def plot_meg_connectome():
'''
Plot the MEG brain connectome for the master figure in MEG paper
'''
megdata = sio.loadmat('MEG_data_info/total_connecivtiy_coord_label.mat')
total_con = megdata['connectivity']
xyz = megdata['ROI_coords']
normal_con = total_con[:53]
smci_con = total_con[53:-28]
pmci_con = total_con[-28:]
edges = smci_con[8, :, :]
sc = SceneObj(bgcolor='black')
c_obj = ConnectObj('default',
xyz,
edges,
select=edges > .026,
line_width=3.,
dynamic=(0., 1.),
dynamic_orientation='center',
cmap='bwr',
color_by='strength')
s_obj = SourceObj('sources', xyz, color='red', radius_min=15.)
cb_obj = ColorbarObj(c_obj, cblabel='Edge strength')
sc.add_to_subplot(c_obj, title='MEG brain network')
sc.add_to_subplot(s_obj)
sc.add_to_subplot(BrainObj('B3'), use_this_cam=True)
sc.add_to_subplot(cb_obj, col=1, width_max=200)
sc.preview()
class PlotBrain:
def __init__(self):
pass
def create_scene(self, bgcolor='white', size=(4000, 3000)):
# Scene creation
self.sc = SceneObj(bgcolor=bgcolor, size=size)
# Colorbar default arguments. See `visbrain.objects.ColorbarObj`
self.CBAR_STATE = dict(cbtxtsz=60,
txtsz=60.,
width=1,
cbtxtsh=60.,
rect=(-3, -2., 10., 4.),
txtcolor="black")
self.KW = dict(title_size=60., zoom=2, title_color="black")
def parcellize_brain(self,
path_to_file1=None,
path_to_file2=None,
cmap="videen_style"):
# Here, we parcellize the brain (using all parcellated included in the file).
# Note that those parcellates files comes from MNE-python.
# Download the annotation file of the left hemisphere lh.aparc.a2009s.annot
if path_to_file1 == None:
path_to_file1 = download_file('lh.aparc.annot',
astype='example_data')
# Define the brain object (now you should know how to do it)
b_obj_parl = BrainObj('inflated', hemisphere='left', translucent=False)
# From the list of printed parcellates, we only select a few of them
select_par = [
b
for b in b_obj_parl.get_parcellates(path_to_file1)['Labels'].values
if b not in
["unknown", "corpuscallosum", "FreeSurfer_Defined_Medial_Wall"]
]
print("Selected parcelations:", select_par)
# Now we define some data for each parcellates (one value per pacellate)
#data_par = self.data[0:34]
data_par = self.data[0:7]
# Finally, parcellize the brain and add the brain to the scene
b_obj_parl.parcellize(
path_to_file1,
select=select_par,
hemisphere='left',
cmap=cmap,
data=data_par,
clim=[self.min_ji, self.max_ji],
#cmap='videen_style', data=data_par, clim=[self.min_ji, self.max_ji],
vmin=self.min_ji,
vmax=self.max_ji,
under='lightgray',
over='darkred')
self.sc.add_to_subplot(b_obj_parl,
row=0,
col=0,
col_span=3,
rotate='left',
title='Left Hemisphere',
**self.KW)
# Again, we download an annotation file, but this time for the right hemisphere
# Download the annotation file of the right hemisphere rh.aparc.annot
if path_to_file2 == None:
path_to_file2 = download_file('rh.aparc.annot',
astype='example_data')
# Define the brain object (again... I know, this is redundant)
b_obj_parr = BrainObj('inflated',
hemisphere='right',
translucent=False)
print(b_obj_parr)
select_par = [
b
for b in b_obj_parr.get_parcellates(path_to_file2)['Labels'].values
if b not in
["unknown", "corpuscallosum", "FreeSurfer_Defined_Medial_Wall"]
]
print("Selected parcelations:", select_par)
#data_par = self.data[49:-1]
data_par = self.data[7:]
b_obj_parr.parcellize(
path_to_file2,
select=select_par,
hemisphere='right',
cmap=cmap,
data=data_par,
clim=[self.min_ji, self.max_ji],
#cmap='videen_style', data=data_par, clim=[self.min_ji, self.max_ji],
vmin=self.min_ji,
vmax=self.max_ji,
under='lightgray',
over='darkred')
# Add the brain object to the scene
self.sc.add_to_subplot(b_obj_parr,
row=0,
col=4,
col_span=3,
rotate='right',
title='Right Hemisphere',
**self.KW)
# Get the colorbar of the brain object and add it to the scene
cb_parr = ColorbarObj(b_obj_parl,
cblabel='Feedback Inhibitory Synaptic Coupling',
**self.CBAR_STATE)
#self.sc.add_to_subplot(cb_parr, row=0, col=3, width_max=2000)
self.b_obj_parl = b_obj_parl
self.path_to_file1 = path_to_file1
self.b_obj_parr = b_obj_parr
self.path_to_file2 = path_to_file2
def plot(self,
data,
path_to_file1=None,
path_to_file2=None,
cmap="videen_style"):
self.data = data
# self.min_ji = np.min(self.data)
# self.max_ji = np.max(self.data)
self.min_ji = 0
self.max_ji = 2
self.create_scene()
self.parcellize_brain(path_to_file1, path_to_file2, cmap=cmap)
self.sc.preview()
