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Subthalamic beta activity and neuroimaging concordance in deep brain stimulation: electrode placement and clinical outcomes.

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  1. # %% [markdown]
  2. # # 4. Motor improvements (UPDRS III & IV analysis)
  4. # %% [markdown]
  5. # This is the fourth Notebook that has to be runned. In this one we will study the motor improvements in the UPDRS scale
  7. # %%
  8. # Load libraries
  9. import numpy as np
  10. import pandas as pd
  11. import matplotlib.pyplot as plt
  12. import seaborn as sns
  13. import scipy.stats as stats
  14. import scipy.stats
  15. from scipy.stats import chi2_contingency, f_oneway, kruskal, shapiro, anderson, ttest_rel, f_oneway, ttest_ind, kstest
  16. from statsmodels.stats.multicomp import pairwise_tukeyhsd
  17. import statsmodels.api as sm
  18. from statsmodels.formula.api import ols
  19. from statsmodels.stats.anova import anova_lm
  21. # %%
  22. df = pd.read_csv('/home/razkinm/projects/6articulodbs/raw/data.csv', sep=';')
  23. df_e = pd.read_csv('/home/razkinm/projects/6articulodbs/derivates/df_preprocessed.csv', sep=',')
  25. df_exploracion = df[['CIC', 'SEXO', 'DD', 'EDAD', 'UPDRS III Off - PREQX', 'UPDRS III On - PREQX', 'UPDRS IV - PRE', 'UPDRS III Off- POSTQX', 'UPDRS III On- POSTQX', 'UPDRS IV - POSTQX', 'LEAD IZQ', 'LEAD DER']]
  26. df_exploracion = df_exploracion.dropna()
  27. df_exploracion['COCANAL IZQ'] = None
  28. df_exploracion['COCANAL DER'] = None
  30. # Iterate over df_exploracion and df_e to compare values
  31. for index_exploracion, row_exploracion in df_exploracion.iterrows():
  32. for index_e, row_e in df_e.iterrows():
  33. if row_exploracion['CIC'] == row_e['ID']:
  34. if row_e['HEMISFERIO'] == 1:
  35. df_exploracion.at[index_exploracion, 'COCANAL DER'] = row_e['COCANAL']
  36. if row_e['HEMISFERIO'] == 0:
  37. df_exploracion.at[index_exploracion, 'COCANAL IZQ'] = row_e['COCANAL']
  39. df_exploracion['COCANAL DER'] = df_exploracion['COCANAL DER'].fillna(0)
  41. df_exploracion['COCANAL_GENERAL'] = np.where(
  42. (df_exploracion['COCANAL IZQ'] == 0) & (df_exploracion['COCANAL DER'] == 0), 0,
  43. np.where((df_exploracion['COCANAL IZQ'] == 1) & (df_exploracion['COCANAL DER'] == 1), 2, 1)
  44. )
  46. # create a new variable called 'COINCIDE IMAGEN' to check if LEAD IZQ and LEAD DER have the same value
  47. df_exploracion['BOTH OPTIMO'] = np.where((df_exploracion['LEAD IZQ'] == 'optimo') & (df_exploracion['LEAD DER'] == 'optimo'), 1, 0)
  48. conditions = [
  49. # 'optimo' paired with either 'optimo' or 'suboptimo' except when paired with 'fuera'
  50. ((df_exploracion['LEAD IZQ'] == 'optimo') & (df_exploracion['LEAD DER'].isin(['optimo', 'suboptimo']))) |
  51. ((df_exploracion['LEAD DER'] == 'optimo') & (df_exploracion['LEAD IZQ'].isin(['optimo', 'suboptimo']))),
  52. # All other cases get 0
