Connecting The Nodes: Leveraging Whole-Brain Networks To Characterize Brain Health Trajectories
Description: This proposal focuses on the characterization of subtle alterations in white matter integrity that occur silently and precede clinical events such as stroke or dementia. Traditional neuroimaging markers—such as white matter hyperintensity volume and diffusion tensor imaging metrics—are lesion-centric, focusing on quantifying alterations by combining focal, voxel-level changes. However, recent research indicates that neurological disorders are less focal than previously believed, shifting our understanding toward distributed, network-level disruptions. This paradigm shift has not yet been applied to the study of white matter alterations in asymptomatic individuals. We propose to bring a network-centric approach to this phenotype by utilizing metrics derived from structural whole-brain connectivity (WBC) mapping. WBC metrics assess the integrity of the brain's network as a whole and have been established and validated in several disorders, including stroke, Alzheimer's disease, and depression, demonstrating their value for early detection and prognosis. Interventions aimed at preventing disease onset or progression in stroke and dementia are most effective when initiated at the earliest stages. By shifting our focus from a lesion-centric to a network-centric approach, we could enable earlier detection of brain health decline, leading to better prevention and outcomes in high-risk individuals. Additionally, WBC metrics could serve as accessible and continuous proxies for the risk of stroke and dementia, aiding in the evaluation of prevention strategies aimed at preserving brain health as we age. If we can show that WBC metrics are linked to the main determinants of brain health (Aim 1) and can detect subtle changes in white matter integrity before symptom onset (Aim 2), they could significantly enhance the characterization of early brain health decline. To achieve these aims, we will derive WBC metrics for more than 100,000 asymptomatic participants from the UK Biobank, the world’s largest neuroimaging study. Furthermore, we will develop and publicly release an end-to-end tool for deriving WBC metrics from raw MRI images (Aim 3), thus facilitating broader adoption of these metrics. In summary, by allowing earlier implementation of risk reduction strategies and opening avenues to increase efficiency in primary prevention trials, WBC metrics could help delay or even prevent the onset of stroke and dementia, thereby contributing to longer and healthier lives.
Funder: American Heart Association
Amount: $231,000
Funding type: fellowship
Funder scheme: Career Development Award
Start date: 2026-04-01
End date: 2029-03-31
Investigators: Cyprien Rivier, Cyprien Rivier
Institution: Yale University
Funded outputs count: 0
Funder award ID: 26CDA1590981
OpenAlex ID: G7400737185