Real-time independent component analysis of fMRI using spatial constraint

Abstract

Real-time functional magnetic resonance imaging (fMRI) is a useful tool that researchers can monitor and assess dynamic brain activity in real time and train individuals to actively control over their brain activation by using neurofeedback. Independent Component Analysis (ICA) is a data-driven method which can recover a set of independent sources from data without using any prior information. Since ICA was firstly proposed to be applied to fMRI data by Mckeown (1998), it has become more and more popular in offline fMRI data analysis. However, ICA was seldom used in real-time fMRI studies due to its large time cost. Although Esposito (2005) proposed a real-time ICA (rtICA) framework by combining FastICA with a sliding-window approach, it was only applied to analyze single-slice data rather than full-brain data and was not stable. The semi-blind rtICA (sb-rtICA) method proposed by Ma (2011) can reduce the computation time and improve stability by adding regularization of certain estimated time course using the experiment paradigm information to rtICA. However, the target independent component (IC) cannot be extracted as the first component in all sliding windows by sb-rtICA, which still adds computation time to some extent. The constrained ICA proposed by Lu (2005) can eliminate the ICA’s indeterminacy on permutation. In this study, we proposed a real-time Constrained Independent Component Analysis (rtCICA) method by combining CICA with the sliding-window technique to improve the performance of rtICA. The basic idea of rtCICA is to induce spatial prior information as constraints into ICA so that the target IC can be always automatically extracted as the first one. Both simulated and real-time fMRI experiments demonstrated that rtCICA outperforms rtICA greatly in the stability and the computational time.