def get_analysis_mask(path, subjects, directory, roi_list):
######## Get matrix infos ###############
conn_test = ConnectivityLoader(path, subjects, directory, roi_list)
# Get nan mask to correctly fill matrix
nan_mask = conn_test.get_results(['Samatha', 'Vipassana'])
# Transform matrix into float of ones
mask_ = np.float_(~np.bool_(nan_mask))
# Get the upper part of the matrix
mask_ = np.triu(mask_, k=1)
return mask_
def write_correlation_matrices(directory, condition):
subjects = np.loadtxt(
'/media/robbis/DATA/fmri/monks/attributes_struct.txt', dtype=np.str)
roi_list = np.loadtxt(
'/media/robbis/DATA/fmri/templates_fcmri/findlab_rois.txt',
delimiter=',',
dtype=np.str)
path = '/media/robbis/DATA/fmri/monks/0_results/'
conn = ConnectivityLoader(path, subjects, directory, roi_list)
nan_mask = conn.get_results(['Samatha', 'Vipassana'])
#nan_mask = conn.get_results(['Rest'])
ds = conn.get_dataset()
mask_ = np.float_(~np.bool_(nan_mask))
mask_ = np.triu(mask_, k=1)
mask_indices = np.nonzero(mask_)
ds_ = ds[np.logical_and(ds.targets == condition, ds.sa.groups == 'E')]
array_ = ds_.samples.mean(0)
mask_[mask_indices] = array_
matrix = np.nan_to_num(copy_matrix(mask_, diagonal_filler=0))
names_lr, colors_lr, index_, coords, networks = get_atlas_info('findlab')
plot_connectomics(
matrix,
20 + 8 * np.abs(matrix.sum(axis=1))**2,
save_path=os.path.join(path, directory),
prename=condition + '_correlation',
save=True,
colormap='bwr',
vmin=np.abs(matrix).max() * -1,
vmax=np.abs(matrix).max(),
node_names=names_lr,
node_colors=colors_lr,
node_coords=coords,
node_order=index_,
networks=networks,
threshold=0.5,
title=condition + ' Correlation',
zscore=False,
)
w_aggregate = aggregate_networks(matrix, roi_list.T[-2])
_, idx = np.unique(networks, return_index=True)
plot_connectomics(w_aggregate,
5 * np.abs(w_aggregate.sum(axis=1))**2,
save_path=os.path.join(path, directory),
prename=condition + '_aggregate_correlation',
save=True,
colormap='bwr',
vmin=-1 * w_aggregate.max(),
vmax=w_aggregate.max(),
node_names=np.unique(networks),
node_colors=colors_lr[idx],
node_coords=coords[idx],
node_order=np.arange(0, len(idx)),
networks=np.unique(networks),
threshold=4,
zscore=False)
delimiter=',',
dtype=np.str)
style_ = 'Samatha'
cv_repetitions = 250
cv_fraction = 0.5
num_exp_subjects = subjects[subjects.T[1] == group_].shape[0]
cv = ShuffleSplit(num_exp_subjects,
n_iter=cv_repetitions,
test_size=cv_fraction)
algorithm = SVR(kernel='linear', C=1)
# Load data
conn = ConnectivityLoader(path, subjects, r, roi_list)
conn.get_results(conditions)
ds = conn.get_dataset()
ds = ds[np.logical_and(ds.sa.meditation == style_, ds.sa.groups == group_)]
# Select data
X = ds.samples
y = np.float_(ds.sa.expertise) * 0.01
# preprocess
X_ = zscore(X, axis=1) # Sample-wise
y_ = zscore(y)
c = Correlation(X_)
corr = c.transform(X_, y_)[0]
