyses tested for associations among medial temporal lobe subregional volumes, regional white matter hyperintensities and memory, while adjusting for age, sex, and education and correcting for multiple comparisons. Increased occipital white matter hyperintensities were related to worse RAVLT delayed recall performance, and to reduced CA1, dentate gyrus, perirhinal cortex (Brodmann area 36), and parahippocampal cortex volumes. These medial temporal lobe subregional volumes were related to delayed recall performance. The association of occipital white matter hyperintensities with delayed recall performance was fully mediated statistically only by perirhinal cortex volume. These results suggest that white matter hyperintensities may be associated with memory decline through their impact on medial temporal lobe atrophy. These findings provide new insights into the role of vascular pathologies in memory loss in older adults and suggest that future studies should further examine the neural mechanisms of these relationships in longitudinal samples.

Deformation-based morphometry identifies deep brain structures protected by ocrelizumab

BACKGROUND

Despite advancements in treatments for multiple sclerosis, insidious disease progression remains an area of unmet medical need, for which atrophy-based biomarkers may help better characterize the progressive biology.

METHODS

We developed and applied a method of longitudinal deformation-based morphometry to provide voxel-level assessments of brain volume changes and identified brain regions that were significantly impacted by disease-modifying therapy.

RESULTS

Using brain MRI data from two identically designed pivotal trials of relapsing multiple sclerosis (total N = 1483), we identified multiple deep brain regions, including the thalamus and brainstem, where volume loss over time was reduced by ocrelizumab (p < 0.05), a humanized anti-CD20 + monoclonal antibody approved for the treatment of multiple sclerosis. Additionally, identified brainstem shrinkage, as well as brain ventricle expansion, was associated with a greater risk for confirmed disability progression (p < 0.05).

CONCLUSIONS

The identification of deep brain structures has a strong implication for developing new biomarkers of brain atrophy reduction to advance drug development for multiple sclerosis, which has an increasing focus on targeting the progressive biology.

Mitigating site effects in covariance for machine learning in neuroimaging data

To acquire larger samples for answering complex questions in neuroscience, researchers have increasingly turned to multi‐site neuroimaging studies. However, these studies are hindered by differences in images acquired across multiple sites. These effects have been shown to bias comparison between sites, mask biologically meaningful associations, and even introduce spurious associations. To address this, the field has focused on harmonizing data by removing site‐related effects in the mean and variance of measurements. Contemporaneously with the increase in popularity of multi‐center imaging, the use of machine learning (ML) in neuroimaging has also become commonplace. These approaches have been shown to provide improved sensitivity, specificity, and power due to their modeling the joint relationship across measurements in the brain. In this work, we demonstrate that methods for removing site effects in mean and variance may not be sufficient for ML. This stems from the fact that such methods fail to address how correlations between measurements can vary across sites. Data from the Alzheimer's Disease Neuroimaging Initiative is used to show that considerable differences in covariance exist across sites and that popular harmonization techniques do not address this issue. We then propose a novel harmonization method called Correcting Covariance Batch Effects (CovBat) that removes site effects in mean, variance, and covariance. We apply CovBat and show that within‐site correlation matrices are successfully harmonized. Furthermore, we find that ML methods are unable to distinguish scanner manufacturer after our proposed harmonization is applied, and that the CovBat‐harmonized data retain accurate prediction of disease group.

Deep Learning-based Quantification of Abdominal Subcutaneous and Visceral Fat Volume on CT Images

RATIONALE AND OBJECTIVES

Develop a deep learning-based algorithm using the U-Net architecture to measure abdominal fat on computed tomography (CT) images.

MATERIALS AND METHODS

Sequential CT images spanning the abdominal region of seven subjects were manually segmented to calculate subcutaneous fat (SAT) and visceral fat (VAT). The resulting segmentation maps of SAT and VAT were augmented using a template-based data augmentation approach to create a large dataset for neural network training. Neural network performance was evaluated on both sequential CT slices from three subjects and randomly selected CT images from the upper, central, and lower abdominal regions of 100 subjects.

RESULTS

Both subcutaneous and abdominal cavity segmentation images created by the two methods were highly comparable with an overall Dice similarity coefficient of 0.94. Pearson's correlation coefficients between the subcutaneous and visceral fat volumes quantified using the two methods were 0.99 and 0.99 and the overall percent residual squared error were 5.5% and 8.5%. Manual segmentation of SAT and VAT on the 555 CT slices used for testing took approximately 46 hours while automated segmentation took approximately 1 minute.

CONCLUSION

Our data demonstrates that deep learning methods utilizing a template-based data augmentation strategy can be employed to accurately and rapidly quantify total abdominal SAT and VAT with a small number of training images.

Image- versus histogram-based considerations in semantic segmentation of pulmonary hyperpolarized gas images

PURPOSE

To characterize the differences between histogram-based and image-based algorithms for segmentation of hyperpolarized gas lung images.

METHODS

Four previously published histogram-based segmentation algorithms (ie, linear binning, hierarchical k-means, fuzzy spatial c-means, and a Gaussian mixture model with a Markov random field prior) and an image-based convolutional neural network were used to segment 2 simulated data sets derived from a public (n = 29 subjects) and a retrospective collection (n = 51 subjects) of hyperpolarized 129Xe gas lung images transformed by common MRI artifacts (noise and nonlinear intensity distortion). The resulting ventilation-based segmentations were used to assess algorithmic performance and characterize optimization domain differences in terms of measurement bias and precision.

RESULTS

Although facilitating computational processing and providing discriminating clinically relevant measures of interest, histogram-based segmentation methods discard important contextual spatial information and are consequently less robust in terms of measurement precision in the presence of common MRI artifacts relative to the image-based convolutional neural network.

