Structural-functional multilayer brain network properties and outcome of combined repetitive transcranial magnetic stimulation and psychotherapy for obsessive-compulsive disorder.

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[1] § Material and methods › Statistical analyses ↔ activity_EF_projects_2024/statistics.py, lines 879–1016 · score 0.69 · linear regression, independent variable, covariates, alpha, models, score
[2] § Material and methods › Graph analyses and network measure extraction ↔ multilayer_EC_glioma_2023/kruskal_algorithm.m, lines 1–58 · score 0.54 · minimum spanning tree, strongest, MST, weighted, Graph, Multilayer
[3] § Material and methods › rsfMRI processing ↔ LENS_paper_2022/SpinTest.m, the whole file · a weak match · score 0.53 · FreeSurfer, Pearson, Parcellation, cortical, atlas, correlations
[4] § Material and methods › Graph analyses and network measure extraction ↔ MST/MST_kruskal.py, lines 110–173 · score 0.51 · spanning tree, strongest, MST, weighted, Graph, connect
[5] § Material and methods › rsfMRI processing ↔ modelling_paper_2021/step1/run_model.m, lines 44–60 · score 0.50 · bandpass filtering, noise, smoothing, network

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The authors' code

Python · 1,036 lines · 39 KB · MIT · 1 match

#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Statistical analyses for the activity EF project
This script contains all statistical analyses for the project on activity and executive functioning in glioma patients.
"""
__author__ = "Christina Ulrich & Mona Zimmermann"
__contact__ = "[email hidden]"
__date__ = "23/05/23" ### Date it was created
__status__ = "Final"
####################
# Review History #
####################
# Reviewed by
## Script for Statistical Analysis ##
####################
# LIBRARIES #
####################
# Standard imports ###
import numpy as np
import pandas as pd
from scipy import stats
from scipy.stats import shapiro, ttest_rel, wilcoxon
import statsmodels.api as sm
import os
import pyreadstat
import matplotlib.pyplot as plt
from statsmodels.stats.outliers_influence import variance_inflation_factor
from statsmodels.compat import lzip
import statsmodels.stats.api as sms
import seaborn as sns
# Third party imports ###
# Internal imports ###
#%%
####################
# DATAFRAME IMPORT #
####################
#ATTENTION>>> to review this code it is easiest to first run the create_df_masks_overlaps.py
#script ,for the patient data, and then use the created variables for the statistical analyses below.
#For the healthy control data we can use the dataframes previously created by Christina, see below.
#%%
### HCs Hemispheric FPN ###
#HCs (N=25)
df_HC_Left = pd.read_csv("/path/to/average_HC_Left_df.csv")
df_HC_Right = pd.read_csv("/path/to/average_HC_Right_df.csv")
### HC Info (i.e. info on EF scores and covariates)
HC_info = pd.read_csv('/path/to/HC_subject_info_after_matching.csv')
#Prepare HC_info df to match other dfs
HC_info.rename(columns = {"Case_ID": "sub"}, inplace = True)
HC_info['sub'] = HC_info['sub'].astype(int).astype(str)
HC_info['sub'] = HC_info['sub'].str.replace('110+', '') #edit sub_ID (remove leading 110)
HC_info['sub'] = HC_info['sub'].astype(int) #change sub back into int to match other dfs
### Dataframe with all HC data (left and right FPN) ###
HCs_std = pd.read_csv("/path/to/df_activity_standardized_HC.csv") #all regions
#%%
#%%
### Patient info and EF scores ###
patient_info = pyreadstat.read_sav("/path/to/info/file/.sav")[0]
patient_info['Case_ID'] = 'sub-' + patient_info['Case_ID'].astype(int).astype(str).str.zfill(4)
# Rename colums of EF_score in patient_info file so that both CST and WFT names match"
patient_info.rename(columns = {'flu_dier_corrected_1_zscore' : 'flu_dier_corrected_1_Zscore',
'flu_dier_corrected_2_zscore' : 'flu_dier_corrected_2_Zscore' }, inplace = True )
### Lateralization
li_subs = pd.read_csv("/path/to/all_subs_list.csv")
#%%
####################
# --- ttest to analyze changes in activity (T1-->T2) --- #
####################
def paired_ttest (df_combined, activity_metrics, area, alpha = 0.05 ):
'''
Paramters
---------
df_combined : pd.DataFrame,
dataframe containing data on activityfor both baseline and FU
activity_metrics: str,
activity that should be investigated (i.e. bbp or offset)
area: str,
determines the area that is being investigated (i.e. peritumoral, ipislateral FPN, contralateral FPN)
alpha : float
set to 0.05
'''
#Remove patients from DFs that have progression before FU
#filter patient_info df based on progression before FU
patient_info_filtered = patient_info[patient_info['progression_before_FU'] == 2]
print(patient_info_filtered.shape)
print(len(patient_info[patient_info['progression_before_FU'] == 1]))
df_combined_filt = df_combined[df_combined['sub'].isin(patient_info_filtered['Case_ID'])]
print(df_combined_filt)
# calculate differences between the paired T1 and T2 measurements
differences = df_combined_filt[f"{activity_metrics}_T2"] - df_combined_filt[f"{activity_metrics}_T1"]
#testing normality of differnces
stat, p = stats.shapiro(differences)
if p > alpha: # null hypothesis (differences has a normal distribution), use paried t-test
print ("The null hypothesis cannot be rejected --> x has normal distribution")
print ("Use paired t-test")
#stat_t, p_t = stats.ttest_rel(df_baseline_filtered[activity_metrics], df_FU_filtered[activity_metrics])
stat_t, p_t = stats.ttest_rel(df_combined_filt[f"{activity_metrics}_T1"], df_combined_filt[f"{activity_metrics}_T2"])
if p_t < alpha:
print('Paired t-test: Reject H0 --> there is a significant difference')
else:
print ('Paired t-test: Fail to reject H0 --> there is no significant difference')
print ('T1 vs T2 t = ', str(round(stat_t, 2)), ' p_t = ', str(round(p_t, 4)))
else: # Differences are not normally distributed, use Wilcoxon signed rank test
print ("The null hypothesis can be rejected --> x is not normally distributed")
print ("Use Wilcoxon signed-rank test")
#stat_w, p_w = stats.wilcoxon(df_baseline_filtered[activity_metrics], df_FU_filtered[activity_metrics])
stat_w, p_w = stats.wilcoxon(df_combined_filt[f"{activity_metrics}_T1"],df_combined_filt[f"{activity_metrics}_T2"])
if p_w < alpha:
print ('Wilcoxon signed rank test: Reject H0 --> there is a significant difference')
else:
print ('Wilcoxon signed rank test: Fail to reject H0 --> no significant difference')
print ('T1 vs T2 t = ', str(round(stat_w, 2)), ' p = ', str(round(p_w, 4)))
#%%
#%%
####################
### Patient paired t-test analysis to investigate change between T1 and T2 ###
####################
#Peritumoral area
paired_ttest(df_peri_combined, 'offset_z', 'peritumoral', alpha = 0.05)
paired_ttest(df_peri_combined, 'BB_welch_z', 'peritumoral', alpha = 0.05)
#%%
#Peri-resection area
paired_ttest(df_cavity_combined, 'offset_z', 'peritumoral', alpha = 0.05)
paired_ttest(df_cavity_combined, 'BB_welch_z', 'peritumoral', alpha = 0.05)
#%%
#Ipsilateral FPN
