Multi-task prediction of infant cognitive scores from longitudinal incomplete neuroimaging data.


Department of Radiology and BRIC, University of North Carolina at Chapel Hill, NC 27599, United States; Department of Psychiatry & Behavioral Sciences, Stanford University, Stanford, CA 94305, United States. Electronic address: [Email]


Early postnatal brain undergoes a stunning period of development. Over the past few years, research on dynamic infant brain development has received increased attention, exhibiting how important the early stages of a child's life are in terms of brain development. To precisely chart the early brain developmental trajectories, longitudinal studies with data acquired over a long-enough period of infants' early life is essential. However, in practice, missing data from different time point(s) during the data gathering procedure is often inevitable. This leads to incomplete set of longitudinal data, which poses a major challenge for such studies. In this paper, prediction of multiple future cognitive scores with incomplete longitudinal imaging data is modeled into a multi-task machine learning framework. To efficiently learn this model, we account for selection of informative features (i.e., neuroimaging morphometric measurements for different time points), while preserving the structural information and the interrelation between these multiple cognitive scores. Several experiments are conducted on a carefully acquired in-house dataset, and the results affirm that we can predict the cognitive scores measured at the age of four years old, using the imaging data of earlier time points, as early as 24 months of age, with a reasonable performance (i.e., root mean square error of 0.18).


Bag-of-words,Brain fingerprinting,Longitudinal incomplete data,Low-rank tensor,Multi-task learning,Postnatal brain development,Sparsity,