Behavioral Indices of Neuropsychological Processing Implicated in Moral Domain Reasoning amongst Children and Adolescents

Moral domain theory posits that moral knowledge is organized in separate domains related to moral and socio-conventional rules, with the latter being reliant on a statement made by authority. Domains may be contingent on different neuropsychological processing that may vary with age. Behavioral indices were measured in three age groups, to detect differences in the neuropsychological processing allegedly involved in the evaluation of rule transgressions in different domains. Acceptance of the transgressions was also investigated. Twenty-four children, 32 early adolescents, and 31 adolescents judged acceptability of rule transgressions when an authority figure allowed the transgression. Across age, moral-rule transgressions were less accepted and took significantly longer to be evaluated. In evaluating moral rule scenarios, children had the longest reaction times. Older adolescents took the least amount of time evaluating socio-conventional rule scenarios. Results suggest differences in the neuropsychological processing underlying decision making for moral and socio-conventional domains and that rule comprehension and distinction amongst domains increase by age

Exploring the benefits and dis-benefits of climate migration as an adaptive strategy along the rural-peri-urban continuum in Namibia

The scale of climate migration across the Global South is expected to increase during this century. By 2050, millions of Africans are likely to consider, or be pushed into, migration because of climate hazards contributing to agricultural disruption, water and food scarcity, desertification, flooding, drought, coastal erosion, and heat waves. However, the migration-climate nexus is complex, as is the question of whether migration can be considered a climate change adaptation strategy across both the rural and urban space. Combining data from household surveys, key informant interviews, and secondary sources related to regional disaster, demographic, resource, and economic trends between 1990 and 2020 from north central and central dryland Namibia, we investigate (i) human migration flows and the influence of climate hazards on these flows and (ii) the benefits and dis-benefits of migration in supporting climate change adaptation, from the perspective of migrants (personal factors and intervening obstacles), areas of origin, and areas of destination. Our analysis suggests an increase in climate-related push factors that could be driving rural out-migration from the north central region to peri-urban settlements in the central region of the country. While push factors play a role in rural-urban migration, there are also several pull factors (many of which have been long-term drivers of urban migration) such as perceived higher wages, diversity of livelihoods, water, health and energy provisioning, remittances, better education opportunities, and the exchange of non-marketed products. Migration to peri-urban settlements can reduce some risks (e.g. loss of crops and income due to climate extremes) but amplify others (e.g. heat stress and insecure land tenure). Adaptation at both ends of the rural–urban continuum is supported by deeply embedded linkages in a model of circular rural–urban-rural migration and interdependencies. Results empirically inform current and future policy debates around climate mobilities in Namibia, with wider implications across Africa

PhysVENeT: a physiologically-informed deep learning-based framework for the synthesis of 3D hyperpolarized gas MRI ventilation

Functional lung imaging modalities such as hyperpolarized gas MRI ventilation enable visualization and quantification of regional lung ventilation; however, these techniques require specialized equipment and exogenous contrast, limiting clinical adoption. Physiologically-informed techniques to map proton (1H)-MRI ventilation have been proposed. These approaches have demonstrated moderate correlation with hyperpolarized gas MRI. Recently, deep learning (DL) has been used for image synthesis applications, including functional lung image synthesis. Here, we propose a 3D multi-channel convolutional neural network that employs physiologically-informed ventilation mapping and multi-inflation structural 1H-MRI to synthesize 3D ventilation surrogates (PhysVENeT). The dataset comprised paired inspiratory and expiratory 1H-MRI scans and corresponding hyperpolarized gas MRI scans from 170 participants with various pulmonary pathologies. We performed fivefold cross-validation on 150 of these participants and used 20 participants with a previously unseen pathology (post COVID-19) for external validation. Synthetic ventilation surrogates were evaluated using voxel-wise correlation and structural similarity metrics; the proposed PhysVENeT framework significantly outperformed conventional 1H-MRI ventilation mapping and other DL approaches which did not utilize structural imaging and ventilation mapping. PhysVENeT can accurately reflect ventilation defects and exhibits minimal overfitting on external validation data compared to DL approaches that do not integrate physiologically-informed mapping

Spatial comparison of CT-based surrogates of lung ventilation with hyperpolarized Helium-3 and Xenon-129 gas MRI in patients undergoing radiation therapy