CONCLUSIONS

Direct optimization within the image domain using convolutional neural networks leverages spatial information, which mitigates problematic issues associated with histogram-based approaches and suggests a preferred future research direction. Further, the entire processing and evaluation framework, including the newly reported deep learning functionality, is available as open source through the well-known Advanced Normalization Tools ecosystem.

Negative Overgeneralization is Associated with Anxiety and Mechanisms of Pattern Completion in Peripubertal Youth

This study examines neural mechanisms of negative overgeneralization, the increased likelihood of generalizing negative information, in peri-puberty. Theories suggest that weak pattern separation (overlapping representations are made distinct, indexed by DG/CA3 hippocampal subfield activation) underlies negative overgeneralization. We alternatively propose that neuro-maturational changes that favor pattern completion (cues reinstate stored representations, indexed by CA1 activation) are modulated by circuitry involved in emotional responding (amygdala, medial prefrontal cortices [mPFC]) to drive negative overgeneralization. Youth (N=34, 9-14 years) recruited from community and clinic settings participated in an emotional mnemonic similarity task while undergoing MRI. At Study, participants indicated the valence of images; at Test, participants made recognition memory judgments. Critical lure stimuli, that were similar to images at Study, were presented at Test, and errors ("false alarms") to negative relative to neutral stimuli reflected negative overgeneralization. Negative overgeneralization was related to greater and more similar patterns of activation in CA1 and both dorsal and ventral mPFC for negative relative to neutral stimuli. At Study, amygdala exhibited greater functional coupling with CA1 and dorsal mPFC during negative items that were later generalized. Negative overgeneralization is rooted in amygdala and mPFC modulation at encoding and pattern completion at retrieval.

Measures of ventilation heterogeneity mapped with hyperpolarized helium-3 MRI demonstrate a T2-high phenotype in asthma

BACKGROUND

Hyperpolarized gas with helium (HHe-3) MR (magnetic resonance) is a noninvasive imaging method which maps and quantifies regions of ventilation heterogeneity (VH) in the lung. VH is an important feature of asthma, but little is known as to how VH informs patient phenotypes.

PURPOSE

To determine if VH indicators quantified by HHe-3 MR imaging (MRI) predict phenotypic characteristics and map to regions of inflammation in children with problematic wheeze or asthma.

METHODS

Sixty children with poorly-controlled wheeze or asthma underwent HHe-3 MRI, including 22 with bronchoalveolar lavage (BAL). The HHe-3 signal intensity defined four ventilation compartments. The non-ventilated and hypoventilated compartments divided by the total lung volume defined a VH index (VHI %).

RESULTS

Children with VHI % in the upper quartile had significantly greater airflow limitation, bronchodilator responsiveness, blood eosinophils, expired nitric oxide (FeNO), and BAL eosinophilic or neutrophilic granulocyte patterns compared to children with VHI % in the lower quartile. Lavage return from hypoventilated bronchial segments had greater eosinophil % than from ventilated segments.

CONCLUSION

In children with asthma, greater VHI % as measured by HHe-3 MRI identifies a severe phenotype with higher type 2 inflammatory markers, and maps to regions of lung eosinophilia. Listed on ClinicalTrials. gov (NCT02577497).

Similarity-driven multi-view embeddings from high-dimensional biomedical data

Diverse, high-dimensional modalities collected in large cohorts present new opportunities for the formulation and testing of integrative scientific hypotheses. Similarity-driven multi-view linear reconstruction (SiMLR) is an algorithm that exploits inter-modality relationships to transform large scientific datasets into smaller, more well-powered and interpretable low-dimensional spaces. SiMLR contributes an objective function for identifying joint signal, regularization based on sparse matrices representing prior within-modality relationships and an implementation that permits application to joint reduction of large data matrices. We demonstrate that SiMLR outperforms closely related methods on supervised learning problems in simulation data, a multi-omics cancer survival prediction dataset and multiple modality neuroimaging datasets. Taken together, this collection of results shows that SiMLR may be applied to joint signal estimation from disparate modalities and may yield practically useful results in a variety of application domains.

Single-Session Bronchial Thermoplasty Guided by 129Xe Magnetic Resonance Imaging. A Pilot Randomized Controlled Clinical Trial

Rationale: Adverse events have limited the use of bronchial thermoplasty (BT) in severe asthma.Objectives: We sought to evaluate the effectiveness and safety of using ¹²⁹Xe magnetic resonance imaging (¹²⁹Xe MRI) to prioritize the most involved airways for guided BT.Methods: Thirty subjects with severe asthma were imaged with volumetric computed tomography and ¹²⁹Xe MRI to quantitate segmental ventilation defects. Subjects were randomized to treatment of the six most involved airways in the first session (guided group) or a standard three-session BT (unguided). The primary outcome was the change in Asthma Quality of Life Questionnaire score from baseline to 12 weeks after the first BT for the guided group compared with after three treatments for the unguided group.Measurements and Main Results: There was no significant difference in quality of life after one guided compared with three unguided BTs (change in Asthma Quality of Life Questionnaire guided = 0.91 [95% confidence interval, 0.28-1.53]; unguided = 1.49 [95% confidence interval, 0.84-2.14]; P = 0.201). After one BT, the guided group had a greater reduction in the percentage of poorly and nonventilated lung from baseline when compared with unguided (-17.2%; P = 0.009). Thirty-three percent experienced asthma exacerbations after one guided BT compared with 73% after three unguided BTs (P = 0.028).Conclusions: Results of this pilot study suggest that similar short-term improvements can be achieved with one BT treatment guided by ¹²⁹Xe MRI when compared with standard three-treatment-session BT with fewer periprocedure adverse events.

