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What are the constraints, cues, and mechanisms that help learners create successful word-meaning mappings? This study takes up linguistic disjunction and looks at cues and mechanisms that can help children learn the meaning of or. We first used a large corpus of parent-child interactions to collect statistics on or uses. Children started producing or between 18-30 months and by 42 months, their rate of production reached a plateau. Second, we annotated for the interpretation of disjunction in child-directed speech. Parents used or mostly as exclusive disjunction, typically accompanied by rise-fall intonation and logically inconsistent disjuncts. But when these two cues were absent, disjunction was generally not exclusive. Our computational modeling suggests that an ideal learner could successfully interpret an English disjunction (as exclusive or not) by mapping forms to meanings after partitioning the input according to the intonational and logical cues available in child-directed speech.

Characterizing animal behavior requires methods to distill 3D movements from video data. Though keypoint tracking has emerged as a widely used solution to this problem, it only provides a limited view of pose, reducing the body of an animal to a sparse set of experimenter-defined points. To more completely capture 3D pose, recent studies have fit 3D mesh models to subjects in image and video data. However, despite the importance of mice as a model organism in neuroscience research, these methods have not been applied to the 3D reconstruction of mouse behavior. Here, we present ArMo, an articulated mesh model of the laboratory mouse, and demonstrate its application to multi-camera recordings of head-fixed mice running on a spherical treadmill. Using an end-to-end gradient based optimization procedure, we fit the shape and pose of a dense 3D mouse model to data-derived keypoint and point cloud observations. The resulting reconstructions capture the shape of the animal’s surface while compactly summarizing its movements as a time series of 3D skeletal joint angles. ArMo therefore provides a novel alternative to the sparse representations of pose more commonly used in neuroscience research.

Elevated vascular disease risk associates with poorer cognitive function, but the mechanism for this link is poorly understood. A leading theory, the structural-functional model argues that vascular risk may drive adverse cardiac remodelling, which in turn leads to chronic cerebral hypoperfusion and subsequent brain structural damage. This model predicts that variation in heart and brain structure should associate with both greater vascular risk and lower cognitive function. This study tests that prediction in a large sample of the UK Biobank (N=11,962). We assemble and summarise vascular risk factors, cardiac magnetic resonance radiomics, brain structural and diffusion MRI indices, and cognitive assessment. We also extract ‘heart-brain axes’ capturing the covariation in heart and brain structure. Many heart and brain measures partially explain the vascular risk – cognitive function association, like left ventricular end-diastolic volume and grey matter volume. Notably, a heart-brain axis, capturing correlation between lower myocardial intensity, lower grey matter volume, and poorer thalamic white matter integrity, completely mediates the association, supporting the structural-functional model. Our findings also complicate this theory by finding that brain structural variation cannot completely explain the heart structure – cognitive function association. Our results broadly offer evidence for the structural functional hypothesis, identify imaging biomarkers for this association by considering covariation in heart and brain structure, and generate novel hypotheses about how cardiovascular risk may link to cognitive function.

Radiomics is a novel image analysis technique, whereby voxel-level information is extracted from digital images and used to derive multiple numerical quantifiers of shape and tissue character. Cardiac magnetic resonance (CMR) is the reference imaging modality for assessment of cardiac structure and function. Conventional analysis of CMR scans is mostly reliant on qualitative image analysis and basic geometric quantifiers. Small proof-of-concept studies have demonstrated the feasibility and superior diagnostic accuracy of CMR radiomics analysis over conventional reporting. CMR radiomics has the potential to transform our approach to defining image phenotypes and, through this, improve diagnostic accuracy, treatment selection, and prognostication. The purpose of this article is to provide an overview of radiomics concepts for clinicians, with particular consideration of application to CMR. We will also review existing literature on CMR radiomics, discuss challenges, and consider directions for future work.

Purpose: To differentiate oncocytoma and chromophobe renal cell carcinoma (RCC) using radiomics features computed from spherical samples of image regions of interest, "radiomic biopsies" (RBs). Approach: In a retrospective cohort study of 102 CT cases [68 males (67%), 34 females (33%); mean age ± SD, 63 ± 12 years ], we pathology-confirmed 42 oncocytomas (41%) and 60 chromophobes (59%). A board-certified radiologist performed two RB rounds. From each RB round, we computed radiomics features and compared the performance of a random forest and AdaBoost binary classifier trained from the features. To control for overfitting, we performed 10 rounds of 70% to 30% train-test splits with feature-selection, cross-validation, and hyperparameter-optimization on each split. We evaluated the performance with test ROC AUC. We tested models on data from the other RB round and compared with the same round testing with the DeLong test. We clustered important features for each round and measured a bootstrapped adjusted Rand index agreement. Results: Our best classifiers achieved an average AUC of 0.71 ± 0.024 . We found no evidence of an effect for RB round ( p = 1 ). We also found no evidence for a decrease in model performance when tested on the other RB round ( p = 0.85 ). Feature clustering produced seven clusters in each RB round with high agreement ( Rand index = 0.981 ± 0.002 , p 0.00001 ). Conclusions: A consistent radiomic signature can be derived from RBs and could help distinguish oncocytoma and chromophobe RCC.

