‘The ischemic fingerprint’ : A predictive model to grade tissue viability and reversibility of ischemic brain injury

Summary

Ischemic brain diseases ranging from small-vessel-disease to vascular-dementia represent a major socio-economic burden via death, disability and costs. Directly assessing the mechanisms of ischemic brain injury (IBI) can impact strategies for personalized treatment, prognosis and follow-up[1]. However, current imaging techniques are limited to providing indirect information on tissue status and/or anatomical evidence secondary to the root causes of IBI. They cannot predict the reversibility or progression of IBI, nor are they sensitive to changes in normal-appearing tissue.
I will address these shortcomings with a predictive model, ‘The ischemic fingerprint’ of the brain tissue, based on novel, non-invasive markers of treatment responsiveness, prognosis and follow-up. I will apply computer-controlled O2 breathing patterns that ‘label’ human blood via modulations in blood oxygenation properties. Physiological responses resulting from this label will be characterized by linking high-resolution hemodynamic and metabolic imaging parameters obtained using an ultra-high field(7-tesla) MRI-scanner.
INNOVATION: The ischemic fingerprint will provide a novel technology to characterize the viability of brain tissue based on cerebral auto-regulatory status and the metabolic consequences of (ir)reversible tissue damage. I will simulate (in-vivo) physiological conditions of IBI in the brain tissue of healthy subjects by safely reducing arterial hemoglobin saturation using mild hypoxic breathing patterns. I will create a predictive model based on hemodynamic and metabolic imaging parameters that I will test in proof-of-concept studies in patient groups displaying different manifestations of IBI. Here, I will instead deploy hyperoxic breathing patterns (re-saturating hemoglobin in ischemic tissue) and relate inverse signal changes.
This follows-up on my PhD-work where I visualized hemodynamic parameters using an 7-tesla MR-scanner with computer-controlled CO2 breathing patterns; moreover, my recent postdoc work exploring techniques for spatial mapping of brain metabolites. My vision is to combine my interdisciplinary knowledge to open a new window to human brain function with direct clinical impact.

Details

Project number

VI.Veni.194.056

Main applicant

Dr. A. Bhogal

Affiliated with

Universiteit Utrecht, Universitair Medisch Centrum Utrecht

Duration

01/10/2019 to 30/09/2022