  53. True # Acts as a catch-all for any cases not covered above
  54. ]
  56. # Define corresponding actions for each condition
  57. choices = [
  58. 1, # Cases where 'optimo' is paired as specified
  59. 0 # All other combinations
  60. ]
  62. # Create the new column based on these conditions
  63. df_exploracion['OPTIMO_vs_no'] = np.select(conditions, choices)
  65. # %% [markdown]
  66. # ## 4.1 UPDRS General Analysis
  68. # %%
  69. # UPDRS III Off - PREQX
  70. mean_UPDRSIIIOffPREQX = df_exploracion['UPDRS III Off - PREQX'].mean()
  71. std_UPDRSIIIOffPREQX = df_exploracion['UPDRS III Off - PREQX'].std()
  73. # UPDRS III On - PREQX
  74. mean_UPDRSIIIOnPREQX = df_exploracion['UPDRS III On - PREQX'].mean()
  75. std_UPDRSIIIOnPREQX = df_exploracion['UPDRS III On - PREQX'].std()
  77. # UPDRS IV - PRE
  78. mean_UPDRSIVPRE = df_exploracion['UPDRS IV - PRE'].mean()
  79. std_UPDRSIVPRE = df_exploracion['UPDRS IV - PRE'].std()
  81. # UPDRS III Off - POSTQX
  82. mean_UPDRSIIIOffPOSTQX = df_exploracion['UPDRS III Off- POSTQX'].mean()
  83. std_UPDRSIIIOffPOSTQX = df_exploracion['UPDRS III Off- POSTQX'].std()
  85. # UPDRS III Off - POSTQX
  86. mean_UPDRSIIIOnPOSTQX = df_exploracion['UPDRS III On- POSTQX'].mean()
  87. std_UPDRSIIIOnPOSTQX = df_exploracion['UPDRS III On- POSTQX'].std()
  89. # UPDRS IV - POSTQX
  90. mean_UPDRSIVPOST = df_exploracion['UPDRS IV - POSTQX'].mean()
  91. std_UPDRSIVPOST = df_exploracion['UPDRS IV - POSTQX'].std()
  93. # UPDRS III Off - PREQX
  94. female_mean_UPDRSIIIOffPREQX = df_exploracion[df_exploracion['SEXO'] == 1]['UPDRS III Off - PREQX'].mean()
  95. female_std_UPDRSIIIOffPREQX = df_exploracion[df_exploracion['SEXO'] == 1]['UPDRS III Off - PREQX'].std()
  97. # UPDRS III On - PREQX
  98. female_mean_UPDRSIIIOnPREQX = df_exploracion[df_exploracion['SEXO'] == 1]['UPDRS III On - PREQX'].mean()
  99. female_std_UPDRSIIIOnPREQX = df_exploracion[df_exploracion['SEXO'] == 1]['UPDRS III On - PREQX'].std()
  101. # UPDRS IV - PRE
  102. female_mean_UPDRSIVPRE = df_exploracion[df_exploracion['SEXO'] == 1]['UPDRS IV - PRE'].mean()
  103. female_std_UPDRSIVPRE = df_exploracion[df_exploracion['SEXO'] == 1]['UPDRS IV - PRE'].std()
  105. # UPDRS III Off - POSTQX
  106. female_mean_UPDRSIIIOffPOSTQX = df_exploracion[df_exploracion['SEXO'] == 1]['UPDRS III Off- POSTQX'].mean()
  107. female_std_UPDRSIIIOffPOSTQX = df_exploracion[df_exploracion['SEXO'] == 1]['UPDRS III Off- POSTQX'].std()
  109. # UPDRS III Off - POSTQX
  110. female_mean_UPDRSIIIOnPOSTQX = df_exploracion[df_exploracion['SEXO'] == 1]['UPDRS III On- POSTQX'].mean()
  111. female_std_UPDRSIIIOnPOSTQX = df_exploracion[df_exploracion['SEXO'] == 1]['UPDRS III On- POSTQX'].std()
  113. # UPDRS IV - POSTQX
  114. female_mean_UPDRSIVPOST = df_exploracion[df_exploracion['SEXO'] == 1]['UPDRS IV - POSTQX'].mean()
  115. female_std_UPDRSIVPOST = df_exploracion[df_exploracion['SEXO'] == 1]['UPDRS IV - POSTQX'].std()
  118. # UPDRS III Off - PREQX
