Perceived Associations between Excessive Sugar Intake and Health Conditions

Excessive sugar intake represents an increased risk of developing non-communicable diseases (e.g., obesity, cardiometabolic diseases, and dental diseases). Still, it is unclear whether people are aware of these adverse health outcomes. The current study systematically examined the extent to which people associate health conditions with excessive sugar intake. Participants (N = 1010 Portuguese volunteers) freely reported all health conditions they associated with excessive sugar consumption and rated the strength of these associations for eight specific health conditions. All participants reported health conditions associated with excessive sugar intake, with the most frequent being risk factors for cardiometabolic diseases (e.g., diabetes), cardiovascular diseases, oral problems, oncological and mental health conditions. Moreover, participants considered diabetes, overweight/obesity, and oral problems as being the conditions most related to excessive sugar intake. Women, participants with children in the household, and experts in health/nutrition rated excessive sugar intake as being more strongly linked to some of the health conditions. The identification of the health conditions that people associate with excessive sugar consumption may inform policymakers, educators, and health professionals and support interventions targeting the general public or specific groups (e.g., overweight people) in raising awareness of potential adverse health outcomes and, ultimately, contribute to reducing sugar intake.     

Test‐retest reliability of the default mode network in a multi‐centric fMRI study of healthy elderly: Effects of data‐driven physiological noise correction techniques

Understanding how to reduce the influence of physiological noise in resting state fMRI data is important for the interpretation of functional brain connectivity. Limited data is currently available to assess the performance of physiological noise correction techniques, in particular when evaluating longitudinal changes in the default mode network (DMN) of healthy elderly participants. In this 3T harmonized multisite fMRI study, we investigated how different retrospective physiological noise correction (rPNC) methods influence the within‐site test‐retest reliability and the across‐site reproducibility consistency of DMN‐derived measurements across 13 MRI sites. Elderly participants were scanned twice at least a week apart (five participants per site). The rPNC methods were: none (NPC), Tissue‐based regression, PESTICA and FSL‐FIX. The DMN at the single subject level was robustly identified using ICA methods in all rPNC conditions. The methods significantly affected the mean z‐scores and, albeit less markedly, the cluster‐size in the DMN; in particular, FSL‐FIX tended to increase the DMN z‐scores compared to others. Within‐site test‐retest reliability was consistent across sites, with no differences across rPNC methods. The absolute percent errors were in the range of 5–11% for DMN z‐scores and cluster‐size reliability. DMN pattern overlap was in the range 60–65%. In particular, no rPNC method showed a significant reliability improvement relative to NPC. However, FSL‐FIX and Tissue‐based physiological correction methods showed both similar and significant improvements of reproducibility consistency across the consortium (ICC = 0.67) for the DMN z‐scores relative to NPC. Overall these findings support the use of rPNC methods like tissue‐based or FSL‐FIX to characterize multisite longitudinal changes of intrinsic functional connectivity. Hum Brain Mapp 37:2114–2132, 2016. © 2016 Wiley Periodicals, Inc.     

Secondary biogenic amine deficiencies: genetic etiology,therapeutic interventions,and clinical effects

neurogenetics - Monoamine neurotransmitter disorders present predominantly with neurologic features, including dystonic or dyskinetic cerebral palsy and movement disorders. Genetic conditions that...