Neural cognitive control moderates the association between insular risk processing and risk-taking behaviors via perceived stress in adolescents

Adolescence is a critical period for the initiation of risk-taking behaviors. We examined the longitudinal interplay between neural correlates of risk processing and cognitive control in predicting risk-taking behaviors via stress. The sample consisted of 167 adolescents (53% males) who were assessed twice (MAgeTime1 = 14.13, MAgeTime2 = 15.05). Neural risk processing was operationalized as blood-oxygen-level-dependent (BOLD) responses in the anterior insula during a lottery choice task and neural cognitive control as BOLD responses during an inhibitory control task. Adolescents reported on perceived stress and risk-taking behaviors. Structural equation modeling analyses indicated that low insular risk processing predicted increases in perceived stress, while perceived stress did not predict changes in insular risk processing across one year. Moreover, significant moderation by neural cognitive control indicated that low insular risk processing predicted increases in risk-taking behaviors via increases in perceived stress among adolescents with poor neural cognitive control, but not among adolescents with good neural cognitive control. The results suggest that risk processing in the anterior insular cortex plays an important role in stress experience and risk-taking behaviors particularly for vulnerable adolescents with poor neural cognitive control

Variability in the analysis of a single neuroimaging dataset by many teams

Data analysis workflows in many scientific domains have become increasingly complex and flexible. To assess the impact of this flexibility on functional magnetic resonance imaging (fMRI) results, the same dataset was independently analyzed by 70 teams, testing nine ex-ante hypotheses. The flexibility of analytic approaches is exemplified by the fact that no two teams chose identical workflows to analyze the data. This flexibility resulted in sizeable variation in hypothesis test results, even for teams whose statistical maps were highly correlated at intermediate stages of their analysis pipeline. Variation in reported results was related to several aspects of analysis methodology. Importantly, meta-analytic approaches that aggregated information across teams yielded significant consensus in activated regions across teams. Furthermore, prediction markets of researchers in the field revealed an overestimation of the likelihood of significant findings, even by researchers with direct knowledge of the dataset. Our findings show that analytic flexibility can have substantial effects on scientific conclusions, and demonstrate factors related to variability in fMRI. The results emphasize the importance of validating and sharing complex analysis workflows, and demonstrate the need for multiple analyses of the same data. Potential approaches to mitigate issues related to analytical variability are discussed

Variability in the analysis of a single neuroimaging dataset by many teams

Data analysis workflows in many scientific domains have become increasingly complex and flexible. To assess the impact of this flexibility on functional magnetic resonance imaging (fMRI) results, the same dataset was independently analyzed by 70 teams, testing nine ex-ante hypotheses. The flexibility of analytic approaches is exemplified by the fact that no two teams chose identical workflows to analyze the data. This flexibility resulted in sizeable variation in hypothesis test results, even for teams whose statistical maps were highly correlated at intermediate stages of their analysis pipeline. Variation in reported results was related to several aspects of analysis methodology. Importantly, meta-analytic approaches that aggregated information across teams yielded significant consensus in activated regions across teams. Furthermore, prediction markets of researchers in the field revealed an overestimation of the likelihood of significant findings, even by researchers with direct knowledge of the dataset. Our findings show that analytic flexibility can have substantial effects on scientific conclusions, and demonstrate factors related to variability in fMRI. The results emphasize the importance of validating and sharing complex analysis workflows, and demonstrate the need for multiple analyses of the same data. Potential approaches to mitigate issues related to analytical variability are discussed

A Minimum Free Energy Model of Motor Learning.

Even highly trained behaviors demonstrate variability, which is correlated with performance on current and future tasks. An objective of motor learning that is general enough to explain these phenomena has not been precisely formulated. In this six-week longitudinal learning study, participants practiced a set of motor sequences each day, and neuroimaging data were collected on days 1, 14, 28, and 42 to capture the neural correlates of the learning process. In our analysis, we first modeled the underlying neural and behavioral dynamics during learning. Our results demonstrate that the densities of whole-brain response, task-active regional response, and behavioral performance evolve according to a Fokker-Planck equation during the acquisition of a motor skill. We show that this implies that the brain concurrently optimizes the entropy of a joint density over neural response and behavior (as measured by sampling over multiple trials and subjects) and the expected performance under this density; we call this formulation of learning minimum free energy learning (MFEL). This model provides an explanation as to how behavioral variability can be tuned while simultaneously improving performance during learning. We then develop a novel variant of inverse reinforcement learning to retrieve the cost function optimized by the brain during the learning process, as well as the parameter used to tune variability. We show that this population-level analysis can be used to derive a learning objective that each subject optimizes during his or her study. In this way, MFEL effectively acts as a unifying principle, allowing users to precisely formulate learning objectives and infer their structure

Associations between peer attachment and neural correlates of risk processing across adolescence

Adolescence is a period of increased risk-taking behavior where individual differences in risk taking may relate to both adverse and positive experiences with peers. Yet, knowledge on how risk processing develops in the adolescent brain and whether this development is related to peer attachment is limited. In this longitudinal functional magnetic resonance imaging (fMRI) study, we collected data from 167 adolescents (53% male) followed for four annual assessments across ages 13-17 years. At each assessment, participants completed a lottery choice task to assess neural risk processing and reported on their perceived attachment to peers and parents. Behaviorally, risk-preference on the lottery choice task decreased linearly with age. Neural activation during risk processing was consistently found in the insula and dACC across the four assessments and increased linearly from ages 13-17 years. Furthermore, higher peer attachment was related to greater right insula risk processing for males but not for females, even after controlling for parental attachment. The magnitudes of this association did not change with age. Findings demonstrate that neural risk processing shows maturation across adolescence and high peer attachment may be associated with low risk taking by heightening neural sensitivity to potential risks for male adolescents

Chaos, danger, and maternal parenting in families: Links with adolescent adjustment in low- and middle-income countries.

The current longitudinal study is the first comparative investigation across low‐ and middle‐income countries (LMICs) to test the hypothesis that harsher and less affectionate maternal parenting (child age 14 years, on average) statistically mediates the prediction from prior household chaos and neighborhood danger (at 13 years) to subsequent adolescent maladjustment (externalizing, internalizing, and school performance problems at 15 years). The sample included 511 urban families in six LMICs: China, Colombia, Jordan, Kenya, the Philippines, and Thailand. Multigroup structural equation modeling showed consistent associations between chaos, danger, affectionate and harsh parenting, and adolescent adjustment problems. There was some support for the hypothesis, with nearly all countries showing a modest indirect effect of maternal hostility (but not affection) for adolescent externalizing, internalizing, and scholastic problems. Results provide further evidence that chaotic home and dangerous neighborhood environments increase risk for adolescent maladjustment in LMIC contexts, via harsher maternal parenting