9 Modelling of Cerebrospinal Fluid Flow by Computational Fluid Dynamics
233
wall was considered rigid. The authors reported that fluid transport occurs in the
direction of arterial wave propagation up to a specific pressure gradient determined
by pulse wave velocity and amplitude. Since the wavelengths investigated in this
model were smaller than physiologically expected, the authors followed up with
a corrected model where the arterial wall was approximated to be uniform [135].
Wang and Olbricht [136] performed a theoretical analysis and reported a lower
maximal adverse pressure gradient for the same conditions. In 2010, Bilston and
co-workers expanded their original CFD model to take into account a time-varying
pressure gradient along the perivascular domain, reflecting the pulsation of CSF in
the subarachnoid space and showing that the relative timing of the arterial pulse
wave and subarachnoid pressure wave could influence perivascular fluid flow [137].
Elliott et al. continued the investigations of wave propagation in an axisymmetric
fluid-structure interaction model [102], while Lloyd et al. used CFD modelling to
predict that Chiari I malformation can change the magnitude and timing of the
subarachnoid pressure-time profiles and lead to greater influx of CSF into the spinal
cord [135].
While the spinal perivascular spaces have been studied primarily within the
context of syrinx formation, their cerebral counterparts have drawn interest because
of their contribution to CNS solute transport. In 2006, Schley et al. formulated
a numerical model to evaluate solute transport in the periarterial space [138].
Almost a decade later, Asgari et al. assessed potential net flow between periarterial
and perivenous fluid spaces [139], and Sharp et al. investigated the possibility of
periartieral amyloid-β clearance by peristalsis [140]. These studies were followed
by a number of computational investigations of flow through perivascular and
interstitial fluid spaces and associated solute transport [141–146]. Because of
the importance of metabolite clearance in the development of neurodegenerative
diseases and limitations of experimental tools to study such cerebral solute transport,
numerical models have drawn attention beyond the computational community. It
can be expected that computational studies of perivascular fluid flow, in particular
in conjunction with CSF dynamics in the subarachnoid space, will continue to gain
traction and that CFD will take on an increasingly important role therein.
9.4 Conclusion
While most of the early CFD models of CSF dynamics focused on method
development, now that the methodology has matured, an increasing number of
studies address specific questions of physiology and pathophysiology and test
hypotheses that cannot be addressed by experimental techniques alone. New insights
provided by these studies include the characterization of the CSF flow regime
(predominantly laminar with transient, local occurrences of turbulence, particularly
in certain disease states), assessment of the contribution of ependymal cilia on CSF
flow (dominates near ventricular wall dynamics) and identification of dispersion and
steady streaming as important mechanisms for solute transport in the perivascular
233
wall was considered rigid. The authors reported that fluid transport occurs in the
direction of arterial wave propagation up to a specific pressure gradient determined
by pulse wave velocity and amplitude. Since the wavelengths investigated in this
model were smaller than physiologically expected, the authors followed up with
a corrected model where the arterial wall was approximated to be uniform [135].
Wang and Olbricht [136] performed a theoretical analysis and reported a lower
maximal adverse pressure gradient for the same conditions. In 2010, Bilston and
co-workers expanded their original CFD model to take into account a time-varying
pressure gradient along the perivascular domain, reflecting the pulsation of CSF in
the subarachnoid space and showing that the relative timing of the arterial pulse
wave and subarachnoid pressure wave could influence perivascular fluid flow [137].
Elliott et al. continued the investigations of wave propagation in an axisymmetric
fluid-structure interaction model [102], while Lloyd et al. used CFD modelling to
predict that Chiari I malformation can change the magnitude and timing of the
subarachnoid pressure-time profiles and lead to greater influx of CSF into the spinal
cord [135].
While the spinal perivascular spaces have been studied primarily within the
context of syrinx formation, their cerebral counterparts have drawn interest because
of their contribution to CNS solute transport. In 2006, Schley et al. formulated
a numerical model to evaluate solute transport in the periarterial space [138].
Almost a decade later, Asgari et al. assessed potential net flow between periarterial
and perivenous fluid spaces [139], and Sharp et al. investigated the possibility of
periartieral amyloid-β clearance by peristalsis [140]. These studies were followed
by a number of computational investigations of flow through perivascular and
interstitial fluid spaces and associated solute transport [141–146]. Because of
the importance of metabolite clearance in the development of neurodegenerative
diseases and limitations of experimental tools to study such cerebral solute transport,
numerical models have drawn attention beyond the computational community. It
can be expected that computational studies of perivascular fluid flow, in particular
in conjunction with CSF dynamics in the subarachnoid space, will continue to gain
traction and that CFD will take on an increasingly important role therein.
9.4 Conclusion
While most of the early CFD models of CSF dynamics focused on method
development, now that the methodology has matured, an increasing number of
studies address specific questions of physiology and pathophysiology and test
hypotheses that cannot be addressed by experimental techniques alone. New insights
provided by these studies include the characterization of the CSF flow regime
(predominantly laminar with transient, local occurrences of turbulence, particularly
in certain disease states), assessment of the contribution of ependymal cilia on CSF
flow (dominates near ventricular wall dynamics) and identification of dispersion and
steady streaming as important mechanisms for solute transport in the perivascular