INCORPORATION OF GRAY MATTER T1 AND T2 * IMPROVES BRAIN ACTIVATION STATISTICS IN FMRI

Purpose: The proton spin density (M0) as well as the MR relaxivities, transverse (T2*), and longitudinal relaxation times, provide the basic contrast mechanisms in functional MRI (fMRI). Although the MR magnetization physically depends on tissue and imaging parameters in a nonlinear fashion, a linear model is what is conventionally used in fMRI activation studies. Furthermore, the standard practice has been to discard the first scans to avoid magnetic saturation effects even though they have important information on the MR relaxivities. It is also well-known that activation occurs in gray matter (GM) while performing a task. We present a statistical fMRI model for Differential T2 * Contrast INcorporating T1 and T2 * of GM, so-called DeTeCT-ING, that uses complex-valued time courses to estimate T1 and T2 * for each voxel, then to incorporate GM MR relaxivities into statistical model in order to better detect brain activation, all from a single pulse sequence by utilizing the first scans. We show the performance of the DeTeCT-ING Model on an fMRI data set and compare it with the conventionally used magnitude-only (MO)1 and newer complex-valued (CA)2 fMRI models. Methods: The temporally varying magnitude of the MR signal, Mt, can be represented by incorporating the effect of the task execution as follows: ܯ௧ ൌ ሾܯ௧ି ଵ expሺെܴܶ / ଵܶ ሻ cosሺ߶ ሻ ܯሺ1 െ expሺെܴܶ / ଵܶሻሻ ሿ sinሺ߶ ሻ expሺെܶܧ / ଶܶ ∗ ߜݖ௧ ሻ ݔ௧́ߚ ଵ where ϕ is the flip angle (FA), TR and TE are the repetition and echo times, δ is the differential task signal change, the coefficient for th