Longitudinal Mapping of Cortical Thickness Measurements: An Alzheimer's Disease Neuroimaging Initiative-Based Evaluation Study

Longitudinal studies of development and disease in the human brain have motivated the acquisition of large neuroimaging data sets and the concomitant development of robust methodological and statistical tools for quantifying neurostructural changes. Longitudinal-specific strategies for acquisition and processing have potentially significant benefits including more consistent estimates of intra-subject measurements while retaining predictive power. Using the first phase of the Alzheimer's Disease Neuroimaging Initiative (ADNI-1) data, comprising over 600 subjects with multiple time points from baseline to 36 months, we evaluate the utility of longitudinal FreeSurfer and Advanced Normalization Tools (ANTs) surrogate thickness values in the context of a linear mixed-effects (LME) modeling strategy. Specifically, we estimate the residual variability and between-subject variability associated with each processing stream as it is known from the statistical literature that minimizing the former while simultaneously maximizing the latter leads to greater scientific interpretability in terms of tighter confidence intervals in calculated mean trends, smaller prediction intervals, and narrower confidence intervals for determining cross-sectional effects. This strategy is evaluated over the entire cortex, as defined by the Desikan-Killiany-Tourville labeling protocol, where comparisons are made with the cross-sectional and longitudinal FreeSurfer processing streams. Subsequent linear mixed effects modeling for identifying diagnostic groupings within the ADNI cohort is provided as supporting evidence for the utility of the proposed ANTs longitudinal framework which provides unbiased structural neuroimage processing and competitive to superior power for longitudinal structural change detection.

An Image Registration-Based Morphing Technique for Generating Subject-Specific Brain Finite Element Models

Finite element (FE) models of the brain are crucial for investigating the mechanisms of traumatic brain injury (TBI). However, FE brain models are often limited to a single neuroanatomy because the manual development of subject-specific models is time consuming. The objective of this study was to develop a pipeline to automatically generate subject-specific FE brain models using previously developed nonlinear image registration techniques, preserving both external and internal neuroanatomical characteristics. To verify the morphing-induced mesh distortions did not influence the brain deformation response, strain distributions predicted using the morphed model were compared to those from manually created voxel models of the same subject. Morphed and voxel models were generated for 44 subjects ranging in age, and simulated using head kinematics from a football concussion case. For each subject, brain strain distributions predicted by each model type were consistent, and differences in strain prediction was less than 4% between model type. This automated technique, taking approximately 2 h to generate a subject-specific model, will facilitate interdisciplinary research between the biomechanics and neuroimaging fields and could enable future use of biomechanical models in the clinical setting as a tool for improving diagnosis.

Functional and Structural Neuroimaging Correlates of Repetitive Low-Level Blast Exposure in Career Breachers

Combat military and civilian law enforcement personnel may be exposed to repetitive low-intensity blast events during training and operations. Persons who use explosives to gain entry (i.e., breach) into buildings are known as “breachers” or dynamic entry personnel. Breachers operate under the guidance of established safety protocols, but despite these precautions, breachers who are exposed to low-level blast throughout their careers frequently report performance deficits and symptoms to healthcare providers. Although little is known about the etiology linking blast exposure to clinical symptoms in humans, animal studies demonstrate network-level changes in brain function, alterations in brain morphology, vascular and inflammatory changes, hearing loss, and even alterations in gene expression after repeated blast exposure. To explore whether similar effects occur in humans, we collected a comprehensive data battery from 20 experienced breachers exposed to blast throughout their careers and 14 military and law enforcement controls. This battery included neuropsychological assessments, blood biomarkers, and magnetic resonance imaging measures, including cortical thickness, diffusion tensor imaging of white matter, functional connectivity, and perfusion. To better understand the relationship between repetitive low-level blast exposure and behavioral and imaging differences in humans, we analyzed the data using similarity-driven multi-view linear reconstruction (SiMLR). SiMLR is specifically designed for multiple modality statistical integration using dimensionality-reduction techniques for studies with high-dimensional, yet sparse, data (i.e., low number of subjects and many data per subject). We identify significant group effects in these data spanning brain structure, function, and blood biomarkers.

Anterolateral entorhinal cortex thickness as a new biomarker for early detection of Alzheimer's disease

INTRODUCTION

Loss of entorhinal cortex (EC) layer II neurons represents the earliest Alzheimer's disease (AD) lesion in the brain. Research suggests differing functional roles between two EC subregions, the anterolateral EC (aLEC) and the posteromedial EC (pMEC).

METHODS

We use joint label fusion to obtain aLEC and pMEC cortical thickness measurements from serial magnetic resonance imaging scans of 775 ADNI-1 participants (219 healthy; 380 mild cognitive impairment; 176 AD) and use linear mixed-effects models to analyze longitudinal associations among cortical thickness, disease status, and cognitive measures.

RESULTS

Group status is reliably predicted by aLEC thickness, which also exhibits greater associations with cognitive outcomes than does pMEC thickness. Change in aLEC thickness is also associated with cerebrospinal fluid amyloid and tau levels.

DISCUSSION

Thinning of aLEC is a sensitive structural biomarker that changes over short durations in the course of AD and tracks disease severity-it is a strong candidate biomarker for detection of early AD.

