Using Biobank Data to Explore Biological Mediators of Education-Related Protection against Dementia
PI: Xiao Liu (Biomedical Engineering)
Co-PI: Liying Luo (Sociology and Criminology)
Extensive research has shown that higher educational attainment is associated with a lower risk of Alzheimer’s disease and related dementias (ADRD). However, the mechanisms underlying this protective effect remain poorly understood—particularly the neural pathways through which education influences dementia risk. Emerging studies have identified a highly organized infraslow (<0.1 Hz) global brain activity across species that is closely linked to the brain’s cholinergic and memory functions, AD pathologies, and associated risk factors. Notably, this global brain activity also differs across groups with different educational levels, suggesting its potential role in linking education and ADRD risk. However, it remains unclear whether, and how, this global brain activity, often measured as global mean BOLD (gBOLD) signal of resting-state functional MRI (fMRI), mediates the effect of education on ADRD risk. Recent large-scale neuroimaging databases, such as the UK Biobank, offer an unprecedented opportunity to investigate this question. The proposed research aims to examine whether and how infra-slow global brain activity mediates the protective effect of education against ADRD. We hypothesize that higher education promotes infra-slow global activity, particularly within high-order cognitive networks, thereby reducing ADRD risk. Using restingstate fMRI data from over 60,000 participants in the UK Biobank, we will compute multiple metrics of gBOLD activity—including amplitude, topology, and spatiotemporal dynamics—and test their mediating role in the relationship between education and ADRD risk, as estimated from longitudinal cognitive outcomes. By investigating infra-slow global brain activity as a potential neural mediator, the study offers a novel biological framework for explaining the cognitive resilience associated with higher education. Leveraging large-scale neuroimaging data from the UK Biobank, the findings could lead to new biomarkers and intervention targets that support brain health across the lifespan, especially in populations at higher risk for ADRD.
List of specific areas of expertise or skills:
The student should have experience with neuroimaging data—especially fMRI—along with a solid understanding of basic statistical methods and proficiency in cloud-based Python programming. Weekly research meetings are required to report progress and discuss the project.
List of specific objectives:
Developing a set of metrics to quantify the global brain activity using resting-state fMRI and then sharing associated tools/code with the neuroimaging community. Key findings will be presented at major neuroimaging and/or ADRD conferences, and submitted as a journal article. The preliminary data will support a grant proposal to the NIA of the NIH, likely the funding calls related to ADRD.
Medium to long-term goal:
To secure an external funding to continue and expand research mining large-scale neuroimaging databases to investigate the role of global brain activity in ADRD.
Connection to ICDS’s mission:
We will use large-scale computing on a big biomedical dataset to better understand how education affects dementia risk.
Recent and/or planned engagement with ICDS:
As a ICDS co-hire, the PI has been, and will continue to be, actively engaged in various ICDS activities, including committee service, symposiums, retreats, and luncheons. Both the coPI and the junior researcher will actively participate in future ICDS activities. The project team will also seek opportunities to present this project in ICDS events, such as ICDS symposium and luncheons.