  119. male_mean_UPDRSIIIOffPREQX = df_exploracion[df_exploracion['SEXO'] == 2]['UPDRS III Off - PREQX'].mean()
  120. male_std_UPDRSIIIOffPREQX = df_exploracion[df_exploracion['SEXO'] == 2]['UPDRS III Off - PREQX'].std()
  122. # UPDRS III On - PREQX
  123. male_mean_UPDRSIIIOnPREQX = df_exploracion[df_exploracion['SEXO'] == 2]['UPDRS III On - PREQX'].mean()
  124. male_std_UPDRSIIIOnPREQX = df_exploracion[df_exploracion['SEXO'] == 2]['UPDRS III On - PREQX'].std()
  126. # UPDRS IV - PRE
  127. male_mean_UPDRSIVPRE = df_exploracion[df_exploracion['SEXO'] == 2]['UPDRS IV - PRE'].mean()
  128. male_std_UPDRSIVPRE = df_exploracion[df_exploracion['SEXO'] == 2]['UPDRS IV - PRE'].std()
  130. # UPDRS III Off - POSTQX
  131. male_mean_UPDRSIIIOffPOSTQX = df_exploracion[df_exploracion['SEXO'] == 2]['UPDRS III Off- POSTQX'].mean()
  132. male_std_UPDRSIIIOffPOSTQX = df_exploracion[df_exploracion['SEXO'] == 2]['UPDRS III Off- POSTQX'].std()
  134. # UPDRS III Off - POSTQX
  135. male_mean_UPDRSIIIOnPOSTQX = df_exploracion[df_exploracion['SEXO'] == 2]['UPDRS III On- POSTQX'].mean()
  136. male_std_UPDRSIIIOnPOSTQX = df_exploracion[df_exploracion['SEXO'] == 2]['UPDRS III On- POSTQX'].std()
  138. # UPDRS IV - POSTQX
  139. male_mean_UPDRSIVPOST = df_exploracion[df_exploracion['SEXO'] == 2]['UPDRS IV - POSTQX'].mean()
  140. male_std_UPDRSIVPOST = df_exploracion[df_exploracion['SEXO'] == 2]['UPDRS IV - POSTQX'].std()
  142. # print all results
  143. print('UPDRS III Off - PREQX')
  144. print('Mean:', mean_UPDRSIIIOffPREQX)
  145. print('Standard Deviation:', std_UPDRSIIIOffPREQX)
  146. print('UPDRS III On - PREQX')
  147. print('Mean:', mean_UPDRSIIIOnPREQX)
  148. print('Standard Deviation:', std_UPDRSIIIOnPREQX)
  149. print('UPDRS IV - PRE')
  150. print('Mean:', mean_UPDRSIVPRE)
  151. print('Standard Deviation:', std_UPDRSIVPRE)
  152. print('UPDRS III Off - POSTQX')
  153. print('Mean:', mean_UPDRSIIIOffPOSTQX)
  154. print('Standard Deviation:', std_UPDRSIIIOffPOSTQX)
  155. print('UPDRS III On - POSTQX')
  156. print('Mean:', mean_UPDRSIIIOnPOSTQX)
  157. print('Standard Deviation:', std_UPDRSIIIOnPOSTQX)
  158. print('UPDRS IV - POSTQX')
  159. print('Mean:', mean_UPDRSIVPOST)
  160. print('Standard Deviation:', std_UPDRSIVPOST)
  162. print('UPDRS FEMALE')
  163. print('UPDRS III Off - PREQX')
  164. print('Mean:', female_mean_UPDRSIIIOffPREQX)
  165. print('Standard Deviation:', female_std_UPDRSIIIOffPREQX)
  166. print('UPDRS III On - PREQX')
  167. print('Mean:', female_mean_UPDRSIIIOnPREQX)
  168. print('Standard Deviation:', female_std_UPDRSIIIOnPREQX)
  169. print('UPDRS IV - PRE')
  170. print('Mean:', female_mean_UPDRSIVPRE)
  171. print('Standard Deviation:', female_std_UPDRSIVPRE)
  172. print('UPDRS III Off - POSTQX')
  173. print('Mean:', female_mean_UPDRSIIIOffPOSTQX)
  174. print('Standard Deviation:', female_std_UPDRSIIIOffPOSTQX)
  175. print('UPDRS III On - POSTQX')
