def generate_level1_stats_table(zmap, mask,
output_html_path,
p_threshold=.001,
z_threshold=None,
method='fpr',
cluster_th=15,
null_zmax='bonferroni',
null_smax=None,
null_s=None,
nmaxima=4,
cluster_pval=.05,
title=None,
):
"""Function to generate level 1 stats table for a contrast.
Parameters
----------
zmap: image object
z-map data image
mask: image object or string
brain mask defining ROI
output_html_path: string,
path where the output html should be written
p_threshold: float (optional, default .001)
(p-variate) frequentist threshold of the activation image
z_threshold: float (optional, default None)
Threshold that has been applied to Z map (input z_map)
method: string (optional, default 'fpr')
to be chosen as height_control in nipy.labs.statistical_mapping
cluster_th: int (optional, default 15)
cluster size threshold (in # voxels)
null_zmax: string (optional, default 'bonferroni')
parameter for cluster level statistics (?)
null_s: strint (optional, default None)
parameter for cluster level statistics (?)
nmaxima: int (optional, default 4)
number of local maxima reported per supra-threshold cluster
title: string (optional)
title of generated stats table
"""
# Delayed import of nipy for more robustness when it is not present
import nipy.labs.statistical_mapping as sm
# sanity
if isinstance(zmap, basestring):
zmap = nibabel.load(zmap)
if isinstance(mask, basestring):
mask = nibabel.load(mask)
# Compute cluster statistics
nulls = {'zmax': null_zmax, 'smax': null_smax, 's': null_s}
"""
if null_smax is not None:
print "a"
clusters, info = sm.cluster_stats(zmap, mask, height_th=threshold,
nulls=nulls)
clusters = [c for c in clusters if c['cluster_pvalue']<cluster_pval]
else:
print "b"
clusters, info = sm.cluster_stats(zmap, mask, height_th=threshold,
height_control=method.lower(),
cluster_th=cluster_th, nulls=nulls)
"""
# do some sanity checks
if title is None:
title = "Level 1 Statistics"
# clusters, info = sm.cluster_stats(zmap, mask, height_th=p_threshold,
# nulls=nulls, cluster_th=cluster_th,)
clusters, _ = sm.cluster_stats(zmap, mask, height_th=p_threshold,
nulls=nulls, cluster_th=cluster_th,)
if clusters is not None:
clusters = [c for c in clusters if c['cluster_pvalue'] < cluster_pval]
#if clusters == None or info == None:
# print "No results were written for %s" % zmap_file_path
# return
if clusters == None:
clusters = []
# Make HTML page
page = open(output_html_path, mode="w")
page.write("<center>\n")
page.write("<b>%s</b>\n" % title)
page.write("<table border = 1>\n")
page.write("<tr><th colspan=4> Voxel significance </th>\
<th colspan=3> Coordinates in MNI referential</th>\
<th>Cluster Size</th></tr>\n")
page.write("<tr><th>p FWE corr<br>(Bonferroni)</th>\
<th>p FDR corr</th><th>Z</th><th>p uncorr</th>")
page.write("<th> x (mm) </th><th> y (mm) </th><th> z (mm) </th>\
<th>(voxels)</th></tr>\n")
for cluster in clusters:
maxima = cluster['maxima']
size = cluster['size']
for j in range(min(len(maxima), nmaxima)):
temp = ["%.3f" % cluster['fwer_pvalue'][j]]
temp.append("%.3f" % cluster['fdr_pvalue'][j])
temp.append("%.2f" % cluster['zscore'][j])
temp.append("%.3f" % cluster['pvalue'][j])
for it in range(3):
temp.append("%.0f" % maxima[j][it])
if j == 0:
# Main local maximum
temp.append('%i' % size)
page.write('<tr><th align="center">' + '</th>\
<th align="center">'.join(temp) + '</th></tr>')
else:
# Secondary local maxima
page.write('<tr><td align="center">' + '</td>\
<td align="center">'.join(temp) + '</td><td></td></tr>\n')
nclust = len(clusters)
nvox = sum([clusters[k]['size'] for k in range(nclust)])
page.write("</table>\n")
page.write("Threshold Z: %.2f (%s control at %.3f)<br/>\n" \
% (z_threshold, method, p_threshold))
page.write("Cluster level p-value threshold: %s<br/>\n" % cluster_pval)
page.write("Cluster size threshold: %i voxels<br/>\n" % cluster_th)
page.write("Number of voxels: %i<br>\n" % nvox)
page.write("Number of clusters: %i<br>\n" % nclust)
# finish up
page.write("</center>\n")
page.close()
return clusters
group_data = example_data.get_filename(
'neurospin', 'language_babies', 'offset_002.npz')
f = np.load(group_data)
data, vardata, xyz = f['mat'], f['var'], f['xyz']
dX = xyz[0].max() + 1
dY = xyz[1].max() + 1
dZ = xyz[2].max() + 1
aux = np.zeros([dX, dY, dZ])
data_images = []