result_ipsi_offset = paired_ttest(df_ipsi_combined, 'offset_z', 'ipsilateral', alpha = 0.05)
result_ipsi_bbp = paired_ttest(df_ipsi_combined,'BB_welch_z', 'ipsilateral', alpha = 0.05)
#%%
#Contralateral FPN
result_contra_offset = paired_ttest(df_contra_combined, 'offset_z', 'contralateral', alpha = 0.05)
result_contra_bbp = paired_ttest(df_contra_combined, 'BB_welch_z', 'contralateral', alpha = 0.05)
#%%
####################
### Post-hoc patient paired t-test analysis for the different freq bands ###
####################
#load in frequency specific data
df_peri_baseline_freqs = pd.read_csv('/path/to/20240517_std_rel_power_baseline.csv')
df_peri_FU_freqs = pd.read_csv('/path/to/20240517_std_rel_power_FU_all.csv')
df_peri_combined_freqs = pd.merge(df_peri_baseline_freqs, df_peri_FU_freqs, on='sub', suffixes=('_T1', '_T2'))
#%%
#paired t-test for the various frequency bands
result_alpha1 = paired_ttest(df_peri_combined_freqs, 'alpha1_z', 'peritumor', alpha = 0.05)
#%%
result_alpha2 = paired_ttest(df_peri_combined_freqs, 'alpha2_z', 'peritumor', alpha = 0.05)
#%%%
result_beta = paired_ttest(df_peri_combined_freqs, 'beta_z', 'peritumor', alpha = 0.05)
#%%
result_delta = paired_ttest(df_peri_combined_freqs, 'delta_z', 'peritumor',alpha = 0.05)
#%%
result_gamma = paired_ttest(df_peri_combined_freqs, 'gamma_z', 'peritumor', alpha = 0.05)
#%%
result_theta = paired_ttest(df_peri_combined_freqs, 'theta_z', 'peritumor', alpha = 0.05)
#%%
#### Test to test whether activity in the FPN is higher or lower than in HCs ####
def test_activity(df_patients, df_HCs_left, df_HCs_right,df_lateralization, area, activity, time):
"""
Function to test for a difference in activity between the ipsilateral or contralateral and HC hemispheres.
Always matches with the right lateralization (i.e. right ipsilateral matches with right HCs hemisphere).
Uses the normal unpaired t-test when normality and equality of variances is confirmed. Uses the Welch test when the normality
is assumed but variances are unequal. If normality is violated uses the Mann-Whitney U test.
Parameters
----------
df_patients : pd.DataFrame,
dataframe containing all patient data
df_HCs_left : pd.Dataframe,
HC dataframe with only left FPN regions
df_HCs_right : pd.DataFrame,
HC dataframe with only right FPN regions
df_lateralization : pd.DataFrame,
dataframe contiaining info on tumor lateralization (left or right)
area : str,
ipsilateral or contralateral: do we compare the ipsilaeral or contralateral hemisphere to HCs?
activity : str,
offset_z or BB_welch_z: do we look at offset or bbp?
time : str,
baseline or FU, do we look at the baseline measurement or FU?
Returns
-------
pd.DataFrame,
results dataframe
"""
df_lat = pd.merge(df_patients, df_lateralization, left_on = "sub", right_on = "Case_ID")
right_tumor = df_lat['lateralization'] == 'right'
left_tumor = df_lat['lateralization'] == 'left'
#filter for subjects that have a right/left tumor
df_right = df_lat[right_tumor]
df_left = df_lat[left_tumor]
if area == 'ipsilateral':
### Statistical tests ###
### Right ipsilateral vs. Right FPN HCs
results_df_right = ind_ttest(df_right, df_HCs_right,area, activity, 'right_ipsi (i.e. right tumor hemisphere)' )
### Left ipsilateral vs. Left FPN HCs
results_df_left = ind_ttest(df_left, df_HCs_left, area, activity, 'left_ipsi (i.e. left tumor hemisphere)')
#results_all = pd.concat([results_df_right,results_df_left])
elif area == 'contralateral':
### Statistical tests ###
### Right ipsilateral vs. Right FPN HCs
results_df_right= ind_ttest(df_right, df_HCs_left,area, activity, 'right_contra (i.e. left non-tumor hemisphere)' )
### Left ipsilateral vs. Left FPN HCs
results_df_left = ind_ttest(df_left, df_HCs_right,area, activity, 'left contra (i.e. right non-tumor hemisphere)')
#results_all = pd.concat([results_df_right,results_df_left])
#results_all.to_csv(f'/data/anw/anw-work/MULTINET/m.zimmermann/01_projects/2023_activity_EF/02_analysis/04_results/test_against_HCs_{activity}_{area}_{time}.csv')
#return(results_all)
#%%
def ind_ttest(df_pat, df_HCs, area, activity, lateralization):
"""
Function to test for differences with the correct test (after checking the assumptions of the data).
Used in function test_activity.
Parameters
----------
df_pat : pd.DataFrame
dataframe containing patient data.
df_HCs : pd.DataFrame,
dataframe containing HCs data.
area : str,
ipsilateral or contralateral
activity : str,
offset_z or BB_welch_z
lateralization : str,
indicates which area of patients is tested against HCs
Returns
-------
None.
"""
results_df = pd.DataFrame(columns= ['Area', 'activity', 'Lateralization', 'Test', 'T-value', 'pvalue'])
### Caclculate mean and std of patients and HCs ###
mean_pat = df_pat[activity].mean()
std_pat = df_pat[activity].std()
mean_HC = df_HCs[activity].mean()
std_HC = df_HCs[activity].std()
#testing normality of data
stat_pat, p_pat = stats.shapiro(df_pat[activity])
stat_HCs, p_HCs = stats.shapiro(df_HCs[activity])
if (p_pat <0.05) or (p_HCs<0.05):
print('Assumption of normality violated: Doing Mann Whitney U test')
results = stats.mannwhitneyu(df_pat[activity], df_HCs[activity])
print(results)
else:
print('Assumption of normality not violated: testing for unequal variances now')
#Testing for equal variances of the two samples (with unequal sample size)
res = stats.levene(df_pat[activity], df_HCs[activity])
if res.pvalue >0.5:
print('Samples have equal variances, doing normal t-test')
results = stats.ttest_ind(a=df_pat[activity], b=df_HCs[activity], equal_var=True)
print(results)
else:
print('Samples have unequal variances, doing Welchs test')
results = stats.ttest_ind(a=df_pat[activity], b=df_HCs[activity], equal_var=False)
print(results)
#%%
### Testing activity against HCs ###
### Offset_z
#Baseline
test_activity(df_ipsi_averaged, df_HC_Left, df_HC_Right, li_subs, 'ipsilateral', 'offset_z', 'baseline')
test_activity(df_contra_averaged, df_HC_Left, df_HC_Right, li_subs, 'contralateral', 'offset_z', 'baseline')
#%%
#FU
test_activity(df_ipsi_averaged_FU, df_HC_Left, df_HC_Right, li_subs, 'ipsilateral', 'offset_z', 'FU')
test_activity(df_contra_averaged_FU, df_HC_Left, df_HC_Right, li_subs, 'contralateral', 'offset_z', 'FU')
### BBP
#Baseline
test_activity(df_ipsi_averaged, df_HC_Left, df_HC_Right, li_subs, 'ipsilateral', 'BB_welch_z', 'baseline')
test_activity(df_contra_averaged, df_HC_Left, df_HC_Right, li_subs, 'contralateral', 'BB_welch_z', 'baseline')
#FU
test_activity(df_ipsi_averaged_FU, df_HC_Left, df_HC_Right, li_subs, 'ipsilateral', 'BB_welch_z', 'FU')
test_activity(df_contra_averaged_FU, df_HC_Left, df_HC_Right, li_subs, 'contralateral', 'BB_welch_z', 'FU')
#%%
####################
# --- Linear regressions --- # --> Multiple Linear Regression
####################
####################
### 1) to test relationship Activity vs EF (T1) ###
####################
def run_multiple_linear_regression(df_activity, df_ef, EF_Score, area, output_dir, covariates=[]):
"""
Parameters:
df_activity: pandas.DataFrame,
