Performance of Trajectory Models with Wind Uncertainty

Typical aircraft trajectory predictors use wind forecasts but do not account for the forecast uncertainty. A method for generating estimates of wind prediction uncertainty is described and its effect on aircraft trajectory prediction uncertainty is investigated. The procedure for estimating the wind prediction uncertainty relies uses a time-lagged ensemble of weather model forecasts from the hourly updated Rapid Update Cycle (RUC) weather prediction system. Forecast uncertainty is estimated using measures of the spread amongst various RUC time-lagged ensemble forecasts. This proof of concept study illustrates the estimated uncertainty and the actual wind errors, and documents the validity of the assumed ensemble-forecast accuracy relationship. Aircraft trajectory predictions are made using RUC winds with provision for the estimated uncertainty. Results for a set of simulated flights indicate this simple approach effectively translates the wind uncertainty estimate into an aircraft trajectory uncertainty. A key strength of the method is the ability to relate uncertainty to specific weather phenomena (contained in the various ensemble members) allowing identification of regional variations in uncertainty

Similar neural pathways link psychological stress and brain-age in health and multiple sclerosis

Clinical and neuroscientific studies suggest a link between psychological stress and reduced brain health in health and neurological disease but it is unclear whether mediating pathways are similar. Consequently, we applied an arterial-spin-labeling MRI stress task in 42 healthy persons and 56 with multiple sclerosis, and investigated regional neural stress responses, associations between functional connectivity of stress-responsive regions and the brain-age prediction error, a highly sensitive machine learning brain health biomarker, and regional brain-age constituents in both groups. Stress responsivity did not differ between groups. Although elevated brain-age prediction errors indicated worse brain health in patients, anterior insula–occipital cortex (healthy persons: occipital pole; patients: fusiform gyrus) functional connectivity correlated with brain-age prediction errors in both groups. Finally, also gray matter contributed similarly to regional brain-age across groups. These findings might suggest a common stress–brain health pathway whose impact is amplified in multiple sclerosis by disease-specific vulnerability factors

Uncovering convolutional neural network decisions for diagnosing multiple sclerosis on conventional MRI using layer-wise relevance propagation

Machine learning-based imaging diagnostics has recently reached or even surpassed the level of clinical experts in several clinical domains. However, classification decisions of a trained machine learning system are typically non-transparent, a major hindrance for clinical integration, error tracking or knowledge discovery. In this study, we present a transparent deep learning framework relying on 3D convolutional neural networks (CNNs) and layer-wise relevance propagation (LRP) for diagnosing multiple sclerosis (MS), the most widespread autoimmune neuroinflammatory disease. MS is commonly diagnosed utilizing a combination of clinical presentation and conventional magnetic resonance imaging (MRI), specifically the occurrence and presentation of white matter lesions in T2-weighted images. We hypothesized that using LRP in a naive predictive model would enable us to uncover relevant image features that a trained CNN uses for decision-making. Since imaging markers in MS are well-established this would enable us to validate the respective CNN model. First, we pre-trained a CNN on MRI data from the Alzheimer's Disease Neuroimaging Initiative (n = 921), afterwards specializing the CNN to discriminate between MS patients (n = 76) and healthy controls (n = 71). Using LRP, we then produced a heatmap for each subject in the holdout set depicting the voxel-wise relevance for a particular classification decision. The resulting CNN model resulted in a balanced accuracy of 87.04% and an area under the curve of 96.08% in a receiver operating characteristic curve. The subsequent LRP visualization revealed that the CNN model focuses indeed on individual lesions, but also incorporates additional information such as lesion location, non-lesional white matter or gray matter areas such as the thalamus, which are established conventional and advanced MRI markers in MS. We conclude that LRP and the proposed framework have the capability to make diagnostic decisions of CNN models transparent, which could serve to justify classification decisions for clinical review, verify diagnosis-relevant features and potentially gather new disease knowledge

