Dissociable effects of 5-HT2C receptor antagonism and genetic inactivation on perseverance and learned non-reward in an egocentric spatial reversal task

Cognitive flexibility can be assessed in reversal learning tests, which are sensitive to modulation of 5-HT2C receptor (5-HT2CR) function. Successful performance in these tests depends on at least two dissociable cognitive mechanisms which may separately dissipate associations of previous positive and negative valence. The first is opposed by perseverance and the second by learned non-reward. The current experiments explored the effect of reducing function of the 5-HT2CR on the cognitive mechanisms underlying egocentric reversal learning in the mouse. Experiment 1 used the 5-HT2CR antagonist SB242084 (0.5 mg/kg) in a between-groups serial design and Experiment 2 used 5-HT2CR KO mice in a repeated measures design. Animals initially learned to discriminate between two egocentric turning directions, only one of which was food rewarded (denoted CS+, CS−), in a T- or Y-maze configuration. This was followed by three conditions; (1) Full reversal, where contingencies reversed; (2) Perseverance, where the previous CS+ became CS− and the previous CS− was replaced by a novel CS+; (3) Learned non-reward, where the previous CS− became CS+ and the previous CS+ was replaced by a novel CS-. SB242084 reduced perseverance, observed as a decrease in trials and incorrect responses to criterion, but increased learned non-reward, observed as an increase in trials to criterion. In contrast, 5-HT2CR KO mice showed increased perseverance. 5-HT2CR KO mice also showed retarded egocentric discrimination learning. Neither manipulation of 5-HT2CR function affected performance in the full reversal test. These results are unlikely to be accounted for by increased novelty attraction, as SB242084 failed to affect performance in an unrewarded novelty task. In conclusion, acute 5-HT2CR antagonism and constitutive loss of the 5-HT2CR have opposing effects on perseverance in egocentric reversal learning in mice. It is likely that this difference reflects the broader impact of 5HT2CR loss on the development and maintenance of cognitive function

Development and validation of the Arizona Cognitive Test Battery for Down syndrome

Neurocognitive assessment in individuals with intellectual disabilities requires a well-validated test battery. To meet this need, the Arizona Cognitive Test Battery (ACTB) has been developed specifically to assess the cognitive phenotype in Down syndrome (DS). The ACTB includes neuropsychological assessments chosen to 1) assess a range of skills, 2) be non-verbal so as to not confound the neuropsychological assessment with language demands, 3) have distributional properties appropriate for research studies to identify genetic modifiers of variation, 4) show sensitivity to within and between sample differences, 5) have specific correlates with brain function, and 6) be applicable to a wide age range and across contexts. The ACTB includes tests of general cognitive ability and prefrontal, hippocampal and cerebellar function. These tasks were drawn from the Cambridge Neuropsychological Testing Automated Battery (CANTAB) and other established paradigms. Alongside the cognitive testing battery we administered benchmark and parent-report assessments of cognition and behavior. Individuals with DS (n = 74, ages 7–38 years) and mental age (MA) matched controls (n = 50, ages 3–8 years) were tested across 3 sites. A subsample of these groups were used for between-group comparisons, including 55 individuals with DS and 36 mental age matched controls. The ACTB allows for low floor performance levels and participant loss. Floor effects were greater in younger children. Individuals with DS were impaired on a number ACTB tests in comparison to a MA-matched sample, with some areas of spared ability, particularly on tests requiring extensive motor coordination. Battery measures correlated with parent report of behavior and development. The ACTB provided consistent results across contexts, including home vs. lab visits, cross-site, and among individuals with a wide range of socio-economic backgrounds and differences in ethnicity. The ACTB will be useful in a range of outcome studies, including clinical trials and the identification of important genetic components of cognitive disability

An epigenetic score for BMI based on DNA methylation correlates with poor physical health and major disease in the Lothian Birth Cohort

Background: The relationship between obesity and adverse health is well established, but little is known about the contribution of DNA methylation to obesity-related health outcomes. This study tests associations between an epigenetic score for\ua0body mass index (BMI) and health-related, cognitive, psychosocial and lifestyle outcomes in the Lothian Birth Cohort 1936. This study also tests whether these associations are independent of phenotypic BMI. Method: Analyses were conducted using data from the Lothian Birth Cohort 1936 (n = 892). Weights for the epigenetic BMI score were derived using penalised regression on methylation data from unrelated Generation Scotland participants (n = 2562). Associations were tested for replication in an independent sample: the Lothian Birth Cohort 1921 (n = 433). Results: A higher\ua0epigenetic BMI score was associated with higher BMI (R = 0.1), greater body weight (R = 0.06), greater time taken to walk 6 m, poorer lung function and poorer general physical health (all R = 0.02), greater levels of triglycerides (R = 0.09), greater %total HbA1c (R = 0.06), lower levels of high-density lipoprotein cholesterol (HDL;\ua0R = 0.08),\ua0higher HDL ratio (HDL/total cholesterol; R = 0.03), lower health-related quality of life, physical inactivity, and greater social deprivation (all\ua0R = 0.02). The epigenetic BMI score (per SD) was also associated with type 2 diabetes (OR 2.17, 95% CI 1.67, 2.84), cardiovascular disease (OR 1.45, 95% CI 1.24, 1.71) and high blood pressure (OR 1.30, 95% CI 1.13, 1.49; all p < 0.00026 after Bonferroni correction). Associations were replicated for BMI (R = 0.06), body weight (R = 0.04), health-related quality of life (R = 0.02), HbA1c (R = 0.07) and triglycerides (R = 0.07; all p < 0.0045 after Bonferroni correction). Conclusions: We observed and replicated associations between an epigenetic score for BMI and variables related to poor physical health and metabolic syndrome. Regression models with both epigenetic and phenotypic BMI scores as predictors accounted for a greater proportion of variance in\ua0all outcome variables than either predictor alone, demonstrating independent and additive effects of epigenetic and phenotypic BMI scores

Classification of Impaired Waist to Height Ratio Using Machine Learning Technique

Metabolic dysfunctions are a set of metabolic risk factors that include abdominal obesity, dyslipidemia, insulin resistance, among others. Individuals with any of these metabolic dysfunctions are at high risk of developing type 2 diabetes and cardiovascular disease. Several parameters and anthropometric indices are used to detect metabolic dysfunctions, such as waist circumference and waist-height ratio (WHtR). The WHtR has an advantage over the body mass index (BMI) since the WHtR provides information on the distribution of body fat, particularly abdominal fat. Central fat distribution is associated with more significant cardio-metabolic health risks than total body fat. Machine learning techniques involve algorithms capable of predicting and analyzing data, increasing our understanding of the events being studied. k-means is a clustering algorithm that has been used in the detection of obesity. This research aims to apply the k-means grouping algorithm to study its capability as an impaired WHtR classifier. Accuracy (Acc), recall (Rec), and precision (P) were calculated. A database of 1863 subjects was used; the database consists of fifteen (15) anthropometric variables and two (2) indices; each anthropometric variable was measured for each participant. The results reported in this research suggest that the k-means clustering algorithm is an acceptable classifier of impaired WHtR subjects ( Acc=0.81 , P=0.83 , and Rec=0.73 ). Besides, the k-means algorithm was able to detect subjects with overweight and fatty tissue deposits in the back and arm areas, suggesting that fat accumulation in these areas is directly related to abdominal fat accumulation.Universidad de Ibagu