the dataframe containing the activity_metrics (bbp and offset)
df_ef: pandas.DataFrame,
the dataframe containing the patient characteristics (EF scores and covariates)
EF_Score: str,
the column name in df representing the dependent variable (i.e. EF Z scores: either CST or WFT)
area: str,
determines the area that is being investigated (i.e. preitumoral, ipsilateral FPN, contralateral FPN)
output_dir: str,
path to directory where results should be stored
covariates: list of str,
a list of column names in df representing the covariates (default: [])
"""
li = []
for activity_metrics in ["BB_welch_z", "offset_z"]:
# Merge the two dataframes based on sub_ID
merge_df = pd.merge(df_activity, df_ef, left_on='sub', right_on='Case_ID')
print(merge_df.shape)
print(len(np.unique(merge_df["sub"])))
assert all(merge_df["sub"] == merge_df["Case_ID"])
#In case EF_Score = CST is analzyed --> remove sub-xxx and sub-xxx from DF
if EF_Score == "cstc_corrected_1_Zscore":
merge_df.drop(merge_df[(merge_df["sub"] == "sub-xxx")|(merge_df["sub"] == "sub-0087")].index, inplace = True)
merge_df.reset_index(inplace = True)
#Check to see if correct participant was excluded
print(merge_df["sub"])
print(merge_df["Case_ID"])
else:
#Check to see if correct participant are still included for WFT
print(merge_df["sub"])
print(merge_df["Case_ID"])
# Define the dependent variable (outcome) and independent variables
X_cols = [activity_metrics] + covariates
print(X_cols)
X = merge_df[X_cols]
y = merge_df[EF_Score]
#Check if X inludes corect columns
print(X)
#Check if Y inludes corect columns
print(y)
# Add a constant term to the independent variables
X = sm.add_constant(X)
# Create a multiple linear regression model
model = sm.OLS(y, X)
# Fit the model to the data
results = model.fit()
# Print the regression coefficients and other results
print(f"Results for {activity_metrics} vs {EF_Score} in {area}:")
print (results.summary())
### --- Testing Assumption of Multiple Linear Regression --- ###
print ('--- Testing Assumptions of Multiple Linear Regression ---')
#Extract the independent variables (IV) only
X_IV = X.drop(columns=['const'])
print(X_IV.head())
# 1) Linear Relationship (between Indepentent and target variables)
print('1) Testing for Linear Relationship: See Graphs')
for IVs in X_IV.columns:
plt.scatter(X[IVs], y)
plt.xlabel(IVs)
plt.ylabel(EF_Score)
plt.title(f"Scatter Plot of {IVs} vs {EF_Score}")
plt.show()
# 2) No Multicollinearity
print ('2) Testing for Multicollinearity')
#Caculate the correlation matrix
corr_matrix = X_IV.corr()
print(corr_matrix)
# Check for variables with high correlation coefficients
high_corr = set()
for i in range(len(corr_matrix.columns)):
for j in range(i):
if abs(corr_matrix.iloc[i, j])> 0.7:
colname = corr_matrix.columns[i]
high_corr.add(colname)
print("Variables with high correlation coefficients:", high_corr)
#Calculate VIF scores for each independent variable (IVs)
vif_scores = pd.DataFrame()
vif_scores["feature"] = X_IV.columns
vif_scores["VIF"] = [variance_inflation_factor(X_IV.values, i) for i in range(X_IV.shape[1])]
print(vif_scores)
#3) Homoscedasticity - constant variance
print ("3) Testing Homoscedasticity: See Graph")
residuals = results.resid
fitted_vals=results.predict(X)
plt.scatter(fitted_vals, residuals)
plt.xlabel('Fitted Values')
plt.ylabel('Residuals')
plt.title("Scatter Plot to test Homoscedasticity")
plt.show()
name = ['Lagranage multiplier statistic', 'p-value', 'f-value', 'f p-value']
test = sms.het_breuschpagan(results.resid, results.model.exog)
print(lzip(name, test))
#4) No Autocorrelation of errors
print ("4) Testing Autocorrelation of errors: See Graph")
plt.plot(residuals.index, residuals)
plt.title("Plot to test Autocorrelation of errors")
plt.show()
#5) Residual Normality
print("5) Testing Residual Normality: See Graph")
sns.distplot(residuals).set(title ="Residual plot: Testing Normality")
print('residual mean:')
print(np.mean(residuals))
stat, p = stats.shapiro(results.resid)
print('Shapiro test:')
print(stat, p)
#6) Checking for outliers
print("6) Checking for Residual relation with independent variables/ outliers: See Graph")
fig, axs = plt.subplots(ncols=X_IV.shape[1], figsize=(15, 5))
for i, col in enumerate (X_IV.columns):
axs[i].scatter(X_IV[col], results.resid)
axs[i].set_xlabel(col)
axs[i].set_ylabel("Residuals")
plt.title("Checking for outliers")
plt.show()
#or
residuals =results.resid
sm.qqplot(residuals, line='s')
plt.title('Residuals Q-Q')
plt.show()
#Calculate Cook's distance values
influence = results.get_influence()
cooks = influence.cooks_distance[0]
#Plot the Cook's distance values against the obersvation numbers
plt.plot(cooks, 'o')
plt.xlabel('Observation number')
plt.ylabel("Cook's distance")
plt.title("Observation number vs Cook's distance")
plt.show()
#Identify influential observations
# n= len(y)
# threshold = 4/n
# influential_observations = np.where(cooks >= threshold)[0]
# print('Influential observations:', influential_observations)
print("--- Testing Assumptions of Multiple Linear Regression Completed --- ")
# get the summary table of the results
table1 = results.summary2().tables[0]
table2 = results.summary2().tables[1]
table2.reset_index(inplace=True)
summary_df = pd.concat([table1, table2], axis=1)
summary_df = summary_df.reset_index()
#Append results to the dataframe
li.append(summary_df)
results_df = pd.concat(li, axis=0)
# Save the dataframe to CSV
#results_df.to_csv(f'{output_dir}linear_regression_baseline_{area}_{EF_Score}_20250701.csv')
#return(results_df)
#%%
###############################
### Patient linear regression_baseline analysis ###
###############################
output_dir = '/path/to/outputdir/'
#Peritumoral
#CST
#rerun 01-07-2025 with improved covariate frontal or not (no variables taken out for multicollinearity)
run_multiple_linear_regression(df_peri_averaged, patient_info, 'cstc_corrected_1_Zscore', 'peritumoral', output_dir, covariates=["Dummy_frontal_or_not_20250627_final", 'Dummy_IDH_WT', 'Dummy_IDHmut_noncodeleted',"epilepsy_dich.1"])
#%%
output_dir = '/path/to/outputdir'
#WFT
#rerun 01-07-2025 with improved covariate frontal or not (no variables taken out for multicollinearity)
run_multiple_linear_regression(df_peri_averaged, patient_info, 'flu_dier_corrected_1_Zscore', 'peritumoral', output_dir, covariates=["Dummy_frontal_or_not_20250627_final", 'Dummy_IDH_WT', 'Dummy_IDHmut_noncodeleted'])
#%%
#Ipsilateral FPN
#CST
#rerun 01-07-2025 with improved covariate frontal or not (no variables taken out for multicollinearity)
run_multiple_linear_regression(df_ipsi_averaged, patient_info, 'cstc_corrected_1_Zscore', 'ipsilateral', output_dir, covariates =["Dummy_frontal_or_not_20250627_final", 'Dummy_IDH_WT', 'Dummy_IDHmut_noncodeleted', "epilepsy_dich.1"])
#%%
#WFT
#rerun 01-07-2025 with improved covariate frontal or not (no variables taken out for multicollinearity)