return (self.b_obj_parl, self.path_to_file1, self.b_obj_parr,
self.path_to_file1, self.path_to_file2)
spec = TimeFrequencyObj('spec', data, sf, method='fourier', cmap='RdBu_r')
sc.add_to_subplot(spec, row=1, col=0, title='Spectrogram', zoom=.9)
###############################################################################
# Time-frequency map
###############################################################################
# Extract time-frequency properties using the wavelet convolution
tf = TimeFrequencyObj('tf', data, sf, method='wavelet')
sc.add_to_subplot(tf, row=1, col=1, title='Time-frequency map', zoom=.9)
###############################################################################
# Multi-taper
###############################################################################
# Extract time-frequency properties using multi-taper (need installation of
# lspopt package)
tf_mt = TimeFrequencyObj('mt',
data,
sf,
method='multitaper',
overlap=.7,
interpolation='bicubic',
cmap='Spectral_r')
sc.add_to_subplot(tf_mt, row=1, col=2, title='Multi-taper', zoom=.9)
cb_tf_win = ColorbarObj(tf_mt, cblabel='Power', **CBAR_STATE)
sc.add_to_subplot(cb_tf_win, row=1, col=3, width_max=150, zoom=.9)
# Display the scene
sc.preview()
column_names = arr[0, :]
arr = np.delete(arr, 0, 0)
n_roi = arr.shape[0]
roi_index = arr[:, 0].astype(int)
roi_labels = arr[:, [1, 2]].astype(object)
# Build the struct array :
label = np.zeros(n_roi, dtype=[('label', object), ('name', object)])
label['label'] = roi_labels[:, 0]
label['name'] = roi_labels[:, 1]
# Get the volume and the hdr transformation :
vol, _, hdr = read_nifti(nifti_file, hdr_as_array=True)
# Define the ROI object and save it :
roi_custom = RoiObj('custom_roi', vol=vol, labels=label, index=roi_index,
hdr=hdr)
# Find thalamus entries :
idx_thalamus = roi_custom.where_is('THALAMUS')
colors = {55: 'slateblue', 56: 'olive', 63: 'darkred', 64: '#ab4642'}
roi_custom.select_roi(idx_thalamus, roi_to_color=colors)
sc.add_to_subplot(roi_custom, row=1, col=2, zoom=.5,
title='Plot dorsal and ventral thalamus with fixed colors')
###############################################################################
# .. note::
# Once your RoiObj is defined, you can save it using
# :class:`visbrain.objects.RoiObj.save`. Once the object is saved, you can
# reload it using the name you've used (here we've used the `custom_roi`
# name which means that you can reload it later using RoiObj('custom_roi'))
# Finally, display the scene
sc.preview()
cmap='hot_r',
vmin=0,
vmax=1,
clim=(0, 1))
b_obj_proj_rl.add_activation(texturerl,
hemisphere='right',
cmap='hot_r',
vmin=0,
vmax=1,
clim=(0, 1),
hide_under=0)
cb_proj = ColorbarObj(b_obj_proj_rl,
cblabel='Correlation',
cmap='hot_r',
vmin=0,
vmax=1,
**CBAR_STATE)
sc2.add_to_subplot(b_obj_proj_ll, row=0, col=0, rotate='left')
sc2.add_to_subplot(b_obj_proj_lr, row=0, col=1, rotate='right')
sc2.add_to_subplot(b_obj_proj_rl, row=0, col=2, rotate='left')
sc2.add_to_subplot(b_obj_proj_rr, row=0, col=3, rotate='right')
sc2.add_to_subplot(cb_proj, row=0, col=4, width_max=100)
sc2.preview()
scene.add_to_subplot(brain_obj, row=0, col=0, rotate='left', use_this_cam=True)
# Finally, add the colorbar :
colorbar = ColorbarObj(source_object,
cblabel='Projection of niEEG data',
**CBAR_STATE)
scene.add_to_subplot(colorbar, row=0, col=1, width_max=200, rotate='up')
# Animation
app_timer = Timer(app=CONFIG['VISPY_APP'], interval='auto', iterations=-1)
def on_timer(*args, **kwargs):
if hasattr(brain_obj, 'camera'):
brain_obj.camera.azimuth += 1
t = app_timer.elapsed
frame = int(np.floor(t * sampling_frequency))
new_data = data[:, frame % data.shape[1]].ravel()
source_object._data = vispy_array(new_data)
source_object.update()
source_object.project_sources(brain_obj)
source_object.color_sources()
app_timer.connect(on_timer)
app_timer.start()
scene.preview()
def vis_sources_by_pval(padj, all_coord, short_label):
'''
1. visualize the sources (brain regions) based on the FDR-adjusted p values
display both the source objects and the brain object
'''
# Define the default camera state used for each subplot
CAM_STATE = dict(
azimuth=0, # azimuth angle
elevation=90, # elevation angle
scale_factor=180 # distance to the camera
)
S_KW = dict(camera_state=CAM_STATE)
# Create the scene
CBAR_STATE = dict(cbtxtsz=12,
txtsz=13.,
width=.5,
cbtxtsh=2.,
rect=(1., -2., 1., 4.))