Longitudinal ComBat: A method for harmonizing longitudinal multi-scanner imaging data

While aggregation of neuroimaging datasets from multiple sites and scanners can yield increased statistical power, it also presents challenges due to systematic scanner effects. This unwanted technical variability can introduce noise and bias into estimation of biological variability of interest. We propose a method for harmonizing longitudinal multi-scanner imaging data based on ComBat, a method originally developed for genomics and later adapted to cross-sectional neuroimaging data. Using longitudinal cortical thickness measurements from 663 participants in the Alzheimer's Disease Neuroimaging Initiative (ADNI) study, we demonstrate the presence of additive and multiplicative scanner effects in various brain regions. We compare estimates of the association between diagnosis and change in cortical thickness over time using three versions of the ADNI data: unharmonized data, data harmonized using cross-sectional ComBat, and data harmonized using longitudinal ComBat. In simulation studies, we show that longitudinal ComBat is more powerful for detecting longitudinal change than cross-sectional ComBat and controls the type I error rate better than unharmonized data with scanner included as a covariate. The proposed method would be useful for other types of longitudinal data requiring harmonization, such as genomic data, or neuroimaging studies of neurodevelopment, psychiatric disorders, or other neurological diseases.

In vivo imaging of canine lung deformation: effects of posture, pneumonectomy, and inhaled erythropoietin

Mechanical stresses on the lung impose the major stimuli for developmental and compensatory lung growth and remodeling. We used computed tomography (CT) to noninvasively characterize the factors influencing lobar mechanical deformation in relation to posture, pneumonectomy (PNX), and exogenous proangiogenic factor supplementation. Post-PNX adult canines received weekly inhalations of nebulized nanoparticles loaded with recombinant human erythropoietin (EPO) or control (empty nanoparticles) for 16 wk. Supine and prone CT were performed at two transpulmonary pressures pre- and post-PNX following treatment. Lobar air and tissue volumes, fractional tissue volume (FTV), specific compliance (Cs), mechanical strains, and shear distortion were quantified. From supine to prone, lobar volume and Cs increased while strain and shear magnitudes generally decreased. From pre- to post-PNX, air volume increased less and FTV and Cs increased more in the left caudal (LCa) than in other lobes. FTV increased most in the dependent subpleural regions, and the portion of LCa lobe that expanded laterally wrapping around the mediastinum. Supine deformation was nonuniform pre- and post-PNX; strains and shear were most pronounced in LCa lobe and declined when prone. Despite nonuniform regional expansion and deformation, post-PNX lobar mechanics were well preserved compared with pre-PNX because of robust lung growth and remodeling establishing a new mechanical equilibrium. EPO treatment eliminated posture-dependent changes in FTV, accentuated the post-PNX increase in FTV, and reduced FTV heterogeneity without altering absolute air or tissue volumes, consistent with improved microvascular blood volume distribution and modestly enhanced post-PNX alveolar microvascular reserves.NEW & NOTEWORTHY Mechanical stresses on the lung impose the major stimuli for lung growth. We used computed tomography to image deformation of the lung in relation to posture, loss of lung units, and inhalational delivery of the growth promoter erythropoietin. Following loss of one lung in adult large animals, the remaining lung expanded and grew while retaining near-normal mechanical properties. Inhalation of erythropoietin promoted more uniform distribution of blood volume within the remaining lung.

Brain Tumor Segmentation Using an Ensemble of 3D U-Nets and Overall Survival Prediction Using Radiomic Features

Accurate segmentation of different sub-regions of gliomas such as peritumoral edema, necrotic core, enhancing, and non-enhancing tumor core from multimodal MRI scans has important clinical relevance in diagnosis, prognosis and treatment of brain tumors. However, due to the highly heterogeneous appearance and shape of these tumors, segmentation of the sub-regions is challenging. Recent developments using deep learning models has proved its effectiveness in various semantic and medical image segmentation tasks, many of which are based on the U-Net network structure with symmetric encoding and decoding paths for end-to-end segmentation due to its high efficiency and good performance. In brain tumor segmentation, the 3D nature of multimodal MRI poses challenges such as memory and computation limitations and class imbalance when directly adopting the U-Net structure. In this study we aim to develop a deep learning model using a 3D U-Net with adaptations in the training and testing strategies, network structures, and model parameters for brain tumor segmentation. Furthermore, instead of picking one best model, an ensemble of multiple models trained with different hyper-parameters are used to reduce random errors from each model and yield improved performance. Preliminary results demonstrate the effectiveness of this method and achieved the 9th place in the very competitive 2018 Multimodal Brain Tumor Segmentation (BraTS) challenge. In addition, to emphasize the clinical value of the developed segmentation method, a linear model based on the radiomics features extracted from segmentation and other clinical features are developed to predict patient overall survival. Evaluation of these innovations shows high prediction accuracy in both low-grade glioma and glioblastoma patients, which achieved the 1st place in the 2018 BraTS challenge.

A randomized trial of an NMDA receptor antagonist for reversing corticosteroid effects on the human hippocampus

Preclinical and clinical research indicates that excess corticosteroid is associated with adverse effects on the hippocampus. Animal model data suggest that N-methyl-D-aspartate (NMDA) receptor antagonists may block corticosteroid effect on the hippocampus. This translational clinical trial investigated the effect of memantine vs. placebo on hippocampal subfield volume in humans receiving chronic corticosteroid therapy. Men and women (N = 46) receiving chronic prescription corticosteroid therapy were randomized to memantine or placebo in a double-blind, crossover design (two 24-week treatment periods, separated by a 4-week washout) for 52 weeks. Structural magnetic resonance imaging was obtained at baseline and after each treatment. Data were analyzed using repeated measures analysis of variance. Mean corticosteroid dose was 7.69 ± 6.41 mg/day and mean duration 4.90 ± 5.61 years. Controlling for baseline volumes, the left DG/CA3 region was significantly larger following memantine than placebo (p = .011). The findings suggest that an NMDA receptor antagonist attenuates corticosteroid effect in the same hippocampal subfields in humans as in animal models. This finding has both mechanistic and clinical implications. Attenuation of the effect of corticosteroids on the human DG/CA3 region implicates the NMDA receptor in human hippocampal volume losses with corticosteroids. In addition, by suggesting a drug class that may, at least in part, block the effects of corticosteroids on the human DG/CA3 subfield, these results may have clinical relevance for people receiving prescription corticosteroids, as well as to those with cortisol elevations due to medical or psychiatric conditions.

