def each_subj_seperate(main_subj_folder, mni_atlas_file_name, idx, atlas_type):
for sub in glob.glob(f'{main_subj_folder}{os.sep}*{os.sep}'):
sn = sub.split(os.sep)[-2]
num_mat_name = sub + 'non-weighted_wholebrain_5d_labmask_yeo7_200_nonnorm.npy'
if os.path.exists(num_mat_name):
num_mat = np.load(num_mat_name)
ncb_num = get_node_betweenness_centrality(num_mat)
add_mat_name = sub + 'weighted_wholebrain_5d_labmask_yeo7_200_nonnorm.npy'
add_mat = np.load(add_mat_name)
ncb_add = get_node_betweenness_centrality(add_mat)
weighted_by_atlas, weights_dict = weight_atlas_by_add(
mni_atlas_file_name, ncb_num, idx)
save_as_nii(weighted_by_atlas, mni_atlas_file_name,
f'Num_node-centrality-betweenness_' + atlas_type,
sub[:-1])
weighted_by_atlas, weights_dict = weight_atlas_by_add(
mni_atlas_file_name, ncb_add, idx)
save_as_nii(weighted_by_atlas, mni_atlas_file_name,
f'ADD_node-centrality-betweenness_' + atlas_type,
sub[:-1])
def grouped_together(main_subj_folder, mni_atlas_file_name, idx, atlas_type):
ncb_num = []
ncb_add = []
for sub in glob.glob(f'{main_subj_folder}{os.sep}*{os.sep}'):
sn = sub.split(os.sep)[-2]
num_mat_name = sub + 'non-weighted_wholebrain_5d_labmask_yeo7_200_nonnorm.npy'
if os.path.exists(num_mat_name):
num_mat = np.load(num_mat_name)
ncb_num.append(get_node_betweenness_centrality(num_mat))
add_mat_name = sub + 'weighted_wholebrain_5d_labmask_yeo7_200_nonnorm.npy'
add_mat = np.load(add_mat_name)
ncb_add.append(get_node_betweenness_centrality(add_mat))
ncb_num = np.asarray(ncb_num)
ncb_add = np.asarray(ncb_add)
ncb_num[ncb_num == 0] = np.nan
ncb_add[ncb_add == 0] = np.nan
ncb_num_mean = np.nanmean(ncb_num, axis=0)
ncb_add_mean = np.nanmean(ncb_add, axis=0)
ncb_num_mean[np.isnan(ncb_num_mean)] = 0
ncb_add_mean[np.isnan(ncb_add_mean)] = 0
weighted_by_atlas, weights_dict = weight_atlas_by_add(
mni_atlas_file_name, ncb_num_mean, idx)
save_as_nii(weighted_by_atlas, mni_atlas_file_name,
f'Num_node-centrality-betweenness_' + atlas_type,
main_subj_folder)
weighted_by_atlas, weights_dict = weight_atlas_by_add(
mni_atlas_file_name, ncb_add_mean, idx)
save_as_nii(weighted_by_atlas, mni_atlas_file_name,
f'ADD_node-centrality-betweenness_' + atlas_type,
main_subj_folder)
wos1.append(table1.find_value_by_scan_Language('Word Order Score 1', sn))
lws.append(table1.find_value_by_scan_Language('Learning words slope', sn))
eff_num_mat =np.zeros((n_subj,len(eff_num_dict)))
eff_add_mat = np.zeros((n_subj,len(eff_add_dict)))
for k in eff_num_dict.keys():
eff_num_mat[:, k] = eff_num_dict[k]
for k in eff_add_dict.keys():
eff_add_mat[:, k] = eff_add_dict[k]
volume_type = 'Num'
r, p = calc_corr(wos1,eff_num_mat, fdr_correct=False, remove_outliers=True)
weighted_by_atlas,weights_dict = weight_atlas_by_add(mni_atlas_file_name,r,idx)
save_as_nii(weighted_by_atlas, mni_atlas_file_name, f'{volume_type}_LocEff-WOS_th_r_'+atlas_type, main_subj_folders)
r, p = calc_corr(lws,eff_num_mat, fdr_correct=False, remove_outliers=True)
weighted_by_atlas,weights_dict = weight_atlas_by_add(mni_atlas_file_name,r,idx)
save_as_nii(weighted_by_atlas, mni_atlas_file_name, f'{volume_type}_LocEff-LWS_th_r_'+atlas_type, main_subj_folders)
volume_type = 'ADD'
