BrainPack: A suite of advanced statistical techniques for multi-subject, multi-group neuroimaging data analysis

National Science Foundation Project (NSF IIS-1607919, 2016-2020)

1. Wang, L.-Y., Chung, J., Park, C., Choi, H., Rodrigue, A., Pierce, J., Clementz, B. A., and McDowell, J. E. (2019), Regularized aggregation of statistical parametric maps, Human Brain Mapping, 40, 65-79. 

Simulation code: document, R code

Real data analysis code: example for combining subjects or voxels in one coordinate

demo R code, demo R function, demo data


2. Samaddar, Jackson, B. S., A., Helms, C. J., Lazar, N. A., Park, C., and McDowell, J. E., A group comparison in fMRI data using a semiparametric model under shape invariance, In preparation.

Simulation code:

Real data analysis code:


3. Samaddar, A., Lazar, N. A., Park, C., Jackson, B. S., and McDowell, J. E., BrainPack: Comparison of groups based on cluster analysis of fMRI data, In preparation.

Simulation code:

Real data analysis code:

4. Wang, L.-Y., Park, C., Yeon, K., and Choi, H. (2017), Tracking concept drift using a constrained penalized regression combiner, Computational Statistics and Data Analysis, 108, 52-69.

5. Moon, C., Giansiracusa, N. and Lazar, N. (2018), Persistence terrace for topological inference of point cloud data, Journal of Computational and Graphical Statistics, 27, 576-586.

6. Son, S., Park, C., and Jeon, Y., Sparse graphical models via calibrated concave convex procedure with application to fMRI data, Submitted.