Development of a New Method to Measure Glymphatic System Dynamics

Context

Unlike other organs the brain has no lymphatic vessels. In mice, clearance of potentially neurotoxic catabolites is performed through the glymphatic system. Homeostasis between the cerebrospinal fluid (CSF) and the interstitial fluid in the space between the cells of the brain parenchyma drains several metabolites, including beta-amyloid, whose deposition in the cerebral extracellular space has been related to the pathogenesis and progression of Alzheimer’s disease. Very recently, it has been shown that the clearance through the cerebral glymphatic system follows sleep-wake dynamics in mice, with a relative increase of the interstitial space by 60% during sleep compared with wake, resulting in two times faster clearance of beta-amyloid during sleep compared to the waking state (Xie et al., 2013).

Perivascular spaces, also known as Virchow-Robin spaces, are extensions of the interstitial space that follow the course of the cerebral penetrating vessels. Enlarged perivascular spaces (EPVS) are visible on MRI, appearing as linear (when imaged parallel to the vessel path) or ovoid small (≤ 3 mm) areas of signal intensity similar to the CSF (when imaged perpendicular to the vessel path). Associations of EPVS with aging, stroke, small vessel disease and cognitive impairment have been established (Maclullich et al., 2004; Potter et al., 2013). However, the presence of EPVS is not considered per se pathological, and physiological fluctuations of these structures have not been extensively studied in healthy volunteers. Since the perivascular space volume reflects the volume of the interstitial space, quantitative assessment of perivascular sleep-wake dynamics has the potential to be developed as an MRI tool to investigate the capacity for catabolite clearance through the glymphatic system in humans in vivo (Ramirez et al., 2016).


Objective

To develop a software tool to analyze within-subject variability in MRI signal reflecting changes in perivascular space volume between wake and sleep, to support and extend to humans the finding of an increase of interstitial space volume during sleep in mice.


Methodology

MRI images are being acquired within an ongoing study of young (18-23 year old) healthy volunteer. The study subjects undergo a baseline MRI scan during wake state, followed by a quasi-continuous overnight scan during sleep. The MRI dataset to be analyzed consists in a time series of T1-MPRAGE images. Image analysis includes:

  •   optimization of co-registration techniques to improve alignment across the image series;
  •   implementation and optimization of an existing algorithm to measure mean shift in signal intensity over time across the longitudinal series of MRI images for detection of T1 changes between wake and sleep (Mure, et al., 2015);
  •   adaptation of existing algorithms for detection of brain lesions (Duan et al., 2008; Van Leemput, et al., 2001) to measure EPVS volume


    Profile

    Programming skills in C++, Matlab, and python (javascript is a plus)

    Image processing background. Knowledge of ITK, VTK is a plus.


   Contact

    Researchers

    Address

        1249 Boylston St, Boston 02215, Massachusetts, USA


References

  1.  Duan, Y., Hildenbrand, P. G., Sampat, M. P., Tate, D. F., Csapo, I., Moraal, B., … Guttmann, C. R. G. (2008). Segmentation of subtraction images for the measurement of lesion change in multiple sclerosis. AJNR. American Journal of Neuroradiology, 29(2), 340–6.
  2. Mure, S., Grenier, T., Meier, D. S., Guttmann, C. R. G., & Benoit-Cattin, H. (2015). Unsupervised spatio-temporal filtering of image sequences. A mean-shift specification. Pattern Recognition Letters, 68(P1), 48–55.
  3. Ramirez, J., Berezuk, C., McNeely, A. A., Gao, F., McLaurin, J., & Black, S. E. (2016). Imaging the Perivascular Space as a Potential Biomarker of Neurovascular and Neurodegenerative Diseases. Cellular and Molecular Neurobiology.
  4. Van Leemput, K., Maes, F., Vandermeulen, D., Colchester,  a, & Suetens, P. (2001). Automated segmentation of multiple sclerosis lesions by model outlier detection. IEEE Transactions on Medical Imaging, 20(8), 677–88.
  5. Xie, L., Kang, H., Xu, Q., Chen, M. J., Liao, Y., Thiyagarajan, M., … Nedergaard, M. (2013). Sleep drives metabolite clearance from the adult brain. Science (New York, N.Y.), 342(6156), 373–7.
See also: Project