  Flow of cerebrospinal fluid in perivascular spaces is a key mechanism
underlying brain transport and clearance. In this paper, we present a
mathematical and numerical formalism for reduced models of pulsatile viscous
fluid flow in networks of generalized annular cylinders. We apply this
framework to study cerebrospinal fluid flow in perivascular spaces induced by
pressure differences, cardiac pulse wave-induced vascular wall motion and
vasomotion. The reduced models provide approximations of the cross-section
average pressure and cross-section flux, both defined over the topologically
one-dimensional centerlines of the network geometry. Comparing the full and
reduced model predictions, we find that the reduced models capture pulsatile
flow characteristics and provide accurate pressure and flux predictions across
the range of idealized and image-based scenarios investigated at a fraction of
the computational cost of the corresponding full models. The framework
presented thus provides a robust and effective computational approach for large
scale in-silico studies of pulsatile perivascular fluid flow and transport.
