def label_surf(pval, colorbar_lim, smooth_iter, colormap, bfp_path='.'):
lsurf = readdfs(os.path.join(bfp_path, 'supp_data/bci32kleft.dfs'))
rsurf = readdfs(os.path.join(bfp_path, 'supp_data/bci32kright.dfs'))
num_vert = lsurf.vertices.shape[0]
lsurf.attributes = sp.zeros((lsurf.vertices.shape[0]))
rsurf.attributes = sp.zeros((rsurf.vertices.shape[0]))
if VTK_INSTALLED:
#smooth surfaces
lsurf = smooth_patch(lsurf, iterations=smooth_iter)
rsurf = smooth_patch(rsurf, iterations=smooth_iter)
else:
print('VTK is not installed, surface will not be smoothed')
# write on surface attributes
lsurf.attributes = pval.squeeze()
lsurf.attributes = lsurf.attributes[:num_vert]
rsurf.attributes = pval.squeeze()
rsurf.attributes = rsurf.attributes[num_vert:2 * num_vert]
lsurf = patch_color_attrib(lsurf, clim=colorbar_lim, cmap=colormap)
rsurf = patch_color_attrib(rsurf, clim=colorbar_lim, cmap=colormap)
return lsurf, rsurf
plt.colorbar()
plt.savefig('dist_motor_before.pdf', dpi=300)
plt.show()
plt.figure()
plt.imshow(sp.absolute(diffafter), aspect='auto', clim=(0, .1))
plt.colorbar()
plt.savefig('dist_motor_after.pdf', dpi=300)
plt.show()
#diffafter = gaussian_filter(diffafter, [2, 0])
nV = len(dfs_right_sm.vertices)
dfs_right_sm.attributes = np.sum(frest * fmotor, axis=0)
dfs_right_sm.attributes = dfs_right_sm.attributes[nV:]
fname1 = 'rest_after_1.png'
fname2 = 'rest_after_2.png'
dfs_right_sm = patch_color_attrib(dfs_right_sm, clim=[0.8, 1])
view_patch_vtk(dfs_right_sm,
azimuth=90,
elevation=180,
roll=90,
outfile=fname1,
show=0)
view_patch_vtk(dfs_right_sm,
azimuth=-90,
elevation=180,
roll=-90,
outfile=fname2,
show=0)
for ind in sp.arange(frest.shape[0]):
dfs_right_sm.attributes = sp.absolute(diffafter[ind, (nV):])
sub_conn_0 = sub_conn_0 / (np.std(sub_conn_0, axis=1) + 1e-16)[:, None]
for ind in range(1, nSub):
sub_conn = sp.corrcoef(sub_data[:, :, ind] + 1e-16)
sub_conn = sub_conn - sp.mean(sub_conn, axis=1)[:, None]
sub_conn = sub_conn / (np.std(sub_conn, axis=1) + 1e-16)[:, None]
dist_all_conn[cc_msk] += sp.mean((sub_conn_0 - sub_conn)**2.0, axis=(1))
print ind,
dist_all_conn = dist_all_conn / nSub
var_all = sp.zeros((sub_data.shape[0], sub_data.shape[1]))
avg_sub_data = sp.mean(sub_data, axis=2)
# azimuth=-90,elevation=-180, roll=-90,
dfs_left_sm = patch_color_attrib(dfs_left_sm, dist_all_conn, clim=[0, 1])
view_patch_vtk(dfs_left_sm,
azimuth=-90,
elevation=-180,
roll=-90,
outfile='dist_conn_view1_1sub_left.png',
show=0)
view_patch_vtk(dfs_left_sm,
azimuth=90,
elevation=180,
roll=90,
outfile='dist_conn_view2_1sub_left.png',
show=0)
sp.savez('conn_pairwise_dist.npz', dist_all_conn)
sub_mean_pc = sp.mean(d, axis=1)
sub_mean_pc = sub_mean_pc - sp.mean(sub_mean_pc)
sub_mean_pc = sub_mean_pc / (sp.std(sub_mean_pc))
rho = np.dot(ref_mean_pc, sub1) / ref_mean_pc.shape[0]
rho[~np.isfinite(rho)] = 0