sc = SceneObj(bgcolor='black', size=(1600, 1000))
# mask = padj<=0.05 # significant regions with true label
data0, max_p, min_p = get_log_pval(padj)
b_obj = BrainObj('white', hemisphere='both', translucent=True)
b_obj.animate(iterations=10, step=30, interval=1)
s_obj = SourceObj(
's1',
all_coord, #all_coord,
# mask = mask,
# mask_color='white',
# mask_radius = 15,
color=padj * 100,
data=data0,
text=short_label,
text_size=10,
text_bold=True,
text_color='yellow',
symbol='disc',
visible=True,
radius_min=10,
radius_max=40,
alpha=0.65)
s_obj_1.set_visible_sources('left')
s_obj.color_sources(data=data0, cmap='jet', clim=(min_p, max_p))
cb_data = ColorbarObj(s_obj,
cblabel='Log FDR-adjusted p value',
border=False,
**CBAR_STATE)
sc.add_to_subplot(b_obj, row=0, col=0, rotate='front')
sc.add_to_subplot(s_obj, row=0, col=0)
sc.add_to_subplot(cb_data, row=0, col=1, width_max=90)
# recording animation and save it
sc.record_animation('output/animate_pvalue_projection.gif', n_pic=10)
sc.preview()
def vis_brainSurface_by_padj(padj_parcellates,
gci_parcellates,
tt,
ptype='significant',
cmap='jet',
use_log=False,
use_1_p=False,
use_p=True):
# '''visualize the brain surface based on the FDR-adjusted GCI values or FDR-adjusted p values'''
# Define the default camera state used for each subplot
CAM_STATE = dict(
azimuth=0, # azimuth angle
elevation=90, # elevation angle
scale_factor=180 # distance to the camera
)
S_KW = dict(camera_state=CAM_STATE)
# Create the scene
CBAR_STATE = dict(cbtxtsz=12,
txtsz=13.,
width=.5,
cbtxtsh=2.,
rect=(1., -2., 1., 4.))
sc = SceneObj(bgcolor='black', size=(1300, 1000))
# n_sources = all_coord.shape[0] #sig_roi_coord.shape[0]
# data = np.random.rand(n_sources)
# 1. significant regions
b_obj_1 = BrainObj('white', translucent=False)
# parcellize brain based on desikan atlas
path_to_file1 = download_file('lh.aparc.annot', astype='example_data')
path_to_file2 = download_file('rh.aparc.annot', astype='example_data')
# dataframe type varaible inlcuding index, label, color for each brain region in DKT atlas
lh_df = b_obj_1.get_parcellates(path_to_file1)
rh_df = b_obj_1.get_parcellates(path_to_file2)
# lh_df.to_excel('output/lh.aparc.xlsx')
def select_rois(df, row=1, col=1):
select_val = list(np.array(df.iloc[row:, col]))
select_val.pop(3)
return select_val
select_roi1 = select_rois(lh_df)
select_roi2 = select_rois(rh_df)
# get log FDR-adjusted p values (optional)
# log_p_parce, l_p, s_p = get_log_pval(padj_parcellates, basis=2)
l_p = np.max(gci_parcellates)
s_p = np.min(gci_parcellates)
# print('-----#####',l_p,s_p)
if ptype == 'significant' or ptype == 'unsignificant':
def get_hemisphere_rois(lh_padj,
lh_gci,
lh_rois,
select='significant',
cal_log=True):
if select == 'significant':
lh_sig_ind = np.where(np.array(lh_padj) <= 0.05)[0]
else:
lh_sig_ind = np.where(np.array(lh_padj) > 0.05)[0]
lh_sig_rois = [lh_rois[i] for i in lh_sig_ind]
if cal_log:
log_p, _, _ = get_log_pval(lh_padj,
basis=2) # calculate "log2(padj)"
lh_sig_padj = list(np.array(log_p)[lh_sig_ind])
else:
# lh_sig_padj = list(np.array(lh_padj)[lh_sig_ind])
lh_sig_gci = list(np.array(lh_gci)[lh_sig_ind])
# max_p= np.max(np.array(lh_sig_padj))
# min_p= np.min(np.array(lh_sig_padj))
# return lh_sig_rois,lh_sig_padj,max_p,min_p
max_gci = np.max(np.array(lh_sig_gci))
