Microwave Radar-Based Breast Cancer Detection:Imaging in Inhomogeneous Breast Phantoms

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Radar-based breast cancer detection using a hemispherical antenna array - experimental results

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Structural subnetwork evolution across the life-span: rich-club, feeder, seeder

The impact of developmental and aging processes on brain connectivity and the connectome has been widely studied. Network theoretical measures and certain topological principles are computed from the entire brain, however there is a need to separate and understand the underlying subnetworks which contribute towards these observed holistic connectomic alterations. One organizational principle is the rich-club - a core subnetwork of brain regions that are strongly connected, forming a high-cost, high-capacity backbone that is critical for effective communication in the network. Investigations primarily focus on its alterations with disease and age. Here, we present a systematic analysis of not only the rich-club, but also other subnetworks derived from this backbone - namely feeder and seeder subnetworks. Our analysis is applied to structural connectomes in a normal cohort from a large, publicly available lifespan study. We demonstrate changes in rich-club membership with age alongside a shift in importance from 'peripheral' seeder to feeder subnetworks. Our results show a refinement within the rich-club structure (increase in transitivity and betweenness centrality), as well as increased efficiency in the feeder subnetwork and decreased measures of network integration and segregation in the seeder subnetwork. These results demonstrate the different developmental patterns when analyzing the connectome stratified according to its rich-club and the potential of utilizing this subnetwork analysis to reveal the evolution of brain architectural alterations across the life-span

Co-prescription of medication for bipolar disorder and diabetes mellitus : a nationwide population based study with focus on gender differences

BackgroundStudies have shown a correlation between bipolar disorder and diabetes mellitus. It is unclear if this correlation is a part of common pathophysiological pathways, or if medication for bipolar disorder has negative effects on blood sugar regulation.MethodsThe Norwegian prescription database was analyzed. Prescriptions for lithium, lamotrigine, carbamazepine and valproate were used as proxies for bipolar disorder. Prescriptions for insulin and oral anti-diabetic agents were used as proxies for diabetes mellitus. We explored the association between medication for bipolar disorder and diabetes medication by logistic regressionResultsWe found a strong association between concomitant use of medication to treat diabetes mellitus and mood stabilizers for the treatment of bipolar disorder. Females had a 30% higher risk compared to men of being treated for both disorders. Persons using oral anti-diabetic agents had higher odds of receiving valproate than either lithium or lamotrigine. Use of insulin as monotherapy seemed to have lower odds than oral anti-diabetic agents of co-prescription of mood stabilizers, compared to the general population.ConclusionsThis study showed a strong association between the use of mood stabilizers and anti-diabetic agents. The association was stronger among women than men

Distinct Neural Signatures of Outcome Monitoring After Selection and Execution Errors

Losing a point in tennis could result from poor shot selection or faulty stroke execution. To explore how the brain responds to these different types of errors, we examined feedback-locked EEG activity while participants completed a modified version of a standard three-armed bandit probabilistic reward task. Our task framed unrewarded outcomes as the result of either errors of selection or errors of execution. We examined whether amplitude of a medial frontal negativity (the feedback-related negativity [FRN]) was sensitive to the different forms of error attribution. Consistent with previous reports, selection errors elicited a large FRN relative to rewards, and amplitude of this signal correlated with behavioral adjustment after these errors. A different pattern was observed in response to execution errors. These outcomes produced a larger FRN, a frontocentral attenuation in activity preceding this component, and a subsequent enhanced error positivity in parietal sites. Notably, the only correlations with behavioral adjustment were with the early frontocentral attenuation and amplitude of the parietal signal; FRN differences between execution errors and rewarded trials did not correlate with subsequent changes in behavior. Our findings highlight distinct neural correlates of selection and execution error processing, providing insight into how the brain responds to the different classes of error that determine future action