Learning with heterogeneous data for longitudinal studies

Abstract

Longitudinal studies are very important to understand cerebral structural changes especially during the course of pathologies. For instance, in the context of mental health research, it is interesting to evaluate how a certain disease degenerates over time in order to discriminate between pathological and normal time dependent brain deformations. However longitudinal data are not easily available, and very often they are characterized by a large variability in both the age of subjects and time between acquisitions (follow up time). This leads to heterogeneous data that may affect the overall study. In this paper we propose a learning method to deal with this kind of heterogeneous data by exploiting covariate measures in a Multiple Kernel Learning (MKL) framework. Cortical thickness and white matter volume of the left middle temporal region are collected from each subject. Then, a subject-dependent kernel weighting procedure is introduced in order to obtain the correction of covariate effect simultaneously with classification. Experiments are reported for First Episode Psychosis detection by showing very promising results.