Researchers have made important discoveries about how disruptions in brain function caused by cerebrovascular disease (CeVD) relate to the pathology of Alzheimer’s disease (AD), influencing neurodegeneration and cognitive abilities in older individuals.
A recent study conducted by Associate Professor Juan Helen Zhou, who leads the Centre for Translational Magnetic Resonance Research at the Yong Loo Lin School of Medicine, National University of Singapore (NUS Medicine), has revealed a specific pattern of brain connectivity that is associated with various indicators of CeVD. This pattern contributes significantly to cognitive decline and neurodegeneration when considered alongside AD. The findings emphasize the significant role of CeVD as a global disruptor of brain connectivity, which shifts our understanding of its contribution to dementia.
CeVD is frequently found in individuals with AD and has been a crucial focus in aging and dementia research. This term encompasses a range of conditions that impair the brain’s blood vessels and flow, including stroke, cerebral atherosclerosis (which occurs when arteries in the brain narrow or harden due to plaque buildup), and small vessel disease that affects the tiny blood vessels. These conditions can harm the brain by obstructing the oxygen and nutrients essential for normal function.
The study, published in Alzheimer’s & Dementia: The Journal of the Alzheimer’s Association, analyzed the brain function of 529 older adults with various degrees of cognitive impairment, from those with normal cognitive function to those diagnosed with AD. By looking at how CeVD markers and patterns of brain activity correlate, researchers identified a distinct connectivity pattern in the brain, which was significantly associated with elevated levels of four CeVD indicators found in brain scans. A crucial result of the study was the differential impact of p-tau181, a blood biomarker for AD, in relation to the CeVD’s connectivity pattern on cognitive decline and brain shrinkage. While both elements contributed to the longitudinal decline in cognitive abilities and brain volume, the study did not find any evidence of them working together in a synergistic manner, suggesting that they may operate through separate pathways in causing neurodegeneration.
A/Prof Zhou stated, “We found that a CeVD-related brain network phenotype, combined with a key Alzheimer’s disease blood biomarker, can provide significant insights into the future development of cognitive decline and neurodegeneration. Our results emphasize the potential of brain connectivity-related markers in tracking cognitive decline, particularly for those at risk of dementia, and highlight the necessity of incorporating neuroimaging and blood biomarker data to gain a better understanding of the mechanisms of these overlapping diseases.”
Dr. Joanna Su Xian Chong, a senior research fellow and the study’s first author from A/Prof Zhou’s team, mentioned, “This pattern illustrates how multiple cerebrovascular disease markers can collectively affect brain function on a broad scale. Notably, the combination of this CeVD-related pattern with plasma p-tau181, a marker for Alzheimer’s, had separate and additive impacts on long-term results. Together, they were linked to cognitive decline and increased brain atrophy from the start and over time, but they did not interact to intensify each other’s effects.” Both A/Prof Zhou and Dr. Chong are also involved in the Centre for Sleep and Cognition and the Healthy Longevity & Human Potential Translational Research Programmes at NUS Medicine.
Looking ahead, the team plans to investigate how the brain communication pattern associated with CeVD is influenced by the severity, cause, and location of CeVD indicators as the disease progresses. They also intend to explore how this pattern interacts with various AD markers and contributes to brain degeneration and cognitive decline across several domains. Additionally, they hope to assess whether these brain network characteristics can serve as reliable biomarkers for tracking both current and future cognitive decline, especially in individuals at risk for dementia. These insights could lead to more accurate predictions than standard imaging techniques and help in the early identification of long-term cognitive outcomes. Ultimately, their goal is to deepen the understanding of the brain mechanisms involved in CeVD and AD, paving the way for advanced imaging tools for early detection and ongoing disease monitoring.