  176. print('Mean:', female_mean_UPDRSIIIOnPOSTQX)
  177. print('Standard Deviation:', female_std_UPDRSIIIOnPOSTQX)
  178. print('UPDRS IV - POSTQX')
  179. print('Mean:', female_mean_UPDRSIVPOST)
  180. print('Standard Deviation:', female_std_UPDRSIVPOST)
  183. print('UPDRS MALE')
  184. print('UPDRS III Off - PREQX')
  185. print('Mean:', male_mean_UPDRSIIIOffPREQX)
  186. print('Standard Deviation:', male_std_UPDRSIIIOffPREQX)
  187. print('UPDRS III On - PREQX')
  188. print('Mean:', male_mean_UPDRSIIIOnPREQX)
  189. print('Standard Deviation:', male_std_UPDRSIIIOnPREQX)
  190. print('UPDRS IV - PRE')
  191. print('Mean:', male_mean_UPDRSIVPRE)
  192. print('Standard Deviation:', male_std_UPDRSIVPRE)
  193. print('UPDRS III Off - POSTQX')
  194. print('Mean:', male_mean_UPDRSIIIOffPOSTQX)
  195. print('Standard Deviation:', male_std_UPDRSIIIOffPOSTQX)
  196. print('UPDRS III On - POSTQX')
  197. print('Mean:', male_mean_UPDRSIIIOnPOSTQX)
  198. print('Standard Deviation:', male_std_UPDRSIIIOnPOSTQX)
  199. print('UPDRS IV - POSTQX')
  200. print('Mean:', male_mean_UPDRSIVPOST)
  201. print('Standard Deviation:', male_std_UPDRSIVPOST)
  205. # %% [markdown]
  206. # ## 4.2 Is there a stadistically difference between people that have optimo/suboptimo or suboptimo/fuera?
  208. # %% [markdown]
  209. # #### For UPDRS III Off
  211. # %%
  212. # Assuming 'UPDRS III Off - PREQX' and 'UPDRS III Off - POSTQX' are your columns
  213. df_exploracion['DIFF UPDRS III Off'] = df_exploracion['UPDRS III Off- POSTQX'] - df_exploracion['UPDRS III Off - PREQX']
  214. from scipy.stats import shapiro, levene, ttest_ind, mannwhitneyu, kstest
  216. # Split the data into two groups
  217. group1 = df_exploracion[df_exploracion['OPTIMO_vs_no'] == 1]['DIFF UPDRS III Off'].dropna()
  218. group2 = df_exploracion[df_exploracion['OPTIMO_vs_no'] == 0]['DIFF UPDRS III Off'].dropna()
  220. # Test for normality
  221. print("Normality Test (Shapiro-Wilk):")
  222. norm1 = scipy.stats.kstest(group1)
  223. norm2 = scipy.stats.kstest(group2)
  224. print("Group 1:", norm1)
  225. print("Group 2:", norm2)
  227. # Test for equality of variances
  228. print("Equality of Variances Test (Levene’s Test):")
  229. lev_test = levene(group1, group2)
  230. print(lev_test)
  232. # Choose the test based on the assumptions
  233. print("\nIndependent Samples Test:")
  234. if norm1.pvalue > 0.05 and norm2.pvalue > 0.05 and lev_test.pvalue > 0.05:
  235. # If both distributions are normal and variances are equal, use t-test
  236. t_stat, p_val = ttest_ind(group1, group2)
  237. print(f"Independent t-test: t={t_stat}, p={p_val}")
  238. else:
  239. # If assumptions are not met, use Mann-Whitney U test
  240. u_stat, p_val = mannwhitneyu(group1, group2)
  241. print(f"Mann-Whitney U test: U={u_stat}, p={p_val}")
  243. # Interpret the p-value
  244. if p_val < 0.05:
  245. print("There is a statistically significant difference between the groups.")
