Impact of hemodynamic effects on diffusion-weighted fMRI signals

In some recent studies, diffusion weighted functional MRI has been proposed to provide contrast immune to vascular changes. Increases in relative signal change during neuronal activation observed under increasing diffusion weighting support the possible diffusion based origin of this contrast. A recent diffusion tensor imaging (DTI) study has also reported the use of Fractional Anisotropy (FA) to track activation in white matter. In this study we aimed to establish if relatively high diffusion weighting (b = 1200 and 1800 s/mm2) eliminates the strong vascular influences brought about by 100% O2 and carbogen (95%O2 + 5% CO2) induced vascular challenges in gray matter (GM) and white matter (WM) of rat brain. We also aimed to characterize the influences of these vascular changes on FA, both in GM and in WM. Our study endorses previous reports that even relatively heavily diffusion weighted data can be significantly influenced by hemodynamic changes. However, this was not only observed in GM, but also in WM. Moreover, our study demonstrates that the estimator used to calculate the relative changes should be carefully chosen in order to avoid biases at low signal-to-noise ratios (SNRs) which accompany increasing diffusion weighting. With the use of robust estimators, we found no increases in relative change with increasing b-value during both vascular challenges. Our data also demonstrate that FA can be significantly influenced by hemodynamics, both in GM and in WM. The observed influence of diffusion weighting direction on relative signal change in GM was shown to be associated with structural differences among various regions. If diffusion based functional contrasts immune to hemodynamics do exist, our results highlight the difficulty in discerning those diffusion changes from accompanying vascular changes.

►We studied the impact of vascular changes on gray and white matter diffusion MR data. ►Hemodynamic responses influenced diffusion weighted data in both brain tissue types. ►DTI parameters (FA and MD) were also significantly influenced by hemodynamics. ►Use of unbiased estimators is critical for quantifying signal changes at low SNR. ►Diffusion based fMRI can be significantly influenced by accompanying hemodynamics.