run_multiple_linear_regression(df_ipsi_averaged, patient_info, 'flu_dier_corrected_1_Zscore', 'ipsilateral', output_dir, covariates=["Dummy_frontal_or_not_20250627_final", 'Dummy_IDH_WT', 'Dummy_IDHmut_noncodeleted'])
#%%
#Contralateral FPN
#CST#
#rerun 01-07-2025 with improved covariate frontal or not (no variables taken out for multicollinearity)
run_multiple_linear_regression(df_contra_averaged, patient_info, 'cstc_corrected_1_Zscore', 'contralateral', output_dir, covariates=["Dummy_frontal_or_not_20250627_final", 'Dummy_IDH_WT', 'Dummy_IDHmut_noncodeleted', "epilepsy_dich.1"])
#%%
#WFT
#rerun 01-07-2025 with improved covariate frontal or not (no variables taken out for multicollinearity)
run_multiple_linear_regression(df_contra_averaged, patient_info, 'flu_dier_corrected_1_Zscore', 'contralateral', output_dir, covariates=["Dummy_frontal_or_not_20250627_final", 'Dummy_IDH_WT', 'Dummy_IDHmut_noncodeleted'])
#%%
####################
### 2) to test relationship Activity vs EF (longitudinally) ###
####################
def run_delta_regression(df_activity, df_ef, EF_Score, area, output_dir, covariates=[]):
"""
Parameters:
df_activity: pandas.DataFrame,
the dataframe containing the activity_metrics (bbp and offset) for both timepoints (baseline and FU)
df_ef: pandas.DataFrame,
the dataframe containing the patient characteristics (EF scores and covariates)
EF_Score: str,
the column name in df representing the dependent variable (i.e. EF Z scores: either CST or WFT)
area: str,
determines the area that is being investigated (i.e. preitumoral, ipsilateral FPN, contralateral FPN)
output_dir: str,
path to directory where results should be stored
covariates: list of str,
a list of column names in df representing the covariates (default: [])
"""
li = []
for activity_metrics in ["BB_welch_z", "offset_z"]:
# Merge the two dataframes based on sub_ID
merge_df = pd.merge(df_activity, df_ef, left_on='sub', right_on='Case_ID')
print(merge_df.shape)
print(len(np.unique(merge_df["sub"])))
print(len(np.unique(merge_df["Case_ID"])))
#In case EF_Score = CST is analzyed --> remove sub-xxx and sub-xxx from DF
if EF_Score == "cstc_corrected":
merge_df.drop(merge_df[(merge_df["sub"] == "sub-xxx")|(merge_df["sub"] == "sub-xxx")].index, inplace = True)
merge_df.reset_index(inplace = True)
#Check to see if correct participant was excluded
print(merge_df["sub"])
print(merge_df["Case_ID"])
#In case EF_Score = WFT is analyzed --> remove sub-xxx from DF
else:
#Check to see if correct participant are still included for WFT
merge_df.drop(merge_df[(merge_df["sub"] == "sub-xxx")].index, inplace = True)
merge_df.reset_index(inplace = True)
#Check to see if correct participant was excluded
print(merge_df["sub"])
print(merge_df["Case_ID"])
#Remove patients from DF that have progression before FU
prog_filt = merge_df["progression_before_FU"] == 1
merge_df = merge_df[~prog_filt]
merge_df.reset_index(inplace = True)
#Check to see if correct participants and correct columns are included in the final version
print(merge_df["sub"])
print(merge_df.columns)
# Calculate delta values
merge_df[f'delta_{activity_metrics}'] = merge_df[activity_metrics + '_T2'] - merge_df[activity_metrics + '_T1']
merge_df[f'delta_{EF_Score}']= merge_df[EF_Score + "_2_Zscore"] - merge_df[EF_Score + "_1_Zscore"]
print(merge_df[f'delta_{EF_Score}'])
# Define the dependent variable (outcome) and independent variables
if len(covariates) > 0:
X = pd.concat([merge_df[f'delta_{activity_metrics}'], merge_df[covariates]], axis=1)
else:
X = merge_df[f'delta_{activity_metrics}']
print(X)
Y = merge_df[f'delta_{EF_Score}']
print(Y)
#Add a constant term to the independent variables
X = sm.add_constant(X)
# Create a multiple linear regression model
model = sm.OLS(Y, X)
#Fit the model to the data
results = model.fit()
# Print the regression coefficients and other results
print(f"Results for delta {activity_metrics} vs delta {EF_Score} in {area}:")
print (results.summary())
### --- Testing Assumption of Multiple Linear Regression --- ###
print ('--- Testing Assumptions of Multiple Linear Regression ---')
#Extract the independent variables (IV) only
X_IV = X.drop(columns=['const'])
print(X_IV.head())
# 1) Linear Relationship (between Indepentent and target variables)
print('1) Testing for Linear Relationship: See Graphs')
for IVs in X_IV.columns:
plt.scatter(X[IVs], Y)
plt.xlabel(IVs)
plt.ylabel(EF_Score)
plt.title(f"Scatter Plot of {IVs} vs {EF_Score}")
plt.show()
# 2) No Multicollinearity
print ('2) Testing for Multicollinearity')
#Caculate the correlation matrix
corr_matrix = X_IV.corr()
print(corr_matrix)
# Check for variables with high correlation coefficients
high_corr = set()
for i in range(len(corr_matrix.columns)):
for j in range(i):
if abs(corr_matrix.iloc[i, j])> 0.7:
colname = corr_matrix.columns[i]
high_corr.add(colname)
print("Variables with high correlation coefficients:", high_corr)
#Calculate VIF scores for each independent variable (IVs)
vif_scores = pd.DataFrame()
vif_scores["feature"] = X_IV.columns
vif_scores["VIF"] = [variance_inflation_factor(X_IV.values, i) for i in range(X_IV.shape[1])]
print(vif_scores)
#3) Homoscedasticity - constant variance
print ("3) Testing Homoscedasticity: See Graph")
residuals = results.resid
fitted_vals=results.predict(X)
plt.scatter(fitted_vals, residuals)
plt.xlabel('Fitted Values')
plt.ylabel('Residuals')
plt.plot(Y, [0]*len(Y))
plt.title("Scatter Plot to test Homoscedasticity")
plt.show()
name = ['Lagranage multiplier statistic', 'p-value', 'f-value', 'f p-value']
test = sms.het_breuschpagan(results.resid, results.model.exog)
print(lzip(name, test))
#4) No Autocorrelation of errors
print ("4) Testing Autocorrelation of errors: See Grahp")
plt.plot(residuals.index, residuals)
plt.title("Plot to test Autocorrelation of errors")
plt.show()
#5) Residual Normality
print("5) Testing Residual Normality: See Graph")
sns.distplot(residuals).set(title ="Residual plot: Testing Normality")
print('residual mean:')
#print(np.mean(residuals))
stat, p = stats.shapiro(results.resid)
print('Shapiro test:')
print(stat, p)
#6) Checking for outliers
print("6) Checking for Residual relation with independent variables/ outliers: See Graph")
fig, axs = plt.subplots(ncols=X_IV.shape[1], figsize=(15, 5))
for i, col in enumerate (X_IV.columns):
axs[i].scatter(X_IV[col], results.resid)
axs[i].set_xlabel(col)
axs[i].set_ylabel("Residuals")
plt.title("Checking for outliers")
plt.show()
#or
residuals =results.resid
sm.qqplot(residuals, line='s')
plt.title('Residuals Q-Q')
plt.show()
print("--- Testing Assumptions of Multiple Linear Regression Completed --- ")
# get the summary table of the results
table1 = results.summary2().tables[0]
table2 = results.summary2().tables[1]
table2.reset_index(inplace=True)
summary_df = pd.concat([table1, table2], axis=1)
summary_df = summary_df.reset_index()
#Append results to the dataframe
li.append(summary_df)
results_delta_df = pd.concat(li, axis=0)
# Save the dataframe to CSV
results_delta_df.to_csv(f'{output_dir}/linear_regression_delta_scores_{area}_{EF_Score}_20250701.csv')