Funding Acknowledgements Type of funding sources: Foundation. Main funding source(s): British Heart Foundation Clinical Research Training Fellowship No. FS/17/81/33318 European Union’s Horizon 2020 research and innovation programme under grant agreement No 825903 (euCanSHare project). Background Multiple epidemiological studies link greater red and processed meat consumption with poorer cardiovascular outcomes. However, the impact of these exposures on directly measured cardiovascular phenotypes has not been examined in large cohorts. Limited existing studies suggest that the observed associations may be mediated by cardiometabolic diseases and/or novel mechanisms acting via the heart-gut axes. However, few studies systematically examine potential confounding and mediating mechanisms. Purpose We assessed, in the UK Biobank, the association between meat intake and cardiovascular structure and function incorporating a comprehensive range of confounders and mediators. Methods We studied 19,408 participants with cardiovascular magnetic resonance (CMR) data. We determined average daily red and processed meat intake using food frequency questionnaires. We used oily fish as a comparator linked to favourable cardiac health. We considered conventional CMR measures (ventricular volumes, left ventricular mass, ejection fraction, stroke volume), novel CMR radiomics features (shape, texture), and arterial stiffness metrics (arterial stiffness index, aortic distensibility). Multivariable linear regression models were used to investigate associations between meat/fish intake and cardiovascular phenotypes, adjusting for age, sex, deprivation, educational level, smoking, alcohol intake, and exercise. In separate models, we investigated the mediating role of cardiometabolic morbidities. Results Higher intake of red and processed meat was associated with an adverse overall pattern of right and left ventricular remodelling, poorer cardiac function, and higher arterial stiffness. Conversely, higher oily fish intake was associated with a healthy cardiovascular phenotype (better ventricular function, greater arterial compliance). Radiomics analysis showed association of the different dietary habits with unique overall geometry of the ventricles and myocardial texture. These associations were partially mediated by cardiometabolic morbidities. Conclusions Higher red and processed meat consumption is associated with adverse cardiovascular phenotypes. These relationships are not fully explained by mediation through cardiometabolic morbidities suggesting importance of alternative disease pathways. Understanding these potential novel disease mechanisms is important for optimising cardiovascular disease prevention strategies.

Brain age inferred from neuroimaging data could reveal important information about the evolution of structural and functional cerebral features across the life span. This has important implications for understanding healthy aging and for identifying Imaging-Derived Phenotypes (IDPs) that characterise age-related neurodegenerative illnesses, such as Alzheimer’s and Parkinson’s disease. The so-called brain age delta refers to the difference between image-derived brain age and chronological age. Accelerated aging (positive delta) or resilience to aging (negative delta) have been found to be useful correlates of factors such as disease and cognitive decline. Multiple studies have proposed prediction models using brain IDPs as predictor variables, mostly relying on simple linear regression. However, methodological and population heterogeneity in these studies precludes definitive conclusions regarding the most informative modelling methodologies or predictor IDPs. To provide first hints in this respect, in this paper we propose to address three questions. First, four different state-of-the-art models are ranked based on well-known performance indices (e.g., mean absolute error) using the UK Biobank brain MRI data in different single/multi-modal settings. Second, for the best model, the association with individual IDPs are calculated to identify those that could play a prominent role in the aging process. Third, associations with non-brain variables are assessed as a first step towards a holistic approach. Our findings demonstrate a prominent role for dMRI IDPs in reducing the mean absolute error and rank high in the association study, dominating the first ten positions and being preceded only by three structural measures that are known to be related to the aging process. This provides evidence of the potential of dMRI IDPs as biomarkers of aging in health and disease.

Aims: To evaluate the repeatability of cardiac magnetic resonance (CMR) radiomics features on test-retest scanning using a multi-centre multi-vendor dataset with a varied case-mix.Methods and Results: The sample included 54 test-retest studies from the VOLUMES resource (thevolumesresource.com). Images were segmented according to a pre-defined protocol to select three regions of interest (ROI) in end-diastole and end-systole: right ventricle, left ventricle (LV), and LV myocardium. We extracted radiomics shape features from all three ROIs and, additionally, first-order and texture features from the LV myocardium. Overall, 280 features were derived per study. For each feature, we calculated intra-class correlation coefficient (ICC), within-subject coefficient of variation, and mean relative difference. We ranked robustness of features according to mean ICC stratified by feature category, ROI, and cardiac phase, demonstrating a wide range of repeatability. There were features with good and excellent repeatability (ICC ≥ 0.75) within all feature categories and ROIs. A high proportion of first-order and texture features had excellent repeatability (ICC ≥ 0.90), however, these categories also contained features with the poorest repeatability (ICC < 0.50).Conclusion: CMR radiomic features have a wide range of repeatability. This paper is intended as a reference for future researchers to guide selection of the most robust features for clinical CMR radiomics models. Further work in larger and richer datasets is needed to further define the technical performance and clinical utility of CMR radiomics.

