MRI-based cerebrovascular reactivity using transfer function analysis reveals temporal group differences between...

Cerebrovascular reactivity (CVR) measures the ability of cerebral blood vessels to change their diameter and, hence, their capacity to regulate regional blood flow in the brain. High resolution quantitative maps of CVR can be produced using blood-oxygen level-dependent (BOLD) magnetic resonance imaging (MRI) in combination with a carbon dioxide stimulus, and these maps have become a useful tool in the clinical evaluation of cerebrovascular disorders. However, conventional CVR analysis does not fully characterize the BOLD response to a stimulus as certain regions of the brain are slower to react to the stimulus than others, especially in disease. Transfer function analysis (TFA) is an alternative technique that can account for dynamic temporal relations between signals and has recently been adapted for CVR computation. We investigated the application of TFA in data on children with sickle cell disease (SCD) and healthy controls, and compared them to results derived from conventional CVR analysis.

Data from 62 pediatric patients with SCD and 34 age-matched healthy controls were processed using conventional CVR analysis and TFA. BOLD data were acquired on a 3 Tesla MRI scanner while a carbon dioxide stimulus was quantified by sampling the end-tidal partial pressures of each exhaled breath. In addition, T1 weighted structural imaging was performed to identify grey and white matter regions for analysis. The TFA method generated maps representing both the relative magnitude change of the BOLD signal in response to the stimulus (Gain), as well as the BOLD signal speed of response (Phase) for each subject. These were compared to CVR maps calculated from conventional analysis. The effect of applying TFA on data from SCD patients versus controls was also examined.

The Gain measures derived from TFA were significantly higher than CVR values based on conventional analysis in both SCD patients and healthy controls, but the difference was greater in the SCD data. Moreover, while these differences were uniform across the grey and white matter regions of controls, they were greater in white matter than grey matter in the SCD group. Phase was also shown to be significantly correlated with the amount that TFA increases CVR estimates in both the grey and white matter.

We demonstrated that conventional CVR analysis underestimates vessel reactivity and this effect is more prominent in patients with SCD. By using TFA, the resulting Gain and Phase measures more accurately characterize the BOLD response as it accounts for the temporal dynamics responsible for the CVR underestimation. We suggest that the additional information offered through TFA can provide insight into the mechanisms underlying CVR compromise in cerebrovascular diseases.