Data stream synchronization for defining meaningful fMRI classification problems

Highlights

• We challenge a popular approach to labelling fMRI data for predictive modelling.

• We propose a new labelling method based on data stream synchronization.

• We validate the proposed method experimentally on real fMRI data.

• We observe major classification accuracy improvement and model complexity reduction.

Abstract

Application of machine learning techniques to the functional Magnetic Resonance Imaging (fMRI) data is recently an active field of research. There is however one area which does not receive due attention in the literature – preparation of the fMRI data for subsequent modelling. In this study we focus on the issue of synchronization of the stream of fMRI snapshots with the mental states of the subject, which is a form of smart filtering of the input data, performed prior to building a predictive model. We demonstrate, investigate and thoroughly discuss the negative effects of lack of alignment between the two streams and propose an original data-driven approach to efficiently address this problem. Our solution involves casting the issue as a constrained optimization problem in combination with an alternative classification accuracy assessment scheme, applicable to both batch and on-line scenarios and able to capture information distributed across a number of input samples lifting the common simplifying i.i.d. assumption. The proposed method is tested using real fMRI data and experimentally compared to the state-of-the-art ensemble models reported in the literature, outperforming them by a wide margin.