Dear Jeff,

The script is using BETA maps and calculates the thresholds and contrasts from these maps. The significance thresholding is using a parametric voxel-wise FDR approach as supplied by CONN (which slightly differs from how SPM computes FDR values), as there is unfortunately no other way to calculate either cluster-wise thresholding nor non-parametric thresholding at the 1st-level, as both are technically ill-defined at the moment.

Here is an excerpt:
«Thresholding techniques for single subject fMRI are more complicated than for multi subject fMRI, as the fMRI time series contain auto correlation (Woolrich et al., 2001). To be able to perform a permutation test on single subject fMRI data, the auto correlations have to be removed prior to the resampling (Locascio et al., 1997; Bullmore et al., 2001; Friman and Westin, 2005), in order to not violate the exchangeability criterion. Single subject fMRI is further complicated by the fact that the spatial smoothing changes the auto correlation structure of the data. This problem is more obvious for CCA based fMRI analysis, where several filters are applied to the fMRI volumes (Friman et al., 2003). The only solution to always have null data with the same properties, is to perform the spatial smoothing in each permutation, which significantly increases the processing time. This problem was recently solved, by doing random permutation tests on the GPU (Eklund et al., 2011a, 2012).»
From: Anders Eklund, Mats Andersson, Camilla Josephson, Magnus Johannesson and Hans Knutsson, Does Parametric fMRI Analysis with SPM Yield Valid Results? - An Empirical Study of 1484 Rest Datasets, 2012, NeuroImage.

However, Adolf et al devised a simple algorithmic scheme to calculate non-parametric permutations on 1st-level fMRI, accounting for the temporal autocorrelation. The strategy is to do a blockwise permutation of temporal volumes, in other words simply split the resting-state scans and then permute to derive the null distribution. This strategy is implemented in the SPM toolbox StabMultip (and maybe also in FSL PALM?). For more info, see: Adolf, D., Weston, S., Baecke, S., Luchtmann, M., Bernarding, J., & Kropf, S. (2014). Increasing the reliability of data analysis of functional magnetic resonance imaging by applying a new blockwise permutation method. Frontiers in neuroinformatics, 8.

However, I never tried this approach, and I am not sure how it would work in practice. To try this approach, you would need to enable saving the denoised bold timeseries in CONN, to then use them as the input for StabMultip. If you try this approach, I would be very interested in hearing how it went :-)

Hope this helps,
Best regards,
Stephen Karl Larroque
GIGA-Consciousness, University of Liège, F.R.S.-F.N.R.S.