The mechanisms behind perivascular fluid flow

PLoS One. 2020 Dec 29;15(12):e0244442. doi: 10.1371/journal.pone.0244442. eCollection 2020.

Abstract

Flow of cerebrospinal fluid (CSF) in perivascular spaces (PVS) is one of the key concepts involved in theories concerning clearance from the brain. Experimental studies have demonstrated both net and oscillatory movement of microspheres in PVS (Mestre et al. (2018), Bedussi et al. (2018)). The oscillatory particle movement has a clear cardiac component, while the mechanisms involved in net movement remain disputed. Using computational fluid dynamics, we computed the CSF velocity and pressure in a PVS surrounding a cerebral artery subject to different forces, representing arterial wall expansion, systemic CSF pressure changes and rigid motions of the artery. The arterial wall expansion generated velocity amplitudes of 60-260 μm/s, which is in the upper range of previously observed values. In the absence of a static pressure gradient, predicted net flow velocities were small (<0.5 μm/s), though reaching up to 7 μm/s for non-physiological PVS lengths. In realistic geometries, a static systemic pressure increase of physiologically plausible magnitude was sufficient to induce net flow velocities of 20-30 μm/s. Moreover, rigid motions of the artery added to the complexity of flow patterns in the PVS. Our study demonstrates that the combination of arterial wall expansion, rigid motions and a static CSF pressure gradient generates net and oscillatory PVS flow, quantitatively comparable with experimental findings. The static CSF pressure gradient required for net flow is small, suggesting that its origin is yet to be determined.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Animals
  • Cerebrospinal Fluid / physiology*
  • Computer Simulation
  • Glymphatic System / physiology*
  • Humans
  • Mice
  • Models, Cardiovascular*
  • Pulsatile Flow / physiology

Grants and funding

CDC and MER are supported by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme under grant agreement 714892, and by the NOTUR grant NN9316K. KAM is supported by the Research Council of Norway under grant agreement 301013. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.