Subject-specific multi-poroelastic model for exploring the risk factors associated with the early stages of Alzheimer’s disease

Liwei Guo, John C. Vardakis, Toni Lassila, Micaela Mitolo, Nishant Ravikumar, Dean Chou, Matthias Lange, Ali Sarrami-Foroushani, Brett J. Tully, Zeike A. Taylor, Susheel Varma, Annalena Venneri, Alejandro F. Frangi, Yiannis Ventikos

Research output: Contribution to journalArticlepeer-review


There is emerging evidence suggesting that Alzheimer’s disease is a vascular disorder, caused by impaired cerebral perfusion, which may be promoted by cardiovascular risk factors that are strongly influenced by lifestyle. In order to develop an understanding of the exact nature of such a hypothesis, a biomechanical understanding of the influence of lifestyle factors is pursued. An extended poroelastic model of perfused parenchymal tissue coupled with separate workflows concerning subject-specific meshes, permeability tensor maps and cerebral blood flow variability is used. The subject-specific datasets used in the modelling of this paper were collected as part of prospective data collection. Two cases were simulated involving male, non-smokers (control and mild cognitive impairment (MCI) case) during two states of activity (high and low). Results showed a marginally reduced clearance of cerebrospinal fluid (CSF)/interstitial fluid (ISF), elevated parenchymal tissue displacement and CSF/ISF accumulation and drainage in the MCI case. The peak perfusion remained at 8 mm s-1 between the two cases.

Original languageEnglish
Article number20170019
JournalInterface Focus
Issue number1
Publication statusPublished - Feb 6 2018


  • Alzheimer’s disease
  • Cerebral blood flow
  • Cerebrospinal fluid
  • Finite-element method
  • Permeability tensor map
  • Poroelasticity

ASJC Scopus subject areas

  • Biotechnology
  • Biophysics
  • Bioengineering
  • Biochemistry
  • Biomaterials
  • Biomedical Engineering


Dive into the research topics of 'Subject-specific multi-poroelastic model for exploring the risk factors associated with the early stages of Alzheimer’s disease'. Together they form a unique fingerprint.

Cite this