r, p = calc_corr(wos1,eff_add_mat, fdr_correct=False, remove_outliers=True)
weighted_by_atlas,weights_dict = weight_atlas_by_add(mni_atlas_file_name,r,idx)
save_as_nii(weighted_by_atlas, mni_atlas_file_name, f'{volume_type}_LocEff-WOS_th_r_'+atlas_type, main_subj_folders)
r, p = calc_corr(lws,eff_add_mat, fdr_correct=False, remove_outliers=True)
weighted_by_atlas,weights_dict = weight_atlas_by_add(mni_atlas_file_name,r,idx)
return r_th, p_corr
if __name__ == '__main__':
subj_main_folder = 'F:\data\V7\TheBase4Ever'
atlas_type = 'yeo7_200'
atlas_main_folder = r'C:\Users\Admin\my_scripts\aal\yeo'
volume_type = 'ADD'
vol_mat, mni_atlas_file_name, idx, subj_idx = volume_based_var(
atlas_type, volume_type, atlas_main_folder, subj_main_folder)
num_of_subj = np.shape(vol_mat)[0]
ages = age_var(subj_main_folder, subj_idx)
r, p = corr_stats(vol_mat, ages)
weighted_by_atlas, weights_dict = weight_atlas_by_add(
mni_atlas_file_name, r, idx)
save_as_nii(weighted_by_atlas, mni_atlas_file_name,
f'{volume_type}_AGE_r_' + atlas_type, subj_main_folder)
r_th, p_corr = multi_comp_correction(r, p)
weighted_by_atlas, weights_dict = weight_atlas_by_add(
mni_atlas_file_name, r_th, idx)
save_as_nii(weighted_by_atlas, mni_atlas_file_name,
f'{volume_type}_AGE_th_r_' + atlas_type, subj_main_folder)
return t, p, t_th
if __name__ == '__main__':
wt = 'ADD'
main_folder = r'F:\data\balance'
atlas_name = 'bna'
atlas_main_folder = r'F:\data\atlases\BNA'
#vol_by_atlas(wt, main_folder, atlas_name, atlas_main_folder)
before_subj = glob.glob(main_folder + f'{os.sep}e*{os.sep}before{os.sep}*')
after_subj = glob.glob(main_folder + f'{os.sep}e*{os.sep}after{os.sep}*')
before_vol_mat, mni_atlas_file_name, idx, subj_idx = volume_based_var(
atlas_name, wt, atlas_main_folder, before_subj)
after_vol_mat, mni_atlas_file_name, idx, subj_idx = volume_based_var(
atlas_name, wt, atlas_main_folder, after_subj)
t, p, t_th = multi_t_test(before_vol_mat, after_vol_mat, fdr_correct=False)
weighted_by_atlas, weights_dict = weight_atlas_by_add(
mni_atlas_file_name, t_th, idx)
save_as_nii(weighted_by_atlas, mni_atlas_file_name,
f'before_vs_after_t_th_' + atlas_name, main_folder)
weighted_by_atlas, weights_dict = weight_atlas_by_add(
mni_atlas_file_name, t, idx)
save_as_nii(weighted_by_atlas, mni_atlas_file_name,
f'before_vs_after_t_' + atlas_name, main_folder)
add_mat = all_subj_add_vals(add_file_name, atlas_labels, subj_main_folder, idx)
from scipy.stats import linregress
r_vec = []
p_vec = []
for i in range(np.shape(add_mat)[1]):
x = fa_mat[:, i]
y = add_mat[:, i]
r, p = linregress(x, y)[2:4]
r_vec.append(r)
p_vec.append(p)
weighted_by_atlas, weights_dict = weight_atlas_by_add(mni_atlas_file_name,
p_vec, idx)
save_as_nii(weighted_by_atlas, mni_atlas_file_name, r'FA_MD_p_' + atlas_type,
subj_main_folder)
weighted_by_atlas, weights_dict = weight_atlas_by_add(mni_atlas_file_name,
r_vec, idx)
save_as_nii(weighted_by_atlas, mni_atlas_file_name, r'FA_MD_r_' + atlas_type,
subj_main_folder)
r_vec = np.asarray(r_vec)
r_vec[np.asarray(p_vec) > 0.05] = 0
r_vec = list(r_vec)
weighted_by_atlas, weights_dict = weight_atlas_by_add(mni_atlas_file_name,
r_vec, idx)
save_as_nii(weighted_by_atlas, mni_atlas_file_name,