Prefrontal-amygdala emotion regulation and depression in multiple sclerosis

Depression is among the most common comorbidities in multiple sclerosis and has severe psychosocial consequences. Alterations in neural emotion regulation in amygdala and prefrontal cortex have been recognized as key mechanism of depression but never been investigated in multiple sclerosis depression. In this cross-sectional observational study, we employed a functional MRI task investigating neural emotion regulation by contrasting regulated versus unregulated negative stimulus perception in 16 persons with multiple sclerosis and depression (47.9 ± 11.8 years; 14 female) and 26 persons with multiple sclerosis but without depression (47.3 ± 11.7 years; 14 female). We tested the impact of depression and its interaction with lesions in amygdala-prefrontal fibre tracts on brain activity reflecting emotion regulation. A potential impact of sex, age, information processing speed, disease duration, overall lesion load, grey matter fraction, and treatment was taken into account in these analyses. Patients with depression were less able (i) to downregulate negative emotions than those without (t = -2.25, P = 0.012, β = -0.33) on a behavioural level according to self-report data and (ii) to downregulate activity in a left amygdala coordinate (t = 3.03, P(Family-wise error [FWE]-corrected) = 0.017, β = 0.39). Moreover, (iii) an interdependent effect of depression and lesions in amygdala-prefrontal tracts on activity was found in two left amygdala coordinates (t = 3.53, p(FWE9 = 0.007, β = 0.48; t = 3.21, p(FWE) = 0.0158, β = 0.49) and one right amygdala coordinate (t = 3.41, p(FWE) = 0.009, β = 0.51). Compatible with key elements of the cognitive depression theory formulated for idiopathic depression, our study demonstrates that depression in multiple sclerosis is characterized by impaired neurobehavioural emotion regulation. Complementing these findings, it shows that the relation between neural emotion regulation and depression is affected by lesion load, a key pathological feature of multiple sclerosis, located in amygdala-prefrontal tracts

Central stress processing, T-cell responsivity to stress hormones, and disease severity in multiple sclerosis

Epidemiological, clinical and neuroscientific studies support a link between psychobiological stress and multiple sclerosis. Neuroimaging suggests that blunted central stress processing goes along with higher multiple sclerosis severity, neuroendocrine studies suggest that blunted immune system sensitivity to stress hormones is linked to stronger neuroinflammation. Until now, however, no effort has been made to elucidate whether central stress processing and immune system sensitivity to stress hormones are related in a disease-specific fashion, and if so, whether this relation is clinically meaningful. Consequently, we conducted two functional MRI analyses based on a total of 39 persons with multiple sclerosis and 25 healthy persons. Motivated by findings of an altered interplay between neuroendocrine stress processing and T-cell glucocorticoid sensitivity in multiple sclerosis, we searched for neural networks whose stress task-evoked activity is differentially linked to peripheral T-cell glucocorticoid signalling in patients versus healthy persons as a potential indicator of disease-specific CNS–immune crosstalk. Subsequently, we tested whether this activity is simultaneously related to disease severity. We found that activity of a network comprising right anterior insula, right fusiform gyrus, left midcingulate and lingual gyrus was differentially coupled to T-cell glucocorticoid signalling across groups. This network’s activity was simultaneously linked to patients’ lesion volume, clinical disability and information-processing speed. Complementary analyses revealed that T-cell glucocorticoid signalling was not directly linked to disease severity. Our findings show that alterations in the coupling between central stress processing and T-cell stress hormone sensitivity are related to key severity measures of multiple sclerosis

MRI Pattern Recognition in Multiple Sclerosis Normal-Appearing Brain Areas

Objective Here, we use pattern-classification to investigate diagnostic information for multiple sclerosis (MS; relapsing­remitting type) in lesioned areas, areas of normal­appearing grey matter (NAGM), and normal-appearing white matter (NAWM) as measured by standard MR techniques. Methods A lesion mapping was carried out by an experienced neurologist for Turbo Inversion Recovery Magnitude (TIRM) images of individual subjects. Combining this mapping with templates from a neuroanatomic atlas, the TIRM images were segmented into three areas of homogenous tissue types (Lesions, NAGM, and NAWM) after spatial standardization. For each area, a linear Support Vector Machine algorithm was used in multiple local classification analyses to determine the diagnostic accuracy in separating MS patients from healthy controls based on voxel tissue intensity patterns extracted from small spherical subregions of these larger areas. To control for covariates, we also excluded group-specific biases in deformation fields as a potential source of information. Results Among regions containing lesions a posterior parietal WM area was maximally informative about the clinical status (96% accuracy, p<10−13). Cerebellar regions were maximally informative among NAGM areas (84% accuracy, p<10−7). A posterior brain region was maximally informative among NAWM areas (91% accuracy, p<10−10). Interpretation We identified regions indicating MS in lesioned, but also NAGM, and NAWM areas. This complements the current perception that standard MR techniques mainly capture macroscopic tissue variations due to focal lesion processes. Compared to current diagnostic guidelines for MS that define areas of diagnostic information with moderate spatial specificity, we identified hotspots of MS associated tissue alterations with high specificity defined on a millimeter scale