Purpose To develop and apply an image acquisition and analysis strategy for spatial comparison of CT-ventilation images with hyperpolarized gas MRI. Methods 11 lung cancer patients underwent 129Xe and 3He ventilation MRI and co-registered 1H anatomical MRI. Expiratory and inspiratory breath-hold CTs were used for deformable image registration and calculation of three CT-ventilation metrics: Hounsfield unit (CTHU), Jacobian (CTJac) and specific gas volume change (CTSGV). Inspiration CT and hyperpolarized gas ventilation MRI were registered via same-breath anatomical 1H-MRI. Voxel-wise Spearman correlation coefficients were calculated between each CT-ventilation image and its corresponding 3He/129Xe-MRI, and for the mean values in regions of interest (ROIs) ranging from fine to coarse in-plane dimensions of 5x5, 10x10, 15x15 and 20x20, located within the lungs as defined by the same-breath 1H-MRI lung mask. Correlation of 3He and 129Xe-MRI was also assessed. Results Spatial correlation of CT-ventilation against 3He/129Xe-MRI increased with ROI size. For example, for CTHU, mean±SD Spearman coefficients were 0.37±0.19/0.33±0.17 at the voxel-level and 0.52±0.20/0.51±0.18 for 20x20 ROIs, respectively. Correlations were stronger for CTHU than for CTJac or CTSGV. Correlation of 3He with 129Xe-MRI was consistently higher than either gas against CT-ventilation maps over all ROIs (p<0.05). No significant differences were observed between CT-ventilation vs 3He-MRI and CT-ventilation vs 129Xe-MRI. Conclusion Comparison of ventilation-related measures from CT and registered hyperpolarized gas MRI is feasible at a voxel level using a dedicated acquisition and analysis protocol. Moderate correlation between CT-ventilation and MRI exists at a regional level. Correlation between MRI and CT is significantly less than between 3He and 129Xe-MRI, suggesting that CT-ventilation surrogate measures may not be measuring lung ventilation alone

Lung MRI with hyperpolarised gases : current & future clinical perspectives

The use of pulmonary MRI in a clinical setting has historically been limited. Whilst CT remains the gold-standard for structural lung imaging in many clinical indications, technical developments in ultrashort and zero echo time MRI techniques are beginning to help realise non-ionising structural imaging in certain lung disorders. In this invited review, we discuss a complementary technique – hyperpolarised (HP) gas MRI with inhaled 3He and 129Xe – a method for functional and microstructural imaging of the lung that has great potential as a clinical tool for early detection and improved understanding of pathophysiology in many lung diseases. HP gas MRI now has the potential to make an impact on clinical management by enabling safe, sensitive monitoring of disease progression and response to therapy. With reference to the significant evidence base gathered over the last two decades, we review HP gas MRI studies in patients with a range of pulmonary disorders, including COPD/emphysema, asthma, cystic fibrosis, and interstitial lung disease. We provide several examples of our experience in Sheffield of using these techniques in a diagnostic clinical setting in challenging adult and paediatric lung diseases

The Lung Image Database Consortium (LIDC):A comparison of different size metrics for pulmonary nodule measurements

RATIONALE AND OBJECTIVES: To investigate the effects of choosing between different metrics in estimating the size of pulmonary nodules as a factor both of nodule characterization and of performance of computer aided detection systems, since the latters are always qualified with respect to a given size range of nodules. MATERIALS AND METHODS: This study used 265 whole-lung CT scans documented by the Lung Image Database Consortium using their protocol for nodule evaluation. Each inspected lesion was reviewed independently by four experienced radiologists who provided boundary markings for nodules larger than 3 mm. Four size metrics, based on the boundary markings, were considered: a uni-dimensional and two bi-dimensional measures on a single image slice and a volumetric measurement based on all the image slices. The radiologist boundaries were processed and those with four markings were analyzed to characterize the inter-radiologist variation, while those with at least one marking were used to examine the difference between the metrics. RESULTS: The processing of the annotations found 127 nodules marked by all of the four radiologists and an extended set of 518 nodules each having at least one observation with three-dimensional sizes ranging from 2.03 to 29.4 mm (average 7.05 mm, median 5.71 mm). A very high inter-observer variation was observed for all these metrics: 95% of estimated standard deviations were in the following ranges [0.49, 1.25], [0.67, 2.55], [0.78, 2.11], and [0.96, 2.69] for the three-dimensional, the uni-dimensional, and the two bi-dimensional size metrics respectively (in mm). Also a very large difference among the metrics was observed: 0.95 probability-coverage region widths for the volume estimation conditional on uni-dimensional, and the two bi-dimensional size measurements of 10mm were 7.32, 7.72, and 6.29 mm respectively. CONCLUSIONS: The selection of data subsets for performance evaluation is highly impacted by the size metric choice. The LIDC plans to include a single size measure for each nodule in its database. This metric is not intended as a gold standard for nodule size; rather, it is intended to facilitate the selection of unique repeatable size limited nodule subsets