Beyond functional connectivity: deep learning applied to resting-state fMRI time series in the prediction of 58 human traits in the HCP

Abstract

Machine learning has made several inroads into the study of brain-behavior relations based on in vivo imaging. While the advent of deep neural networks was expected to further improve predictions, the current literature based on resting-state functional connectivity presents mixed results. We hypothesize that the representation of the data, i.e. in the form of functional connectivity, could restrict an advantage of deep learning techniques, namely that of learning complex representations directly from the data. Thus, we investigated if bypassing this feature extraction resulted in improved performance in the prediction of 58 widely studied behavioral traits from a large sample of Human Connectome Project subjects, using deep learning techniques. For this task, we adapted the InceptionTime architecture, which jointly predicts traits directly from regional time series through representation learning, and compared results with a strong kernel-based baseline. Results revealed that both models achieve comparable performance in most traits. Eleven significant differences in mean squared error were detected, however, with seven favoring the neural network approach, and this number increased when accounting for covariates. We additionally show that contrary to the expectation, the neural network approach was more robust to reductions in the training set size. On the other hand, it was more sensitive to reductions in the length of the time series at test time. Our results present a more nuanced view of the potential of deep learning for the prediction of behavior from neuroimaging, which allows learning features directly from the data.