ed.The occurrence of neuronal spikes recorded directly from sensory cortex is highly irregular within and between presentations of an invariant stimulus. The traditional solution has been to average responses across many trials. However, with this approach, response variability is downplayed as noise, so it is assumed that statistically controlling it will reveal the brain's true response to a stimulus. A mounting body of evidence suggests that this approach is inadequate. For example, experiments show that response variability itself varies as a function of stimulus dimensions like contrast and state dimensions like attention. In other words, response variability has structure, is therefore potentially informative and should be incorporated into models which try to explain neural encoding. In this article we provide commentary on a recently published study by Coen-Cagli and Solomon that incorporates spike variability in a quantitative model, by explaining it as a function of divisive normalization. We consider the potential role of neural oscillations in this process as a potential bridge between the current microscale findings and response variability at the mesoscale/macroscale level. To evaluate the value of dual-energy computed tomography (DECT) in differentiating cerebral hemorrhage from blood brain barrier (BBB) disruption after neuro-interventional procedures with intra-arterial injection of iodinated contrast material. This prospective study was approved by the local ethics committee, and informed consent was obtained for all patients. Thirty five patients with acute ischemic stroke or un-ruptured brain aneurysm who had received intra-arterial administration of iodinated contrast material were evaluated using DECT at 80 and 150 kV immediately after the procedure.A three-material decomposition algorithm was used to obtain virtual non-contrast (VNC) images and iodine overlay maps (IOM). A follow-up examination (brain magnetic resonance imaging MRI or conventional CT) was used as the standard of reference for hemorrhage, defined as a persistant hyperdensity on a conventional CT or T2* hypo-intensity on brain MRI. The diagnostic values of DECT in differentiating hemorrhage and iodinated contrast material were obtained. Mixed images obtained with DECT showed intra-parenchymal or subarachnoid hyperattenuation in 18/35 patients. Among these, 16 were classified (according to VNC images and IOM) as contrast extravasations and two with a mixture of hemorrhage and contrast material. On follow-up imaging, there were two patients with hemorrhage. The sensitivity, specificity, and accuracy of DECT in the identifying hemorrhage was calculated as 67% (2/3), 100% (32/32) and 97% (32/33) respectively. DECT allows an early and accurate differentiation between cerebral hemorrhage and BBB disruption after intra-arterial neuro-interventional procedures. DECT allows an early and accurate differentiation between cerebral hemorrhage and BBB disruption after intra-arterial neuro-interventional procedures. To perform a meta-analysis comparing the diagnostic performance of increased signal intensity on T1- and T2-weighted magnetic resonance images and apparent diffusion coefficient (ADC) values in differentiating uterine leiomyosarcoma (LMS) from benign leiomyoma (LM). A systematic literature search for original studies was performed using PubMed/MEDLINE, the Cochrane Library, Embase, and Web of Science. Data necessary for the meta-analysis was extracted from the selected articles and analyzed. Eight studies with 795 patients met our predefined inclusion criteria and were included in the analysis. Increased signal on T1-weighted imaging had a pooled sensitivity of 56.8% (95% CI 20%-87.4%) for LMS (n = 60) which was significantly higher than 7.6% (95% CI 2.2%-22.7%) for LM (n = 1272) ( = 0.0094). Increased signal analysis on T2-weighted imaging had a pooled sensitivities of 93.2% and 93.2% (95% CI 45.7%-99.6% and 42.9%-99.6%) for LMS (n = 90), which were not significantly different from the 54.5% and 53.9% (95% CI 33.6%-74%, 32%-74%) for LM (n = 215) ( = 0.102 and 0.112). https://www.selleckchem.com/products/zotatifin.html On ADC value analysis, LMS (n = 43) had a weighted mean and standard deviation of 0.896 ± 0.19 10 mm /s, 0.929 ± 0.182 10 mm /s, which were significantly lower from 1.258 ± 0.303 10 mm /s, 1.304 ± 0.303 10 mm /s for LM (n = 159) ( = < 0.0001, < 0.0001). Our meta-analysis demonstrated that high signal intensity on T1-weighted images and low ADC values can accurately differentiate LMS from LM. Although, LMS had a higher pooled sensitivity for T2-weighted increased signal intensity compared to LM, there was no statistical significance. Our meta-analysis demonstrated that high signal intensity on T1-weighted images and low ADC values can accurately differentiate LMS from LM. Although, LMS had a higher pooled sensitivity for T2-weighted increased signal intensity compared to LM, there was no statistical significance.Teaching point Early depiction of systemic air embolism after percutaneous lung biopsy allows for timely adequate management to prevent potentially fatal complications.Teaching point CT may help distinguishing benign from life-threatening pneumatosis intestinalis.Callous-unemotional (CU) traits are early-emerging personality features characterized by deficits in empathy, concern for others, and remorse following social transgressions. One of the interpersonal deficits most consistently associated with CU traits is impaired behavioral and neurophysiological responsiveness to fearful facial expressions. However, the facial expression paradigms traditionally employed in neuroimaging are often ambiguous with respect to the nature of threat (i.e., is the perceiver the threat, or is something else in the environment?). In the present study, 30 adolescents with varying CU traits viewed fearful facial expressions cued to three different contexts ("afraid for you," "afraid of you," "afraid for self") while undergoing functional magnetic resonance imaging (fMRI). Univariate analyses found that mean right amygdala activity during the "afraid for self" context was negatively associated with CU traits. With the goal of disentangling idiosyncratic stimulus-driven neural responses, we employed intersubject representational similarity analysis to link intersubject similarities in multivoxel neural response patterns to contextualized fearful expressions with differential intersubject models of CU traits.