Meditation and breathwork on anxiety in Parkinson's disease: A pilot randomized controlled trial.

Normative models of neuroimaging data benchmark interindividual differences in quantitative features against population reference models. Similar to pediatric growth charts of height or weight, such “brain charts,” can boost interpretability and power to detect clinical differences. Analyses using deviation scores derived from normative models of brain features compared with analyses of raw measures can increase detection of meaningful patterns in populations as well as specific individuals. Previous normative modeling studies largely model longitudinal data using cross-sectional brain charts, which do not directly address variability in longitudinal change. Over time, individuals with extreme centiles tend to move closer to the mean, with different expected growth velocities based on baseline centiles. Longitudinal normative models can account for the regression to the mean by benchmarking change between time points, which is particularly useful in highly dynamic periods of brain development such as adolescence. Here, we present conditional-longitudinal models where within-individual change is quantified by conditioning follow-up brain-volume measurements on baseline values. Harnessing anatomical brain magnetic resonance imaging (MRI) data from the Adolescent Brain Cognitive Development (ABCD) Study, we tested for brain trajectory differences associated with perinatal developmental factors (eg, birth weight) and with contemporaneous measures of psychopathology. Extensive research links perinatal factors, including birth weight and gestational age, to brain MRI differences persisting into adolescence and adulthood. In general, prematurity and low birth weight are associated with smaller regional brain volumes. However, few studies provide a longitudinal perspective on whether outcomes of perinatal adversity are stable or whether they play a role in the dynamics of brain maturation. Prematurity may result in slower brain growth in childhood, and there may be age-varying associations between birth weight and brain volume. However, prior work has relied on modeling approaches that do not fully disentangle static differences from differences in how the brain changes. For instance, if birth weight is associated with brain volume at baseline and if individuals with larger brains tend to have larger absolute change between time points, then associations with change might simply reflect a propagation of baseline differences rather than meaningful differences in longitudinal trajectories. Conditional-longitudinal models resolve this uncertainty by quantifying degree of expected change for each individual based on their baseline brain volumes. Adverse perinatal factors have also been linked to increased susceptibility to psychiatric conditions. Adolescence is a critical period for both brain development and sensitivity to mental illness, and individual deviations from normative brain-growth patterns may be related to changes in mental health. Baseline neuroimaging features in the ABCD Study have previously been linked to overall psychopathology. Smaller studies have also suggested that structural brain changes can be factors in severity of psychiatric symptoms over time. However, the potential utility of longitudinal normative models remains untested. Understanding the interactions between perinatal factors, brain maturation, and emerging psychopathology in adolescence may elucidate potential links between adverse perinatal factors and elevated risk for psychiatric conditions. In this study, we introduce conditional-longitudinal models to address this gap. Our aim was to ascertain the association of longitudinal change in brain volumes with birth weight, gestational age, and longitudinal changes in psychopathology.