pySPFM 0.0.1b22

A python package for Paradigm Free Mapping (3dPFM and 3dMEPFM).

pySPFM

The Python version of AFNI's 3dPFM and 3dMEPFM with some extra features like the addition of a spatial regularization similar to the one used by Total Activation.

References

• Caballero-Gaudes, C., Moia, S., Panwar, P., Bandettini, P. A., & Gonzalez-Castillo, J. (2019). A deconvolution algorithm for multi-echo functional MRI: Multi-echo Sparse Paradigm Free Mapping. NeuroImage, 202, 116081–116081. https://doi.org/10.1016/j.neuroimage.2019.116081
• Caballero Gaudes, C., Petridou, N., Francis, S. T., Dryden, I. L., & Gowland, P. A. (2013). Paradigm free mapping with sparse regression automatically detects single-trial functional magnetic resonance imaging blood oxygenation level dependent responses. Human Brain Mapping. https://doi.org/10.1002/hbm.21452
• Gaudes, C. C., Ville, D. V. D., Petridou, N., Lazeyras, F., & Gowland, P. (2011). Paradigm-free mapping with morphological component analysis: Getting most out of fMRI data. Wavelets and Sparsity XIV, 8138, 81381K. https://doi.org/10.1117/12.893920
• Karahanoǧlu, F. I., Caballero-Gaudes, C., Lazeyras, F., & Van De Ville, D. (2013). Total activation: FMRI deconvolution through spatio-temporal regularization. NeuroImage. https://doi.org/10.1016/j.neuroimage.2013.01.067
• Uruñuela, E., Bolton, T. A. W., Van De Ville, D., & Caballero-Gaudes, C. (2021). Hemodynamic Deconvolution Demystified: Sparsity-Driven Regularization at Work. ArXiv:2107.12026 [q-Bio]. http://arxiv.org/abs/2107.12026