An innovative method for assessing the relationship between longitudinal brain volume measurements and neurodevelopmental outcomes in preterm infants

Abstract

Predicting neurodevelopmental outcomes in very preterm infants is critical, but clinically-acquired data like longitudinal total brain volume (TBV), as macroscopic index of brain growth, are often sparse and irregular, hindering accurate prognosis. We studied 294 very preterm infants with TBV measured longitudinally and neurodevelopmental outcomes assessed at 2 years (Bayley-III) and 8 years (WISC-V). To handle missingness and irregular sampling, we compared six imputation strategies (mean, MissForest, MICE, GP, MGP, and MGP (initGP)) and trained a semi-supervised classifier on the imputed TBV trajectories. Performance was evaluated using multiple classification metrics, and results were summarized by averaging across outcomes and ages of study. Across analyses, a novel variant of Missing Gaussian Process (MGP) initialized with a GP fit (MGP (initGP)), which leverages individual patient trajectories to stabilize estimates, achieved the best average performance. Its advantages were most consistent and significant for 8-year outcomes, highlighting its strength in modeling longer developmental trajectories. While performance at 2 years was more modest, this likely reflects the intrinsic challenges of early-term prediction from TBV alone. We therefore recommend MGP (initGP) as a strong default for imputing longitudinal TBV for prognostic studies.