MRI-based cerebrovascular reactivity using transfer function analysis reveals temporal group differences between patients with sickle cell disease and healthy controls

Abstract

Objectives: 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.

Materials and methods: 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.

Results: 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.

Conclusions: 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.

Keywords: BOLD MRI; Cerebrovascular reactivity; Hypercapnia; Sickle cell disease; Temporal lag; Transfer function analysis.

Fig. 1

a) The targeted end-tidal values of the CVR protocol shown in grey, and the corresponding sampled PetCO₂ and PetO₂ waveforms for a single subject are overlaid in red and blue, respectively. b) Examples of the BOLD response to CO₂ step change for an SCD patient and a healthy control subject. The BOLD signal was averaged over the entire grey matter (black line) and white matter (grey line) and plotted over time. A low pass filter was performed on the data to reduce signal fluctuations due to background and physiological noise. The blue shaded region represents the periods of normocapnia, and the orange shaded region indicates the administration of the hypercapnic stimulus. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

Fig. 2

Representative slices from a CVR_Conv, CVR_Gain, and Phase map for a) a 11 year old female with SCD and b) a healthy 12 year old female control subject.

Fig. 3

Scatter plot showing the non-linear relation when comparing the difference between WM and GM. ΔPhase is calculated as the mean Phase difference between the WM and GM of each subject. The GM/WM ratio of the CVR increase due to TFA (CVR_Gain/CVR_Conv) is plotted along the x-axis.

Fig. 4

Plot of mean CVR_Gain versus CVR_Conv in the a) GM and b) WM. Subject data points are identified as either Patients (■) or Controls (◊). Linear trend lines have been added in black to show differences in slope between the Patient and Control group.

Fig. 5

Scatter plots demonstrating the relation between Phase and the CVR_Gain/CVR_Conv ratio in the a) GM and b) WM. Subject data points are identified as either Patients (■) or Controls (◊). In both plots, the effect of TFA on CVR estimates is linearly correlated with the Phase lag detected by TFA.