min_gci = np.min(np.array(lh_sig_gci))
return lh_sig_rois, lh_sig_gci, max_gci, min_gci
# (1). set (log-padj) as values for color mapping
# select_regions_L,lh_padj,_,_ = get_hemisphere_rois(padj_parcellates[:34], gci_parcellates[:34], select_roi1, select = ptype, cal_log=use_log)
# select_regions_R,rh_padj,_,_ = get_hemisphere_rois(padj_parcellates[34:], gci_parcellates[34:], select_roi2, select = ptype, cal_log=use_log)
# clab = 'Log FDR-adjusted p value'
# b_obj_1.parcellize(path_to_file1, select= select_regions_L,data=lh_padj,cmap=cmap,clim=[s_p,l_p])
# b_obj_1.parcellize(path_to_file2, select= select_regions_R,data=rh_padj,cmap=cmap,clim=[s_p,l_p])
# cb_1 = ColorbarObj(b_obj_1, clim= [s_p,l_p], cblabel=clab, border=False, **CBAR_STATE)
# plot GCI value-4/23/2020
select_regions_L, lh_gci, _, _ = get_hemisphere_rois(
padj_parcellates[:34],
gci_parcellates[:34],
select_roi1,
select=ptype,
cal_log=use_log)
select_regions_R, rh_gci, _, _ = get_hemisphere_rois(
padj_parcellates[34:],
gci_parcellates[34:],
select_roi2,
select=ptype,
cal_log=use_log)
clab = 'GCI value'
b_obj_1.parcellize(path_to_file1, select=select_regions_L,
data=lh_gci) #clim=[s_p,l_p]
b_obj_1.parcellize(path_to_file2,
select=select_regions_R,
data=rh_gci,
cmap=cmap) #, clim=[s_p,l_p])
cb_1 = ColorbarObj(b_obj_1,
clim=[1.76, 1.80],
cblabel=clab,
border=False,
**CBAR_STATE)
elif ptype == 'together':
select_regions_L = select_roi1
select_regions_R = select_roi2
if use_log:
# (1). set (log-padj) as values for color mapping
clab = 'Log FDR-adjusted p value'
lh_padj = log_p_parce[:34]
rh_padj = log_p_parce[34:]
b_obj_1.parcellize(path_to_file1,
select=select_regions_L,
data=lh_padj,
cmap=cmap,
clim=[s_p, l_p])
b_obj_1.parcellize(path_to_file2,
select=select_regions_R,
data=rh_padj,
cmap=cmap,
clim=[s_p, l_p])
cb_1 = ColorbarObj(b_obj_1,
clim=[s_p, l_p],
cblabel=clab,
border=False,
**CBAR_STATE)
if use_1_p:
# (2). set (1-padj) as values for color mapping
clab = tt #'1-FDR-adjusted p value'
# clab = '1-FDR-adjusted p value'
padj_0 = [1 - i for i in padj_parcellates]
b_obj_1.parcellize(path_to_file1,
select=select_regions_L,
data=padj_0[:34],
cmap=cmap)
b_obj_1.parcellize(path_to_file2,
select=select_regions_R,
data=padj_0[34:],
cmap=cmap)
cb_1 = ColorbarObj(b_obj_1,
cblabel=clab,
border=False,
**CBAR_STATE) #Log FDR-adjusted p value
if use_p:
# (2). set (1-padj) as values for color mapping
print('--------use p-------')
clab = tt #'1-FDR-adjusted p value'
mx = np.array(gci_parcellates).max()
mi = np.array(gci_parcellates).min()
b_obj_1.parcellize(path_to_file1,
select=select_regions_L,
data=gci_parcellates[:34],
cmap=cmap,
clim=[mi, mx])
b_obj_1.parcellize(path_to_file2,
select=select_regions_R,
data=gci_parcellates[34:],
cmap=cmap,
clim=[mi, mx])
cb_1 = ColorbarObj(b_obj_1,
cblabel=clab,
border=False,
**CBAR_STATE) #Log FDR-adjusted p value
b_obj_1.animate(iterations=10, step=30, interval=1.2)
sc.add_to_subplot(b_obj_1, row=0, col=0, rotate='front')
sc.add_to_subplot(cb_1, row=0, col=1, width_max=90)
# sc.record_animation('output/%s_pvalue_projection.gif'%ptype, n_pic=10)
# sc.record_animation('output/pmci_degree.gif', n_pic=8)
sc.preview()