Controllability of structural brain networks.

Cognitive function is driven by dynamic interactions between large-scale neural circuits or networks, enabling behaviour. However, fundamental principles constraining these dynamic network processes have remained elusive. Here we use tools from control and network theories to offer a mechanistic explanation for how the brain moves between cognitive states drawn from the network organization of white matter microstructure. Our results suggest that densely connected areas, particularly in the default mode system, facilitate the movement of the brain to many easily reachable states. Weakly connected areas, particularly in cognitive control systems, facilitate the movement of the brain to difficult-to-reach states. Areas located on the boundary between network communities, particularly in attentional control systems, facilitate the integration or segregation of diverse cognitive systems. Our results suggest that structural network differences between cognitive circuits dictate their distinct roles in controlling trajectories of brain network function

Inflammation leads through PGE/EP3 signaling to HDAC5/MEF2-dependent transcription in cardiac myocytes

The myocyte enhancer factor 2 (MEF2) regulates transcription in cardiac myocytes and adverse remodeling of adult hearts. Activators of G protein-coupled receptors (GPCRs) have been reported to activate MEF2, but a comprehensive analysis of GPCR activators that regulate MEF2 has to our knowledge not been performed. Here, we tested several GPCR agonists regarding their ability to activate a MEF2 reporter in neonatal rat ventricular myocytes. The inflammatory mediator prostaglandin E2 (PGE2) strongly activated MEF2. Using pharmacological and protein-based inhibitors, we demonstrated that PGE2 regulates MEF2 via the EP3 receptor, the betagamma subunit of Gi/o protein and two concomitantly activated downstream pathways. The first consists of Tiam1, Rac1, and its effector p21-activated kinase 2, the second of protein kinase D. Both pathways converge on and inactivate histone deacetylase 5 (HDAC5) and thereby de-repress MEF2. In vivo, endotoxemia in MEF2-reporter mice induced upregulation of PGE2 and MEF2 activation. Our findings provide an unexpected new link between inflammation and cardiac remodeling by de-repression of MEF2 through HDAC5 inactivation, which has potential implications for new strategies to treat inflammatory cardiomyopathies

Shot at Dawn: Late Photography and the Anti-War Memorial

The military executions of World War One are the subject of Chloe Dewe Mathews’s 2014 photographic series Shot at Dawn. These events—in which hundreds of soldiers were court-martialled and executed for cowardice and desertion—remain controversial, without consensus or established collective narrative. This article charts historic negotiations with the subject but also considers more recent efforts to integrate these proceedings within memorial practice. World War One remembrance activities, whilst diverse, have often emphasised sacrifice, heroism and community. Correspondingly, participation and engagement were core values in the major British World War One centenary arts project, titled 14-18 NOW, from which Shot at Dawn was commissioned. Chloe Dewe Mathews’s contribution to the programme, however, presents a photographic aesthetic of resistance to the principles of inclusivity and remembrance elsewhere embraced by the project. As such, the work challenges the consensual politics of commemoration and—through the practices of late photography, land art and performance pilgrimage— substitutes trauma and forgetfulness for reconciliation and memory

Task-Specific Codes for Face Recognition: How they Shape the Neural Representation of Features for Detection and Individuation

The variety of ways in which faces are categorized makes face recognition challenging for both synthetic and biological vision systems. Here we focus on two face processing tasks, detection and individuation, and explore whether differences in task demands lead to differences both in the features most effective for automatic recognition and in the featural codes recruited by neural processing.Our study appeals to a computational framework characterizing the features representing object categories as sets of overlapping image fragments. Within this framework, we assess the extent to which task-relevant information differs across image fragments. Based on objective differences we find among task-specific representations, we test the sensitivity of the human visual system to these different face descriptions independently of one another. Both behavior and functional magnetic resonance imaging reveal effects elicited by objective task-specific levels of information. Behaviorally, recognition performance with image fragments improves with increasing task-specific information carried by different face fragments. Neurally, this sensitivity to the two tasks manifests as differential localization of neural responses across the ventral visual pathway. Fragments diagnostic for detection evoke larger neural responses than non-diagnostic ones in the right posterior fusiform gyrus and bilaterally in the inferior occipital gyrus. In contrast, fragments diagnostic for individuation evoke larger responses than non-diagnostic ones in the anterior inferior temporal gyrus. Finally, for individuation only, pattern analysis reveals sensitivity to task-specific information within the right "fusiform face area".OUR RESULTS DEMONSTRATE: 1) information diagnostic for face detection and individuation is roughly separable; 2) the human visual system is independently sensitive to both types of information; 3) neural responses differ according to the type of task-relevant information considered. More generally, these findings provide evidence for the computational utility and the neural validity of fragment-based visual representation and recognition