simil_mtx = sp.pi / 2.0 + sp.arcsin(rho)
# simil_mtx=0.3*sp.ones(rho.shape)
#SC = SpectralClustering(n_clusters=nClusters, affinity='precomputed')
#labs_all = SC.fit_predict(simil_mtx)+1
#rho = smooth_surf_function(dfs_left_sm, rho)
dfs_left_sm.attributes = rho
dfs_left_sm = patch_color_attrib(dfs_left_sm, rho, clim=[-1, 1])
view_patch_vtk(dfs_left_sm,
azimuth=90,
elevation=180,
roll=90,
outfile='sub1to1_view1_pc.png',
show=1)
view_patch_vtk(dfs_left_sm,
azimuth=-90,
elevation=-180,
roll=-90,
outfile='sub1to1_view2_pc.png',
show=1)
rho = np.dot(sub_mean_pc, sub2) / sub_mean_pc.shape[0]
rho[~np.isfinite(rho)] = 0
p_dir, sub, sub + '.rfMRI_REST2_LR.\
reduce3.ftdata.NLM_11N_hvar_25.mat'))
data = data['ftdata_NLM']
sub2L, _, _ = normalizeData(data[~LR_flag, :].T)
sub2R, _, _ = normalizeData(data[LR_flag, :].T)
_, R = brainSync(X=sub1L, Y=sub2L)
avgCorrL += sp.sum(sub1L * sp.dot(R, sub2L), axis=0)
avgCorrR += sp.sum(sub1R * sp.dot(R, sub2R), axis=0)
nSub += 1
print nSub,
avgCorrL = avgCorrL / nSub
avgCorrR = avgCorrR / nSub
# plot correlations in right hemisphere
dfs_right_sm = patch_color_attrib(dfs_right_sm, avgCorrR, clim=[0, 1])
view_patch_vtk(dfs_right_sm,
azimuth=-90,
elevation=-180,
roll=-90,
outfile='corrLR_right1.png',
show=0)
view_patch_vtk(dfs_right_sm,
azimuth=90,
elevation=180,
roll=90,
outfile='corr_LR_right2.png',
show=0)
# Plot correlations in left hemisphere
dfs_left_sm = patch_color_attrib(dfs_left_sm, avgCorrL, clim=[0, 1])
ls = readdfs(
os.path.join(p_dir_ref, 'reference', ref + '.aparc.\
a2009s.32k_fs.reduce3.very_smooth.right.dfs'))
lsurf = ls
#lind = np.where(ls.labels > -10)[0]
lsurf.attributes = np.zeros((lsurf.vertices.shape[0]))
#lsurf.attributes = X[150,:lsurf.vertices.shape[0]] #
nVert = lsurf.vertices.shape[0]
diffafter = Xtsk - Xnew
lsurf.attributes = np.sum((diffafter)**2, axis=0)
lsurf.attributes = lsurf.attributes[nVert:]
#lsurf.attributes = smooth_surf_function(lsurf, lsurf.attributes)#, a1=1.1, a2=1.1)
lsurf = patch_color_attrib(lsurf, clim=[1, 2])
view_patch_vtk(lsurf,
azimuth=90,
elevation=180,
roll=90,
outfile=fname1,
show=0)
view_patch_vtk(lsurf,
azimuth=-90,
elevation=180,
roll=-90,
outfile=fname2,
show=0)
#%%
for ind in np.arange(Xtsk.shape[0]):
for iter1 in sp.arange(1000):
perm1 = np.random.permutation(temp.shape[1])
d2 = temp[:, perm1]
d2, R = brainSync(X=d1, Y=d2)
null_corr[:, iter1] = sp.sum(d1 * d2, axis=0)
print iter1,
d2, R = brainSync(X=d1, Y=temp)
scorr = sp.sum(d1 * d2, axis=0)
c = scorr[:, None] < null_corr
pval = sp.mean(c, axis=1)
dfs_left_sm = patch_color_attrib(dfs_left_sm, 1 - pval, clim=[0.95, 1])
view_patch_vtk(dfs_left_sm,
azimuth=-90,
elevation=-180,
roll=-90,
outfile='pval_perm1.png',
show=0)
view_patch_vtk(dfs_left_sm,
azimuth=90,
elevation=180,
roll=90,
outfile='pval_perm2.png',
show=0)
fmri_sub22 = loadmat('/deneb_disk/studyforrest/sub-02-run2\
