## Level 2
# merge param estimates across all subjects per seed
merge = Node(Merge(dimension='t'), name='merge')
# FSL randomise for higher level analysis
highermodel = Node(Randomise(tfce=True,
raw_stats_imgs=True,
design_mat=group_mat,
tcon=group_con),
name='highermodel')
## Cluster results
# make binary masks of sig clusters
binarize = Node(Binarize(min=0.95, max=1.0),
name='binarize',
iterfield='in_file')
# mask T-map before clustering
mask_tmaps = Node(ApplyMask(), name='mask_tmaps')
# clusterize and extract cluster stats/peaks
clusterize = Node(Cluster(threshold=2.3,
out_index_file='outindex.nii',
out_localmax_txt_file='localmax.txt'),
name='clusterize')
# make pictures if time
# In[ ]:
