key: cord-024629-5q7abusm authors: Luna, Beatriz; Tervo-Clemmens, Brenden; Calabro, Finnegan J. title: Considerations when Characterizing Adolescent Neurocognitive Development date: 2020-05-11 journal: Biol Psychiatry DOI: 10.1016/j.biopsych.2020.04.026 sha: doc_id: 24629 cord_uid: 5q7abusm nan disentangle pandemic-related effects (e.g., due to stress, time out of school, etc) from agerelated developmental effects. Cohort-sequential/accelerated longitudinal designs, which follow multiple cohorts with various starting ages, may be better able to isolate developmental and visit effects through the inclusion of cross-sectional effects. However, they may be more limited in the ability to leverage within-subject developmental effects, given the potential moderating effects of initial age. Importantly, both designs have limitations in attrition and scan failures, which can introduce bias (e.g., over representing willing participants who may be more mature). We suggest investigators optimize data collection for their planned analyses and known sources of error. When the aim is to model subject-specific developmental trajectories, as in the neurodevelopment of individual differences, a single cohort design is optimal, but with consideration to confounding non-developmental visit effects. Alternatively, group-level normative developmental changes and their physiological underpinnings may be better suited by an accelerated longitudinal design that can control for non-developmental visit effects. Simulation studies can be particularly useful in modeling these effects prior to data collection, informing sample size and requisite power that can be tailored to study-specific factors (e.g., reliability of included measurements, predicted attrition rate). A growing number of large-scale Big Data collection efforts, in both the US (e.g., Philadelphia Neurodevelopmental Cohort (PNC); Pediatric Imaging, Neurocognition, and Genetics (PING) study; National Consortium on Alcohol and Neurodevelopment in Adolescence (NCANDA); Adolescent Brain Cognitive Development (ABCD) study; Lifespan Human Connectome Project Development (HCP-D)) and Europe (e.g., NeuroScience in Psychiatry Network (NSPN), Braintime; Center for Lifespan Changes in Brain and Cognition (LCBC)), as well as multi-site aggregation of existing data (e.g., Enigma Consortium)) have the potential, for the first time, to provide a rigorous understanding of the replicability and effect sizes of various developmental neuroimaging outcomes. This is critically needed especially with the multiplecomparison thresholding techniques used in neuroimaging and the relatively low reliability of many functional measures. Initial investigations in the ABCD dataset have already delineated important implications in this regard, including effects of data collection site and scanner manufacturer (2) . Importantly, large datasets can also be leveraged to replicate findings regarding normative development and deviations associated with psychopathology (3). Modeling frameworks that allow for curvilinear longitudinal trajectories and age as a continuous variable, such as multi-level and structural equation models, have the ability to capture the uniqueness of the adolescent period. Inverse forms of age (e.g., 1/age) are particularly important for modelling adolescence as they can capture fast growth during childhood, deceleration in adolescence, and stabilization in adulthood. In comparison, quadratic models provide the opportunity to identify peaks during adolescence. Exploratory analyses that test several functional forms should balance model fit with model complexity and use information criteria (e.g., Akaike Information Criterion (AIC) / Bayesian Information Criterion (BIC)) for model selection. More recently, algorithmic approaches have been adopted for fitting non-linear developmental trajectories (e.g., General Additive Models). These approaches have many key advantages, including flexible and quantitatively-defined functional forms and permit the examination of age-periods of significant change (4), which can delineate plasticity and growth that can inform predictive models for risk for psychopathology (5) and opportunities for effective interventions, though they often require very large sample sizes. In addition, wellpowered models of normative development can be used as a template from which to assess impairment given the age of subjects (6) . Potential confounds in developmental neuroimaging studies are well-recognized including head motion, non-developmental visit effects, and missing data. Thus, limiting artifacts during imaging acquisition and use of state-of-the-science approaches to identify and mitigate these effects in the acquired data (e.g., global signal regression, despiking, and template-based artifact removal) are