Real-time independent component analysis of fMRI time-series

Abstract

Real-time functional magnetic resonance imaging (fMRI) enables one to monitor a subject's brain activity during an ongoing session. The availability of online information about brain activity is essential for developing and refining interactive fMRI paradigms in research and clinical trials and for neurofeedback applications. Data analysis for real-time fMRI has traditionally been based on hypothesis-driven processing methods. Off-line data analysis, conversely, may be usefully complemented by data-driven approaches, such as independent component analysis (ICA), which can identify brain activity without a priori temporal assumptions on brain activity. However, ICA is commonly considered a time-consuming procedure and thus unsuitable to process the high flux of fMRI data while they are acquired. Here, by specific choices regarding the implementation, we exported the ICA framework and implemented it into real-time fMRI data analysis. We show that, reducing the ICA input to a few points within a time-series in a sliding-window approach, computational times become compatible with real-time settings. Our technique produced accurate dynamic readouts of brain activity as well as a precise spatiotemporal history of quasistationary patterns in the form of cumulative activation maps and time courses. Results from real and simulated motor activation data show comparable performances for the proposed ICA implementation and standard linear regression analysis applied either in a sliding-window or in a cumulative mode. Furthermore, we demonstrate the possibility of monitoring transient or unexpected neural activities and suggest that real-time ICA may provide the fMRI researcher with a better understanding and control of subjects' behaviors and performances.

Another kind of ‘BOLD Response’: answering multiple-choice questions via online decoded single-trial brain signals

“…real-time independent component analysis (Esposito et al, 2003), e.g., to automatically detect artifacts (caused by motion or undesired thought processes during encoding) and 2. real-time multivariate analysis techniques, such as real-time multi-voxel pattern analysis (LaConte et al, 2007), e.g., support vector machines (Lee et al, 2009a), that might help to increase the sensitivity to detect more subtle spatial differences of brain activation patterns.…”

A Review on Dependence Measures in Exploring Brain Networks from fMRI Data

“…The second approach that solves such limitation is the use of model-free method. This approach does not need to define a seed region or reference region from prior knowledge, and it may use some mathematical techniques, such as principal component analysis (PCA) in [17,18,19,20] or independent component analysis (ICA) in [21,22,19], for defining voxels. PCA maps fMRI data to a new space via an orthogonal transformation and defines a set of components having the greatest variances as voxels.…”

“…• Model-free method: Examples that use this method include [17,18,21,22,25,26,27,19,20]. The result from this method may often be difficult for interpretation by using anatomical knowledge.…”

Real-Time Functional MRI: Development and Emerging Applications

“…Alternatively, real-time fMRI can also be implemented using a sliding-window approach 15,16,22 in which aˆxed number of images, called the window width, are used in the analysis throughout the scanning session. Although the size of the data set increases as the scan progresses, the window width is kept constant by discarding the oldest image in the analysis window when a new image becomes available.…”