| Title: | A functional connectome signature of blood pressure in >30 000 participants from the UK biobank. |
| Journal: | Cardiovascular Research |
| Published: | 25 Jul 2022 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/35875865/ |
| DOI: | https://doi.org/10.1093/cvr/cvac116 |
| URL: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10262183 |
| Citations: | 10 (10 in last 2 years) as of 8 Aug 2024 |
Publication 12293
WARNING: the interactive features of this website use CSS3, which your browser does not support. To use the full features of this website, please update your browser.
Abstract
AIMS: Elevated blood pressure (BP) is a prevalent modifiable risk factor for cardiovascular diseases and contributes to cognitive decline in late life. Despite the fact that functional changes may precede irreversible structural damage and emerge in an ongoing manner, studies have been predominantly informed by brain structure and group-level inferences. Here, we aim to delineate neurobiological correlates of BP at an individual level using machine learning and functional connectivity.</p>
METHODS AND RESULTS: Based on whole-brain functional connectivity from the UK Biobank, we built a machine learning model to identify neural representations for individuals' past (∼8.9 years before scanning, N = 35 882), current (N = 31 367), and future (∼2.4 years follow-up, N = 3 138) BP levels within a repeated cross-validation framework. We examined the impact of multiple potential covariates, as well as assessed these models' generalizability across various contexts.The predictive models achieved significant correlations between predicted and actual systolic/diastolic BP and pulse pressure while controlling for multiple confounders. Predictions for participants not on antihypertensive medication were more accurate than for currently medicated patients. Moreover, the models demonstrated robust generalizability across contexts in terms of ethnicities, imaging centres, medication status, participant visits, gender, age, and body mass index. The identified connectivity patterns primarily involved the cerebellum, prefrontal, anterior insula, anterior cingulate cortex, supramarginal gyrus, and precuneus, which are key regions of the central autonomic network, and involved in cognition processing and susceptible to neurodegeneration in Alzheimer's disease. Results also showed more involvement of default mode and frontoparietal networks in predicting future BP levels and in medicated participants.</p>
CONCLUSION: This study, based on the largest neuroimaging sample currently available and using machine learning, identifies brain signatures underlying BP, providing evidence for meaningful BP-associated neural representations in connectivity profiles.</p>
7 Keywords
- Biological Specimen Banks
- Blood Pressure
- Brain
- Connectome
- Humans
- Magnetic Resonance Imaging
- United Kingdom
7 Authors
- Rongtao Jiang
- Vince D Calhoun
- Stephanie Noble
- Jing Sui
- Qinghao Liang
- Shile Qi
- Dustin Scheinost
1 Application
| Application ID | Title |
|---|---|
| 34175 | Identify biomarkers for distinguishing different mental disorders using brain images and their associations with genetic risk |
Enabling scientific discoveries that improve human health