vardata_images = []
mask_images = []
for i in range(data.shape[0]):
aux[list(xyz)] = data[i]
data_images.append(Image(aux.copy(), np.eye(4)))
aux[list(xyz)] = vardata[i]
vardata_images.append(Image(aux.copy(), np.eye(4)))
aux[list(xyz)] = 1
mask_images.append(aux)
return data_images, vardata_images, mask_images
data_images, vardata_images, mask_images = remake_images()
zimg, mask, nulls = sm.onesample_test(data_images,
None,
mask_images,
'wilcoxon',
permutations=1024,
cluster_forming_th=0.01)
clusters, info = sm.cluster_stats(zimg, mask, 0.01, nulls=nulls)
def get_activations(narps,
hyp,
logfile,
fdr=0.05,
pthresh=0.001,
simulate_noise=False,
cluster_kthresh=10,
use_mean_fdr_thresh=False):
assert fdr is not None or pthresh is not None
region = hyp_regions[hyp]
# load mask, create if necessary
maskimg_file = get_mask_img(narps, region)
maskimg = nibabel.load(maskimg_file)
# load data
zstat_imgs = get_zstat_images(narps, hyp)
# setup masker
MNI_img = os.path.join(os.environ['FSLDIR'],
'data/standard/MNI152_T1_2mm_brain_mask.nii.gz')
masker = nilearn.input_data.NiftiMasker(MNI_img)
# get roi mask
roi_mask = masker.fit_transform(maskimg)[0, :]
mean_fdr_thresh, fdr_thresh = get_mean_fdr_thresh(zstat_imgs, masker,
roi_mask, simulate_noise)
results = pandas.DataFrame({
'$p < %0.3f$, $k > %d$' % (pthresh, cluster_kthresh):
numpy.zeros(len(zstat_imgs))
})
results['FDR'] = 0.0
for i, img in enumerate(zstat_imgs):
z = masker.fit_transform(img)[0, :]
if simulate_noise:
z = numpy.random.randn(z.shape[0])
p = 1 - scipy.stats.norm.cdf(z) # convert Z to p
# compute per-team FDR
results.iloc[i, 1] = numpy.sum(p[roi_mask > 0] < fdr_thresh[i, 0])
# cluster z image
nii = nibabel.load(img)
c = cluster_stats(nii, maskimg, scipy.stats.norm.ppf(1 - pthresh),
'none', cluster_kthresh)
if c[0] is not None:
results.iloc[i, 0] = len(c[0])
# load ALE and consensus results for comparison
meta_results = numpy.zeros(2)
ale_img = os.path.join(narps.dirs.dirs['output'],
'ALE/hypo%d_fdr_thresholded.nii.gz' % hyp)
if os.path.exists(ale_img):
ale_data = masker.fit_transform(ale_img)[0, :]
meta_results[0] = numpy.sum(ale_data[roi_mask > 0])
else:
meta_results[0] = numpy.nan
# consensus not performed for 3 and 4, so use 1/2 instead
hyp_fix = {1: 1, 2: 2, 3: 1, 4: 2, 5: 5, 6: 6, 7: 7, 8: 8, 9: 9}
consensus_img = os.path.join(
narps.dirs.dirs['output'],
'consensus_analysis/hypo%d_1-fdr.nii.gz' % hyp_fix[hyp])
if os.path.exists(ale_img):
consensus_data = masker.fit_transform(consensus_img)[0, :]
meta_results[1] = numpy.sum(consensus_data[roi_mask > 0] > (1 - fdr))
else:
meta_results[1] = numpy.nan
message = '\nHypothesis: %s\n' % hyp
if simulate_noise:
message += 'SIMULATING WITH RANDOM NOISE\n'
message += 'Region (%d voxels): %s\n' % (numpy.sum(roi_mask), region)
message += 'ROI image: %s\n' % maskimg_file
message += '\nProportion teams with nonzero activation:\n'
message += (results > 0).mean(0).to_string() + '\n'
message += 'Activated voxels in ALE map: %d\n' % meta_results[0]
message += 'Activated voxels in consensus map: %d\n' % meta_results[1]
log_to_file(logfile, message)
return (results, mean_fdr_thresh, meta_results, numpy.sum(roi_mask))
group_data = example_data.get_filename(
'neurospin', 'language_babies', 'offset_002.npz')
f = np.load(group_data)
data, vardata, xyz = f['mat'], f['var'], f['xyz']
dX = xyz[0].max() + 1
dY = xyz[1].max() + 1
dZ = xyz[2].max() + 1
aux = np.zeros([dX, dY, dZ])
data_images = []
vardata_images = []
mask_images = []
for i in range(data.shape[0]):
aux[list(xyz)] = data[i]
data_images.append(Image(aux.copy(), np.eye(4)))
aux[list(xyz)] = vardata[i]
vardata_images.append(Image(aux.copy(), np.eye(4)))
aux[list(xyz)] = 1
mask_images.append(aux)
return data_images, vardata_images, mask_images
data_images, vardata_images, mask_images = remake_images()
zimg, mask, nulls = sm.onesample_test(data_images,
None,
mask_images,
'wilcoxon',
permutations=1024,
cluster_forming_th=0.01)
clusters, info = sm.cluster_stats(zimg, mask, 0.01, nulls=nulls)
def generate_level1_stats_table(
zmap,
mask,
output_html_path,
p_threshold=.001,
z_threshold=None,
method='fpr',
cluster_th=15,
null_zmax='bonferroni',
null_smax=None,
null_s=None,
nmaxima=4,
cluster_pval=.05,
title=None,
):
"""Function to generate level 1 stats table for a contrast.
