Combining complex signal change in functional MRI

With the development of functional magnetic resonance imaging (fMRI) techniques, data analysis methods based on complex MR data have been proposed. However, the methods have not been popular for fMRI community, in part because the phase activation in conventional GRE fMRI has been suggested to originate from the large veins (1). Recently, novel fMRI methods such as transition-band SSFP fMRI and an alternating balanced SSFP method for neuronal current measurement (2) have been proposed. In these methods, the functional contrasts exist in the complex domain, providing significant and localized complex signal change. Hence, the usefulness of the complex-data analysis methods has become increasingly important for these applications by allowing them to reliably obtain complex activation. As mentioned in his letter, Dr. Rowe has proposed a complex-data analysis method based on the generalized likelihood ratio test (3). Despite the usefulness of the method, the computational complexity of the method, which requires multiple iterations to estimate the parameters, hampers the routine use of the method. This is particularly true for high-resolution studies that we targeted in our study. To overcome this inefficiency, we have proposed a new method based on T 2 statistics combined with generalized linear model (4). Dr. Rowe’s letter expressed concerns about the relationship of our model to his model and some mathematical errors. Here we present our responses to his points: