Hormone effects on fMRI and cognitive measures of encoding: Importance of hormone preparation

We compared fMRI and cognitive data from nine hormone therapy (HT)-naive women with data from women exposed to either opposed conjugated equine estrogens (CEE) (n = 10) or opposed estradiol (n = 4). Exposure to either form of HT was associated with healthier fMRI response; however, CEE-exposed women exhibited poorer memory performance than either HT-naive or estradiol-exposed subjects. These preliminary findings emphasize the need to characterize differential neural effects of various HTs. ©2006AAN Enterprises, Inc

Crosswalk study on blood collection-tube types for Alzheimer's disease biomarkers

Introduction: Blood-based Alzheimer's disease (AD) biomarkers show promise, but pre-analytical protocol differences may pose problems. We examined seven AD blood biomarkers (amyloid beta [ A β ] 42 , A β 40 , phosphorylated tau [ p - ta u 181 , total tau [t-tau], neurofilament light chain [NfL], A β 42 40 , and p - ta u 181 A β 42 ) in three collection tube types (ethylenediaminetetraacetic acid [EDTA] plasma, heparin plasma, serum). Methods: Plasma and serum were obtained from cerebrospinal fluid or amyloid positron emission tomography-positive and -negative participants (N = 38) in the Wisconsin Registry for Alzheimer's Prevention. We modeled AD biomarker values observed in EDTA plasma versus heparin plasma and serum, and assessed correspondence with brain amyloidosis. Results: Results suggested bias due to tube type, but crosswalks are possible for some analytes, with excellent model fit for NfL ( R 2 = 0.94), adequate for amyloid ( R 2 = 0.40-0.69), and weaker for t-tau ( R 2 = 0.04-0.42) and p - ta u 181 ( R 2 = 0.22-0.29). Brain amyloidosis differentiated several measures, especially EDTA plasma pTa u 181 A β 42 ( d = 1.29). Discussion: AD biomarker concentrations vary by tube type. However, correlations for some biomarkers support harmonization across types, suggesting cautious optimism for use in banked blood

Tomosynthesis in pulmonary cystic fibrosis with comparison to radiography and computed tomography: a pictorial review

The purpose of this pictorial review is to illustrate chest imaging findings of cystic fibrosis (CF) using tomosynthesis (digital tomography), in comparison to radiography and computed tomography (CT). CF is a chronic systemic disease where imaging has long been used for monitoring chest status. CT exposes the patient to a substantially higher radiation dose than radiography, rendering it unsuitable for the often needed repeated examinations of these patients. Tomosynthesis has recently appeared as an interesting low dose alternative to CT, with an effective dose of approximately 0.08 mSv for children and 0.12 mSv for adults. Tomosynthesis is performed on the same X-ray system as radiography, adding only about 1 min to the normal examination time. Typical pulmonary changes in CF such as mucus plugging, bronchial wall thickening, and bronchiectases are shown in significantly better detail with tomosynthesis than with traditional radiography. In addition, the cost for a tomosynthesis examination is low compared to CT. To reduce the radiation burden of patients with CF it is important to consider low dose alternatives to CT, especially in the paediatric population. Tomosynthesis has a lower radiation dose than CT and gives a superior visualisation of pulmonary CF changes compared to radiography. It is important to further determine the role of tomosynthesis for monitoring disease progression in CF

Positive Affect Predicts Cerebral Glucose Metabolism in Late Middle-aged Adults.

Positive affect is associated with a number of health benefits; however, few studies have examined the relationship between positive affect and cerebral glucose metabolism, a key energy source for neuronal function and a possible index of brain health. We sought to determine if positive affect was associated with cerebral glucose metabolism in late middle-aged adults (n = 133). Participants completed the positive affect subscale of the Center for Epidemiological Studies Depression Scale at two time points over a two-year period and underwent 18F-fluorodeoxyglucose-positron emission tomography scanning. After controlling for age, sex, perceived health status, depressive symptoms, anti-depressant use, family history of Alzheimer’s disease, APOE ε4 status and interval between visits, positive affect was associated with greater cerebral glucose metabolism across para-/limbic, frontal, temporal and parietal regions. Our findings provide evidence that positive affect in late midlife is associated with greater brain health in regions involved in affective processing and also known to be susceptible to early neuropathological processes. The current findings may have implications for interventions aimed at increasing positive affect to attenuate early neuropathological changes in at-risk individuals

The Neural Basis of Following Advice

Learning by following explicit advice is fundamental for human cultural evolution, yet the neurobiology of adaptive social learning is largely unknown. Here, we used simulations to analyze the adaptive value of social learning mechanisms, computational modeling of behavioral data to describe cognitive mechanisms involved in social learning, and model-based functional magnetic resonance imaging (fMRI) to identify the neurobiological basis of following advice. One-time advice received before learning had a sustained influence on people's learning processes. This was best explained by social learning mechanisms implementing a more positive evaluation of the outcomes from recommended options. Computer simulations showed that this “outcome-bonus” accumulates more rewards than an alternative mechanism implementing higher initial reward expectation for recommended options. fMRI results revealed a neural outcome-bonus signal in the septal area and the left caudate. This neural signal coded rewards in the absence of advice, and crucially, it signaled greater positive rewards for positive and negative feedback after recommended rather than after non-recommended choices. Hence, our results indicate that following advice is intrinsically rewarding. A positive correlation between the model's outcome-bonus parameter and amygdala activity after positive feedback directly relates the computational model to brain activity. These results advance the understanding of social learning by providing a neurobiological account for adaptive learning from advice

Identification of Small Molecule Inhibitors of Pseudomonas aeruginosa Exoenzyme S Using a Yeast Phenotypic Screen

Pseudomonas aeruginosa is an opportunistic human pathogen that is a key factor in the mortality of cystic fibrosis patients, and infection represents an increased threat for human health worldwide. Because resistance of Pseudomonas aeruginosa to antibiotics is increasing, new inhibitors of pharmacologically validated targets of this bacterium are needed. Here we demonstrate that a cell-based yeast phenotypic assay, combined with a large-scale inhibitor screen, identified small molecule inhibitors that can suppress the toxicity caused by heterologous expression of selected Pseudomonas aeruginosa ORFs. We identified the first small molecule inhibitor of Exoenzyme S (ExoS), a toxin involved in Type III secretion. We show that this inhibitor, exosin, modulates ExoS ADP-ribosyltransferase activity in vitro, suggesting the inhibition is direct. Moreover, exosin and two of its analogues display a significant protective effect against Pseudomonas infection in vivo. Furthermore, because the assay was performed in yeast, we were able to demonstrate that several yeast homologues of the known human ExoS targets are likely ADP-ribosylated by the toxin. For example, using an in vitro enzymatic assay, we demonstrate that yeast Ras2p is directly modified by ExoS. Lastly, by surveying a collection of yeast deletion mutants, we identified Bmh1p, a yeast homologue of the human FAS, as an ExoS cofactor, revealing that portions of the bacterial toxin mode of action are conserved from yeast to human. Taken together, our integrated cell-based, chemical-genetic approach demonstrates that such screens can augment traditional drug screening approaches and facilitate the discovery of new compounds against a broad range of human pathogens

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