Real‐time functional MRI using pseudo‐continuous arterial spin labeling

Abstract

The first implementation of real-time acquisition and analysis of arterial spin labeling-based functional magnetic resonance imaging time series is presented in this article. The implementation uses a pseudo-continuous labeling scheme followed by a spiral k-space acquisition trajectory. Real-time reconstruction of the images, preprocessing, and regression analysis of the functional magnetic resonance imaging data were implemented on a laptop computer interfaced with the MRI scanner. The method allows the user to track the current raw data, subtraction images, and the cumulative t-statistic map overlaid on a cumulative subtraction image. The user is also able to track the time course of individual time courses and interactively selects a region of interest as a nuisance covariate. The pulse sequence allows the user to adjust acquisition and labeling parameters while observing their effect on the image within two successive pulse repetition times. This method is demonstrated by two functional imaging experiments: a simultaneous finger-tapping and visual stimulation paradigm, and a bimanual finger-tapping task. Conventional functional magnetic resonance imaging (fMRI) collects blood oxygen level-dependent (BOLD)-contrast MR images of a subject's brain while performing a cognitive task, whereas subsequent image reconstruction and analysis are performed offline (i.e., on a separate computer after the experiment is completed). Real-time fMRI is an exciting extension to conventional fMRI techniques that enables the user to analyze fMRI data as it is being collected. Thus, in real-time fMRI, the results are immediately available as the subject is being scanned, and the results can be used to reveal and guide the subject's cognitive processes. It can also facilitate the experimenter's parameter selections or a clinician's interventions (1).Several real-time analysis methods have been implemented for online processing of BOLD data, including cumulative correlation (2), sliding-window correlations with reference vector optimization (3), online general linear model analysis (4), and combined methods to collect behavioral, physiological, and MRI data while performing near real-time statistical analysis (5). All the above methods can facilitate real-time analysis, e.g., the incremental algorithms are useful in monitoring ongoing activation, and the sliding window approaches can improve localization of dynamic activity in time (4). Given current computer-processing speed, any of these approaches can be used to display realtime activation maps. This allows for several real-time applications: online data quality control, real-time functional activation monitoring, interactive paradigms based on the subject's dynamic functional activity (fMRI biofeedback or brain-computer interface; Ref. 6), and autonomous control of neural activation using real-time fMRI. These techniques have been used to study subjects' modulation of motor-area cortical activation and emotional processing (7-11).However, despite the efficiency of BOLD-contrast...

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