Real-time independent component analysis of fMRI time-series

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...

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Veröffentlicht in:NeuroImage (Orlando, Fla.) Jg. 20; H. 4; S. 2209 - 2224
Hauptverfasser: Esposito, Fabrizio, Seifritz, Erich, Formisano, Elia, Morrone, Renato, Scarabino, Tommaso, Tedeschi, Gioacchino, Cirillo, Sossio, Goebel, Rainer, Di Salle, Francesco
Format: Journal Article
Sprache:Englisch
Veröffentlicht: United States Elsevier Inc 01.12.2003
Elsevier Limited
Schlagworte:
Accuracy
Algorithms
Brain Mapping - methods
Computer Simulation
Computer Systems
Data analysis
Data Interpretation, Statistical
Decomposition
Descriptive statistics
Exploratory data-driven analysis
Fixed-point algorithm
fMRI
Functional magnetic resonance imaging
Humans
Image Processing, Computer-Assisted
Independent component analysis
Magnetic Resonance Imaging - statistics & numerical data
Methods
NMR
Nuclear magnetic resonance
Principal components analysis
Real-time analysis
Receiver operating characteristics
Regression Analysis
Reproducibility of Results
Sliding-window analysis
Tomography
fMRI
Real-time analysis
Independent component analysis
Functional magnetic resonance imaging
Sliding-window analysis
Exploratory data-driven analysis
Receiver operating characteristics
Descriptive statistics
Fixed-point algorithm
ISSN:1053-8119, 1095-9572
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Zusammenfassung: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.
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ISSN:1053-8119
1095-9572
DOI:10.1016/j.neuroimage.2003.08.012