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

Abstract

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).

Keywords: Bag-of-words; Brain fingerprinting; Longitudinal incomplete data; Low-rank tensor; Multi-task learning; Postnatal brain development; Sparsity.

Figure 1

Multi-task multi-linear regression model for prediction of infant cognitive scores. Top: longitudinal data from a sample subject with missing data in some time points (M stands for months); Bottom: Multiple cognitive scores, each of which defines one prediction task; Middle: Multiple linear models to predict the cognitive scores, each from one time point.

Figure 2

Longitudinal infant dataset, containing 24 subjects (columns), each scanned at 9 different time points (rows). Each block contains the cortical morphological attributes of all vertices on the cortical surface for a specific subject at a specific time point. Black blocks show the missing data at the respective time points. Our task is to predict the cognitive scores assessed at the age of 48M.

Figure 3

Brain fingerprinting procedure, using a model similar to BoW: A pool of attribute vectors from all training subjects is created, which are then clustered into d different clusters (in this case d = 3). Then, a d-dimensional vector represents each given subject, containing the frequencies of its vertices lying in each of these d clusters.

Figure 4

Percentage of selected features from each time point.

Figure 5

RMSE of the proposed method through 10-fold cross-validation to predict the ELC score for each time interval. Elements in the first column of each row indicate the starting time point of the interval and the elements in the first row of each column are the ending time point for the interval.

Figure 6

RMSE of ELC prediction (using all time points) as a function of different values for d (the number of clusters for creating the brain fingerprints).

Figure 7

Percentage of the vertices that are not rejected at the 5% significance level for predicting the Early Learning Composite (ELC) score from each of the five features, at different time points. The last one in the second row shows the average value across all time points for the features.

Figure 8

Scatter plots of the actual (horizontal axis) and the predicted (vertical axis) values of the five scores (VRS, FMS, RLS, ELS and ELC), at the 24M time point, for 10 different runs.