  246. else:
  247. print("There is no statistically significant difference between the groups.")
  251. # %% [markdown]
  252. # #### For UPDRS III On
  254. # %%
  255. # Assuming 'UPDRS III Off - PREQX' and 'UPDRS III Off - POSTQX' are your columns
  256. df_exploracion['DIFF UPDRS III On'] = df_exploracion['UPDRS III On- POSTQX'] - df_exploracion['UPDRS III On - PREQX']
  257. from scipy.stats import shapiro, levene, ttest_ind, mannwhitneyu, norm
  259. # Split the data into two groups
  260. group1 = df_exploracion[df_exploracion['OPTIMO_vs_no'] == 1]['DIFF UPDRS III On'].dropna()
  261. group2 = df_exploracion[df_exploracion['OPTIMO_vs_no'] == 0]['DIFF UPDRS III On'].dropna()
  263. data = group1
  264. mean, std = norm.fit(data) # Fit a normal distribution to get mean and std
  265. stat, p = kstest(data, 'norm', args=(mean, std))
  266. print("Statistic:", stat, "P-value:", p)
  268. data = group2
  269. mean, std = norm.fit(data) # Fit a normal distribution to get mean and std
  270. stat, p = kstest(data, 'norm', args=(mean, std))
  271. print("Statistic:", stat, "P-value:", p)
  273. if p > 0.05:
  274. print("Data is normally distributed (fail to reject H0).")
  275. else:
  276. print("Data is not normally distributed (reject H0).")
  278. if p > 0.05:
  279. print("Data is normally distributed (fail to reject H0).")
  280. else:
  281. print("Data is not normally distributed (reject H0).")
  283. # Test for normality
  284. print("Normality Test (Shapiro-Wilk):")
  285. norm1 = shapiro(group1)
  286. norm2 = shapiro(group2)
  287. print("Group 1:", norm1)
  288. print("Group 2:", norm2)
  290. # Test for equality of variances
  291. print("Equality of Variances Test (Levene’s Test):")
  292. lev_test = levene(group1, group2)
  293. print(lev_test)
  295. # Choose the test based on the assumptions
  296. print("\nIndependent Samples Test:")
  297. if norm1.pvalue > 0.05 and norm2.pvalue > 0.05 and lev_test.pvalue > 0.05:
  298. # If both distributions are normal and variances are equal, use t-test
  299. t_stat, p_val = ttest_ind(group1, group2)
  300. print(f"Independent t-test: t={t_stat}, p={p_val}")
  301. else:
  302. # If assumptions are not met, use Mann-Whitney U test
  303. u_stat, p_val = mannwhitneyu(group1, group2)
  304. print(f"Mann-Whitney U test: U={u_stat}, p={p_val}")
  306. # Interpret the p-value
  307. if p_val < 0.05:
  308. print("There is a statistically significant difference between the groups.")
  309. else:
  310. print("There is no statistically significant difference between the groups.")