Introduction Cardiac magnetic resonance (CMR) radiomics use voxel-level data to derive quantitative indices of myocardial tissue texture, which may provide complementary risk information to traditional CMR measures. Purpose In this first stage of our work, establishing the performance characteristics of CMR radiomics in relation to disease outcomes, we aimed to elucidate differences in radiomic features by sex and age in apparently healthy adults. Methods We defined a healthy cohort from the first 5,065 individuals completing the UK Biobank Imaging Enhancement, limiting to white Caucasian ethnicity, and excluding those with major co-morbidities, or cardiovascular risk factors/symptoms. We created evenly distributed age groups: 45–54 years, 55–64 years, 65–74 years. Radiomics features were extracted from left ventricle segmentations, with normalisation to body surface area. We compared mean values of individual features between the sexes, stratified by age and separately between the oldest and youngest age groups for each sex. Results We studied 657 (309 men, 358 women) healthy individuals. There were significant differences between radiomics features of men and women. Different features appeared more important at different age groups. For instance, in the youngest age group “end-systolic coarseness” showed greatest difference between men and women, whilst “end-diastolic run percentage” and “end-diastolic high grey level emphasis” showed most variation in the oldest and middle age groups. In the oldest age groups, differences between men and women were most predominant in the texture features, whilst in the younger groups a mixture of shape and texture differences were observed. We demonstrate significant variation between radiomics features by age, these differences are exclusively in texture features with different features implicated in men and women (“end-diastolic mean intensity” in women, “end-systolic sum entropy in men”). Conclusions There are significant age and sex differences in CMR radiomics features of apparently healthy adults, demonstrating alterations in myocardial architecture not appreciated by conventional indices. In younger ages, shape and texture differences are observed, whilst in older ages texture differences dominate. Furthermore, texture features are the most different features between the youngest and oldest hearts. We provide proof-of-concept data indicating CMR radiomics has discriminatory value with regard to two characteristics strongly linked to cardiovascular outcomes. We will next elucidate relationships between CMR radiomics, cardiac risk factors, and clinical outcomes, establishing predictive value incremental to existing measures. Funding Acknowledgement Type of funding source: Other. Main funding source(s): European Union's Horizon 2020 research and innovation programme (825903),British Heart Foundation Clinical Research Training Fellowship (FS/17/81/33318)

Several institutions have developed image feature extraction software to compute quantitative descriptors of medical images for radiomics analyses. With radiomics increasingly proposed for use in research and clinical contexts, new techniques are necessary for standardizing and replicating radiomics findings across software implementations. We have developed a software toolkit for the creation of 3D digital reference objects with customizable size, shape, intensity, texture, and margin sharpness values. Using user-supplied input parameters, these objects are defined mathematically as continuous functions, discretized, and then saved as DICOM objects. Here, we present the definition of these objects, parameterized derivations of a subset of their radiomics values, computer code for object generation, example use cases, and a user-downloadable sample collection used for the examples cited in this paper.

Radiomic features are being increasingly studied for clinical applications. We aimed to assess the agreement among radiomic features when computed by several groups by using different software packages under very tightly controlled conditions, which included standardized feature definitions and common image data sets. Ten sites (9 from the NCI's Quantitative Imaging Network] positron emission tomography–computed tomography working group plus one site from outside that group) participated in this project. Nine common quantitative imaging features were selected for comparison including features that describe morphology, intensity, shape, and texture. The common image data sets were: three 3D digital reference objects (DROs) and 10 patient image scans from the Lung Image Database Consortium data set using a specific lesion in each scan. Each object (DRO or lesion) was accompanied by an already-defined volume of interest, from which the features were calculated. Feature values for each object (DRO or lesion) were reported. The coefficient of variation (CV), expressed as a percentage, was calculated across software packages for each feature on each object. Thirteen sets of results were obtained for the DROs and patient data sets. Five of the 9 features showed excellent agreement with CV &lt, 1%, 1 feature had moderate agreement (CV &lt, 10%), and 3 features had larger variations (CV ≥ 10%) even after attempts at harmonization of feature calculations. This work highlights the value of feature definition standardization as well as the need to further clarify definitions for some features.