#return(results_delta_df)
#%%
###############################
### Patient linear regression_delta_scores analysis ###
###############################
output_dir = "/path/to/outputdir/"
#Peritumoral
#CST
run_delta_regression(df_peri_combined, patient_info, 'cstc_corrected', 'peritumoral', output_dir, covariates=['Dummy_IDH_WT', 'Dummy_IDHmut_noncodeleted',"Dummy_CT_during_FU", "Interval_surgery_NPA"])
#%%
#WFT
#rerun 01-07-2025 with improved covariate frontal or not (no variables taken out for multicollinearity)
run_delta_regression(df_peri_combined, patient_info, 'flu_dier_corrected', 'peritumoral', output_dir, covariates=['Dummy_IDH_WT', 'Dummy_IDHmut_noncodeleted','Syntax_dummy_only_RTH', 'Syntax_dummy_RTHandXT'])
#%%
output_dir = "/path/to/output_dir/"
#Ipsilateral FPN
#CST
run_delta_regression(df_ipsi_combined, patient_info, 'cstc_corrected', 'ipsilateral', output_dir, covariates=['Dummy_IDH_WT', 'Dummy_IDHmut_noncodeleted',"Dummy_CT_during_FU", "Interval_surgery_NPA"])
#%%
#WFT
#rerun 01-07-2025 with improved covariate frontal or not (no variables taken out for multicollinearity)
run_delta_regression(df_ipsi_combined, patient_info, 'flu_dier_corrected', 'ipsilateral', output_dir, covariates=['Dummy_IDH_WT', 'Dummy_IDHmut_noncodeleted','Syntax_dummy_only_RTH', 'Syntax_dummy_RTHandXT'])
#%%
#Contralateral FPN
output_dir = "/path/to/outputdir/"
#CST
run_delta_regression(df_contra_combined, patient_info, 'cstc_corrected', 'contralateral', output_dir, covariates=['Dummy_IDH_WT', 'Dummy_IDHmut_noncodeleted',"Dummy_CT_during_FU", "Interval_surgery_NPA"])
#%%
#WFT
#rerun 01-07-2025 with improved covariate frontal or not (no variables taken out for multicollinearity)
run_delta_regression(df_contra_combined, patient_info, 'flu_dier_corrected', 'contralateral', output_dir, covariates=['Dummy_IDH_WT', 'Dummy_IDHmut_noncodeleted','Syntax_dummy_only_RTH', 'Syntax_dummy_RTHandXT'])
#%%
####################
### 3) to test relationship Activity vs EF in HCs ###
####################
def run_HC_linear_regression(df_activity, df_ef, EF_Score, area, output_dir, covariates=[]):
"""
Parameters:
df_activity: pandas.DataFrame,
the dataframe containing the activity_metrics (bbp and offset)
df_ef: pandas.DataFrame,
the dataframe containing the patient characteristics (EF scores and covariates)
EF_Score: str,
the column name in df representing the dependent variable (i.e. EF Z scores: either CST or WFT)
area: str,
determines the area that is being investigated (i.e. left or right FPN)
output_dir: str,
path to directory where results should be stored
covariates: list of str,
a list of column names in df_ef representing the covariates (default: [])
"""
li = []
for activity_metrics in ["BB_welch_z", "offset_z"]:
# Merge the two dataframes based on sub_ID
merged_df = pd.merge(df_activity, df_ef, on='sub')
print(merged_df.head)
print(merged_df[[activity_metrics, EF_Score]])
# Define the dependent variable (outcome) and independent variables
X_cols = [activity_metrics] + covariates
X = merged_df[X_cols]
y = merged_df[EF_Score]
#Check if X inludes corect columns
print(X)
#Check if Y inludes corect columns
print(y)
# Add a constant term to the independent variables
X = sm.add_constant(X)
# Create a multiple linear regression model
model = sm.OLS(y, X)
# Fit the model to the data
results = model.fit()
# Print the regression coefficients and other results
print(f"Results for {activity_metrics} vs {EF_Score} in {area}:")
print (results.summary())
### --- Creating Graphs --- ###
sns.lmplot(x=X.columns[1], y=EF_Score, data=merged_df, scatter_kws={'alpha':0.5})
plt.title(f'for HCs: {activity_metrics} vs. {EF_Score}_in {area}')
plt.axhline(y= -1.5, linestyle ='--', color ='grey')
# plt.savefig(f'/data/anw/anw-work/MULTINET/culrich/03_analysis/05_Graphs/04_HC_Linear_Regression/Plot of {activity_metrics} vs {EF_Score} in HCs in {area}".png', bbox_inches = "tight")
plt.show()
### --- Testing Assumption of Multiple Linear Regression --- ###
print ('--- Testing Assumptions of inear Regression ---')
#Extract the independent variables (IV) only
X_IV = X.drop(columns=['const'])
print(X_IV.head())
# 1) Linear Relationship (between Indepentent and target variables)
print('1) Testing for Linear Relationship: See Graphs')
for IVs in X_IV.columns:
plt.scatter(X[IVs], y)
plt.xlabel(IVs)
plt.ylabel(EF_Score)
plt.title(f"Scatter Plot of {IVs} vs {EF_Score}")
plt.show()
#2) Homoscedasticity - constant variance
print ("2) Testing Homoscedasticity: See Graph")
residuals = results.resid
fitted_vals=results.predict(X)
plt.scatter(fitted_vals, residuals)
plt.xlabel('Fitted Values')
plt.ylabel('Residuals')
plt.plot(y, [0]*len(y))
plt.title("Scatter Plot to test Homoscedasticity")
plt.show()
name = ['Lagranage multiplier statistic', 'p-value', 'f-value', 'f p-value']
test = sms.het_breuschpagan(results.resid, results.model.exog)
print(lzip(name, test))
#3) Independence of observations
#create plot of the residuals against the independent variable
print('3) Testing for Independence: See Graphs')
for IVs in X_IV.columns:
plt.scatter(X[IVs], residuals)
plt.xlabel(IVs)
plt.ylabel('residuals')
plt.title(f"Scatter Plot of {IVs} vs residuals")
plt.show()
#4) No Autocorrelation of errors
print ("4) Testing Autocorrelation of errors: See Grahp")
plt.plot(residuals.index, residuals)
plt.title("Plot to test Autocorrelation of errors")
plt.show()
#5) Residual Normality
print("5) Testing Residual Normality: See Graph")
sns.distplot(residuals).set(title ="Residual plot: Testing Normality")
print('residual mean:')
print(np.mean(residuals))
stat, p = stats.shapiro(results.resid)
print('Shapiro test:')
print(stat, p)
#6) Checking for outliers
print("6) Checking for outliers: See Graph")
residuals =results.resid
sm.qqplot(residuals, line='s')
plt.title('Residuals Q-Q')
plt.show()
print("--- Testing Assumptions of Linear Regression Completed --- ")
# get the summary table of the results
table1 = results.summary2().tables[0]
table2 = results.summary2().tables[1]
table2.reset_index(inplace=True)
summary_df = pd.concat([table1, table2], axis=1)
summary_df = summary_df.reset_index()
li.append(summary_df)
results_HC_df = pd.concat(li, axis=0)
# Save the dataframe to CSV
results_HC_df.to_csv(output_dir + f'/linear_regression_HC_{area}_{EF_Score}.csv')
#return(results_HC_df)
#%%
###############################
### HC linear regression_baseline analysis ###
###############################
output_dir = '/path/to/output_dir/'
#Left Hemisphere FPN
#CST
run_HC_linear_regression(df_HC_Left, HC_info, 'CST_Zscore_shifting', 'left_FPN', output_dir, covariates=[])
#WFT
run_HC_linear_regression(df_HC_Left, HC_info, 'AnimalFluency_Zscore', 'left_FPN', output_dir, covariates=[])
#Rigt Hemisphere FPN
#CST
run_HC_linear_regression(df_HC_Right, HC_info, 'CST_Zscore_shifting', 'right_FPN', output_dir, covariates=[])
#WFT
run_HC_linear_regression(df_HC_Right, HC_info, 'AnimalFluency_Zscore', 'right_FPN', output_dir, covariates=[])

