Synopsis

Keywords: Neuro, White Matter, relaxometry

This study used myelin water fraction (MWF) and geometric mean T2 time of the intra- and extra-cellular water fraction (IET2) derived from a multi-spin echo sequence with compressed sensing (METRICS)2, as well as T1 mapping derived from MP1RAGE to evaluate the long-term changes in myelination and white matter integrity in youths with neonatal brain injury. We observed significant differences in T1 values and IET2 of the normal appearing white matter, but not in the white matter lesions in a population of young adults with neonatal brain injury compared to controls.

introduction

Many individuals with neonatal brain injury demonstrate diffuse white matter hyperintensities, which may be associated with damage to premyelinating oligodendrocytes1. However, despite the consequences on a child’s global development, the long-term neurodevelopmental sequalae of perinatal white matter injury are yet unclear. Recent developments in MRI have enabled whole brain multicomponent T2 relaxometry in clinically feasible scan times. Thus, this study uses myelin water fraction (MWF) and geometric mean T2 time of the intra- and extra-cellular water fraction (IET2) derived from a multi-spin echo sequence with compressed sensing (METRICS)2, as well as T1 mapping derived from MP1RAGE to evaluate the long-term changes in myelination and white matter integrity in youths with neonatal brain injury.

methods

25 participants (10 neonatal brain injury (mean age: 22.62 years (5.2 S.D.), range 14.70 – 30.36), 15 control (mean age: 24.77 years (5.3 S.D.), range 18.42 – 36.12), gestational age at birth = 26-40 weeks) were scanned on a 3T Philips Ingenia Elition X (Philips Medical Systems, Best, the Netherlands) system using a 32-channel SENSE head coil. T1w, 3D FLAIR, and 56-echo CPMG2 data were acquired on each participant. Lesions (FLAIR hyperintensities) were manually segmented by CMB. Normal appearing white matter (NAWM) was classified as any WM not included in the lesion. The DEcomposition and Component Analysis of Exponential Signals (DECAES) package5 was used to compute voxel-wise T2-distributions and calculate GMT2 and IET2 maps based on the T2 decay curve fit using non-negative least-squares. GMT2, MWF, and IET2 were calculated for each subject in WM lesions, perilesional WM, and NAWM.

results

Within the NAWM, T1 values were significantly higher in the neonatal brain injury group (mean = 835.8 ms, 270 SD) compared to controls (mean = 427 ms, 37.8 SD; t(5.29) = 3.65, p = 0.0133). There was a trend for increased IET2 in the neonatal brain injury group (mean = 0.0731, 0.0025 SD) compared to controls (mean = 0.0714, 0.0012 SD), but this did not reach statistical significant (t(15) = 2.08, p = 0.056. There were no significant differences between groups for the MWF of NAWM. Comparing lesions values in the early brain injury group to the NAWM of controls, there was a significant increase in IET2 (mean lesion= 0.077, 0.008 SD, mean control NAWM = 0.071, 0.001 SD, p = 0.029); however no other comparisons reached statistical significance. Within the early brain injury group, there were no significant differences in MWF, IET2, or T1 values between lesion and NAWM. However, within the early brain injury group there are a number of different underlying etiologies which may contribute to this null finding.

conclusions

These results suggest that despite the presence of FLAIR hyperintensities in regions of neonatal white matter injury, it is possible that neuroplastic mechanisms may enable for degree of recovery depending on the etiology. This study included multiple etiologies of neonatal brain injury, thus the differing physiological mechanisms may have contributed to the null findings in the lesion data. Nonetheless the results suggest that there are indeed long term changes in the normal appearing white matter distant from the visible lesions.

Acknowledgements

This work was supported by R01 EY030877 to CMB

References

1. Volpe, J. J. Brain injury in premature infants: a complex amalgam of destructive and developmental disturbances. Lancet Neurol. 8, 110–124 (2009). 2. Dvorak, A. V. et al. Multi-spin echo T2 relaxation imaging with compressed sensing (METRICS) for rapid myelin water imaging. Magn. Reson. Med. 84, 1264–1279 (2020). 3. Schmidt, P. et al. An automated tool for detection of FLAIR-hyperintense white-matter lesions in Multiple Sclerosis. NeuroImage 59, 3774–3783 (2012). 4. Zhang, Y., Brady, M. & Smith, S. Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm. IEEE Trans. Med. Imaging 20, 45–57 (2001). 5. Doucette, J., Kames, C. & Rauscher, A. DECAES - DEcomposition and Component Analysis of Exponential Signals. Z. Med. Phys. 30, 271–278 (2020).