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.
authors.affiliation: Second Division of Neurology, Second University of Naples, 8013 Naples, Italy. faesposi@unina.it
authors.forename: Fabrizio
authors.initials: F
authors.lastname: Esposito
authors.forename: Erich
authors.initials: E
authors.lastname: Seifritz
authors.forename: Elia
authors.initials: E
authors.lastname: Formisano
authors.forename: Renato
authors.initials: R
authors.lastname: Morrone
authors.forename: Tommaso
authors.initials: T
authors.lastname: Scarabino
authors.forename: Gioacchino
authors.initials: G
authors.lastname: Tedeschi
authors.forename: Sossio
authors.initials: S
authors.lastname: Cirillo
authors.forename: Rainer
authors.initials: R
authors.lastname: Goebel
authors.forename: Francesco
authors.initials: F
authors.lastname: Di Salle
mesh_terms: D000465:Algorithms; D001931:Brain Mapping / Q000379:methods; D003198:Computer Simulation; D003199:Computer Systems; D003627:Data Interpretation, Statistical; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008279:Magnetic Resonance Imaging / Q000706:statistics & numerical data*; D012044:Regression Analysis; D015203:Reproducibility of Results; D014054:Tomography