statistics.py at commit 704ca7e, under MIT · at the source

Overview

Authors: Sophie MDD Fitzsimmons^1,2, Coen Coomans^1,2, Lucas Breedt², Neeltje M Batelaan^1,2,3,4, Ysbrand D van der Werf^2,5, Odile A van den Heuvel^1,2,5, Linda Douw², Chris Vriend^1,2,5

ORCID iDs: Sophie MDD Fitzsimmons

Institutions: Amsterdam University Medical Centers (Netherlands); Vrije Universiteit Amsterdam (Netherlands); Amsterdam Public Health (Netherlands); GGZ inGeest (Netherlands); Amsterdam Neuroscience (Netherlands)

Journal: NeuroImage. Clinical, volume 50, article 104012

Dates: received 5 December 2025; accepted 25 May 2026; published online 26 May 2026; in print 2026

Type: Research article · Language: English

License: CC BY

Identifiers: DOI 10.1016/j.nicl.2026.104012 · PMID 42217473 · PMCID PMC13241641 · OpenAlex W7162471095

Open access: gold, a free copy (OpenAlex)

Status: code verified

Categories: structural MRI / diffusion (modality), fMRI (modality), other (modality), human (organism), other condition (population)

Methods: Spectral & time-frequency, Statistics, Smoothing, state filtering, decompositions, Evoked potentials, Connectivity, Graphs, fMRI & imaging

Keywords: Repetitive transcranial magnetic stimulation, Obsessive–compulsive disorder, Graph analysis, Multilayer networks, Functional MRI, Diffusion MRI

MeSH: Brain*, Nerve Net*, Obsessive-Compulsive Disorder*, Psychotherapy*, Transcranial Magnetic Stimulation*, Adult, Combined Modality Therapy, Dorsolateral Prefrontal Cortex, Female, Humans, Magnetic Resonance Imaging, Male, Middle Aged, Treatment Outcome, Young Adult (* major topic)

Topic: Transcranial Magnetic Stimulation Studies (Neurology, Neuroscience), according to OpenAlex

Funding: Dutch Research Council (NWO) (016.176.306, 198.015); ZonMw

Citations: not cited yet (Europe PMC); 61 references in the paper

Abstract

Background: Repetitive transcranial magnetic stimulation (rTMS) is a promising treatment for obsessive–compulsive disorder (OCD), but response rates are variable. Pre-treatment characteristics of the stimulated region can influence rTMS outcome.

Objective/hypothesis: We investigated the relationship between network features of the rTMS stimulation location and treatment outcome in a randomised trial of rTMS combined with exposure and response prevention psychotherapy (ERP) for OCD, using graph analysis of single-layer functional and structural brain networks, and of structural–functional multilayer networks.

Methods: We analysed data from 58 treatment-refractory adult OCD patients. Participants received either: high frequency (HF) rTMS to the left dorsolateral prefrontal cortex (DLPFC) (n = 19); HF rTMS to the left pre-supplementary motor area (preSMA) (n = 21); or control rTMS to the vertex (n = 18), all combined with ERP. We used pre-treatment resting state functional MRI (rs-fMRI) and diffusion MRI (dMRI) scans to construct single-layer and structural–functional multilayer networks for each participant. We computed various centrality measures of the stimulated location and subnetwork, and examined their relationship with treatment outcome.

Results: We found no associations between functional/structural single-layer network characteristics and treatment outcome. However, higher average multilayer betweenness centrality of the stimulated subnetwork in the DLPFC rTMS group (but not in the preSMA/vertex groups) was associated with symptom reduction (p = 0.013).

Conclusions: Participants with greater integration of the stimulated subnetwork with the rest of the structural–functional network showed greater improvement following DLPFC rTMS-ERP. Our exploratory results give a preliminary indication of the importance of the interplay between structural and functional networks for rTMS outcome.

