John Flournoy

Current methods and limitations for longitudinal fMRI analysis across development

The human brain is remarkably plastic. The brain changes dramatically across development, with ongoing functional development continuing well into the third decade of life and substantial changes occurring again in older age. Dynamic changes in brain function are thought to underlie the innumerable changes in cognition, emotion, and behavior that occur across development. The brain also changes in response to experience, which raises important questions about how the environment influences the developing brain. Longitudinal functional magnetic resonance imaging (fMRI) studies are an essential means of understanding these developmental changes and their cognitive, emotional, and behavioral correlates. This paper provides an overview of common statistical models of longitudinal change applicable to developmental cognitive neuroscience, and a review of the functionality provided by major software packages for longitudinal fMRI analysis. We demonstrate that there are important developmental questions that cannot be answered using available software. We propose alternative approaches for addressing problems that are commonly faced in modeling developmental change with fMRI data.

Longitudinal modeling in developmental neuroimaging research: Common challenges, and solutions from developmental psychology

Hypotheses about change over time are central to informing our understanding of development. Developmental neuroscience is at critical juncture: although the majority of longitudinal imaging studies have observations with two time points, researchers are increasingly obtaining three or more observations of the same individuals. The goals of the proposed manuscript are to draw upon the long history of methodological and applied literature on longitudinal statistical models to summarize common problems and issues that arise in their use. We also provide suggestions and solutions to improve the design, analysis and interpretation of longitudinal data, and discuss the importance of matching the theory of change with the appropriate statistical model used to test the theory. Researchers should articulate a clear theory of change and to design studies to capture that change and use appropriately sensitive measures to assess that change during development. Simulated data are used to demonstrate several common analytic approaches to longitudinal analyses. We provide the code for our simulations and figures in an online supplement to aid researchers in exploring and plotting their data. We provide brief examples of best practices for reporting such models. Finally, we clarify common misunderstandings in the application and interpretation of these analytic approaches.

Neurodevelopmental changes across adolescence in viewing and labeling dynamic peer emotions

Highlights • Dynamic peer facial stimuli recruit key regions involved in emotion processing.• LPFC shows a nonlinear age trend across adolescence to labeling dynamic peer faces.• MOFC/vMPFC shows a linear decrease with age to viewing dynamic peer faces.• No significant age trends were observed in amygdala during viewing or labeling dynamic peer faces.

Presentation and validation of the DuckEES child and adolescent dynamic facial expressions stimulus set

The stimulus sets presently used to study emotion processing are primarily static pictures of individuals (primarily adults) making emotional facial expressions. However, the dynamic, stereotyped movements associated with emotional expressions contain rich information missing from static pictures, such as the difference between happiness and pride. We created a set of 1.1 s dynamic emotional facial stimuli representing boys and girls aged 8–18. A separate group of 36 individuals (mean [M] age = 19.5 years, standard deviation [SD] = 1.95, 13 male) chose the most appropriate emotion label for each video from a superset of 250 videos. Validity and reliability statistics were performed across all stimuli, which were then used to determine which stimuli should be included in the final stimulus set. We set a criterion for inclusion of 70% agreement with the modal response made for each video. The final stimulus set contains 142 videos of 36 actors (M age = 13.24 years, SD = 2.09, 14 male) making negative (disgust, embarrassment, fear, sadness), positive (happiness, pride), and neutral facial expressions. The percent correct among the final stimuli was high (median = 88.89%; M = 88.38%, SD = 7.74%), as was reliability (κ = 0.753).

Making an unknown unknown a known unknown: Missing data in longitudinal neuroimaging studies

The analysis of longitudinal neuroimaging data within the massively univariate framework provides the opportunity to study empirical questions about neurodevelopment. Missing outcome data are an all-to-common feature of any longitudinal study, a feature that, if handled improperly, can reduce statistical power and lead to biased parameter estimates. The goal of this paper is to provide conceptual clarity of the issues and non-issues that arise from analyzing incomplete data in longitudinal studies with particular focus on neuroimaging data. This paper begins with a review of the hierarchy of missing data mechanisms and their relationship to likelihood-based methods, a review that is necessary not just for likelihood-based methods, but also for multiple-imputation methods. Next, the paper provides a series of simulation studies with designs common in longitudinal neuroimaging studies to help illustrate missing data concepts regardless of interpretation. Finally, two applied examples are used to demonstrate the sensitivity of inferences under different missing data assumptions and how this may change the substantive interpretation. The paper concludes with a set of guidelines for analyzing incomplete longitudinal data that can improve the validity of research findings in developmental neuroimaging research.

Novel insights from the Yellow Light Game: Safe and risky decisions differentially impact adolescent outcome-related brain function

ABSTRACT Changes across the span of adolescence in the adolescent reward system are thought to increase the tendency to take risks. While developmental differences in decision and outcome‐related reward processes have been studied extensively, existing paradigms have largely neglected to measure how different types of decisions modulate reward‐related outcome processes. We modified an existing decision‐making paradigm (the Stoplight Task; Chein et al., 2011) to create a flexible laboratory measure of decision‐making and outcome processing, including the ability to assess modulatory effects of safe versus risky decisions on reward‐related outcome processes: the Yellow Light Game (YLG). We administered the YLG in the MRI scanner to 81 adolescents, ages 11–17 years, recruited from the community. Results showed that nucleus accumbens activation was enhanced for (1) risky>safe decisions, (2) positive>negative outcomes, and (3) outcomes following safe decisions compared to outcomes following risky decisions, regardless of whether these outcomes were positive or negative. Outcomes following risky decisions (compared to outcomes following safe decisions) were associated with enhanced activity in cortical midline structures. Furthermore, while there were no developmental differences in risk‐taking behavior, more pubertally mature adolescents showed enhanced nucleus accumbens activation during positive>negative outcomes. These findings suggest that outcome processing is modulated by the types of decisions made by adolescents and highlight the importance of investigating processes involved in safe as well as risky decisions to better understand the adolescent tendency to take risks. HIGHLIGHTSThe Yellow Light Game measures decision‐making and associated outcome processes.Safe decisions elicited greater outcome‐related activity in nucleus accumbens (NAcc).Risky decisions produced more outcome‐related activity in cortical midline structures.More mature adolescents showed greater NAcc activation for positive outcomes.