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White matter microstructure changes as a function of time-of-day: a biomarker of accumulating sleep pressure?
Irene Voldsbekk1, Daniel Roelfs1, Atle Bjørnerud1, Inge Groote1, and Ivan Maximov2

1Department of Diagnostic Physics, Oslo University Hospital, Oslo, Norway, 2Norwegian Centre for Mental Disorders Research, Oslo University Hospital, Oslo, Norway

Synopsis

There is an increasing awareness of time-of-day (TOD) effects in MRI. The underlying causes of these fluctuations may be related to diurnal physiological variations, reflecting the processes that govern transition between sleep and wake. We probed TOD effects in a prospective manner, scanning 47 healthy individuals in the morning and the same evening using a five-shell diffusion-weighted imaging protocol at 3T, where a number of important Zeitgeber signals were rigorously controlled. We found significant changes in an number of derived DWI parameters (FA, MD, AWF, MK), and speculate that these changes are pointing towards underlying mechanisms of sleep-wake homeostasis.

Introduction

The role of diurnal fluctuations in the human brain derived by magnetic resonance imaging (MRI) is receiving increasing attention. However, little is known about the physiological changes underlying these fluctuations. Transition between stages of sleep and wake is regulated by circadian and homeostatic processes1. Contemporary theories of sleep regulation emphasise accumulation of learning-induced increases in synaptic strength2 and metabolic waste3 during wakefulness, which sleep then serves to renormalise. Diurnal fluctuations in MRI measures may thus reflect sleep regulating processes, and as such represent MRI-derived biomarkers of sleep pressure. Recent evidence suggests that structural measures by diffusion tensor imaging (DTI)4 change as a function of time-of-day (TOD), both in the white matter (WM) skeleton5, the whole cerebrum6 and at the interface of grey matter and cerebrospinal fluid (CSF)7. However, these studies lack rigorous experimental control between scans and only Elvsåshagen and colleagues5 assessed changes spanning more than a few hours. In contrast, the current study addressed the impact of TOD from morning to evening in a prospective controlled within-subject experimental design in which participants were continuously monitored from the first to the second scan. Moreover, the current study included estimations of non-Gaussian diffusion metrics obtained from diffusion kurtosis imaging (DKI) 8 and WM tract integrity9 in addition to classic DTI-derived parameters, in order to probe underlying axonal properties.

Methods

Participants were 47 healthy adults (30 women) who underwent diffusion-weighted (DWI) MRI in the morning and evening of the same day. The imaging protocol consisted of a multi-shell full-brain multiband-accelerated 2 mm isotropic acquisition with b [0, 500, 1000, 2000, 3000]; directions [5, 12, 30, 40, 50]. Between scans, they stayed at the hospital premises under constant supervision and followed a standardised protocol of physical activity, food and fluid intake. Diffusion data were preprocessed in accordance with the following steps: noise correction10, Gibbs ringing corrections11, topup/eddy application12, B1 field normalisation and spatial smoothing with Gaussian kernet 1mm3. All diffusion metrics were then estimated using the Veraart and colleagues approach13. MRI images were analysed with voxel-wise tract based spatial statistics14, threshold free cluster enhancement15 and permutation testing in FSL16,17, correcting for multiple comparisons and thresholded at p < .05.

Results

A full day of wakefulness was found to be associated with widespread increases in fractional anisotropy (FA), mean kurtosis (MK) and axonal water fraction (AWF), in addition to widespread decreases in mean diffusivity (MD). See figures 1-4. These changes were adjusted for age, gender and chronotype. Chronotype did not show a significant relationship with changes in any of the diffusion parameters. Moreover, changes spanned most of the WM skeleton, suggesting a global effect.

Discussion

These findings indicate a temporal component to DWI-derived structural measures, in which white matter microstructure fluctuate as a function of TOD. These fluctuations may reflect physiological changes underlying accumulating sleep pressure, as the fluctuation is prevalent as a global change in WM microstructure. Importantly, we found an increase in estimations of WM integrity and specifically in axonal density, which suggest wakefulness may accommodate activity-dependent structural plasticity in WM. Specifically, this could reflect processes related to the activity-dependent accumulation of synapse strength2 and metabolic waste3. However, caution must be exerted in interpreting specific underlying biology on the basis of DTI and DKI18. Although the increases in the estimation of axonal density from AWF is a promising preliminary finding, introducing more complex diffusion models will afford more detailed investigation of this relationship. Despite this, these findings highlight the potential of MRI derived measures to probe sleep regulating processes in the human brain. Moreover, these findings have important implications for experimental considerations, as diurnal fluctuations would confound any design in which one experimental group is consistently scanned at a different TOD than another experimental group.

Conclusion

The WM microstructure of the human brain is not static and fluctuate as a function of TOD. The fluctuations detected by DWI parameters may represent biomarkers of accumulating sleep pressure.

Acknowledgements

We would like to thank the great team who helped with data collection.

References

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Figures

Figure 1. Changes observed in FA from morning to evening. A) Clusters with significant increases in FA. B) Mean increase across significant FA clusters for different chronotypes.

Figure 2. Changes observed in MK from morning to evening. A) Clusters with significant increases in MK. B) Mean increase across significant MK clusters for different chronotypes.

Figure 3. Changes observed in AWF from morning to evening. A) Clusters with significant increases in AWF. B) Mean increase across significant AWF clusters for different chronotypes.

Figure 4. Changes observed in MD from morning to evening. A) Clusters with significant decreases in MD. B) Mean increase across significant MD clusters for different chronotypes.

Proc. Intl. Soc. Mag. Reson. Med. 27 (2019)
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