/fmri_tnlm_5_reduce3_v2.mat') # h5py.File(fname1);
dfs_ref = readdfs(
os.path.join(
p_dir_ref, 'reference', ref + '.aparc\
.a2009s.32k_fs.reduce3.smooth.' + hemi + '.dfs'))
sub1seg1 = normdata(fmri_sub11['func_' + hemi][:, :420])
sub1seg2 = normdata(fmri_sub12['func_' + hemi][:, :420])
sub2seg1 = normdata(fmri_sub21['func_' + hemi][:, :420])
sub2seg2 = normdata(fmri_sub22['func_' + hemi][:, :420])
rho_before = sp.sum(sub1seg1 * sub1seg2, axis=1) / sub1seg1.shape[1]
dfs_ref = patch_color_attrib(dfs_ref, rho_before, clim=[0, .7])
view_patch_vtk(dfs_ref,
azimuth=90,
elevation=180,
roll=90,
outfile='before2_seg1to2_1.png')
view_patch_vtk(dfs_ref,
azimuth=-90,
elevation=180,
roll=-90,
outfile='before2_seg1to2_2.png')
_, Rot12 = rot_sub_data(ref=sub2seg2, sub=sub1seg1)
sub1seg1rot = sp.dot(sub1seg1, Rot12.T)
#%% Read surfaces for visualization
lsurf = readdfs('/home/ajoshi/coding_ground/bfp/supp_data/bci32kleft.dfs')
rsurf = readdfs('/home/ajoshi/coding_ground/bfp/supp_data/bci32kright.dfs')
lsurf.attributes = np.zeros((lsurf.vertices.shape[0]))
rsurf.attributes = np.zeros((rsurf.vertices.shape[0]))
smooth_surf_
nVert = lsurf.vertices.shape[0]
#%% Visualization of normal diff from the atlas
lsurf.attributes = np.sqrt(np.sum((diff), axis=0))
lsurf.attributes = lsurf.attributes[:nVert] / 15
rsurf.attributes = np.sqrt(np.sum((diff), axis=0))
rsurf.attributes = rsurf.attributes[nVert:2 * nVert] / 15
lsurf = patch_color_attrib(lsurf, clim=[0.1, .3])
rsurf = patch_color_attrib(rsurf, clim=[0.1, .3])
view_patch_vtk(lsurf,
azimuth=-90,
elevation=180,
roll=-90,
outfile='l1normal.png',
show=1)
view_patch_vtk(rsurf,
azimuth=-90,
elevation=180,
roll=-90,
outfile='r1normal.png',
show=1)
freq1[(bci_labs > 0) & (freq1 == 0)] = \
sp.amin(freq1[(bci_labs_orig > 0) & (freq1 != 0)])
freq[(bci_bst.labels == roino * 10)] += freq1[(bci_bst.labels == roino *
10)]
bci_bst.labels += sp.uint16(bci_labs)
freq[freq == 0] = 1
bci_bst.attributes = freq
bci_bst = patch_color_labels(bci_bst, freq=freq, cmap='Paired')
# bci_bst = smooth_patch(bci_bst, iterations=90, relaxation=10.8)
view_patch_vtk(bci_bst, show=1)
####writedfs('/home/ajoshi/data/BCI-DNI_brain_atlas/BCI-DNI_brain.left.\
####mid.cortex_refined_labs_uncorr.dfs', bci_bst)
bci_bst = patch_color_attrib(bci_bst, bci_bst.attributes)
view_patch_vtk(bci_bst, show=1)
bci_bst = patch_color_labels(bci_bst, freq=freq, cmap='Paired')
view_patch_vtk(bci_bst, show=1)
####writedfs('/home/ajoshi/data/BCI-DNI_brain_atlas/BCI-DNI_brain.left.\
####mid.cortex_refined_labs_mod_freq_uncorr.dfs', bci_bst)
bci_labs = reduce3_to_bci_lh(bci_bst.labels)
bci_freq = reduce3_to_bci_lh(bci_bst.attributes)
bci_bst = readdfs('/big_disk/ajoshi/coding_ground/hbci_atlas/BCI-DNI_brain_\
atlas_refined_4_11_2017/BCI-DNI_brain.left.mid.cortex.dfs')
