Indexed on: 22 Jul '17Published on: 22 Jul '17Published in: NeuroImage
The blood oxygenation level-dependent (BOLD) fMRI response to neuronal activation results from a complex interplay of induced metabolic and vascular changes. Thus, its transients, such as initial overshoot and post-stimulus undershoot, provide a window into the dynamic relationships of the underlying physiological variables. In this study, we propose multi-echo fMRI as a tool to investigate the physiological underpinnings of the BOLD signal, in particular, and brain functional physiology, in general. In the human visual cortex at 3 T, we observed that the BOLD response is nonlinearly dependent on echo-time (TE) and the amount of nonlinearity varies during the entire time-course. Fitting a linear model to this nonlinear relationship resulted in a positive intercept at TE = 0 ms. The time-course of the intercept exhibited fast and slow modulations, distinctly different both from the BOLD response and cerebral blood flow (CBF). In order to shed light on the TE-dependence of the BOLD signal and the intercept time-course, we performed simulations based on a nonlinear two-compartmental BOLD signal model combined with the dynamic balloon model. The modeling suggests that the intercept time-course reflects a weighted sum of deoxyhemoglobin concentration and venous CBV signal changes. We demonstrate that only CBF-venous blood volume (CBV) uncoupling but not CBF-oxygen metabolism (CMRO2) uncoupling can fully account for our experimental observations. In particular, these results strongly argue for a slow evolution of the venous CBV together with stimulus-type-dependent CBF transients (the latter being tightly coupled with CMRO2) to be responsible for the BOLD signal adaptation during stimulation and for the post-stimulus undershoot. Thus, BOLD signal transients are composed of smoothed version of neuronal time-course as reflected in CBF and CMRO2 and secondary vascular processes due to biomechanics of venous blood vessels, and multi-echo fMRI in combination with modeling provides invaluable insights into these physiological processes.