  The study of healthy brain development helps to better understand the brain
transformation and brain connectivity patterns which happen during childhood to
adulthood. This study presents a sparse machine learning solution across
whole-brain functional connectivity (FC) measures of three sets of data,
derived from resting state functional magnetic resonance imaging (rs-fMRI) and
task fMRI data, including a working memory n-back task (nb-fMRI) and an emotion
identification task (em-fMRI). These multi-modal image data are collected on a
sample of adolescents from the Philadelphia Neurodevelopmental Cohort (PNC) for
the prediction of brain ages. Due to extremely large variable-to-instance ratio
of PNC data, a high dimensional matrix with several irrelevant and highly
correlated features is generated and hence a pattern learning approach is
necessary to extract significant features. We propose a sparse learner based on
the residual errors along the estimation of an inverse problem for the extreme
learning machine (ELM) neural network. The purpose of the approach is to
overcome the overlearning problem through pruning of several redundant features
and their corresponding output weights. The proposed multimodal sparse ELM
classifier based on residual errors (RES-ELM) is highly competitive in terms of
the classification accuracy compared to its counterparts such as conventional
ELM, and sparse Bayesian learning ELM.
