A new approach to measure single-event related brain activity using real-time fMRI: feasibility of sensory, motor, and higher cognitive tasks.

Real-time fMRI is a rapidly emerging methodology that enables monitoring changes in brain activity during an ongoing experiment. In this article we demonstrate the feasibility of performing single-event sensory, motor, and higher cognitive tasks in real-time on a clinical whole-body scanner. This approach requires sensitivity optimized fMRI methods: Using statistical parametric mapping we quantified the spatial extent of BOLD contrast signal changes as a function of voxel size and demonstrate that sacrificing spatial resolution and readout bandwidth improves the detection of signal changes in real time. Further increases in BOLD contrast sensitivity were obtained by using real-time multi-echo EPI. Real-time image analysis was performed using our previously described Functional Imaging in REal time (FIRE) software package, which features real-time motion compensation, sliding window correlation analysis, and automatic reference vector optimization. This new fMRI methodology was validated using single-block design paradigms of standard visual, motor, and auditory tasks. Further, we demonstrate the sensitivity of this method for online detection of higher cognitive functions during a language task using single-block design paradigms. Finally, we used single-event fMRI to characterize the variability of the hemodynamic impulse response in primary and supplementary motor cortex in consecutive trials using single movements. Real-time fMRI can improve reliability of clinical and research studies and offers new opportunities for studying higher cognitive functions.

Functional magnetic resonance imaging in real time (FIRE): sliding-window correlation analysis and reference-vector optimization.

New algorithms for correlation analysis are presented that allow the mapping of brain activity from functional MRI (fMRI) data in real time during the ongoing scan. They combine the computation of the correlation coefficients between measured fMRI time-series data and a reference vector with "detrending", a technique for the suppression of non-stimulus-related signal components, and the "sliding-window technique". Using this technique, which limits the correlation computation to the last N measurement time points, the sensitivity to changes in brain activity is maintained throughout the whole experiment. For increased sensitivity in activation detection a fast and robust optimization of the reference vector is proposed, which takes into account a realistic model of the hemodynamic response function to adapt the parameterized reference vector to the measured data. Based on the described correlation method, real-time fMRI experiments using visual stimulation paradigms have been performed successfully on a clinical MR scanner, which was linked to an external workstation for image analysis.

Enhancement of BOLD-contrast sensitivity by single-shot multi-echo functional MR imaging.

Improved data acquisition and processing strategies for blood oxygenation level-dependent (BOLD)-contrast functional magnetic resonance imaging (fMRI), which enhance the functional contrast-to-noise ratio (CNR) by sampling multiple echo times in a single shot, are described. The dependence of the CNR on T2*, the image encoding time, and the number of sampled echo times are investigated for exponential fitting, echo summation, weighted echo summation, and averaging of correlation maps obtained at different echo times. The method is validated in vivo using visual stimulation and turbo proton echoplanar spectroscopic imaging (turbo-PEPSI), a new single-shot multi-slice MR spectroscopic imaging technique, which acquires up to 12 consecutive echoplanar images with echo times ranging from 12 to 213 msec. Quantitative T2*-mapping significantly increases the measured extent of activation and the mean correlation coefficient compared with conventional echoplanar imaging. The sensitivity gain with echo summation, which is computationally efficient provides similar sensitivity as fitting. For all data processing methods sensitivity is optimum when echo times up to 3.2 T2* are sampled. This methodology has implications for comparing functional sensitivity at different magnetic field strengths and between brain regions with different magnetic field inhomogeneities.