Modeling sparse longitudinal data in early neurodevelopment - Chen, Yaqing; Dubey, Paromita; Müller, Hans-Georg; Bruchhage, Muriel; Wang, Jane-Ling; Deoni, Sean (NeuroImage (Orlando, Fla.), 2021)
•Obtaining mean trend and modes of variation for sparse longitudinal neuroimaging data.•Reconstructing individual smooth trajectories using PACE approach.•The PACE method outperforms established random-effects models for trajectory recovery.•Nonparametric estimation of dynamic quantiles to construct brain-for-age growth charts.•Concurrent regression where responses and predictors both vary across age. Early childhood is a period marked by rapid brain growth accompanied by cognitive and motor development. However, it remains unclear how early developmental skills relate to neuroanatomical growth across time with no growth quantile trajectories of typical brain development currently available to place and compare individual neuroanatomical development. Even though longitudinal neuroimaging data have become more common, they are often sparse, making dynamic analyses at subject level a challenging task. Using the Principal Analysis through Conditional Expectation (PACE) approach geared towards sparse longitudinal data, we investigate the evolution of gray matter, white matter and cerebrospinal fluid volumes in a cohort of 446 children between the ages of 1 and 120 months. For each child, we calculate their dynamic age-varying association between the growing brain and scores that assess cognitive functioning, applying the functional varying coefficient model. Using local Fréchet regression, we construct age-varying growth percentiles to reveal the evolution of brain development across the population. To further demonstrate its utility, we apply PACE to predict individual trajectories of brain development.