The intracranial Windkessel implies arteriovenous pulsatile coupling increased by venous resistances

Giuseppe Baselli, Maria Marcella Laganà

Research output: Contribution to journalArticlepeer-review

Abstract

Various detailed models of cerebral circulation have been proposed, recently fostered by the hypothesized relationship between extracranial venous drainage impairment and neurodegenerative diseases. However, some basic model is missing, analogous to the 2-element Windkessel (WK) of the systemic circulation. This theoretical study focuses, in the simplest way, on the dependence of intracranial venous pressure (VP) pulsatility on the intracranial arterial 2-element WK and the venous resistance RV. The WK compliance is shown to exert an arteriovenous capacitive coupling (AV-CC), agumented by RV increments. The WK was estimated based on the intracranial arterial pressure (AP) and flow (ФICA) waves of an open database of 3325 virtual subjects. A normal RV was estimated imposing a mean VP of 10 mmHg, doubled to mimic hindered extracranial veins. The AP to VP transfer function showed: i) a gain almost proportional to RV; ii) a zero slightly below heart rate (HR) corresponding to the arterial WK pole; iii) a mid-frequency derivative band up to the AV-CC pole, the frequency of which was almost inversely proportional to RV; iv) full coupling at high frequencies, yet above the pulse harmonic content, at normal HR. In conclusion, besides the well-known effect of venous hindering on the mean VP, the results of this model support the hypothesis that abnormal pulsatility of cerebral veins may play a significant role in cerebrovascular imbalance and related neurodegeneration.

Original languageEnglish
Article number103092
JournalBiomedical Signal Processing and Control
Volume71
DOIs
Publication statusPublished - Jan 2022

Keywords

  • Cerebrovascular circulation
  • Lumped-parameter model
  • Neurodegeneration
  • Rehabilitation
  • Venous insufficiency
  • Venous pulsatility

ASJC Scopus subject areas

  • Signal Processing
  • Health Informatics

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