output_names=['out_path'],
function=plot_stat_maps),
name='plot_contrasts',
iterfield=['thresh'])
# input: plot data with set of different thresholds:
plot_contrasts.inputs.thresh = [None, 1, 2, 3]
# set expected thread and memory usage for the node:
plot_contrasts.interface.num_threads = 1
plot_contrasts.interface.mem_gb = 0.2
# ======================================================================
# DEFINE NODE: THRESHOLD
# ======================================================================
# function: Topological FDR thresholding based on cluster extent/size.
# Smoothness is estimated from GLM residuals but is assumed to be the
# same for all of the voxels.
thresh = Node(Threshold(), name="thresh")
# input: whether to use FWE (Bonferroni) correction for initial threshold
# (a boolean, nipype default value: True):
thresh.inputs.use_fwe_correction = False
# input: whether to use FDR over cluster extent probabilities (boolean)
thresh.inputs.use_topo_fdr = True
# input: value for initial thresholding (defining clusters):
thresh.inputs.height_threshold = 0.05
# input: is the cluster forming threshold a stat value or p-value?
# ('p-value' or 'stat', nipype default value: p-value):
thresh.inputs.height_threshold_type = 'p-value'
# input: which contrast in the SPM.mat to use (an integer):
thresh.inputs.contrast_index = 1
# input: p threshold on FDR corrected cluster size probabilities (float):
thresh.inputs.extent_fdr_p_threshold = 0.05
# input: minimum cluster size in voxels (an integer, default = 0):
