Paper Info
Title
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.
Year
DOI
Venue
2003
10.1016/j.neuroimage.2003.08.012
NeuroImage
Keywords
DocType
Volume
Functional magnetic resonance imaging,fMRI,Real-time analysis,Exploratory data-driven analysis,Descriptive statistics,Sliding-window analysis,Independent component analysis,Fixed-point algorithm,Receiver operating characteristics
Journal
20
Issue
ISSN
Citations 
4
1053-8119
30
PageRank 
References 
Authors
3.21
10
9
Name
Order
Citations
PageRank
Fabrizio Esposito142136.61
E SEIFRITZ218617.42
Elia Formisano377858.91
Renato Morrone4303.21
Tommaso Scarabino51098.57
Gioacchino Tedeschi612510.97
Sossio Cirillo7303.21
Rainer Goebel867056.00
Francesco Di Salle915516.25