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Human intracranial pulsatility during the cardiac cycle: a computational modelling framework.

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Summary

This study presents a computational model of brain biomechanics, simulating intracranial dynamics and validating it against clinical indicators of brain pulsatility. The model accurately depicts the complex interplay of cerebrospinal fluid (CSF) flow and brain tissue movement.

Keywords:
Cerebral blood flowCerebrospinal fluidFinite element modelInterstitial fluidIntracranial pressureIntracranial pulsatilityPoroelasticity

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Area of Science:

  • Biomechanics
  • Computational Neuroscience
  • Medical Imaging

Background:

  • Advanced medical imaging and computational power enable high-fidelity modeling of brain biomechanics.
  • The brain's environment involves complex interactions between tissue, blood, cerebrospinal fluid (CSF), and interstitial fluid (ISF).
  • Pulsatile cerebral blood flow drives dynamic interactions within the intracranial environment.

Purpose of the Study:

  • To design and validate a computational platform for simulating intracranial dynamics.
  • To assess model validity using clinically relevant indicators of brain pulsatility.
  • To investigate the interplay between tissue motion and ISF/CSF flow.

Main Methods:

  • Developed finite element models for coupled CSF flow and brain tissue motion.
  • Utilized detailed 3D geometry from MRI scans, including brain tissue, ventricles, and cranial subarachnoid space (SAS).
  • Modeled brain parenchyma as a poroelastic medium and CSF flow as viscous fluid movement, driven by pulsatile blood flow.

Main Results:

  • Predicted complex interplay between CSF spaces and poroelastic parenchyma affecting intracranial pressure (ICP), CSF flow, and tissue displacement.
  • Observed dominant temporal ICP variations with minor spatial changes.
  • Found substantial CSF flow in ventricles and cranial-spinal spaces, some SAS flow, and minor ISF velocities, alongside a funnel-shaped parenchymal deformation.

Conclusions:

  • The developed model accurately represents the coupled dynamics of ICP, CSF flow, and brain tissue movement.
  • Model predictions align well with clinical observations.
  • The platform provides a robust tool for investigating intracranial dynamics under physiological and pathophysiological conditions.