Structural neuroimaging in mild traumatic brain injury: A chronic effects of neurotrauma consortium study

OBJECTIVES

The chronic effects of neurotrauma consortium (CENC) observational study is a multisite investigation designed to examine the long-term longitudinal effects of mild traumatic brain injury (mTBI). All participants in this initial CENC cohort had a history of deployment in Operation Enduring Freedom (Afghanistan), Operation Iraqi Freedom (Iraq), and/or their follow-on conflicts (Operation Freedom's Sentinel). All participants undergo extensive medical, neuropsychological, and neuroimaging assessments and either meet criteria for any lifetime mTBI or not. These assessments are integrated into six CENC core studies-Biorepository, Biostatistics, Data and Study Management, Neuroimaging, and Neuropathology.

METHODS

The current study outlines the quantitative neuroimaging methods managed by the Neuroimaging Core using FreeSurfer automated software for image quantification.

RESULTS

At this writing, 319 participants from the CENC observational study have completed all baseline assessments including the imaging protocol and tertiary data quality assurance procedures.

CONCLUSIONS/DISCUSSION

The preliminary findings of this initial cohort are reported to describe how the Neuroimaging Core manages neuroimaging quantification for CENC studies.

Multi-Site Meta-Analysis of Morphometry

Genome-wide association studies (GWAS) link full genome data to a handful of traits. However, in neuroimaging studies, there is an almost unlimited number of traits that can be extracted for full image-wide big data analyses. Large populations are needed to achieve the necessary power to detect statistically significant effects, emphasizing the need to pool data across multiple studies. Neuroimaging consortia, e.g., ENIGMA and CHARGE, are now analyzing MRI data from over 30,000 individuals. Distributed processing protocols extract harmonized features at each site, and pool together only the cohort statistics using meta analysis to avoid data sharing. To date, such MRI projects have focused on single measures such as hippocampal volume, yet voxelwise analyses (e.g., tensor-based morphometry; TBM) may help better localize statistical effects. This can lead to $10^{13}$1013 tests for GWAS and become underpowered. We developed an analytical framework for multi-site TBM by performing multi-channel registration to cohort-specific templates. Our results highlight the reliability of the method and the added power over alternative options while preserving single site specificity and opening the doors for well-powered image-wide genome-wide discoveries.

Deep convolutional neural network for segmentation of thoracic organs-at-risk using cropped 3D images

PURPOSE

Automatic segmentation of organs-at-risk (OARs) is a key step in radiation treatment planning to reduce human efforts and bias. Deep convolutional neural networks (DCNN) have shown great success in many medical image segmentation applications but there are still challenges in dealing with large 3D images for optimal results. The purpose of this study is to develop a novel DCNN method for thoracic OARs segmentation using cropped 3D images.

METHODS

To segment the five organs (left and right lungs, heart, esophagus and spinal cord) from the thoracic CT scans, preprocessing to unify the voxel spacing and intensity was first performed, a 3D U-Net was then trained on resampled thoracic images to localize each organ, then the original images were cropped to only contain one organ and served as the input to each individual organ segmentation network. The segmentation maps were then merged to get the final results. The network structures were optimized for each step, as well as the training and testing strategies. A novel testing augmentation with multiple iterations of image cropping was used. The networks were trained on 36 thoracic CT scans with expert annotations provided by the organizers of the 2017 AAPM Thoracic Auto-segmentation Challenge and tested on the challenge testing dataset as well as a private dataset.

RESULTS

The proposed method earned second place in the live phase of the challenge and first place in the subsequent ongoing phase using a newly developed testing augmentation approach. It showed superior-than-human performance on average in terms of Dice scores (spinal cord: 0.893 ± 0.044, right lung: 0.972 ± 0.021, left lung: 0.979 ± 0.008, heart: 0.925 ± 0.015, esophagus: 0.726 ± 0.094), mean surface distance (spinal cord: 0.662 ± 0.248 mm, right lung: 0.933 ± 0.574 mm, left lung: 0.586 ± 0.285 mm, heart: 2.297 ± 0.492 mm, esophagus: 2.341 ± 2.380 mm) and 95% Hausdorff distance (spinal cord: 1.893 ± 0.627 mm, right lung: 3.958 ± 2.845 mm, left lung: 2.103 ± 0.938 mm, heart: 6.570 ± 1.501 mm, esophagus: 8.714 ± 10.588 mm). It also achieved good performance in the private dataset and reduced the editing time to 7.5 min per patient following automatic segmentation.

CONCLUSIONS

The proposed DCNN method demonstrated good performance in automatic OAR segmentation from thoracic CT scans. It has the potential for eventual clinical adoption of deep learning in radiation treatment planning due to improved accuracy and reduced cost for OAR segmentation.

Convolutional Neural Networks with Template-Based Data Augmentation for Functional Lung Image Quantification

RATIONALE AND OBJECTIVES

We propose an automated segmentation pipeline based on deep learning for proton lung MRI segmentation and ventilation-based quantification which improves on our previously reported methodologies in terms of computational efficiency while demonstrating accuracy and robustness. The large data requirement for the proposed framework is made possible by a novel template-based data augmentation strategy. Supporting this work is the open-source ANTsRNet-a growing repository of well-known deep learning architectures first introduced here.

MATERIALS AND METHODS

Deep convolutional neural network (CNN) models were constructed and trained using a custom multilabel Dice metric loss function and a novel template-based data augmentation strategy. Training (including template generation and data augmentation) employed 205 proton MR images and 73 functional lung MRI. Evaluation was performed using data sets of size 63 and 40 images, respectively.

