| Application: | 20576, Brain Shape Analysis from very Large Datasets |
| Title: | Biophysics-based statistical learning: Application to heart and brain interactions |
| Size: | 4.0 MB |
| Archived: | 6 Oct 2021 |
| Stability: | Complete |
| Personal: | Contains individual-level data |
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Notes
Heart and brain are linked by pathophysiological and physiological mechanisms sharing several risk factors. However, until now, the lack of databases integrating heart and brain data of the same subjects prevented the study of their relationship in depth. Nowadays, this analysis is possible thanks to initiatives such as UK Biobank. However, most research on large multimodal databases is focused on studying the associations among the available measurements through standard statistical models. Nevertheless, these approaches do not provide insights into the underlying mechanisms and are often hampered by the lack of prior knowledge on the physiological relationships between measurements. For instance, important indices of cardiovascular function, such as the stiffness or the contractility of the cardiac fibers cannot be measured in-vivo. These non-observable parameters that reflect physiological properties can be estimated through the personaliation of cardiovascular mechanistic models. Therefore, we estimated the parameters of a cardiovascular lumped model and identified differences between clinical subgroups. Subjects diagnosed with atrial fibrillation have lower cardiac contractility compared to subjects without any diagnosed cardiovascular disease. Moreover, there are significant differences between the groups in the correlations of brain volumetric features with vascular indicators.Application 20576
Brain Shape Analysis from very Large Datasets
An extensive study of the brain anatomy could lead to powerful biomarkers to detect brain diseases affecting the brain shape. The UK Biobank initiative offers a unique opportunity to build for the first time a truly extensive study of the brain over a significantly large cohort. Our proposal is threefold: (1) to identify possible methodological bottlenecks in processing very large datasets of brain images, (2) to provide a new state-of-the-art statistical atlas across ages and populations, and (3) to further study possible links between brain anatomy and biological criteria, such as age, gender, and other factors. The study of the brain anatomy, with its variation across ages and populations, is key for establishing a precise characterization of normal evolutions in healthy subjects. Any deviation from such established standard is a possible risk indicator, for instance, of the Alzheimer?s disease. Research on building and exploiting statistical atlases from very large datasets potentially improves diagnostics and follow-ups in brain diseases affecting the brain shape. A statistical atlas will be built using the neuro images from the UK Biobank participants. The average shape of the brain, and its variations, will be studied by considering possible links with several biological criteria, such as age, gender, and other factors. Full cohort.
| Lead investigator: | Professor Nicholas Ayache |
| Lead institution: | Inria |
Enabling scientific discoveries that improve human health