Reproduced under the paper's license (CC BY), from the paper cited above.

Repositories

Its files are read in the Code ↔ Paper reader above, with 5 matches between paragraphs and lines of code.

multinetlab-amsterdam/projects

License: MIT

State: the link answers, verified on 28 September 2026

Evidence: files inventoried

Commit: 704ca7e4d769d5d77dc2a14aef0ad67ebaa2d765, 17 September 2026

Languages: Python (36), MATLAB (35), R (9), SPSS (2)

Size: 119 files, 82 scripts

Software Heritage: not archived

Found in: the text, “Graph analyses and network measure extraction”

Holds: license file, environment (activity_EF_projects_2024/requirements.txt, activity_network_project_2023/requirements.txt, glioblastoma_embedding_2026/requirements.txt), 4 notebooks

Not found: README, CITATION.cff, tests, continuous integration, documentation

Tools: pandas (35 files), NumPy (28 files), Matplotlib (21 files), seaborn (15 files), SciPy (13 files), tidyverse (9 files), statsmodels (7 files), Brain Connectivity Toolbox (6 files), Signal Processing Toolbox (6 files), scikit-learn (6 files), NiBabel (5 files), ggpubr (4 files), Statistics and Machine Learning Toolbox (4 files), rstatix (4 files), SHAP (4 files), car (3 files), ggplot2 (3 files), FreeSurfer (2 files), lme4 (2 files), NetworkX (2 files), nlme (2 files), SPM (2 files), afex (1 file), imbalanced-learn (1 file), lmerTest (1 file), Image Processing Toolbox (1 file), Pingouin (1 file), psych (1 file), reshape2 (1 file), survival (1 file), XGBoost (1 file)

Availability: 1 check, the latest on 28 September 2026: the link answers

28 September 2026: the link answers

83 files

KPS_prediction_2026/src_modeling/01_model_train_xgbordreg.py, Python, 281 lines
KPS_prediction_2026/src_modeling/02_model_comparison.py, Python, 206 lines
KPS_prediction_2026/src_modeling/03_model_test_held-out_set.py, Python, 166 lines
KPS_prediction_2026/src_modeling/04_mcp_prediction.py, Python, 267 lines
KPS_prediction_2026/src_modeling/05_EDA.py, Python, 38 lines
KPS_prediction_2026/src_modeling/utils.py, Python, 2,600 lines
KPS_prediction_2026/src_preprocessing/00_combine_gsi_rads_reports.py, Python, 104 lines
KPS_prediction_2026/src_preprocessing/01_brain_imaging_subj_selection_gsi_reports.py, Python, 125 lines
KPS_prediction_2026/src_preprocessing/02_clinical_merge_datasets_fill_nans.py, Python, 234 lines
KPS_prediction_2026/src_preprocessing/03_clinical_subj_selection.py, Python, 86 lines
KPS_prediction_2026/src_preprocessing/04_overlap_subjs_brain_imaging_clinical.py, Python, 94 lines
KPS_prediction_2026/src_preprocessing/05_clinical_EDA.py, Python, 135 lines
KPS_prediction_2026/src_preprocessing/06_tumor_volume_components.py, Python, 86 lines
KPS_prediction_2026/src_preprocessing/07_tumor_volume_components_corrections.py, Python, 118 lines
KPS_prediction_2026/src_preprocessing/08_combine_gsi_rads_output.py, Python, 170 lines
KPS_prediction_2026/src_preprocessing/09_combine_all_features.py, Python, 183 lines
KPS_prediction_2026/src_preprocessing/10_external_holdout_clinical_set.py, Python, 246 lines
KPS_prediction_2026/src_preprocessing/11_final_EDA_patient_table.py, Python, 91 lines
KPS_prediction_2026/src_preprocessing/utils.py, Python, 415 lines
LENS_paper_2022/LENS_git_20220425_revision.m, MATLAB, 673 lines
LENS_paper_2022/SpinPermuFS.m, MATLAB, 78 lines
LENS_paper_2022/SpinTest.m, MATLAB, 104 lines, 1 match
LENS_paper_2022/cortical2surface.m, MATLAB, 176 lines
LENS_paper_2022/syntax_LENS_20220415.sps, SPSS, 263 lines
activity_EF_projects_2024/anova_cavity.R, R, 209 lines
activity_EF_projects_2024/anova_enhancing_tumor.R, R, 119 lines
activity_EF_projects_2024/anova_peritumoral.R, R, 452 lines
activity_EF_projects_2024/cox_model.R, R, 57 lines
activity_EF_projects_2024/create_df.py, Python, 687 lines
activity_EF_projects_2024/create_freq_df.py, Python, 162 lines
activity_EF_projects_2024/post_hoc_FPN.py, Python, 190 lines
activity_EF_projects_2024/statistics.py, Python, 1,036 lines, 1 match
activity_network_project_2023/EC_CC_pipeline.py, Python, 193 lines
activity_network_project_2023/PLI.py, Python, 289 lines
activity_network_project_2023/masks.py, Python, 157 lines
activity_network_project_2023/master_df.py, Python, 701 lines
activity_network_project_2023/spin-test_binom.py, Python, 49 lines
activity_network_project_2023/statistics_network_activity_github.py, Python, 994 lines
activity_network_project_2023/statistics_profiles_github.py, Python, 530 lines
clustering_paper_2021/results_manuscript_AECc_new_HCs_JD_SK.m, MATLAB, 350 lines
clustering_paper_2021/results_manuscript_PLI_new_HCs_2020.m, MATLAB, 603 lines
clustering_paper_2021/shuf_cl_pl.m, MATLAB, 146 lines
fatigue_glioma_manuscript_2022/Code.Rmd, R, 390 lines
glioblastoma_embedding_2026/main.py, Python, 291 lines
glioblastoma_embedding_2026/src/dTOR_compute_fiber_weights.m, MATLAB, 58 lines
glioblastoma_embedding_2026/src/data_loading.py, Python, 1,331 lines
glioblastoma_embedding_2026/src/mixed_anovas.r, R, 84 lines
glioblastoma_embedding_2026/src/statistics.py, Python, 373 lines
glioblastoma_embedding_2026/src/tumor_tract_density_indices.m, MATLAB, 95 lines
glioblastoma_embedding_2026/src/whole_brain_tdm.m, MATLAB, 77 lines
meetomes_2022/correlations.py, Python, 318 lines
modelling_paper_2021/step1/Jansen_network_RK2.m, MATLAB, 99 lines
modelling_paper_2021/step1/envelopecorr.m, MATLAB, 37 lines
modelling_paper_2021/step1/fft_filt.m, MATLAB, 31 lines
modelling_paper_2021/step1/phaselockvalue_leak.m, MATLAB, 39 lines
modelling_paper_2021/step1/pli_matteo.m, MATLAB, 23 lines
modelling_paper_2021/step1/rangenorm.m, MATLAB, 9 lines
modelling_paper_2021/step1/run_model.m, MATLAB, 164 lines, 1 match
modelling_paper_2021/step2/average_SC_emp_all.m, MATLAB, 113 lines
modelling_paper_2021/step2/corr_sim_mod.m, MATLAB, 107 lines
modelling_paper_2021/step2/vectorise.m, MATLAB, 10 lines
modelling_paper_2021/step3/check_higher_coupling_ranges.m, MATLAB, 59 lines
modelling_paper_2021/step3/test_FC_measures_max_correlations.m, MATLAB, 46 lines
modelling_paper_2021/step3/test_between_maximum_coupling_values.m, MATLAB, 39 lines
modelling_paper_2021/step4/Random_coupling.m, MATLAB, 233 lines
modelling_paper_2021/step5/paired_rainclouds.py, Python, 124 lines
multilayer_EC_glioma_2023/Supra_adjacencymatrix_github.m, MATLAB, 226 lines
multilayer_EC_glioma_2023/adjusted_multilayer_main_code.py, Python, 854 lines
multilayer_EC_glioma_2023/fft_filt_BNA.m, MATLAB, 31 lines
multilayer_EC_glioma_2023/kruskal_algorithm.m, MATLAB, 81 lines, 1 match
multilayer_EC_glioma_2023/pli_matteo.m, MATLAB, 23 lines
multiscale_integration_cortex_2021/kruskal_algorithm.m, MATLAB, 76 lines
multiscale_integration_cortex_2021/multiscale_integration_memory_tle_final.m, MATLAB, 1,131 lines
mumo_paper_2021/data/mumo_manuscript_syntax.sps, SPSS, 52 lines
mumo_paper_2021/functions/fft_filt_BNA.m, MATLAB, 31 lines
mumo_paper_2021/functions/kruskal_algorithm.m, MATLAB, 81 lines
mumo_paper_2021/functions/pli_matteo.m, MATLAB, 23 lines
mumo_paper_2021/functions/remove_empty_regions.m, MATLAB, 171 lines
mumo_paper_2021/script/mumo_script_clean.m, MATLAB, 639 lines
physical_fitness_2024/analyses_MB_SF36_CIS20_part1_2_Github.Rmd, R, 1,188 lines
physical_fitness_2024/analyses_MB_SF36_CIS20_part3_Github.Rmd, R, 1,442 lines
symptomnetwork_glioma_paper_2022/code.Rmd, R, 421 lines
LICENSE, License, 21 lines