bci_bst_sm = readdfs('/big_disk/ajoshi/data/BCI-DNI_brain_atlas/BCI-DNI_brain.\
left.mid.cortex_smooth10.dfs')
dist_all_orig += sp.mean(
(sub_data_orig1[:, :, ind] - sub_data_orig2[:, :, ind])**2.0, axis=(1))
sub_data2[:, :, ind], _ = rot_sub_data(ref=sub_data1[:, :, ind],
sub=sub_data2[:, :, ind])
dist_all_rot += sp.mean((sub_data1[:, :, ind] - sub_data2[:, :, ind])**2.0,
axis=(1))
print ind,
dist_all_rot = dist_all_rot / (nSub)
dist_all_orig = dist_all_orig / (nSub)
var_all = sp.zeros((sub_data1.shape[0], sub_data2.shape[1]))
avg_sub_data = sp.mean(sub_data1, axis=2)
dfs_right_sm = patch_color_attrib(dfs_right_sm, (2 - dist_all_orig) / 2.0,
clim=[0, 1])
view_patch_vtk(dfs_right_sm,
azimuth=-90,
elevation=-180,
roll=-90,
outfile='dist_sess_orig_view1_1sub_right.png',
show=0)
view_patch_vtk(dfs_right_sm,
azimuth=90,
elevation=180,
roll=90,
outfile='dist_sess_orig_view2_1sub_right.png',
show=0)
dfs_right_sm = patch_color_attrib(dfs_right_sm, (2 - dist_all_rot) / 2.0,
clim=[0.75, 1])
#
# pval=sp.zeros((rho_all.shape[0],1))
#
# for jj in range(rho_all.shape[0]):
# _, pval[jj] = sp.stats.mannwhitneyu(rho_null[jj,:], rho_all[jj,:]) #,
# # alternative='greater')
# print jj
# sns.distplot(pval)
dfs_hemi_sm = readdfs(
os.path.join(
p_dir_ref, 'reference', ref + '.aparc\
.a2009s.32k_fs.reduce3.smooth.' + hemi + '.dfs'))
dfs_hemi_sm.attributes = sp.squeeze(rho_all.mean(axis=1))
dfs_hemi_sm = patch_color_attrib(dfs_hemi_sm, clim=[0, 1])
view_patch_vtk(dfs_hemi_sm,
azimuth=90,
elevation=180,
roll=90,
outfile='rest_rot1_fcon1000_subject1_' + hemi + '.png',
show=1)
view_patch_vtk(dfs_hemi_sm,
azimuth=-90,
elevation=180,
roll=-90,
outfile='rest_rot2_fcon1000_subject1_' + hemi + '.png',
show=1)
rho_null = rho_null.T
rho_all1 = sp.mean(rho_all, axis=1)[:, None]
vrest = data
m = np.mean(vrest, 1)
vrest = vrest - m[:, None]
s = np.std(vrest, 1) + 1e-116
vrest1 = vrest / s[:, None]
rho1 = 0
rho1rot = 0
diffafter = 0
diffbefore = 0
a = sp.load('fcon1000_null_all_left.npz')
rho_null = sp.mean(a['rho_null'], axis=0)
dfs_left_sm.attributes = sp.mean(rho_null, axis=0)
dfs_left_sm = patch_color_attrib(dfs_left_sm, clim=[0, 1])
view_patch_vtk(dfs_left_sm,
azimuth=90,
elevation=180,
roll=90,
outfile='rest_mean1_left.png',
show=1)
view_patch_vtk(dfs_left_sm,
azimuth=-90,
elevation=180,
roll=-90,
outfile='rest_mean2_left.png',
show=1)
dfs_left_sm.attributes = sp.std(rho_null, axis=0)
dfs_left_sm = patch_color_attrib(dfs_left_sm, clim=[0, .1])
freq1[(bci_labs > 0) & (freq1 == 0)] = \
sp.amin(freq1[(bci_labs_orig > 0) & (freq1 != 0)])
freq[(bci_bst.labels == roino * 10)] += freq1[(bci_bst.labels == roino *
10)]
bci_bst.labels += sp.uint16(bci_labs)
freq[freq == 0] = 1
bci_bst.attributes = freq
bci_bst = patch_color_labels(bci_bst, cmap='Paired')
# bci_bst = smooth_patch(bci_bst, iterations=90, relaxation=10.8)
view_patch_vtk(bci_bst, show=1)
