Hemodynamic effects on cognitive function - Evaluating multi- parametric hemodynamic changes causing cognitive impairments in patients with asymptomatic high-grade internal carotid artery stenosis

• Applicants: Dr. Jens Göttler; Dr. Stephan Kaczmarz
• Subject Area: Clinical Neurology; Neurosurgery and Neuroradiology ; Medical Physics, Biomedical Technology
• Term: since 2024
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 547163214

Internal carotid artery stenosis (ICAS) is a major public health issue and accounts for 10-15% of all ischemic strokes. Furthermore, most patients even with high-grade ICAS are clinically ‘asymptomatic’, i.e., they do not show any signs of transient or permanent cerebral ischemia. This neglects, however, that many of these patients have subtle cognitive impairments and often show a progressive increase up to severe dementia symptoms. At the same time, cerebral hemodynamics were found to be altered in these patients, supposedly due to a reduction of perfusion pressure in the vasculature distal to the stenosis. These hemodynamic alterations can be assessed by a multitude of different cerebral perfusion and oxygenation parameters. Furthermore, preliminary evidence exists for a link between such hemodynamic impairments and cognitive decline. As hemodynamic impairments are potentially reversible by revascularization, reliable tools are urgently needed, which allow clinicians to better select ICAS patients at risk, for more aggressive treatment options. Especially patients without obvious neurological deficits but harmful hemodynamic circumstances could benefit by preventing further brain damage and cognitive decline. The proposed project will develop a tool to evaluate hemodynamic impairments that cause cognitive impairments in patients with asymptomatic high-grade ICAS by integrating novel multi-parametric magnetic resonance imaging (MRI) with careful neuropsychological assessment and machine learning approaches. More specifically, the MRI protocol includes quantitative multi-parametric hemodynamic imaging of cerebrovascular reactivity (CVR), cerebral blood flow (CBF), cerebral blood volume (CBV), oxygen extraction capacity (OEC), capillary transit-time heterogeneity (CTH), oxygen extraction fraction (OEF) and cerebral metabolic rate of oxygen consumption (CMRO2). Since watershed area locations in ICAS patients are vulnerable and individually varying, a non-invasive and fully automated segmentation approach will be employed using deep learning. Neuropsychological assessment will evaluate mild cognitive deficits in these ICAS patients using an established clinical test. Based on these tools, our approach aims at integrating perfusion and oxygenation sensitive MRI by means of machine learning in order to predict cognitive impairment in individual ICAS patients. This represents an important step forward towards the development of clinically applicable multi-parametric imaging-based biomarkers with the aim to guide therapy in individual ICAS patients.

• DFG Programme: Research Grants
• Co-Investigators: Professorin Dr. Christine Preibisch; Privatdozent Dr. Benedikt Wiestler