critical. Given that artifact removal techniques can also remove meaningful signal, it becomes important to characterize information loss that varies systematically with age (e.g., global signal regression (7)). After addressing artifacts in "pre-processing" stages, it is also important to test, report, and potentially control for any remaining associations with age/pubertal status. Additionally, missing data needs to be integrated in analyses to ensure representative sampling. The interpretation of findings on adolescent brain development should always be informed by conceptual models of neurodevelopment, broader theories from the neuroscience literature, and in consideration of limitations of data. This is particularly important given our stillemerging field, which may lead to multiple interpretations that are critical to move towards increased transparency and reproducibility. For example, task-based fMRI group differences showing lower BOLD activation in adolescents or clinical groups compared to adults or healthy populations could be due to limited regional engagement or alternatively, varied strategies, or compensatory processes (8) . Consideration of alternative interpretations, including behavioral performance (e.g., task difficulty, distinguishing between correct and error trials, response speed), and systems-level changes are thus critical. Within this context, both exploratory and hypothesis driven designs are needed, but with clear rationale and predictions based on the current status of the literature and informed by open science approaches. Critically, wellvalidated negative findings are as important as "positive results" in moving the field forward. A next step in developmental cognitive neuroscience is to characterize the physiological neural mechanisms underlying development. Multimodal imaging approaches that concurrently assess multiple aspects of brain maturation, including those that animal and postmortem studies have shown undergo unique changes through puberty (myelination, neurotransmitters), can inform underlying developmental mechanisms (9) . Several acquisitions that go beyond structural, task-, and resting state-fMRI, such as characterizing white matter microstructure (e.g, Diffusion Tensor Imaging and Magnetization Transfer Ratio), tissue iron as a marker for dopamine (e.g., Quantitative Susceptibility Mapping, R2', and neurotransmitter systems (Positron Emission Tomography, MR Spectroscopy), can move our understanding of normative developmental mechanisms forward and inform the etiology of psychopathology and potential interventions. Overall, methodological and analytical approaches that characterize developmental change are critical in characterizing adolescent development and informing normative neurocognitive growth and risk for psychopathology. Notably, development through adolescence must be conceptualized in a nonlinear fashion characterizing the transition to adult-level trajectories and integrating the multiple and independent brain maturational mechanisms that underlie behavior and determine adult trajectories ( Figure 1 ). Depiction of curvilinear trajectories from childhood through adolescence and into adulthood, including processes that increase (green) (e.g., myelination, cognitive control, and prefrontal GABA) or decrease (blue) (e.g., synaptic pruning, cortico-subcortical functional connectivity and prefrontal glutamate) and stabilize into adulthood or show unique peaks in adolescence (red) (e.g., cortico-subcortical connectivity, dopamine function, affective processes) (10) . The age of adolescence Identifying reproducible individual differences in childhood functional brain networks: An ABCD study Development of white matter microstructure and intrinsic functional connectivity between the amygdala and ventromedial prefrontal cortex: associations with anxiety and depression Development of Hippocampal-Prefrontal Cortex Interactions through Adolescence Early Cannabis Use and Neurocognitive Risk: A Prospective Functional Neuroimaging Study. Biological Psychiatry: Cognitive Neuroscience and Neuroimaging Age-Associated Deviations of Amygdala Functional Connectivity in Youths With Psychosis Spectrum Disorders: Relevance to Psychotic Symptoms Topography and behavioral relevance of the global signal in the human brain What has fMRI told us about the Development of Cognitive Control through Adolescence? Maturation of the human striatal dopamine system revealed by PET and quantitative MRI Adolescence as a neurobiological critical period for the development of higher-order cognition The authors are supported by the National Institute of Mental Health: R01MH08024, R03MH113090, R01MH067924, and the Staunton Farm Foundation. The authors report no biomedical financial interests or potential conflicts of interest.