####writedfs('/home/ajoshi/data/BCI-DNI_brain_atlas/BCI-DNI_brain.right.\
####mid.cortex_refined_labs_uncorr.dfs', bci_bst)
bci_bst = patch_color_attrib(bci_bst, bci_bst.attributes)
view_patch_vtk(bci_bst, show=1)
bci_bst = patch_color_labels(bci_bst, freq=freq, cmap='Paired')
# bci_bst = smooth_patch(bci_bst, iterations=90, relaxation=10.8)
view_patch_vtk(bci_bst, show=1)
####writedfs('/home/ajoshi/data/BCI-DNI_brain_atlas/BCI-DNI_brain.right.\
####mid.cortex_refined_labs_mod_freq_uncorr.dfs', bci_bst)
bci_bst = patch_color_attrib(bci_bst, bci_bst.labels)
view_patch_vtk(bci_bst, show=1)
bci_labs = reduce3_to_bci_rh(bci_bst.labels)
bci_freq = reduce3_to_bci_rh(bci_bst.attributes)
bci_bst = readdfs(
'/big_disk/ajoshi/data/BCI-DNI_brain_atlas/BCI-DNI_brain.right.\
faceseg1 = faceseg1[:180]
faceseg2 = faceseg2[:180]
faceseg1_2 = faceseg1_2[:180]
print(sp.linalg.norm(fseg1 - fseg2), sp.linalg.norm(fseg1_2 - fseg2),
sp.linalg.norm(fseg1_2 - fseg1))
print(
sp.dot(faceseg1, faceseg2) / len(faceseg2),
sp.dot(faceseg1_2, faceseg2) / len(faceseg2),
sp.dot(faceseg1_2, faceseg1) / len(faceseg2))
plt.plot(faceseg1, 'b')
plt.plot(faceseg2, 'r')
plt.plot(faceseg1_2, 'k')
plt.savefig('face_annotation_sync.png')
rho_21 = sp.sum(fseg1_2 * fseg2, axis=1) / fseg2.shape[1]
dfs_ref = patch_color_attrib(dfs_ref, rho_21, clim=[0.5, 1])
view_patch_vtk(dfs_ref,
azimuth=90,
elevation=180,
roll=90,
outfile='sync1_2_1_right.png')
view_patch_vtk(dfs_ref,
azimuth=-90,
elevation=180,
roll=-90,
outfile='sync1_2_2_right.png')
y3 = np.einsum('ij,ij->i', yunit, (V3 - V1)) / np.linalg.norm(yunit, axis=1)
sqrt_DT = (np.abs((x1 * y2 - y1 * x2) + (x2 * y3 - y2 * x3) +
(x3 * y1 - y3 * x1)))
Ar = 0.5 * (np.abs((x1 * y2 - y1 * x2) + (x2 * y3 - y2 * x3) +
(x3 * y1 - y3 * x1)))
TC = face_v_conn(surf1)
Wt = (1.0 / 3.0) * (TC)
# Wt = sp.sparse.spdiags(Wt*Ar, (0), NumTri, NumTri)
surf_weight = Wt * Ar
surf1.attributes = surf_weight
surf_weight = surf_weight[:, None]
# smooth_surf_function(dfs_right_sm, Wt*Ar*0.1, a1=0, a2=1)
surf1.attributes = ind_rois
surf1 = patch_color_attrib(surf1)
view_patch_vtk(surf1, show=1)
# sub = '110411'
# p_dir = '/home/ajoshi/data/HCP_data'
lst = os.listdir('/big_disk/ajoshi/HCP5')
rho1 = 0
rho1rot = 0
rho2 = 0
rho2rot = 0
# lst = [lst[0]]
diffbefore = 0
diffafter = 0
sub = lst[0]
lsurf.attributes = np.zeros((lsurf.vertices.shape[0]))
rsurf.attributes = np.zeros((rsurf.vertices.shape[0]))
lsurf = smooth_patch(lsurf, iterations=1500)
rsurf = smooth_patch(rsurf, iterations=1500)
labs[sp.isnan(labs)] = 0
diff = diff * (labs.T > 0)
diffAdhdInatt = diffAdhdInatt * (labs.T > 0)
nVert = lsurf.vertices.shape[0]
#%% Visualization of normal diff from the atlas
lsurf.attributes = np.sqrt(np.sum((diff), axis=1))
lsurf.attributes = lsurf.attributes[:nVert] / 50
rsurf.attributes = np.sqrt(np.sum((diff), axis=1))