RESULTS

Accuracy for CNN-based proton lung MRI segmentation (in terms of Dice overlap) was left lung: 0.93 ± 0.03, right lung: 0.94 ± 0.02, and whole lung: 0.94 ± 0.02. Although slightly less accurate than our previously reported joint label fusion approach (left lung: 0.95 ± 0.02, right lung: 0.96 ± 0.01, and whole lung: 0.96 ± 0.01), processing time is <1 second per subject for the proposed approach versus ∼30 minutes per subject using joint label fusion. Accuracy for quantifying ventilation defects was determined based on a consensus labeling where average accuracy (Dice multilabel overlap of ventilation defect regions plus normal region) was 0.94 for the CNN method; 0.92 for our previously reported method; and 0.90, 0.92, and 0.94 for expert readers.

CONCLUSION

The proposed framework yields accurate automated quantification in near real time. CNNs drastically reduce processing time after offline model construction and demonstrate significant future potential for facilitating quantitative analysis of functional lung MRI.

Probing Changes in Lung Physiology in COPD Using CT, Perfusion MRI, and Hyperpolarized Xenon-129 MRI

RATIONALE AND OBJECTIVES

Chronic obstructive pulmonary disease (COPD) is highly heterogeneous and not well understood. Hyperpolarized xenon-129 (Xe129) magnetic resonance imaging (MRI) provides a unique way to assess important lung functions such as gas uptake. In this pilot study, we exploited multiple imaging modalities, including computed tomography (CT), gadolinium-enhanced perfusion MRI, and Xe129 MRI, to perform a detailed investigation of changes in lung morphology and functions in COPD. Utility and strengths of Xe129 MRI in assessing COPD were also evaluated against the other imaging modalities.

MATERIALS AND METHODS

Four COPD patients and four age-matched normal subjects participated in this study. Lung tissue density measured by CT, perfusion measures from gadolinium-enhanced MRI, and ventilation and gas uptake measures from Xe129 MRI were calculated for individual lung lobes to assess regional changes in lung morphology and function, and to investigate correlations among the different imaging modalities.

RESULTS

No significant differences were found for all measures among the five lobes in either the COPD or age-matched normal group. Strong correlations (R > 0.5 or < -0.5, p < 0.001) were found between ventilation and perfusion measures. Also gas uptake by blood as measured by Xe129 MRI showed strong correlations with CT tissue density and ventilation measures (R > 0.5 or < -0.5, p < 0.001) and moderate to strong correlations with perfusion measures (R > 0.4 or < -0.5, p < 0.01). Four distinctive patterns of functional abnormalities were found in patients with COPD.

CONCLUSION

Xe129 MRI has high potential to uniquely identify multiple changes in lung physiology in COPD using a single breath-hold acquisition.

ABCA7 risk variant in healthy older African Americans is associated with a functionally isolated entorhinal cortex mediating deficient generalization of prior discrimination training

Using high-resolution resting state functional magnetic resonance imaging (fMRI), the present study tested the hypothesis that ABCA7 genetic risk differentially affects intra-medial temporal lobe (MTL) functional connectivity between MTL subfields, versus internetwork connectivity of the MTL with the medial prefrontal cortex (mPFC), in nondemented older African Americans. Although the association of ABCA7 risk variants with Alzheimer's disease (AD) has been confirmed worldwide, its effect size on the relative odds of being diagnosed with AD is significantly higher in African Americans. However, little is known about the neural correlates of cognitive function in older African Americans and how they relate to AD risk conferred by ABCA7. In a case-control fMRI study of 36 healthy African Americans, we observed ABCA7 related impairments in behavioral generalization that was mediated by dissociation in entorhinal cortex (EC) resting state functional connectivity. Specifically, ABCA7 risk variant was associated with EC-hippocampus hyper-synchronization and EC-mPFC hypo-synchronization. Carriers of the risk genotype also had a significantly smaller anterolateral EC, despite our finding no group differences on standardized neuropsychological tests. Our findings suggest a model where impaired cortical connectivity leads to a more functionally isolated EC at rest, which translates into aberrant EC-hippocampus hyper-synchronization resulting in generalization deficits. While we cannot identify the exact mechanism underlying the observed alterations in EC structure and network function, considering the relevance of Aβ in ABCA7 related AD pathogenesis, the results of our study may reflect the synergistic reinforcement between amyloid and tau pathology in the EC, which significantly increases tau-induced neuronal loss and accelerates synaptic alterations. Finally, our results add to a growing literature suggesting that generalization of learning may be a useful tool for assessing the mild cognitive deficits seen in the earliest phases of prodromal AD, even before the more commonly reported deficits in episodic memory arise.

Deep learning-based quantification of abdominal fat on magnetic resonance images

Obesity is increasingly prevalent and associated with increased risk of developing type 2 diabetes, cardiovascular diseases, and cancer. Magnetic resonance imaging (MRI) is an accurate method for determination of body fat volume and distribution. However, quantifying body fat from numerous MRI slices is tedious and time-consuming. Here we developed a deep learning-based method for measuring visceral and subcutaneous fat in the abdominal region of mice. Congenic mice only differ from C57BL/6 (B6) Apoe knockout (Apoe^-/-) mice in chromosome 9 that is replaced by C3H/HeJ genome. Male congenic mice had lighter body weight than B6-Apoe^-/- mice after being fed 14 weeks of Western diet. Axial and coronal T1-weighted sequencing at 1-mm-thickness and 1-mm-gap was acquired with a 7T Bruker ClinScan scanner. A deep learning approach was developed for segmenting visceral and subcutaneous fat based on the U-net architecture made publicly available through the open-source ANTsRNet library—a growing repository of well-known neural networks. The volumes of subcutaneous and visceral fat measured through our approach were highly comparable with those from manual measurements. The Dice score, root-mean-square error (RMSE), and correlation analysis demonstrated the similarity between two methods in quantifying visceral and subcutaneous fat. Analysis with the automated method showed significant reductions in volumes of visceral and subcutaneous fat but not non-fat tissues in congenic mice compared to B6 mice. These results demonstrate the accuracy of deep learning in quantification of abdominal fat and its significance in determining body weight.