N-Octanoyl-Dopamine inhibits cytokine production in activated T-cells and diminishes MHC-class-II expression as well as adhesion molecules in IFN gamma-stimulated endothelial cells

IFN gamma enhances allograft immunogenicity and facilitates T-cell mediated rejection. This may cause interstitial fibrosis and tubular atrophy (IFTA), contributing to chronic allograft loss. We assessed if inhibition of T-cell activation by N-octanoyl dopamine (NOD) impairs adherence of activated T-cells to endothelial cells and the ability of activated T-cells to produce IFN gamma. We also assessed if NOD affects IFN gamma mediated gene expression in endothelial cells. The presence of NOD during T-cell activation significantly blunted their adhesion to unstimulated and cytokine stimulated HUVEC. Supernatants of these T-cells displayed significantly lower concentrations of TNF alpha and IFN gamma and were less capable to facilitate T-cell adhesion. In the presence of NOD VLA-4 (CD49d/CD29) and LFA-1 (CD11a/CD18) expression on T-cells was reduced. NOD treatment of IFN gamma stimulated HUVEC reduced the expression of MHC class II transactivator (CIITA), of MHC class II and its associated invariant chain CD74. Since IFTA is associated with T-cell mediated rejection and IFN gamma to a large extent regulates immunogenicity of allografts, our current data suggest a potential clinical use of NOD in the treatment of transplant recipients. Further in vivo studies are warranted to confirm these in vitro findings and to assess the benefit of NOD on IFTA in clinically relevant models

Variability in the analysis of a single neuroimaging dataset by many teams

Data analysis workflows in many scientific domains have become increasingly complex and flexible. To assess the impact of this flexibility on functional magnetic resonance imaging (fMRI) results, the same dataset was independently analyzed by 70 teams, testing nine ex-ante hypotheses. The flexibility of analytic approaches is exemplified by the fact that no two teams chose identical workflows to analyze the data. This flexibility resulted in sizeable variation in hypothesis test results, even for teams whose statistical maps were highly correlated at intermediate stages of their analysis pipeline. Variation in reported results was related to several aspects of analysis methodology. Importantly, meta-analytic approaches that aggregated information across teams yielded significant consensus in activated regions across teams. Furthermore, prediction markets of researchers in the field revealed an overestimation of the likelihood of significant findings, even by researchers with direct knowledge of the dataset. Our findings show that analytic flexibility can have substantial effects on scientific conclusions, and demonstrate factors related to variability in fMRI. The results emphasize the importance of validating and sharing complex analysis workflows, and demonstrate the need for multiple analyses of the same data. Potential approaches to mitigate issues related to analytical variability are discussed

Variability in the analysis of a single neuroimaging dataset by many teams

Data analysis workflows in many scientific domains have become increasingly complex and flexible. To assess the impact of this flexibility on functional magnetic resonance imaging (fMRI) results, the same dataset was independently analyzed by 70 teams, testing nine ex-ante hypotheses. The flexibility of analytic approaches is exemplified by the fact that no two teams chose identical workflows to analyze the data. This flexibility resulted in sizeable variation in hypothesis test results, even for teams whose statistical maps were highly correlated at intermediate stages of their analysis pipeline. Variation in reported results was related to several aspects of analysis methodology. Importantly, meta-analytic approaches that aggregated information across teams yielded significant consensus in activated regions across teams. Furthermore, prediction markets of researchers in the field revealed an overestimation of the likelihood of significant findings, even by researchers with direct knowledge of the dataset. Our findings show that analytic flexibility can have substantial effects on scientific conclusions, and demonstrate factors related to variability in fMRI. The results emphasize the importance of validating and sharing complex analysis workflows, and demonstrate the need for multiple analyses of the same data. Potential approaches to mitigate issues related to analytical variability are discussed