Increased cognitive demands boost the spatial interference effect in bimanual pointing

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Altered processing of food stimuli in adolescents with loss of control eating

Loss of control eating (LOC) constitutes a common eating pathology in childhood and adolescence. Models developed for adult patients stress a biased processing of food-related stimuli as an important maintaining factor. To our knowledge, however, no EEG study to date investigated the processing of visual food stimuli in children or adolescents with LOC. Adolescents with at least one self-reported episode of LOC in the last four weeks and a matched control group completed a modified Go/NoGo task, with a numerical target or non-target stimulus being presented on one side of the screen and an irrelevant high-calorie food or neutral stimulus being presented on the opposite side. Mean P3 amplitudes were analyzed. In Go trials, the LOC group’s mean P3 amplitudes were comparable irrespective of distractor category, while for NoGo trials, mean P3 amplitudes were significantly higher when the distractor was a high-calorie food stimulus. This pattern was reversed in the control group. Results are interpreted in light of Gray’s reinforcement sensitivity theory. They might reflect altered processes of behavioral inhibition in adolescents with LOC upon confrontation with visual food stimuli

Neural changes related to motion processing in healthy aging

The authors wish to thank the research staff at the Aberdeen Biomedical Imaging Centre for their assistance during experimental setup and data collection, and James Urquhart for technical assistance. Funding: This work was supported by the Biotechnology and Biological Sciences Research Council [grant number BB/K007173/1].Peer reviewedPublisher PD

Effects of Differential Strategies of Emotion Regulation

Patients suffering from mental disorders, especially anxiety disorders, are often impaired by inadequate emotional reactions. Specific aspects are the insufficient perception of their own emotional states and the use of dysfunctional emotion regulation strategies. Both aspects are interdependent. Thus, Cognitive Behavioral Therapy (CBT) comprises the development and training of adequate emotion regulation strategies. Traditionally, reappraisal is the most common strategy, but strategies of acceptance are becoming more important in the course of advancing CBT. Indeed, there is evidence that emotion regulation strategies differ in self-reported effectiveness, psychophysiological reactions, and underlying neural correlates. However, comprehensive comparisons of different emotion regulation strategies are sparse. The present study, therefore, compared the effect of three common emotion regulation strategies (reappraisal, acceptance, and suppression) on self-reported effectiveness, recollection, and psychophysiological as well as electroencephalographic dimensions. Twenty-nine healthy participants were instructed to either reappraise, accept, suppress, or passively observe their upcoming emotional reactions while anxiety- and sadness-inducing pictures were presented. Results showed a compelling effect of reappraisal on emotional experience, skin conductance response, and P300 amplitude. Acceptance was almost as effective as reappraisal, but led to increased emotional experience. Combining all results, suppression was shown to be the least effective but significantly decreased emotional experience when thoughts and feelings had to be suppressed. Moreover, results show that greater propensity for rumination differentially impairs strategies of emotion regulation

The impact of task relevance and degree of distraction on stimulus processing

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Does adult ADHD interact with COMT val 158 met genotype to influence working memory performance?

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Urine steroid metabolomics as a diagnostic tool in primary aldosteronism

Primary aldosteronism (PA) causes 5-10% of hypertension cases, but only a minority of patients are currently diagnosed and treated because of a complex, stepwise, and partly invasive workup. We tested the performance of urine steroid metabolomics, the computational analysis of 24-hour urine steroid metabolome data by machine learning, for the identification and subtyping of PA. Mass spectrometry-based multi-steroid profiling was used to quantify the excretion of 34 steroid metabolites in 24-hour urine samples from 158 adults with PA (88 with unilateral PA [UPA] due to aldosterone-producing adenomas [APAs]; 70 with bilateral PA [BPA]) and 65 sex- and age-matched healthy controls. All APAs were resected and underwent targeted gene sequencing to detect somatic mutations associated with UPA. Patients with PA had increased urinary metabolite excretion of mineralocorticoids, glucocorticoids, and glucocorticoid precursors. Urine steroid metabolomics identified patients with PA with high accuracy, both when applied to all 34 or only the three most discriminative steroid metabolites (average areas under the receiver-operating characteristics curve [AUCs-ROC] 0.95-0.97). Whilst machine learning was suboptimal in differentiating UPA from BPA (average AUCs-ROC 0.65-0.73), it readily identified APA cases harbouring somatic KCNJ5 mutations (average AUCs-ROC 0.79-85). These patients showed a distinctly increased urine excretion of the hybrid steroid 18-hydroxycortisol and its metabolite 18-oxo-tetrahydrocortisol, the latter identified by machine learning as by far the most discriminative steroid. In conclusion, urine steroid metabolomics is a non-invasive candidate test for the accurate identification of PA cases and KCNJ5-mutated APAs

Urine steroid metabolomics as a diagnostic tool in primary aldosteronism

Primary aldosteronism (PA) causes 5-10% of hypertension cases, but only a minority of patients are currently diagnosed and treated because of a complex, stepwise, and partly invasive workup. We tested the performance of urine steroid metabolomics, the computational analysis of 24-hour urine steroid metabolome data by machine learning, for the identification and subtyping of PA. Mass spectrometry-based multi-steroid profiling was used to quantify the excretion of 34 steroid metabolites in 24-hour urine samples from 158 adults with PA (88 with unilateral PA [UPA] due to aldosterone-producing adenomas [APAs]; 70 with bilateral PA [BPA]) and 65 sex- and age-matched healthy controls. All APAs were resected and underwent targeted gene sequencing to detect somatic mutations associated with UPA. Patients with PA had increased urinary metabolite excretion of mineralocorticoids, glucocorticoids, and glucocorticoid precursors. Urine steroid metabolomics identified patients with PA with high accuracy, both when applied to all 34 or only the three most discriminative steroid metabolites (average areas under the receiver-operating characteristics curve [AUCs-ROC] 0.95-0.97). Whilst machine learning was suboptimal in differentiating UPA from BPA (average AUCs-ROC 0.65-0.73), it readily identified APA cases harbouring somatic KCNJ5 mutations (average AUCs-ROC 0.79-85). These patients showed a distinctly increased urine excretion of the hybrid steroid 18-hydroxycortisol and its metabolite 18-oxo-tetrahydrocortisol, the latter identified by machine learning as by far the most discriminative steroid. In conclusion, urine steroid metabolomics is a non-invasive candidate test for the accurate identification of PA cases and KCNJ5-mutated APAs.</p