Background and Purpose: Diffusion-weighted imaging (DWI) and perfusion MRI were used to examine the spatiotemporal evolution of stroke lesions in adult macaques with ischemic occlusion. Methods: Permanent MCA occlusion was induced with silk sutures through an interventional approach via the femoral artery in adult rhesus monkeys (n = 8, 10-21 years old). The stroke lesions were examined with high-resolution DWI and perfusion MRI, and T2-weighted imaging (T2W) on a clinical 3T scanner at 1-6, 48, and 96 hours post occlusion and validated with H&E staining. Results: The stroke infarct evolved via a natural logarithmic pattern with the mean infarct growth rate = 1.38 ± 1.32 ml per logarithmic time scale (hours) (n = 7) in the hyperacute phase (1-6 hours). The mean infarct volume after 6 hours post occlusion was 3.6±2.8 ml (n = 7, by DWI) and increased to 3.9±2.9 ml (n = 5, by T2W) after 48 hours, and to 4.7±2.2ml (n = 3, by T2W) after 96 hours post occlusion. The infarct volumes predicted by the natural logarithmic function were correlated significantly with the T2W-derived lesion volumes (n = 5, r = 0.92, p = 0.01) at 48 hours post occlusion. The final infarct volumes derived from T2W were correlated significantly with those from H&E staining (r = 0.999, p < 0.0001, n = 4). In addition, the diffusion-perfusion mismatch was visible generally at 6 hours but nearly diminished at 48 hours post occlusion. Conclusion: The infarct evolution follows a natural logarithmic pattern in the hyperacute phase of stroke. The logarithmic pattern of evolution could last up to 48 hours after stroke onset and may be used to predict the infarct volume growth during the acute phase of ischemic stroke. The nonhuman primate model, MRI protocols, and post data processing strategy may provide an excellent platform for characterizing the evolution of acute stroke lesion in mechanistic studies and therapeutic interventions of stroke disease.
Background: Medication-overuse headache (MOH) is often comorbid with emotional disturbances, contributing to poorer outcomes. The aims of the present study were to assess the psychometric properties of the Stagnation Scale in a sample of MOH patients, and to compare two factor models: a three-factor model reported in previous studies and a proposed bi-factor model. Methods: Consecutive adult outpatients (N = 310) admitted to the Regional Referral Headache Centre of the Sant’Andrea Hospital in Rome (Italy) were administered the Stagnation Scale and two questionnaires measuring depression and perceived disability. Results: The original three-factor model demonstrated an adequate fit to the data (χ<sup>2</sup> <inf>101</inf> = 238.70; p < 0.001; Root Mean Square Error of Approximation [RMSEA] = 0.07; 90% CI of RMSEA = 0.06 / 0.08; Comparative Fit Index [CFI] = 0.98; Weighted Root Mean Square Residual [WRMR] = 0.75). However, the bi-factor model had a comparable or even better fit, with a RMSEA of 0.05 (90% CI: 0.04 / 0.07), providing strong evidence for an absolute fit to the data (χ<sup>2</sup> <inf>88</inf> = 161.43; p < 0.001; RMSEA = 0.05; 90% CI of RMSEA = 0.04 / 0.07; CFI = 0.99; WRMR = 0.56). The stagnation general factor and all the group factors correlated significantly and positively with convergent measures. Conclusions: There is support for the use of the Stagnation Scale in MOH patients, with the goal of better understanding the role of psychological factors in the evolution and course of the disorder.
Causal composition allows people to generate new causal relations by combining existing causal knowledge. We introduce a new computational model of such reasoning, the force theory, which holds that people compose causal relations by simulating the processes that join forces in the world, and compare this theory with the mental model theory (Khemlani et al., 2014) and the causal model theory (Sloman et al., 2009), which explain causal composition on the basis of mental models and structural equations, respectively. In one experiment, the force theory was uniquely able to account for people's ability to compose causal relationships from complex animations of real-world events. In three additional experiments, the force theory did as well as or better than the other two theories in explaining the causal compositions people generated from linguistically presented causal relations. Implications for causal learning and the hierarchical structure of causal knowledge are discussed.
This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Recent evidence suggests that grammatical aspect can bias how individuals perceive criminal intentionality during discourse comprehension. Given that criminal intentionality is a common criterion for legal definitions (e.g., first-degree murder), the present study explored whether grammatical aspect may also impact legal judgments. In a series of four experiments participants were provided with a legal definition and a description of a crime in which the grammatical aspect of provocation and murder events were manipulated. Participants were asked to make a decision (first- vs. second-degree murder) and then indicate factors that impacted their decision. Findings suggest that legal judgments can be affected by grammatical aspect but the most robust effects were limited to temporal dynamics (i.e., imperfective aspect results in more murder actions than perfective aspect), which may in turn influence other representational systems (i.e., number of murder actions positively predicts perceived intentionality). In addition, findings demonstrate that the influence of grammatical aspect on situation model construction and evaluation is dependent upon the larger linguistic and semantic context. Together, the results suggest grammatical aspect has indirect influences on legal judgments to the extent that variability in aspect changes the features of the situation model that align with criteria for making legal judgments.