Longitudinal data are becoming increasingly available in developmental neuroimaging. To maximize thepromise of this wealth of information on how biology, behavior, and cognition change over time,... Show moreLongitudinal data are becoming increasingly available in developmental neuroimaging. To maximize thepromise of this wealth of information on how biology, behavior, and cognition change over time, there is aneed to incorporate broad and rigorous training in longitudinal methods into the repertoire of developmentalneuroscientists. Fortunately, these models have an incredibly rich tradition in the broader developmentalsciences that we can draw from. Here, we provide a primer on longitudinal models, written in a beginnerfriendly (and slightly irreverent) manner, with a particular focus on selecting among different modeling frameworks (e.g., multilevel versus latent curve models) to build the theoretical model of development a researcher wishes to test. Our aims are three-fold: (1) lay out a heuristic framework for longitudinal model selection, (2) build a repository of references that ground each model in its tradition of methodological development and practical implementation with a focus on connecting researchers to resources outside traditional neuroimaging journals, and (3) provide practical resources in the form of a codebook companion demonstrating how to fit these models. These resources together aim to enhance training for the next generation of developmental neuroscientists by providing a solid foundation for future forays into advanced modeling applications. Show less
While it is well understood that the brain experiences changes across short-term experience/learning and long-term development, it is unclear how these two mechanisms interact to produce... Show moreWhile it is well understood that the brain experiences changes across short-term experience/learning and long-term development, it is unclear how these two mechanisms interact to produce developmental outcomes. Here we test an interactive model of learning and development where certain learning-related changes are constrained by developmental changes in the brain against an alternative development-as-practice model where outcomes are determined primarily by the accumulation of experience regardless of age. Participants (8-29 years) participated in a three-wave, accelerated longitudinal study during which they completed a feedback learning task during an fMRI scan. Adopting a novel longitudinal modeling approach, we probed the unique and moderated effects of learning, experience, and development simultaneously on behavioral performance and network modularity during the task. We found nonlinear patterns of development for both behavior and brain, and that greater experience supported increased learning and network modularity relative to naive subjects. We also found changing brain-behavior relationships across adolescent development, where heightened network modularity predicted improved learning, but only following the transition from adolescence to young adulthood. These results present compelling support for an interactive view of experience and development, where changes in the brain impact behavior in context-specific fashion based on developmental goals. Show less
While it is well understood that the brain experiences changes across short-term experience/learning and long-term development, it is unclear how these two mechanisms interact to produce... Show moreWhile it is well understood that the brain experiences changes across short-term experience/learning and long-term development, it is unclear how these two mechanisms interact to produce developmental outcomes. Here we test an interactive model of learning and development where certain learning-related changes are constrained by developmental changes in the brain against an alternative development-as-practice model where outcomes are determined primarily by the accumulation of experience regardless of age. Participants (8–29 years) participated in a three-wave, accelerated longitudinal study during which they completed a feedback learning task during an fMRI scan. Adopting a novel longitudinal modeling approach, we probed the unique and moderated effects of learning, experience, and development simultaneously on behavioral performance and network modularity during the task. We found nonlinear patterns of development for both behavior and brain, and that greater experience supported increased learning and network modularity relative to naïve subjects. We also found changing brain-behavior relationships across adolescent development, where heightened network modularity predicted improved learning, but only following the transition from adolescence to young adulthood. These results present compelling support for an interactive view of experience and development, where changes in the brain impact behavior in context-specific fashion based on developmental goals. Show less
There has been a large spike in longitudinal fMRI studies in recent years, and so it is essential that researchers carefully assess the limitations and challenges afforded by longitudinal designs.... Show moreThere has been a large spike in longitudinal fMRI studies in recent years, and so it is essential that researchers carefully assess the limitations and challenges afforded by longitudinal designs. In this article, we provide an overview of important considerations for longitudinal fMRI research in developmental samples, including task design, sampling strategies, and group-level analyses. We first discuss considerations for task designs, weighing the pros and cons of many commonly used tasks, as well as outlining how the tasks may be impacted by repeated exposure. Secondly, we review the types of group-level analyses that can be conducted on longitudinal fMRI data, analyses which must account for repeated measures. Finally, we review and critique recent longitudinal studies that have emerged in the past few years. Show less
