The Temporal Resolution of Functional MRI

Temporal resolving power of spin echo and gradient echo fMRI at 3T with apparent diffusion coefficient compartmentalization

The temporal resolving power of blood oxygenation level-dependent (BOLD) functional magnetic resonance imaging (fMRI) at 3T was investigated in the visual and auditory cortices of the human brain. By using controlled temporal delays and selective visual hemifield stimulation, regions with similar (left vs. right occipital cortex) and different (occipital cortex vs. auditory cortex) vascular architectures were compared. Estimates of the time-to-peak (TTP) of the BOLD hemodynamic response function (hrf) were obtained using a spin echo (SE) sequence and compared to those acquired using a traditional gradient echo (GE) sequence. The hrf TTP in the visual cortex was found to be 4.73 s and 4.21 s for GE and SE, respectively. The auditory cortex response was significantly delayed, with TTPs of 4.95 s and 4.51 s for GE and SE, respectively. The GE response was able to resolve visual stimuli separated by 250 ms, whereas SE could resolve stimuli 500 ms apart. Apparent-diffusion-coefficient (ADC) compartmentalization of the BOLD signal was applied to restrict the vascular sensitivity of the SE and GE sequences. Limiting the response to voxels with ADCs < 0.8 x 10(-3) mm(2)/s improved the temporal resolving power of GE and SE BOLD to 125 ms and 250 ms, respectively.

Wavelet-based estimation of a semiparametric generalized linear model of fMRI time-series

This paper addresses the problem of detecting significant changes in fMRI time series that are correlated to a stimulus time course. This paper provides a new approach to estimate the parameters of a semiparametric generalized linear model of fMRI time series. The fMRI signal is described as the sum of two effects: a smooth trend and the response to the stimulus. The trend belongs to a subspace spanned by large scale wavelets. The wavelet transform provides an approximation to the Karhunen-Loève transform for the long memory noise and we have developed a scale space regression that permits to carry out the regression in the wavelet domain while omitting the scales that are contaminated by the trend. In order to demonstrate that our approach outperforms the state-of-the art detrending technique, we evaluated our method against a smoothing spline approach. Experiments with simulated data and experimental fMRI data, demonstrate that our approach can infer and remove drifts that cannot be adequately represented with splines.