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Background: Preliminary studies suggest that sports-related concussion (SRC) is associated with alterations in cerebral blood flow (CBF) regulation. Here, we use advanced magnetic resonance imaging (MRI) techniques to measure CBF and cerebrovascular responsiveness (CVR) in individual SRC patients and healthy control subjects. Methods: 15 SRC patients (mean age = 16.3, range 14-20 years) and 27 healthy control subjects (mean age = 17.6, range 13-21 years) underwent anatomical MRI, pseudo-continuous arterial spin labeling (pCASL) MRI and model-based prospective end-tidal targeting (MPET) of CO2 during blood oxygenation level-dependent (BOLD) MRI. Group differences in global mean resting CBF were examined. Voxel-by-voxel group and individual differences in regional CVR were examined using statistical parametric mapping (SPM). Leave-one-out receiver operating characteristic curve analysis was used to evaluate the utility of brain MRI CO2 stress testing biomarkers to correctly discriminate between SRC patients and healthy control subjects. Results: All studies were tolerated with no complications. Traumatic structural findings were identified in one SRC patient. No significant group differences in global mean resting CBF were observed. There were no significant differences in the CO2 stimulus and O2 targeting during BOLD MRI. Significant group and patient-specific differences in CVR were observed with SRC patients demonstrating a predominant pattern of increased CVR. Leave-one-out ROC analysis for voxels demonstrating a significant increase in CVR was found to reliably discriminate between SRC patients and healthy control subjects (AUC of 0.879, p = 0.0001). The optimal cutoff for increased CVR declarative for SRC was 1,899 voxels resulting in a sensitivity of 0.867 and a specificity of 0.778 for this specific ROC analysis. There was no correlation between abnormal voxel counts and Postconcussion Symptom Scale scores among SRC patients. Conclusion: Acute and subacute SRCs are associated with alterations in CVR that can be reliably detected by brain MRI CO2 stress testing in individual patients. [ABSTRACT FROM AUTHOR]

This article examines how commerce promotes peace between states. It diverges from the commercial peace literature and its predominant focus on international trade by looking inside the domestic economy to see how its structure influences conflict. Drawing on selectorate theory, I argue that substantial quantities of public property generate fiscal autonomy for governments, strengthen their hold on the domestic reigns of power, and create opportunities to pursue more aggressive foreign policies. A series of statistical tests shows that greater quantities of publicly held assets increase the likelihood that a state will participate in military conflict. Given that the predominance of privately held property is one of the defining institutions of capitalism, these results support the claim that capitalism promotes peace. [ABSTRACT FROM AUTHOR]

This study argues that a subtle shift in the primary independent variable of the commercial peace literature-from trade to free trade-provides an opportunity to respond to the some of the strongest criticisms of this research program. Free trade, and not just trade, promotes peace by removing an important foundation of domestic privilege-protective barriers to international commerce-that enhances the domestic power of societal groups likely to support war, reduces the capacity of free-trading interests to limit aggression in foreign policy, and simultaneously generates political support for the state often used to build its war machine. A series of statistical tests demonstrates that higher levels of free trade, rather than trade alone, reduce military conflict between states. Moreover, contrary to conventional wisdom, these arguments suggest how the puzzling case of World War I may confirm, rather than contradict, the central claims of com- mercial liberalism. [ABSTRACT FROM AUTHOR]

This article explores how the dramatic expansion of British trade in the decades prior to World War I affected Britain's ability to raise an army. We first develop a simple institutionally based model of British army recruiting which we then perturb by expanding trade while holding all other variables constant. Our theoretical analysis suggests that the expansion of trade would impede Britain's ability to raise an army, a prediction that finds substantial support in the historical record using both quantitative and qualitative analysis. Contrary to the conventional wisdom that trade enhances a state's military power, we find that the expansion of trade did not ease Britain's resource constraints by making labor more freely available for military purposes. Rather, by raising the civilian demand for labor, the expansion of trade made labor more expensive and difficult to mobilize, even as a more effective army became more important to British strategy. [ABSTRACT FROM AUTHOR]

Purpose: This Hydrocephalus Clinical Research Network (HCRN) study had two aims: (1) to compare the predictive performance of the original ETV Success Score (ETVSS) using logistic regression modeling with other newer machine learning models and (2) to assess whether inclusion of imaging variables improves prediction performance using machine learning models. Methods: We identified children undergoing first-time ETV for hydrocephalus that were enrolled prospectively at HCRN sites between 200 and 2020. The primary outcome was ETV success 6 months after index surgery. The cohort was randomly divided into training (70%) and testing (30%) datasets. The classic ETVSS variables were used for logistic regression and machine learning models. Predictive performance of each model was evaluated on the testing dataset using area under the receiver operating characteristic curve (AUROC). Results: There were 752 patients that underwent first time ETV, of which 185 patients (24.6%) experienced ETV failure within 6 months. For aim 1, using the classic ETVSS variables, machine learning models did not outperform logistic regression with AUROC 0.60 (95% CI: 0.52–0.69) for Naïve Bayes (highest machine learning model performance) and 0.68 (95% CI: 0.60–0.76) for logistic regression. After inclusion of imaging features (aim 2), machine learning model prediction improved but remained no better than the above logistic regression with the highest AUROC of 0.67 (95% CI: 0.59–0.75) attained using Naïve Bayes architecture compared to 0.68 (95% CI: 0.59–0.76) for logistic regression. Conclusions: This contemporary multicenter observational cohort study demonstrated that machine learning modeling strategies did not improve performance of the ETVSS model over logistic regression. [ABSTRACT FROM AUTHOR]