Biology subjects

Dahl, R. E.

Publications and source records attributed to Dahl, R. E..

6 recordsLinked to original sources

bioRxiv2023

The association between cortical gyrification and sleep in adolescents and young adults.

Study objectivesHealthy sleep is important for adolescent neurodevelopment, and relationships between brain structure and sleep can vary in strength over this maturational window. Although cortical gyrification is increasingly considered a useful index for understanding cognitive and emotional outcomes in adolescence, and sleep is also a strong predictor of such outcomes, we know relatively little about associations between cortical gyrification and sleep. MethodsUsing Local gyrification index (lGI) of 34 bilateral brain regions and regularized regression for feature selection, we examined gyrification-sleep relationships in the Neuroimaging and Pediatric Sleep databank (252 participants; 9-26 years; 58.3% female) and identified developmentally invariant (stable across age) or developmentally specific (observed only during discrete age intervals) brain-sleep associations. Naturalistic sleep characteristics (duration, timing, continuity, and regularity) were estimated from wrist actigraphy. ResultsFor most brain regions, greater lGI was associated with longer sleep duration, earlier sleep timing, lower variability in sleep regularity, and shorter time awake after sleep onset. lGI in frontoparietal network regions showed associations with sleep patterns that were stable across age. However, in default mode network regions, lGI was only associated with sleep patterns from late childhood through early-to-mid adolescence, a period of vulnerability for mental health disorders. ConclusionsWe detected both developmentally invariant and developmentally specific ties between local gyrification and naturalistic sleep patterns. Default mode network regions may be particularly susceptible to interventions promoting more optimal sleep during childhood and adolescence.

neuroscience↗

bioRxiv2021

Learning Rates Are Not All the Same: The Interpretation of Computational Model Parameters Depends on the Context

Reinforcement Learning (RL) models have revolutionized the cognitive and brain sciences, promising to explain behavior from simple conditioning to complex problem solving, to shed light on developmental and individual differences, and to anchor cognitive processes in specific brain mechanisms. However, the RL literature increasingly reveals contradictory results, which might cast doubt on these claims. We hypothesized that many contradictions arise from two commonly-held assumptions about computational model parameters that are actually often invalid: That parameters generalize between contexts (e.g., tasks, models) and that they capture interpretable (i.e., unique, distinctive) neurocognitive processes. To test this, we asked 291 participants aged 8-30 years to complete three learning tasks in one experimental session, and fitted RL models to each. We found that some parameters (exploration / decision noise) showed significant generalization: they followed similar developmental trajectories, and were reciprocally predictive between tasks. However, generalization was significantly below ceiling. Furthermore, other parameters (learning rates, forgetting) did not show evidence of generalization, and sometimes even opposite developmental trajectories. Interpretability was low for all parameters. We conclude that the systematic study of context factors (e.g., reward stochasticity; task volatility) will be necessary to enhance the generalizability and interpretability of computational cognitive models. HighlightsO_LIComputational cognitive modeling often makes two implicit assumptions: 1) Model parameters generalize between studies and models. 2) Parameters are interpretable, i.e., neurally, cognitively, and/or mechanistically specific. C_LIO_LIWe empirically investigate these assumptions in Reinforcement Learning (RL), using a large developmental dataset featuring three different learning tasks in a within-participant design. C_LIO_LIWe find that no parameters generalize between tasks in terms of absolute parameter values. However, RL decision noise/exploration parameters generalize in terms of between-participant variation, showing similar age trajectories across tasks. Interpretability was low for all parameters, especially those related to learning and memory. C_LIO_LIEven for parameters that showed generalization, the degree of generalization was significantly lower than in an artificial sample with perfect generalization for most parameters, revealing a relative lack of generalization. C_LIO_LIThese findings are in accordance with previous claims about the developmental trajectory of decision noise/exploration parameters, but suggest that claims about the development of learning rates or Forgetting might not be generalizable: these parameters are unlikely to capture the same neuro-cognitive processes across different tasks. C_LIO_LIWe suggest ways forward to improve the generalizability and interpretability of model parameters, in particular by focusing more on task context. C_LI

neuroscience↗

bioRxiv2021

Associations between brain structure and sleep patterns across adolescent development

ImportanceStructural brain maturation and sleep are complex processes that exhibit significant changes over adolescence and are linked to healthy physical and mental development. The precise timing and magnitude of these changes influence function throughout the lifespan. However, the relationships between gray matter structure and sleep patterns during adolescence are not fully understood. A detailed characterization of brain-sleep associations during this sensitive period is crucial for understanding factors contributing to optimal neurodevelopmental trajectories in adolescence. ObjectiveTo investigate whether sleep-gray matter relationships are developmentally-invariant (i.e., stable across age) or developmentally-specific (i.e., only present during discrete time windows) from late childhood through young adulthood. SettingThe Neuroimaging and Pediatric Sleep Databank was constructed from 8 research studies conducted at the University of Pittsburgh between 2009 and 2020. ParticipantsThe final sample consisted of 240 participants without current psychiatric diagnoses (9-25 years), and with good quality sleep tracking and structural MRI (sMRI) data. DesignParticipants completed a sMRI scan and 5-7 days of wrist actigraphy to assess naturalistic sleep. We examined cross-sectional associations between sMRI measures and sleep patterns, as well as the effects of age, sex, and their interaction with sMRI measures on sleep. Main Outcome(s) and Measure(s)Using Freesurfer software, we extracted cortical thickness and subcortical volumes from T1-weighted MRI. Sleep patterns (duration, timing, continuity, regularity) were estimated from wrist actigraphy. ResultsShorter sleep duration, later sleep timing, and poorer sleep continuity were associated with a stable pattern of thinner cortex and altered subcortical volumes in diverse brain regions across adolescence. In a discrete subset of regions (e.g., posterior cingulate), thinner cortex was associated with these sleep patterns from late childhood through early-to-mid adolescence, but not in late adolescence and young adulthood. Conclusions and RelevanceIn childhood and adolescence, developmentally-invariant and developmentally-specific associations exist between sleep patterns and gray matter structure, in a wide array of brain regions linked to many sensory, cognitive, and emotional processes. Sleep intervention during specific developmental periods could potentially promote healthier neurodevelopmental outcomes. KEY POINTSO_ST_ABSQuestionC_ST_ABSDoes age modulate associations between gray matter structure and actigraphic sleep patterns across adolescent development? FindingsThis cross-sectional study reports stable associations between regional gray matter structure and shorter duration, later timing, and poorer continuity of sleep from ages 9 to 25 years-old, as well as developmentally-specific associations that are present only from late childhood to early-to-mid adolescence. MeaningStronger coupling of gray matter and sleep patterns from late childhood to early-to-mid adolescence potentially implicates this discrete developmental window as a period of vulnerability to adverse sleep-brain interactions. Sleep intervention during this developmental stage may support healthier neurodevelopmental trajectories.