Parameters
----------
zmap: image object
z-map data image
mask: image object or string
brain mask defining ROI
output_html_path: string,
path where the output html should be written
p_threshold: float (optional, default .001)
(p-variate) frequentist threshold of the activation image
z_threshold: float (optional, default None)
Threshold that has been applied to Z map (input z_map)
method: string (optional, default 'fpr')
to be chosen as height_control in nipy.labs.statistical_mapping
cluster_th: int (optional, default 15)
cluster size threshold (in # voxels)
null_zmax: string (optional, default 'bonferroni')
parameter for cluster level statistics (?)
null_s: strint (optional, default None)
parameter for cluster level statistics (?)
nmaxima: int (optional, default 4)
number of local maxima reported per supra-threshold cluster
title: string (optional)
title of generated stats table
"""
# Delayed import of nipy for more robustness when it is not present
import nipy.labs.statistical_mapping as sm
# sanity
if isinstance(zmap, basestring):
zmap = nibabel.load(zmap)
if isinstance(mask, basestring):
mask = nibabel.load(mask)
# Compute cluster statistics
nulls = {'zmax': null_zmax, 'smax': null_smax, 's': null_s}
"""
if null_smax is not None:
print "a"
clusters, info = sm.cluster_stats(zmap, mask, height_th=threshold,
nulls=nulls)
clusters = [c for c in clusters if c['cluster_pvalue']<cluster_pval]
else:
print "b"
clusters, info = sm.cluster_stats(zmap, mask, height_th=threshold,
height_control=method.lower(),
cluster_th=cluster_th, nulls=nulls)
"""
# do some sanity checks
if title is None:
title = "Level 1 Statistics"
# clusters, info = sm.cluster_stats(zmap, mask, height_th=p_threshold,
# nulls=nulls, cluster_th=cluster_th,)
clusters, _ = sm.cluster_stats(
zmap,
mask,
height_th=p_threshold,
nulls=nulls,
cluster_th=cluster_th,
)
if clusters is not None:
clusters = [c for c in clusters if c['cluster_pvalue'] < cluster_pval]
#if clusters == None or info == None:
# print "No results were written for %s" % zmap_file_path
# return
if clusters == None:
clusters = []
# Make HTML page
page = open(output_html_path, mode="w")
page.write("<center>\n")
page.write("<b>%s</b>\n" % title)
page.write("<table border = 1>\n")
page.write("<tr><th colspan=4> Voxel significance </th>\
<th colspan=3> Coordinates in MNI referential</th>\
<th>Cluster Size</th></tr>\n")
page.write("<tr><th>p FWE corr<br>(Bonferroni)</th>\
<th>p FDR corr</th><th>Z</th><th>p uncorr</th>")
page.write("<th> x (mm) </th><th> y (mm) </th><th> z (mm) </th>\
<th>(voxels)</th></tr>\n")
for cluster in clusters:
maxima = cluster['maxima']
size = cluster['size']
for j in range(min(len(maxima), nmaxima)):
temp = ["%.3f" % cluster['fwer_pvalue'][j]]
temp.append("%.3f" % cluster['fdr_pvalue'][j])
temp.append("%.2f" % cluster['zscore'][j])
temp.append("%.3f" % cluster['pvalue'][j])
for it in range(3):
temp.append("%.0f" % maxima[j][it])
if j == 0:
# Main local maximum
temp.append('%i' % size)
page.write('<tr><th align="center">' + '</th>\
<th align="center">'.join(temp) + '</th></tr>')
else:
# Secondary local maxima
page.write('<tr><td align="center">' + '</td>\
<td align="center">'.join(temp) + '</td><td></td></tr>\n')
nclust = len(clusters)
nvox = sum([clusters[k]['size'] for k in range(nclust)])
page.write("</table>\n")
page.write("Threshold Z: %.2f (%s control at %.3f)<br/>\n" \
% (z_threshold, method, p_threshold))
page.write("Cluster level p-value threshold: %s<br/>\n" % cluster_pval)
page.write("Cluster size threshold: %i voxels<br/>\n" % cluster_th)
page.write("Number of voxels: %i<br>\n" % nvox)
page.write("Number of clusters: %i<br>\n" % nclust)
# finish up
page.write("</center>\n")
page.close()
return clusters