radhanila / sparse_dense

GLM and MVPA Analysis of the sparse_dense dataset.

MVPA analysis of the sparse-dense Experiment.

This repository contains scripts for the analysis of the behavioral part of the fMRI data using R [1], scripts for GLM based Beta-Series least-squares-separate (LSS) estimation [2] and scripts for a whole-brain searchlight [3] analysis using a support-vector-classifier implemented in Nilearn [4].

Important Notes

• The goal is to keep the analysis dynamic enough so that it can be adapted for future projects
• GLM and MVPA analysis use HTCondor .submit files for parallel computing on a HPC
• A fold-wise permutation scheme needs to be implemented [5] or we use pyMVPA [6] instead

References

[1] Singmann, H., & Kellen, D. (2019). An introduction to mixed models for experimental psychology. In New methods in cognitive psychology (pp. 4-31). Routledge.

[2] Mumford, J. A., Turner, B. O., Ashby, F. G., & Poldrack, R. A. (2012). Deconvolving BOLD activation in event-related designs for multivoxel pattern classification analyses. Neuroimage, 59(3), 2636-2643.

[3] Kriegeskorte, N., Goebel, R., & Bandettini, P. (2006). Information-based functional brain mapping. Proceedings of the National Academy of Sciences, 103(10), 3863-3868.

[4] Abraham, A., Pedregosa, F., Eickenberg, M., Gervais, P., Mueller, A., Kossaifi, J., ... & Varoquaux, G. (2014). Machine learning for neuroimaging with scikit-learn. Frontiers in neuroinformatics, 8, 14.

[5] Etzel, J. A., & Braver, T. S. (2013). MVPA permutation schemes: Permutation testing in the land of cross-validation. In 2013 International Workshop on Pattern Recognition in Neuroimaging (pp. 140-143). IEEE.

[6] Hanke, M., Halchenko, Y. O., Sederberg, P. B., Hanson, S. J., Haxby, J. V., & Pollmann, S. (2009). PyMVPA: A python toolbox for multivariate pattern analysis of fMRI data. Neuroinformatics, 7(1), 37-53.