Unstable Inflation Causing Injury. Insight from Prone Position and Paired Computed Tomography Scans

RATIONALE

It remains unclear how prone positioning improves survival in acute respiratory distress syndrome. Using serial computed tomography (CT), we previously reported that "unstable" inflation (i.e., partial aeration with large tidal density swings, indicating increased local strain) is associated with injury progression.

OBJECTIVES

We prospectively tested whether prone position contains the early propagation of experimental lung injury by stabilizing inflation.

METHODS

Injury was induced by tracheal hydrochloric acid in rats; after randomization to supine or prone position, injurious ventilation was commenced using high tidal volume and low positive end-expiratory pressure. Paired end-inspiratory (EI) and end-expiratory (EE) CT scans were acquired at baseline and hourly up to 3 hours. Each sequential pair (EI, EE) of CT images was superimposed in parametric response maps to analyze inflation. Unstable inflation was then measured in each voxel in both dependent and nondependent lung. In addition, five pigs were imaged (EI and EE) prone versus supine, before and (1 hour) after hydrochloric acid aspiration.

MEASUREMENTS AND MAIN RESULTS

In rats, prone position limited lung injury propagation and increased survival (11/12 vs. 7/12 supine; P = 0.01). EI-EE densities, respiratory mechanics, and blood gases deteriorated more in supine versus prone rats. At baseline, more voxels with unstable inflation occurred in dependent versus nondependent regions when supine (41 ± 6% vs. 18 ± 7%; P < 0.01) but not when prone. In supine pigs, unstable inflation predominated in dorsal regions and was attenuated by prone positioning.

CONCLUSIONS

Prone position limits the radiologic progression of early lung injury. Minimizing unstable inflation in this setting may alleviate the burden of acute respiratory distress syndrome.

Longitudinal and cross-sectional structural magnetic resonance imaging correlates of AV-1451 uptake

We examined the relationship between in vivo estimates of tau deposition as measured by 18F-AV-1451 tau positron emission tomography imaging and cross-sectional cortical thickness, as well as rates of antecedent cortical thinning measured from magnetic resonance imaging in individuals with and without evidence of cerebral amyloid in 63 participants from the Alzheimer's Disease Neuroimaging Initiative study, including 32 cognitively normal individuals (mean age 74 years), 27 patients with mild cognitive impairment (mean age 76.8 years), and 4 patients diagnosed with Alzheimer's disease (mean age 80 years). We hypothesized that structural measures would correlate with 18F-AV-1451 in a spatially local manner and that this correlation would be stronger for longitudinal compared to cross-sectional measures of cortical thickness and in those with cerebral amyloid versus those without. Cross-sectional and longitudinal estimates of voxelwise atrophy were made from whole brain maps of cortical thickness and rates of thickness change. In amyloid-β-positive individuals, the correlation of voxelwise atrophy across the whole brain with a summary measure of medial temporal lobe (MTL) 18F-AV-1451 uptake demonstrated strong local correlations in the MTL with longitudinal atrophy that was weaker in cross-sectional analysis. Similar effects were seen in correlations between 31 bilateral cortical regions of interest. In addition, several nonlocal correlations between atrophy and 18F-AV-1451 uptake were observed, including association between MTL atrophy and 18F-AV-1451 uptake in parietal lobe regions of interest such as the precuneus. Amyloid-β-negative individuals only showed weaker correlations in data uncorrected for multiple comparisons. While these data replicate previous reports of associations between 18F-AV-1451 uptake and cross-sectional structural measures, the current results demonstrate a strong relationship with longitudinal measures of atrophy. These data support the notion that in vivo measures of tau pathology are tightly linked to the rate of neurodegenerative change.

APOE ε4 status in healthy older African Americans is associated with deficits in pattern separation and hippocampal hyperactivation

African Americans are 1.4 times more likely than European Americans to carry the apolipoprotein E (APOE) ε4 allele, a risk factor for Alzheimer's disease (AD). However, little is known about the neural correlates of cognitive function in older African Americans and how they relate to genetic risk for AD. In particular, no past study on African Americans has examined the effect of APOE ε4 status on pattern separation-mnemonic discrimination performance and its corresponding neural computations in the hippocampus. Previous work using the mnemonic discrimination paradigm has localized increased activation in the DG/CA3 hippocampal subregions as being correlated with discrimination deficits. In a case-control high-resolution functional magnetic resonance imaging study of 30 healthy African Americans, aged 60 years and older, we observed APOE ε4-related impairments in mnemonic discrimination, coincident with dysfunctional hyperactivation in the DG/CA3, and CA1 regions, despite no evidence of structural differences in the hippocampus between carriers and noncarriers. Our results add to the growing body of evidence that deficits in pattern separation may be an early marker for AD-related neuronal dysfunction.

Functional Imbalance of Anterolateral Entorhinal Cortex and Hippocampal Dentate/CA3 Underlies Age-Related Object Pattern Separation Deficits

The entorhinal cortex (EC) is among the earliest brain areas to deteriorate in Alzheimer's disease (AD). However, the extent to which functional properties of the EC are altered in the aging brain, even in the absence of clinical symptoms, is not understood. Recent human fMRI studies have identified a functional dissociation within the EC, similar to what is found in rodents. Here, we used high-resolution fMRI to identify a specific hypoactivity in the anterolateral EC (alEC) commensurate with major behavioral deficits on an object pattern separation task in asymptomatic older adults. Only subtle deficits were found in a comparable spatial condition, with no associated differences in posteromedial EC between young and older adults. We additionally linked this condition to dentate/CA3 hyperactivity, and the ratio of activity between the regions was associated with object mnemonic discrimination impairment. These results provide novel evidence of alEC-dentate/CA3 circuit dysfunction in cognitively normal aged humans.

