Physiological Fluctuations in the White Matter of Children with Sickle Cell Disease

1Physiology and Experimental Medicine, The Hospital for Sick Children, Toronto, ON, Canada, 2Medical Biophysics, University of Toronto, Toronto, ON, Canada, 3Sunnybrook Health Sciences Centre, Toronto, ON, Canada, 4Medical Imaging, University of Toronto, Toronto, ON, Canada

### Synopsis

The pulsatility of the brain has been previously shown to be associated with cerebrovascular dysfunction. Recently, the use resting state BOLD imaging has been proposed to non-invasively assess this pulsatility by calculating the temporal variance in the white matter. This measure is known as physiological fluctuation in the white matter (PFwm). In this study, we compared PFwm acquired in children with sickle cell disease to healthy controls. The results show increased pulsatility in the disease group, providing evidence that this approach has the potential to be a clinically relevant tool in the assessment of cerebrovascular diseases.

### Introduction

Sickle cell disease (SCD) is a genetic hemoglobinopathy affecting oxygen transport throughout the body due to the transformation of red blood cells into a rigid sickled shape. This leads to a cascade of physiological changes, including vascular impairment in the brain. Cerebrovascular dysfunction in SCD may be further exacerbated by arterial stiffness, which is a potential biomarker for cardiovascular mortality [1]. One of the potential consequences associated with poor arterial compliance (stiffening of the vessels) is increased intracranial pulsatility. Stiff arterial walls are unable to act as an elastic buffer to convert arterial pulsatile flow into steady peripheral flow [2]. Because the brain is contained within the fixed skull, temporal changes in blood flow and pressure across the cardiac cycle is transferred into the surrounding tissue [3]. This effect has been observed in studies on small vessel disease and adult dementia, and has been termed physiological fluctuation in the white matter (PFwm), and can serve as a proxy of cerebrovascular dysfunction [4]. Using resting state blood-oxygen level dependent (BOLD) MR imaging the fluctuations can be detected as temporal variations in the white matter (WM) signal, as defined by $PF^2_{wm} = \frac{\sigma^2_{wm} - \sigma^2_{thermal}}{ \mu^2_{wm}}$, where σ2 is the temporal variance and μ is the temporal mean. In the present study, we acquired PFwm data in children with SCD, and compared them to healthy controls. We hypothesize that SCD patients will exhibit greater WM fluctuations reflecting their underlying pathophysiology.

### Materials and Methods

Nine pediatric patients with SCD (5 male, 4 female, age 10-17 years) and 6 age-matched healthy controls were imaged on a clinical 3T MRI scanner (MAGNETOM Tim Trio; Siemens Medical Solutions, Germany) using a 32-channel head coil. Baseline fluctuation data was acquired using a BOLD sequence (TR/TE = 2000/40ms, FOV = 220mm, matrix size = 64×64, slices = 25, slice thickness = 4.5mm, volumes = at least 120). Subjects were instructed to remain awake during the scan. In addition, a T1-weighted image (MPRAGE, voxel = 1 mm isotropic) was acquired for anatomical information. A WM mask was generated by segmenting the T1-weighted data and co-registered into the BOLD space using AFNI. The mask was further eroded by a 4 mm kernel to minimize partial voluming and slices containing the cerebellum were removed. The BOLD data was corrected for motion (MCFLIRT, FSL), and dynamics with excessive motion were discarded. The temporal mean and variance across the time-series in each voxel was computed. Next, we calculated the spatial average of the variance (σ2wm) and mean (μwm) within the regions defined by the WM mask. Thermal noise variance (σ2thermal) was calculated from a user-defined region of interest drawn outside of the brain. Differences in PFWM between groups were calculated using Student's t-test, with significance defined as a p-value < 0.05.

### Results

All subject data used for analysis exhibited head motion of less than 0.6 mm averaged across the BOLD scan. Figure 1 shows representative PFwm maps superimposed onto anatomical slices comparing a SCD and healthy subject. The mean PFwm was significantly higher in children with SCD (0.182 ± 0.096) compared to the control group (0.085 ± 0.019), with a p-value = 0.016. A plot comparing the two groups is provided in Figure 2.

### Discussion

The increased WM pulsatility in SCD patients supports a possible link between PFwm and cerebrovascular dysfunction. This increase may be driven by the combination of anemia-induced vasodilation and reduced nitric oxide bioavailability [5], each contributing to arterial stiffening. Our results show that PFwm has the potential to be a clinically relevant tool in the assessment of SCD and other cerebrovascular diseases.

### Acknowledgements

This work was supported by funding from the Canadian Institutes of Health Research (CIHR) and Canada Research Chairs (CRC).

### References

1. Belizna C, et al. Stroke 2012; 43:1129-30.
2. Bateman GA, et al. Neuroradiology 2008; 50:491-7.
3. Wagshul ME, et al. Fluids and Barriers of the CNS 2011; 8:5.
4. Makedonov I, et al. Neurobiology of Aging 2015; 73(1).
5. Wood KC, et al. Free Radical Biology and Medicine 2008; 44:1506-28.

### Figures

Figure 1. Representative PFwm maps in a sickle cell and healthy subject.

Figure 2. Group mean and standard deviation of PFwm in sickle cell patients and healthy controls.

Proc. Intl. Soc. Mag. Reson. Med. 24 (2016)
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