157 research outputs found

    Context-specific activations are a hallmark of the neural basis of individual differences in general executive function

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    Common executive functioning (cEF) is a domain-general factor that captures shared variance in performance across diverse executive function tasks. To investigate the neural mechanisms of individual differences in cEF (e.g., goal maintenance, biasing), we conducted the largest fMRI study of multiple executive tasks to date (N = 546). Group average activation during response inhibition (antisaccade task), working memory updating (keep track task), and mental set shifting (number–letter switch task) overlapped in classic cognitive control regions. However, there were no areas across tasks that were consistently correlated with individual differences in cEF ability. Although similar brain areas are recruited when completing different executive function tasks, activation levels of those areas are not consistently associated with better performance. This pattern is inconsistent with a simple model in which higher cEF is associated with greater or less activation of a set of control regions across different task contexts; however, it is potentially consistent with a model in which individual differences in cEF primarily depend on activation of domain-specific targets of executive function. Brain features that explain commonalities in executive function performance across tasks remain to be discovered

    The Relationship Between Resting State Network Connectivity and Individual Differences in Executive Functions

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    The brain is organized into a number of large networks based on shared function, for example, high-level cognitive functions (frontoparietal network), attentional capabilities (dorsal and ventral attention networks), and internal mentation (default network). The correlations of these networks during resting-state fMRI scans varies across individuals and is an indicator of individual differences in ability. Prior work shows higher cognitive functioning (as measured by working memory and attention tasks) is associated with stronger negative correlations between frontoparietal/attention and default networks, suggesting that increased ability may depend upon the diverging activation of networks with contrasting function. However, these prior studies lack specificity with regard to the higher-level cognitive functions involved, particularly with regards to separable components of executive function (EF). Here we decompose EF into three factors from the unity/diversity model of EFs: Common EF, Shifting-specific EF, and Updating-specific EF, measuring each via factor scores derived from a battery of behavioral tasks completed by 250 adult participants (age 28) at the time of a resting-state scan. We found the hypothesized segregated pattern only for Shifting-specific EF. Specifically, after accounting for one’s general EF ability (Common EF), individuals better able to fluidly switch between task sets have a stronger negative correlation between the ventral attention network and the default network. We also report non-predicted novel findings in that individuals with higher Shifting-specific abilities exhibited more positive connectivity between frontoparietal and visual networks, while those individuals with higher Common EF exhibited increased connectivity between sensory and default networks. Overall, these results reveal a new degree of specificity with regard to connectivity/EF relationships

    Multi-Polygenic Analysis of Nicotine Dependence in Individuals of European Ancestry

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    Introduction: Heritability estimates of nicotine dependence (ND) range from 40% to 70%, but discovery GWAS of ND are underpowered and have limited predictive utility. In this work, we leverage genetically correlated traits and diseases to increase the accuracy of polygenic risk prediction. Methods: We employed a multi-trait model using summary statistic-based best linear unbiased predictors (SBLUP) of genetic correlates of DSM-IV diagnosis of ND in 6394 individuals of European Ancestry (prevalence = 45.3%, %female = 46.8%, mu(age) = 40.08 [s.d. = 10.43]) and 3061 individuals from a nationally-representative sample with Fagerstrom Test for Nicotine Dependence symptom count (FTND; 51.32% female, mean age = 28.9 [s.d. = 1.70]). Polygenic predictors were derived from GWASs known to be phenotypically and genetically correlated with ND (i.e., Cigarettes per Day [CPD], the Alcohol Use Disorders Identification Test [AUDIT-Consumption and AUDIT-Problems], Neuroticism, Depression, Schizophrenia, Educational Attainment, Body Mass Index [BMI], and Self-Perceived Risk-Taking); including Height as a negative control. Analyses controlled for age, gender, study site, and the first 10 ancestral principal components. Results: The multi-trait model accounted for 3.6% of the total trait variance in DSM-IV ND. Educational Attainment (beta = -0.125; 95% CI: [-0.149,-0.101]), CPD (0.071 [0.047,0.095]), and Self-Perceived Risk-Taking (0.051 [0.026,0.075]) were the most robust predictors. PGS effects on FTND were limited. Conclusions: Risk for ND is not only polygenic, but also pleiotropic. Polygenic effects on ND that are accessible by these traits are limited in size and act additively to explain risk.Peer reviewe

    The Etiology of Observed Negative Emotionality from 14 to 24 Months

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    We examined the magnitude of genetic and environmental influences on observed negative emotionality at age 14, 20, and 24 months. Participants were 403 same-sex twin pairs recruited from the Longitudinal Twin Study whose emotional responses to four different situations were coded by independent raters. Negative emotionality showed significant consistency across settings, and there was evidence of a latent underlying negative emotionality construct. Heritability decreased, and the magnitude of shared environmental influences increased, for the latent negative emotionality construct from age 14 to 24 months. There were significant correlations between negative emotionality assessed at age 14, 20, and 24 months, and results suggested common genetic and shared environmental influences affecting negative emotionality across age, and that age-specific influences are limited to non-shared environmental influences, which include measurement error
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