Preterm birth has been shown to induce an altered developmental trajectory of brain structure and function. With the aid support vector machine (SVM) classification methods we aimed to investigate whether MRI data, collected in adolescence, could be used to predict whether an individual had been born preterm or at term. To this end we collected T1-weighted anatomical MRI data from 143 individuals (69 controls, mean age 14.6y). The inclusion criteria for those born preterm were birth weight ≤ - 1500g and gestational age < 37w. A linear SVM was trained on the grey matter segment ofMR images in two different ways. First, all the individuals were used for training and classification was performed by the leave-one-out method, yielding 93%correct classification (sensitivity = 0.905, specificity = 0.942). Separately, a random half of the available data were used for training twice and each time the other, unseen, half of the data was classified, resulting 86%and 91%accurate classifications. Both gestational age (R = -0.24, p<0.04) and birth weight (R = -0.51, p < 0.001) correlated with the distance to decision boundary within the group of individuals born preterm. Statistically significant correlations were also found between IQ (R = -0.30, p < 0.001) and the distance to decision boundary. Those born small for gestational age did not form a separate subgroup in these analyses. The high rate of correct classification by the SVM motivates further investigation. The long-term goal is to automatically and non-invasively predict the outcome of preterm-born individuals on an individual basis using as early a scan as possible.
Chu, C., Lagercrantz, H., Forssberg, H., & Nagy, Z. (2015). Investigating the use of support vector machine classification on structural brain images of preterm-born teenagers as a biological marker. PLoS ONE, 10(4). https://doi.org/10.1371/journal.pone.0123108