Modeling sparse longitudinal data in early neurodevelopment
Yaqing Chen,
Paromita Dubey,
Hans-Georg Müller,
Muriel Bruchhage,
Jane-Ling Wang,
Sean Deoni
Affiliations
Yaqing Chen
Department of Statistics, University of California, Davis, Davis, CA, 95616, USA
Paromita Dubey
Department of Statistics, Stanford University, Stanford, CA, 94305, USA
Hans-Georg Müller
Department of Statistics, University of California, Davis, Davis, CA, 95616, USA
Muriel Bruchhage
Advanced Baby Imaging Lab, Hasbro Children’s Hospital, Rhode Island Hospital, Providence, RI, 02903, USA; Department of Pediatrics, Warren Alpert Medical School at Brown University, Providence, RI, 02912, USA
Jane-Ling Wang
Department of Statistics, University of California, Davis, Davis, CA, 95616, USA
Sean Deoni
Corresponding author at: Department of Pediatrics, Warren Alpert Medical School at Brown University, Providence, RI, 02912, USA.; Advanced Baby Imaging Lab, Hasbro Children’s Hospital, Rhode Island Hospital, Providence, RI, 02903, USA; Department of Pediatrics, Warren Alpert Medical School at Brown University, Providence, RI, 02912, USA; Department of Radiology, Warren Alpert Medical School at Brown University, Providence, RI, 02912, USA; Maternal, Newborn, and Child Health Discovery & Tools, Bill & Melinda Gates Foundation, Seattle, WA, USA
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.
