Abstract

During aging, accumulating cerebral vascular and metabolic dysfunction significantly contributes to cognitive decline and neurodegenerative diseases. Identifying early cerebral biomarkers linked to age-related conditions like Alzheimer’s disease (AD) and coronary artery disease (CAD), and modifiable factors like cardiorespiratory fitness, is crucial for disease understanding and prevention.
This thesis comprises five interrelated studies using multimodal neuroimaging, including dual-calibrated functional magnetic resonance imaging (fMRI), and advanced computational modeling. Manuscript One investigated cognitively healthy older adults with familial AD risk, showing that decreasing cerebrospinal fluid amyloid-beta—a marker of amyloid accumulation—was associated with increased cerebral blood flow (CBF), particularly among individuals with higher cardiovascular risk. Elevated baseline inflammation predicted faster longitudinal declines in CBF within this group, highlighting cardiovascular health as a critical mediator of early cerebrovascular changes.
Manuscript Two extended this investigation to severe vascular impairment in CAD, revealing widespread cerebrovascular and metabolic dysfunction characterized by decreased CBF, cerebrovascular reactivity (CVR), cerebral metabolic rate of oxygen consumption (CMRO₂), and elevated oxygen extraction fraction (OEF). Notably, CVR and OEF were directly associated with cognitive deficits, emphasizing their role as early markers of cerebrovascular compromise.
Manuscripts Three and Four explored how cardiorespiratory fitness (VO₂peak) influences cerebral physiology in healthy aging and CAD. In healthy older adults, higher fitness correlated positively with greater CBF and negatively with OEF, suggesting enhanced oxygen delivery efficiency. Among CAD patients, higher fitness uniquely improved CMRO₂ in addition to CBF and CVR, underscoring fitness’s potential to reverse metabolic dysfunction clinically.
Manuscript Five addressed calibrated fMRI limitations to estimate metabolic biomarkers, including poor arterial spin labeling signal quality and breathing manipulation dependency. It introduced a robust deep learning-based computational model, providing accurate, gas-independent estimates of CBF, CVR, OEF, CMRO₂, and mitochondrial oxygen diffusivity directly from fMRI data, enhancing biomarker reliability and clinical application.
Collectively, these studies reveal the complex interplay between vascular, metabolic, and fitness-related factors influencing cerebral health during aging and cardiovascular disease. Integrating longitudinal data, advanced neuroimaging, and computational modeling, this thesis provides novel insights for early detection and targeted interventions to maintain cognitive function and reduce neurodegenerative risk.