  314. # %% [markdown]
  315. # #### For UPDRS IV
  317. # %%
  318. # Assuming 'UPDRS III Off - PREQX' and 'UPDRS III Off - POSTQX' are your columns
  319. df_exploracion['DIFF UPDRS IV'] = df_exploracion['UPDRS IV - POSTQX'] - df_exploracion['UPDRS IV - PRE']
  320. from scipy.stats import shapiro, levene, ttest_ind, mannwhitneyu
  322. # Split the data into two groups
  323. group1 = df_exploracion[df_exploracion['OPTIMO_vs_no'] == 1]['DIFF UPDRS IV'].dropna()
  324. group2 = df_exploracion[df_exploracion['OPTIMO_vs_no'] == 0]['DIFF UPDRS IV'].dropna()
  326. # Test for normality
  327. print("Normality Test (Shapiro-Wilk):")
  328. norm1 = shapiro(group1)
  329. norm2 = shapiro(group2)
  330. print("Group 1:", norm1)
  331. print("Group 2:", norm2)
  333. # Test for equality of variances
  334. print("Equality of Variances Test (Levene’s Test):")
  335. lev_test = levene(group1, group2)
  336. print(lev_test)
  338. # Choose the test based on the assumptions
  339. print("\nIndependent Samples Test:")
  340. if norm1.pvalue > 0.05 and norm2.pvalue > 0.05 and lev_test.pvalue > 0.05:
  341. # If both distributions are normal and variances are equal, use t-test
  342. t_stat, p_val = ttest_ind(group1, group2)
  343. print(f"Independent t-test: t={t_stat}, p={p_val}")
  344. else:
  345. # If assumptions are not met, use Mann-Whitney U test
  346. u_stat, p_val = mannwhitneyu(group1, group2)
  347. print(f"Mann-Whitney U test: U={u_stat}, p={p_val}")
  349. # Interpret the p-value
  350. if p_val < 0.05:
  351. print("There is a statistically significant difference between the groups.")
  352. else:
  353. print("There is no statistically significant difference between the groups.")
  357. # %% [markdown]
  358. # ## 4.3 Analysis for differences related to sex, age or disease duration
  360. # %% [markdown]
  361. # ## Any difference related to age?
  363. # %%
  364. df = df_exploracion.rename(columns={
  365. 'UPDRS III Off - PREQX': 'UPDRS_III_Off_PREQX',
  366. 'UPDRS III Off- POSTQX': 'UPDRS_III_Off_POSTQX',
  367. 'UPDRS III On - PREQX': 'UPDRS_III_On_PREQX',
  368. 'UPDRS III On- POSTQX': 'UPDRS_III_On_POSTQX',
  369. 'UPDRS IV - PRE': 'UPDRS_IV_PRE',
  370. 'UPDRS IV - POSTQX': 'UPDRS_IV_POSTQX',
  371. 'EDAD': 'EDAD'
  372. })
  374. df['UPDRS_III_Off_Diff'] = df['UPDRS_III_Off_POSTQX'] - df['UPDRS_III_Off_PREQX']
  375. df['UPDRS_III_On_Diff'] = df['UPDRS_III_On_POSTQX'] - df['UPDRS_III_On_PREQX']
  376. df['UPDRS_IV_Diff'] = df['UPDRS_IV_POSTQX'] - df['UPDRS_IV_PRE']
  378. # Análisis de regresión para verificar el efecto de la edad
  379. def regress_age_effect(df, dependent_var):
  380. X = sm.add_constant(df['EDAD'])
  381. model = sm.OLS(df[dependent_var], X).fit()
  382. print(f"Regression analysis for {dependent_var} on AGE")
  383. print(model.summary())
  385. regress_age_effect(df, 'UPDRS_III_Off_Diff')
  386. regress_age_effect(df, 'UPDRS_III_On_Diff')
  387. regress_age_effect(df, 'UPDRS_IV_Diff')
  390. # %% [markdown]
  391. # No hay evidencia significativa de que la edad tenga un efecto en las diferencias en las puntuaciones UPDRS_III_Off (p-valor = 0.794).
  392. # No hay evidencia significativa de que la edad tenga un efecto en las diferencias en las puntuaciones UPDRS_III_On (p-valor = 0.653).
