Brain Aging and 4D Flow
We are interested in examining changes in cardiac hemodynamics with age and the relationship to cerebral blood flow and distribution. Hemodynamic information from velocity encoded MR imaging can be combined with quantitative brain imaging to determine the prognostic significance for structural and microstructural brain changes, including atrophy and volume loss in specific regions of interest. This approach can be used to evaluate subtle, clinically silent changes that occur in the brain in association with factors such as hypertension and cerebral small vessel disease and to determine the impact on brain network organization. This approach has considerable potential to yield new insights concerning how vascular factors influence individual differences in neurological outcome in older adults and the shared role in age-related neurological disorders.
Node-specific path length for 90 different brain regions in Normo/Pre-hypertensive vs. Hypertensive groups. These results reflect brain network reorganization in response to hypertension.
Clustering results for brain network hubs in Normo/Pre-hypertensives and Hypertensives. Differences in clustering patterns for critical subcortical brain network hubs, including thalamus, putamen and hippocampus, can be observed in hypertensives (evidenced by different colors than in normo/pre-hypertensives). These regions play central roles as relay centers and in memory.
Investigators: Ann Ragin (PhD), Susanne Schnell (PhD), Guixiang Ma (MS), Philip Yu (PhD), Can Wu (PhD), Sameer Ansari (MD), Ali Shabani (MD), Michael Markl (PhD)
Publications, Presentations:
Ragin A, Wu C, Ma G, Ansari SA, Markl M, Schnell S. Aortic flow and cerebral hemodynamics in age-related brain volume loss. To be presented, International Society of Magnetic Resonance in Medicine, Paris, France, 2018.
Ma G, Cao B, Yu PS, Ragin A. Hypertension induces changes in brain network organization. To be presented, International Society of Magnetic Resonance in Medicine, Paris, France, 2018.
Ma, G., Lu, C. T., He, L., Yu, P. S., & Ragin, A. B. (2017). Multi-view graph embedding with hub detection for brain network analysis. In Proceedings – 17th IEEE International Conference on Data Mining, ICDM 2017 (Vol. 2017-November, pp. 967-972). Institute of Electrical and Electronics Engineers Inc.. DOI: 10.1109/ICDM.2017.123
Ma, G., He, L., Lu, C. T., Shao, W., Yu, P. S., Leow, A. D., & Ragin, A. B. (2017). Multi-view clustering with graph embedding for connectome analysis. In CIKM 2017 – Proceedings of the 2017 ACM Conference on Information and Knowledge Management (Vol. Part F131841, pp. 127-136). Association for Computing Machinery. DOI: 10.1145/3132847.3132909
Ma, G., He, L., Cao, B., Zhang, J., Yu, P. S., & Ragin, A. B. (2016). Multi-graph clustering based on interior-node topology with applications to brain networks. In Machine Learning and Knowledge Discovery in Databases – European Conference, ECML PKDD 2016, Proceedings (Vol. 9851 LNAI, pp. 476-492). Springer Verlag. DOI: 10.1007/978-3-319-46128-1_30