con.save('{dir}NEMO_{c}_{f}-average-connectivity.h5'.format(
dir=meg_dir, c=cond, f=freq))
# Compute contrast between conditions
contrast = ga_con[exp_conds[0]] - ga_con[exp_conds[1]]
contrast.save(
'{dir}NEMO_pos_vs_ton_contrast_{f}-avg-connectivity.h5'.format(
dir=meg_dir, f=freq))
# Perform a permutation test to only retain connections that are part of a significant bundle.
stats = conpy.cluster_permutation_test(cons['pos'],
cons['ton'],
cluster_threshold=5,
src=fs_src,
n_permutations=1000,
verbose=True,
alpha=0.05,
n_jobs=2,
seed=10,
return_details=True,
max_spread=0.01)
connection_indices, bundles, bundle_ts, bundle_ps, H0 = stats
con_clust = contrast[connection_indices]
# Save some details about the permutation stats to disk
write_hdf5('{dir}NEMO_pos_vs_ton_contrast_{f}-stats.h5'.format(dir=meg_dir,
f=freq),
dict(connection_indices=connection_indices,
bundles=bundles,
bundle_ts=bundle_ts,
bundle_ps=bundle_ps,
ga_con[cond] = con
con.save(fname.ga_con(condition=cond))
# Compute contrast between faces and scrambled pictures
contrast = ga_con[conditions[0]] - ga_con[conditions[1]]
contrast.save(fname.ga_con(condition='contrast'))
# Perform a permutation test to only retain connections that are part of a
# significant bundle.
stats = conpy.cluster_permutation_test(
cons['face'],
cons['scrambled'],
cluster_threshold=5,
src=fsaverage,
n_permutations=1024,
verbose=True,
alpha=0.05,
n_jobs=2,
seed=10,
return_details=True,
max_spread=0.01,
)
connection_indices, bundles, bundle_ts, bundle_ps, H0 = stats
con_clust = contrast[connection_indices]
# Save some details about the permutation stats to disk
write_hdf5(fname.stats,
dict(connection_indices=connection_indices,
bundles=bundles,
bundle_ts=bundle_ts,
bundle_ps=bundle_ps,
]
for subject in subjects:
con_face = conpy.read_connectivity('%s-face-con.h5' % subject)
con_face = con_face.to_original_src(fsaverage)
con_scrambled = conpy.read_connectivity('%s-scrambled-con.h5' % subject)
con_scrambled = con_scrambled.to_original_src(fsaverage)
face.append(con_face)
scrambled.append(con_scrambled)
contrast.append(con_face - con_scrambled) # Create contrast
# Compute the grand-average contrast
contrast = reduce(operator.add, contrast) / len(subjects)
# Perform a permutation test to only retain connections that are part of a
# significant bundle.
connection_indices = conpy.cluster_permutation_test(
face,
scrambled, # The two conditions
cluster_threshold=5, # The initial t-value threshold to form bundles
max_spread=0.01, # Maximum distance (in m) between connections
# that are assigned to the same bundle.
src=fsaverage, # The source space for distance computations
n_permutations=1000, # The number of permutations for estimating
# the distribution of t-values.
alpha=0.05 # The p-value at which to reject the null-hypothesis
)
# Prune the contrast connectivity to only contain connections that are part of
# significant bundles.
contrast = contrast[connection_indices]