Multi-task Joint Prediction of Infant Cortical Morphological and Cognitive Development

During the early postnatal period, the human brain undergoes rapid and dynamic development. Over the past decades, there has been increased attention in studying the cognitive and cortical development of infants. However, accurate prediction of the infant cognitive and cortical development at an individual-level is a significant challenge, due to the huge complexities in highly irregular and incomplete longitudinal data that is commonly seen in current studies. Besides, joint prediction of cognitive scores and cortical morphology is barely investigated, despite some studies revealing the tight relationship between cognitive ability and cortical morphology and suggesting their potential mutual benefits. To tackle this challenge, we develop a flexible multi-task framework for joint prediction of cognitive scores and cortical morphological maps, namely, disentangled intensive triplet spherical adversarial autoencoder (DITSAA). First, we extract the mixed representative latent vector through a triplet spherical ResNet and further disentangles latent vector into identity-related and age-related features with an attention-based module. The identity recognition and age estimation tasks are introduced as supervision for a reliable disentanglement of the two components. Then we formulate the individualized cortical profile at a specific age by combining disentangled identity-related information and corresponding age-related information. Finally, an adversarial learning strategy is integrated to achieve a vivid and realistic prediction of cortical morphology, while a cognitive module is employed to predict cognitive scores. Extensive experiments are conducted on a public dataset, and the results affirm our method's ability to predict cognitive scores and cortical morphology jointly and flexibly using incomplete longitudinal data.