At the source: github.com/multinetlab-amsterdam/projects/tree/master/mumo_paper_2021

multinetlab-amsterdam/data_analysis

License: MIT

State: the link answers, verified on 28 September 2026

Evidence: files inventoried

Commit: e8a057a5247b4a698792a3fedc7c9107cd08d781, 26 August 2022

Languages: Python (2)

Size: 5 files, 2 scripts

Software Heritage: archived

Found in: the text, “Graph analyses and network measure extraction”

Holds: license file

Not found: README, CITATION.cff, environment file, tests, continuous integration, documentation

Tools: NumPy (2 files), Matplotlib (1 file), pandas (1 file), SciPy (1 file), specparam (formerly FOOOF) (1 file)

Availability: 1 check, the latest on 28 September 2026: the link answers

28 September 2026: the link answers

3 files

MST/MST_kruskal.py, Python, 173 lines, 1 match
fooof/alternative_Compute_broadband.py, Python, 727 lines
LICENSE, License, 21 lines

At the source: github.com/multinetlab-amsterdam/data_analysis/tree/Multilayer/Multilayer

Tracing map

Proposed by the machine: these links were found in the paper and verified at the source, without human review. The map will receive a Zenodo DOI once one of the paper's authors has validated it with their ORCID.

What the map holds:

2 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
84 scripts, each with its path and the digest of its content;
5 matches between paragraphs of the paper and lines of the code (method lexical-v1);
neither the text of the paper nor the code itself.

Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

No dataset and no data link were found in the paper.

Data availability

The authors do not have permission to share data.

Reproduced under the paper's license (CC BY), from the paper cited above.

Versions

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Version 1, 28 September 2026: the first record

Recorded: type, language, journal, volume, pages, dates, 8 authors, 6 keywords, 15 MeSH terms, 2 funders, 59 references.

Cite

This paper

Fitzsimmons, S. M., Coomans, C., Breedt, L., Batelaan, N. M., van der Werf, Y. D., van den Heuvel, O. A., Douw, L., & Vriend, C. (2026). Structural-functional multilayer brain network properties and outcome of combined repetitive transcranial magnetic stimulation and psychotherapy for obsessive-compulsive disorder. NeuroImage. Clinical, 50, 104012. https://doi.org/10.1016/j.nicl.2026.104012

BibTeX

@article{fitzsimmons2026structural, author = {Fitzsimmons, Sophie MDD and Coomans, Coen and Breedt, Lucas and Batelaan, Neeltje M and van der Werf, Ysbrand D and van den Heuvel, Odile A and Douw, Linda and Vriend, Chris}, title = {{Structural-functional multilayer brain network properties and outcome of combined repetitive transcranial magnetic stimulation and psychotherapy for obsessive-compulsive disorder}}, journal = {NeuroImage. Clinical}, year = {2026}, month = may, volume = {50}, pages = {104012}, publisher = {Elsevier}, issn = {2213-1582}, doi = {10.1016/j.nicl.2026.104012}, url = {https://doi.org/10.1016/j.nicl.2026.104012}, pmid = {42217473}, pmcid = {PMC13241641} }

RIS

TY - JOUR AU - Fitzsimmons, Sophie MDD AU - Coomans, Coen AU - Breedt, Lucas AU - Batelaan, Neeltje M AU - van der Werf, Ysbrand D AU - van den Heuvel, Odile A AU - Douw, Linda AU - Vriend, Chris TI - Structural-functional multilayer brain network properties and outcome of combined repetitive transcranial magnetic stimulation and psychotherapy for obsessive-compulsive disorder T2 - NeuroImage. Clinical J2 - Neuroimage Clin PY - 2026 DA - 2026/05/26 VL - 50 SP - 104012 SN - 2213-1582 PB - Elsevier DO - 10.1016/j.nicl.2026.104012 UR - https://doi.org/10.1016/j.nicl.2026.104012 LA - en ER -

CSL-JSON

{ "id": "10.1016/j.nicl.2026.104012", "type": "article-journal", "title": "Structural-functional multilayer brain network properties and outcome of combined repetitive transcranial magnetic stimulation and psychotherapy for obsessive-compulsive disorder", "container-title": "NeuroImage. Clinical", "author": [ { "family": "Fitzsimmons", "given": "Sophie MDD" }, { "family": "Coomans", "given": "Coen" }, { "family": "Breedt", "given": "Lucas" }, { "family": "Batelaan", "given": "Neeltje M" }, { "family": "van der Werf", "given": "Ysbrand D" }, { "family": "van den Heuvel", "given": "Odile A" }, { "family": "Douw", "given": "Linda" }, { "family": "Vriend", "given": "Chris" } ], "container-title-short": "Neuroimage Clin", "volume": "50", "page": "104012", "DOI": "10.1016/j.nicl.2026.104012", "PMID": "42217473", "PMCID": "PMC13241641", "ISSN": "2213-1582", "publisher": "Elsevier", "URL": "https://doi.org/10.1016/j.nicl.2026.104012", "language": "en", "issued": { "date-parts": [ [ 2026, 5, 26 ] ] } }

The tracing map gets a citation of its own once an author has validated it and it has a DOI.

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