Complex data analysis in high-resolution SSFP fMRI.

In transition-band steady-state free precession (SSFP) functional MRI (fMRI), functional contrast originates from a bulk frequency shift induced by a deoxygenated hemoglobin concentration change in the activated brain regions. This frequency shift causes a magnitude and/or phase-signal change depending on the off-resonance distribution of a voxel in the balancedSSFP (bSSFP) profile. However, in early low-resolution studies, only the magnitude signal activations were shown. In this paper the task-correlated phase-signal change is presented in a highresolution (1 1 1m m 3 ) study. To include this phase activation in a functional analysis, a new complex domain data analysis method is proposed. The results show statistically significant phase-signal changes in a large number of voxels comparable to that of the magnitude-activated voxels. The complex-data analysis method successfully includes these phase activations in the activation map and thus provides wider coverage compared to magnitude-data analysis results. Magn Reson Med 57:905–917, 2007. © 2007 Wiley-Liss, Inc. Since its first inception in the early 1990s, blood oxygen level-dependent (BOLD) functional MRI (fMRI) has had a tremendous impact on human brain research. Most studies performed a functional data analysis using the magnitude data of the time-series images while ignoring phase-signal changes. Recently a few high-resolution studies explored the phase information and revealed that the task-correlated phase-signal change can provide additional information regarding brain activation (1,2). Investigators found that a large draining vein induced phase-signal changes in addition to magnitude-signal changes by creating a relatively uniform frequency shift within a voxel. This information was utilized to determine the oxygen saturation change in veins (1) and to suppress BOLD signals from large vessels (2). However, in transition-band SSFP, or blood oxygenation sensitive steady state (BOSS) fMRI, a recently developed functional imaging method that provides greater signal-to-noise ratio (SNR) efficiency with less imaging distortion (3– 6), the origin of the phase-signal change differs from that in conventional gradient-recalled echo (GRE) fMRI (5). Moreover, this task-correlated phasesignal change was always expected, since a sharp phase transition near the on-resonance frequency (also at every multiple of 1/TR) is considered to be a primary source of the functional contrast in transition-band SSFP fMRI. However, early low-resolution studies measured functional activations from the magnitude signals (5). This has raised questions regarding the existence of the phase activation and the contribution of the SSFP phase profile to the functional contrast. In this article we present the results of a 3D high-resolution (1 mm 3 ) functional study to show the presence and characteristics of task-correlated phase-signal changes in transition-band SSFP fMRI. Simulation results are also included to further elucidate the sensitivity and characteristics of the phase-signal changes. If functional contrast exists in both magnitude and phase signals, and if both signals provide spatially localized information, an analysis based only on magnitude cannot fully exploit the data. Some voxels that contain phase activation without magnitude activation will not be detected, and some voxels that contain both activations will show less activation in the magnitude-only analysis. Therefore, to acquire more reliable results, a complex domain data analysis method that encompasses both magnitude- and phase-signal activations is necessary. Recently several complex-data analysis methods (7–15), most of which are based on a generalized likelihood ratio test (GLRT) (13), have been proposed. Here we propose a new method that combines a Hotelling’s T 2 -test (which can be derived from GLRT) (16) with a generalized linear model (GLM) (17) to calculate the statistical significance of the activation from complex data. This method is computationally efficient compared to the previously proposed methods, and generates full activation maps that include both magnitude and phase activations.