def extract_vector(self):
if self.level == 2:
self.df_cifti_load = pd.DataFrame(
self.fmri_data_np_arr.mean(axis=2))
if type(self.seed_ROI_name) == list and len(self.seed_ROI_name) > 1:
if self.seed_analysis_output == 'parcellated':
self.df_cifti_load = pd.DataFrame(
self.parcellated_cifti_load.get_fdata())
self.df_cifti_load.columns = self.parcel_labels
self.df_cifti_load['avg'] = self.df_cifti_load[
self.seed_ROI_name].mean(axis=1)
self.parcel_labels = self.df_cifti_load.columns.to_list()
else:
self.df_cifti_load = pd.DataFrame(self.cifti_load.get_fdata())
df_parcellated_cifti_load = pd.DataFrame(
self.parcellated_cifti_load.get_fdata())
df_parcellated_cifti_load.columns = self.parcel_labels
self.df_cifti_load['avg'] = df_parcellated_cifti_load[
self.seed_ROI_name].mean(axis=1)
self.seed_ROI_name = 'avg'
else:
if self.seed_analysis_output == 'dense':
self.df_cifti_load = pd.DataFrame(self.cifti_load.get_fdata())
df_parcellated_cifti_load = pd.DataFrame(
self.parcellated_cifti_load.get_fdata())
df_parcellated_cifti_load.columns = self.parcel_labels
self.df_cifti_load[
self.seed_ROI_name] = df_parcellated_cifti_load[
self.seed_ROI_name]
else:
self.df_cifti_load = pd.DataFrame(
self.parcellated_cifti_load.get_fdata())
cifti_np_array = self.df_cifti_load.to_numpy()
if self.method == 'correlation':
#Pearson correlation coefficients with LedoitWolf covariance estimator
#measure = ConnectivityMeasure(kind='correlation',cov_estimator='LedoitWolf')
#Pearson correlation coefficients based oemperical covariance (i.e. standard)
measure = ConnectivityMeasure(kind='correlation',
cov_estimator=EmpiricalCovariance())
elif self.method == 'covariance':
#LedoitWolf estimator
measure = ConnectivityMeasure(kind='covariance')
elif self.method == 'partial_correlation':
# Partial correlation with LedoitWolf covariance estimator
measure = ConnectivityMeasure(kind='partial correlation')
elif self.method == 'precision':
measure = ConnectivityMeasure(kind='precision')
elif 'sparse' in self.method:
measure = GraphicalLassoCV()
if 'sparse' in self.method:
measure.fit(cifti_np_array)
if 'covariance' in self.method:
network_matrix = measure.covariance_
elif 'precision' in self.method:
network_matrix = measure.precision_
else:
network_matrix = measure.fit_transform([cifti_np_array])[0]
df_network_matrix = pd.DataFrame(network_matrix)
df_network_matrix.columns = self.parcel_labels
if self.seed_ROI_name == 'avg':
# take everything except last element, i.e. avg. Need to do this because downstream this object must match grayordinate_file
self.r_functional_vector = df_network_matrix[
self.seed_ROI_name][:-1].to_numpy()
else:
self.r_functional_vector = np.squeeze(
df_network_matrix[self.seed_ROI_name].to_numpy())
self.z_functional_vector = 0.5 * (
np.log(1 + self.r_functional_vector) -
np.log(1 - self.r_functional_vector))
