I used machine learning to predict cardiovascular risk factor ie. blood pressure variability based on brain data as subjects performed stressful tasks inside fMRI scanner. People with a tendency to exhibit exaggerated blood pressure reactions to psychological stressors are at risk for hypertension, adverse clinical cardiovascular events, and premature cardiovascular mortality. Exaggerated blood pressure reactions to psychological stressors may be determined, in part, by a “brain phenotype” that is characterized by reliable neural activity changes in brain areas that regulate cardiovascular physiology during stressful experiences. The clinical implication is that brain phenotypes determined by neuroimaging methodologies could be used in translational efforts to better monitor, predict, and possibly reduce stress-related risk for cardiovascular disease.
Delivered brain phenotype for peripheral blood pressure reactivity. [Github]
Developed PCA-LASSO toolbox to predict cardiovascular risk based on brain activity.
Co-authored top-tier journal article which received media coverage. [Reuters]
Techniques: Matlab, FSL, Freesurfer, gnu parallel, SPSS, mediation, PCA, LASSO