rsurf.attributes = rsurf.attributes[nVert:2 * nVert] / 50
lsurf = patch_color_attrib(lsurf, clim=[0, .2])
rsurf = patch_color_attrib(rsurf, clim=[0, .2])
view_patch_vtk(lsurf,
azimuth=100,
elevation=180,
roll=90,
outfile='l1normal.png',
show=1)
view_patch_vtk(rsurf,
azimuth=-100,
elevation=180,
roll=-90,
outfile='r1normal.png',
show=1)
vrest, _, _ = normalizeData(vrest)
vrest, Rot = brainSync(X=vsub, Y=vrest)
t = sp.sum(vrest*vsub, axis=0)
# print('rho(%d)=%g' % (ind1, sp.mean(t)), end=' ')
print 'rho(%d)=%g' % (ind1, sp.mean(t))
rho_sub[ind1, :] = t
# %%
# Hypothesis test
rho_sub1 = sp.mean(rho_sub, axis=0)
pval = sp.mean(rho_sub1 > rho_null, axis=0)
r, corrPval,_,_ = multipletests(pvals=pval, alpha=0.05, method='fdr_bh')
sl = readdfs(os.path.join(BFPPATH, 'bci32kleft.dfs'))
sl.attributes = corrPval[:sl.vertices.shape[0]]
sl = patch_color_attrib(sl, clim=[0, 1])
sr = readdfs(os.path.join(BFPPATH, 'bci32kright.dfs'))
sr.attributes = corrPval[sl.vertices.shape[0]:2*sl.vertices.shape[0]]
sr = patch_color_attrib(sr, clim=[0, 1])
writedfs('right_pval_sn7915.dfs',sr);
writedfs('left_pval_sn7915.dfs',sl);
view_patch_vtk(sl, azimuth=90, elevation=180, roll=90, show=1)
view_patch_vtk(sl, azimuth=-90, elevation=180, roll=-90, show=1)
y3 = np.einsum('ij,ij->i', yunit, (V3 - V1)) / np.linalg.norm(yunit, axis=1)
sqrt_DT = (np.abs((x1 * y2 - y1 * x2) + (x2 * y3 - y2 * x3) +
(x3 * y1 - y3 * x1)))
Ar = 0.5 * (np.abs((x1 * y2 - y1 * x2) + (x2 * y3 - y2 * x3) +
(x3 * y1 - y3 * x1)))
TC = face_v_conn(surf1)
Wt = (1.0 / 3.0) * (TC)
# Wt = sp.sparse.spdiags(Wt*Ar, (0), NumTri, NumTri)
surf_weight = Wt * Ar
surf1.attributes = surf_weight
surf_weight = surf_weight[:, None]
# smooth_surf_function(dfs_left_sm, Wt*Ar*0.1, a1=0, a2=1)
surf1.attributes = ind_rois
surf1 = patch_color_attrib(surf1)
view_patch_vtk(surf1, show=1)
# sub = '110411'
# p_dir = '/home/ajoshi/data/HCP_data'
lst = os.listdir('/big_disk/ajoshi/HCP5')
rho1 = 0
rho1rot = 0
rho2 = 0
rho2rot = 0
# lst = [lst[0]]
diffbefore = 0
diffafter = 0
sub = lst[0]
all_centroid = []
centroid = []
label_count = 0
# for n in range(nClusters.shape[0]):
# print n
n = 0
roiregion = left_hemisphere[n]
s_a = readdfs('100307.reduce3.very_smooth.left.refined.dfs')
seeds = sp.zeros(nClusters)
# seeds_roi = sp.zeros(nClusters)
col = sp.zeros(s_a.vertices.shape[0])
col[sp.int16(seeds)] = sp.arange(len(seeds)) + 1
s_a = patch_color_attrib(s_a, col)
ref_dir = os.path.join(p_dir, 'reference')
ref = '100307'
fn1 = ref + '.reduce' + str(3) + '.LR_mask.mat'
fname1 = os.path.join(ref_dir, fn1)
msk = scipy.io.loadmat(fname1)
data = scipy.io.loadmat(
os.path.join(
p_dir, sub, sub + '.rfMRI_REST' + str(1) + '_LR' +
'.reduce3.ftdata.NLM_11N_hvar_25.mat'))
LR_flag = 0
data = data['ftdata_NLM']
LR_flag = msk['LR_flag'].squeeze()
LR_flag = np.squeeze(LR_flag) == 1