Supervised learning technique for the automated identification of white matter hyperintensities in traumatic brain injury

BACKGROUND

White matter hyperintensities (WMHs) are foci of abnormal signal intensity in white matter regions seen with magnetic resonance imaging (MRI). WMHs are associated with normal ageing and have shown prognostic value in neurological conditions such as traumatic brain injury (TBI). The impracticality of manually quantifying these lesions limits their clinical utility and motivates the utilization of machine learning techniques for automated segmentation workflows.

METHODS

This study develops a concatenated random forest framework with image features for segmenting WMHs in a TBI cohort. The framework is built upon the Advanced Normalization Tools (ANTs) and ANTsR toolkits. MR (3D FLAIR, T2- and T1-weighted) images from 24 service members and veterans scanned in the Chronic Effects of Neurotrauma Consortium's (CENC) observational study were acquired. Manual annotations were employed for both training and evaluation using a leave-one-out strategy. Performance measures include sensitivity, positive predictive value, [Formula: see text] score and relative volume difference.

RESULTS

Final average results were: sensitivity = 0.68 ± 0.38, positive predictive value = 0.51 ± 0.40, [Formula: see text] = 0.52 ± 0.36, relative volume difference = 43 ± 26%. In addition, three lesion size ranges are selected to illustrate the variation in performance with lesion size.

CONCLUSION

Paired with correlative outcome data, supervised learning methods may allow for identification of imaging features predictive of diagnosis and prognosis in individual TBI patients.

A population level atlas of Mus musculus craniofacial skeleton and automated image-based shape analysis

Laboratory mice are staples for evo/devo and genetics studies. Inbred strains provide a uniform genetic background to manipulate and understand gene-environment interactions, while their crosses have been instrumental in studies of genetic architecture, integration and modularity, and mapping of complex biological traits. Recently, there have been multiple large-scale studies of laboratory mice to further our understanding of the developmental basis, evolution, and genetic control of shape variation in the craniofacial skeleton (i.e. skull and mandible). These experiments typically use micro-computed tomography (micro-CT) to capture the craniofacial phenotype in 3D and rely on manually annotated anatomical landmarks to conduct statistical shape analysis. Although the common choice for imaging modality and phenotyping provides the potential for collaborative research for even larger studies with more statistical power, the investigator (or lab-specific) nature of the data collection hampers these efforts. Investigators are rightly concerned that subtle differences in how anatomical landmarks were recorded will create systematic bias between studies that will eventually influence scientific findings. Even if researchers are willing to repeat landmark annotation on a combined dataset, different lab practices and software choices may create obstacles for standardization beyond the underlying imaging data. Here, we propose a freely available analysis system that could assist in the standardization of micro-CT studies in the mouse. Our proposal uses best practices developed in biomedical imaging and takes advantage of existing open-source software and imaging formats. Our first contribution is the creation of a synthetic template for the adult mouse craniofacial skeleton from 25 inbred strains and five F1 crosses that are widely used in biological research. The template contains a fully segmented cranium, left and right hemi-mandibles, endocranial space, and the first few cervical vertebrae. We have been using this template in our lab to segment and isolate cranial structures in an automated fashion from a mixed population of mice, including craniofacial mutants, aged 4-12.5 weeks. As a secondary contribution, we demonstrate an application of nearly automated shape analysis, using symmetric diffeomorphic image registration. This approach, which we call diGPA, closely approximates the popular generalized Procrustes analysis (GPA) but negates the collection of anatomical landmarks. We achieve our goals by using the open-source advanced normalization tools (ANT) image quantification library, as well as its associated R library (ANTsR) for statistical image analysis. Finally, we make a plea to investigators to commit to using open imaging standards and software in their labs to the extent possible to increase the potential for data exchange and improve the reproducibility of findings. Future work will incorporate more anatomical detail (such as individual cranial bones, turbinals, dentition, middle ear ossicles) and more diversity into the template.

Tidal changes on CT and progression of ARDS

Background

Uncertain prediction of outcome in acute respiratory distress syndrome (ARDS) impedes individual patient management and clinical trial design.

Objectives

To develop a radiological metric of injurious inflation derived from matched inspiratory and expiratory CT scans, calibrate it in a model of experimental lung injury, and test it in patients with ARDS.

Methods

73 anaesthetised rats (acid aspiration model) were ventilated (protective or non-protective) for up to 4 hours to generate a spectrum of lung injury. CT was performed (inspiratory and expiratory) at baseline each hour, paired inspiratory and expiratory images were superimposed and voxels tracked in sequential scans. In nine patients with ARDS, paired inspiratory and expiratory CT scans from the first intensive care unit week were analysed.

Results

In experimental studies, regions of lung with unstable inflation (ie, partial or reversible airspace filling reflecting local strain) were the areas in which subsequent progression of injury was greatest in terms of progressive infiltrates (R=0.77) and impaired compliance (R=0.67, p<0.01). In patients with ARDS, a threshold fraction of tissue with unstable inflation was apparent: >28% in all patients who died and ≤28% in all who survived, whereas segregation of survivors versus non-survivors was not possible based on oxygenation or lung mechanics.

Conclusions

A single set of superimposed inspiratory–expiratory CT scans may predict progression of lung injury and outcome in ARDS; if these preliminary results are validated, this could facilitate clinical trial recruitment and individualised care.