neuroscience↗

bioRxiv2020

Modeling Changes in Probabilistic Reinforcement Learning during Adolescence

In the real world, many relationships between events are uncertain and probabilistic. Uncertainty is also likely to be a more common feature of daily experience for youth because they have less experience to draw from than adults. Some studies suggests probabilistic learning may be inefficient in youth compared to adults [1], while others suggest it may be more efficient in youth that are in mid adolescence [2, 3]. Here we used a probabilistic reinforcement learning task to test how youth age 8-17 (N = 187) and adults age 18-30 (N = 110) learn about stable probabilistic contingencies. Performance increased with age through early-twenties, then stabilized. Using hierarchical Bayesian methods to fit computational reinforcement learning models, we show that all participants performance was better explained by models in which negative outcomes had minimal to no impact on learning. The performance increase over age was driven by 1) an increase in learning rate (i.e. decrease in integration time horizon); 2) a decrease in noisy/exploratory choices. In mid-adolescence age 13-15, salivary testosterone and learning rate were positively related. We discuss our findings in the context of other studies and hypotheses about adolescent brain development. Author summaryAdolescence is a time of great uncertainty. It is also a critical time for brain development, learning, and decision making in social and educational domains. There are currently contradictory findings about learning in adolescence. We sought to better isolate how learning from stable probabilistic contingencies changes during adolescence with a task that previously showed interesting results in adolescents. We collected a relatively large sample size (297 participants) across a wide age range (8-30), to trace the adolescent developmental trajectory of learning under stable but uncertain conditions. We found that age in our sample was positively associated with higher learning rates and lower choice exploration. Within narrow age bins, we found that higher saliva testosterone levels were associated with higher learning rates in participants age 13-15 years. These findings can help us better isolate the trajectory of maturation of core learning and decision making processes during adolescence.

neuroscience↗

bioRxiv2020

Understanding the Unique Advantage of Adolescents in Stochastic, Volatile Environments: Combining Reinforcement Learning and Bayesian Inference

During adolescence, youth venture out, explore the wider world, and are challenged to learn how to navigate novel and uncertain environments. We investigated whether adolescents are uniquely adapted to this transition, compared to younger children and adults. In a stochastic, volatile reversal-learning task with a sample of 291 participants aged 8-30, we found that adolescents outperformed both younger and older participants. We developed two independent cognitive models, based on Reinforcement learning (RL) and Bayesian inference (BI). The RL parameter for learning from negative outcomes and the BI parameters specifying participants mental models peaked closest to optimal in adolescents, suggesting a central role in adolescent cognitive processing. By contrast, persistence and noise parameters improved monotonously with age. We distilled the insights of RL and BI using principal component analysis and found that three shared components interacted to form the adolescent performance peak: adult-like behavioral quality, child-like time scales, and developmentally-unique processing of positive feedback. This research highlights adolescence as a neurodevelopmental window that may be specifically adapted for volatile and uncertain environments. It also shows how detailed insights can be gleaned by using cognitive models in new ways.

neuroscience↗

bioRxiv2017

Development of subcortical volumes across adolescence in males and females: A multisample study of longitudinal changes

The developmental patterns of subcortical brain volumes in males and females observed in previous studies have been inconsistent. To help resolve these discrepancies, we examined developmental trajectories using three independent longitudinal samples of participants in the age-span of 8-22 years (total 216 participants and 467 scans). These datasets, including Pittsburgh (PIT; University of Pittsburgh, USA), NeuroCognitive Development (NCD; University of Oslo, Norway), and Orygen Adolescent Development Study (OADS; The University of Melbourne, Australia), span three countries and were analyzed together and in parallel using mixed-effects modeling with both generalized additive models and general linear models. For all regions and across all samples, males were found to have significantly larger volumes as compared to females, and significant sex differences were seen in age trajectories over time. However, direct comparison of sample trajectories and sex differences identified within samples were not consistent. The trajectories for the amygdala, putamen, and nucleus accumbens were most consistent between the three samples. Our results suggest that even after using similar preprocessing and analytic techniques, additional factors, such as image acquisition or sample composition may contribute to some of the discrepancies in sex specific patterns in subcortical brain changes across adolescence, and highlight region-specific variations in congruency of developmental trajectories.

neuroscience↗