Similar neural pathways link psychological stress and brain-age in health and multiple sclerosis

Clinical and neuroscientific studies suggest a link between psychological stress and reduced brain health in health and neurological disease but it is unclear whether mediating pathways are similar. Consequently, we applied an arterial-spin-labeling MRI stress task in 42 healthy persons and 56 with multiple sclerosis, and investigated regional neural stress responses, associations between functional connectivity of stress-responsive regions and the brain-age prediction error, a highly sensitive machine learning brain health biomarker, and regional brain-age constituents in both groups. Stress responsivity did not differ between groups. Although elevated brain-age prediction errors indicated worse brain health in patients, anterior insula–occipital cortex (healthy persons: occipital pole; patients: fusiform gyrus) functional connectivity correlated with brain-age prediction errors in both groups. Finally, also gray matter contributed similarly to regional brain-age across groups. These findings might suggest a common stress–brain health pathway whose impact is amplified in multiple sclerosis by disease-specific vulnerability factors

Blunted neural and psychological stress processing predicts future grey matter atrophy in multiple sclerosis

BACKGROUND: Multiple sclerosis (MS) is characterized by two neuropathological key aspects: inflammation and neurodegeneration. Clinical studies support a prospective link between psychological stress and subsequent inflammatory disease activity. However, it is unknown if a similar link exists for grey matter (GM) degeneration as the key driver of irreversible disability. METHODS: We tested whether neural network activity triggered in a psychological fMRI stress paradigm (a mental arithmetic task including social evaluation) conducted at a baseline time point predicts future GM atrophy in 25 persons with MS (14 females). Atrophy was determined between the baseline and a follow-up time point with a median delay of 1012 (Rg: 717–1439) days. Additionally, atrophy was assessed in 22 healthy subjects (13 females; median delay 771 [Rg: 740–908] days between baseline and follow-up) for comparison. RESULTS: An analysis of longitudinal atrophy in patients revealed GM loss in frontal, parietal, and cerebellar areas. Cerebellar atrophy was more pronounced in patients than controls. Future parietal and cerebellar atrophy could be predicted based on activity of two networks. Perceived psychological stress was negatively related to future parietal atrophy in patients and activity of the network predictive of parietal atrophy was positively linked to perceived stress. CONCLUSIONS: We have shown that blunted neural and psychological stress processing have a detrimental effect on the course of MS and are interrelated. Together with research showing that psychological and neural stress processing can be altered through interventions, our findings suggest that stress processing might constitute an important modifiable disease factor

Disparity in Reimbursement for Tuberculosis Care Among Different Health Insurance Schemes: Evidence from Three Counties in Central China

Background: Health inequity is an important issue all around the world. The Chinese basic medical security system comprises three major insurance schemes, namely the Urban Employee Basic Medical Insurance (UEBMI), the Urban Resident Basic Medical Insurance (URBMI), and the New Cooperative Medical Scheme (NCMS). Little research has been conducted to look into the disparity in payments among the health insurance schemes in China. In this study, we aimed to evaluate the disparity in reimbursements for tuberculosis (TB) care among the abovementioned health insurance schemes. Methods: This study uses a World Health Organization (WHO) framework to analyze the disparities and equity relating to the three dimensions of health insurance: population coverage, the range of services covered, and the extent to which costs are covered. Each of the health insurance scheme’s policies were categorized and analyzed. An analysis of the claims database of all hospitalizations reimbursed from 2010 to 2012 in three counties of Yichang city (YC), which included 1506 discharges, was conducted to identify the differences in reimbursement rates and out-of-pocket (OOP) expenses among the health insurance schemes. Results: Tuberculosis patients had various inpatient expenses depending on which scheme they were covered by (TB patients covered by the NCMS have less inpatient expenses than those who were covered by the URBMI, who have less inpatient expenses than those covered by the UEBMI). We found a significant horizontal inequity of healthcare utilization among the lower socioeconomic groups. In terms of financial inequity, TB patients who earned less paid more. The NCMS provides modest financial protection, based on income. Overall, TB patients from lower socioeconomic groups were the most vulnerable. Conclusion: There are large disparities in reimbursement for TB care among the three health insurance schemes and this, in turn, hampers TB control. Reducing the gap in health outcomes between the three health insurance schemes in China should be a focus of TB care and control. Achieving equity through integrated policies that avoid discrimination is likely to be effective