  393. # No hay evidencia significativa de que la edad tenga un efecto en las diferencias en las puntuaciones UPDRS_IV_Diff (p-valor = 0.412).
  395. # %% [markdown]
  396. # ## Any difference related to sex?
  398. # %% [markdown]
  399. # ### Linear Regression Analysis
  401. # %%
  402. df = df_exploracion.rename(columns={
  403. 'UPDRS III Off - PREQX': 'UPDRS_III_Off_PREQX',
  404. 'UPDRS III Off- POSTQX': 'UPDRS_III_Off_POSTQX',
  405. 'UPDRS III On - PREQX': 'UPDRS_III_On_PREQX',
  406. 'UPDRS III On- POSTQX': 'UPDRS_III_On_POSTQX',
  407. 'UPDRS IV - PRE': 'UPDRS_IV_PRE',
  408. 'UPDRS IV - POSTQX': 'UPDRS_IV_POSTQX',
  409. 'SEXO': 'SEXO'
  410. })
  412. # Calcular las diferencias en las puntuaciones
  413. df['UPDRS_III_Off_Diff'] = df['UPDRS_III_Off_POSTQX'] - df['UPDRS_III_Off_PREQX']
  414. df['UPDRS_III_On_Diff'] = df['UPDRS_III_On_POSTQX'] - df['UPDRS_III_On_PREQX']
  415. df['UPDRS_IV_Diff'] = df['UPDRS_IV_POSTQX'] - df['UPDRS_IV_PRE']
  417. # Análisis de regresión para verificar el efecto del sexo
  418. def regress_sex_effect(df, dependent_var):
  419. X = sm.add_constant(df['SEXO'])
  420. model = sm.OLS(df[dependent_var], X).fit()
  421. print(f"Regression analysis for {dependent_var} on SEX")
  422. print(model.summary())
  424. # Verificar el efecto del sexo en las diferencias UPDRS
  425. regress_sex_effect(df, 'UPDRS_III_Off_Diff')
  426. regress_sex_effect(df, 'UPDRS_III_On_Diff')
  427. regress_sex_effect(df, 'UPDRS_IV_Diff')
  431. # %%
  432. # Filtrar los datos según LEAD y SEXO
  433. df_filtered_optimosub_male = df_exploracion[(df_exploracion['OPTIMO_vs_no'] == 1) & (df_exploracion['SEXO'] == 2)]
  434. df_filtered_optimosub_female = df_exploracion[(df_exploracion['OPTIMO_vs_no'] == 1) & (df_exploracion['SEXO'] == 1)]
  435. df_filtered_subfuera_male = df_exploracion[(df_exploracion['OPTIMO_vs_no'] == 0) & (df_exploracion['SEXO'] == 2)]
  436. df_filtered_subfuera_female = df_exploracion[(df_exploracion['OPTIMO_vs_no'] == 0) & (df_exploracion['SEXO'] == 1)]
  438. # Diccionario para almacenar los resultados
  439. results = {
  440. 'Group': [],
  441. 'UPDRS III Off': [],
  442. 'UPDRS III On': [],
  443. 'UPDRS IV': []
  444. }
  446. def paired_t_tests(df, group_name):
  447. # Calcular el tamaño de la muestra
  448. n = len(df)
  450. # Prueba t pareada para UPDRS III Off
  451. ttest_off = ttest_rel(df['UPDRS III Off - PREQX'], df['UPDRS III Off- POSTQX'])
  452. print(f"Paired t-test {group_name} UPDRS III Off (N={n}): t={ttest_off.statistic}, p={ttest_off.pvalue:.3f}")
  454. # Prueba t pareada para UPDRS III On
  455. ttest_on = ttest_rel(df['UPDRS III On - PREQX'], df['UPDRS III On- POSTQX'])
  456. print(f"Paired t-test {group_name} UPDRS III On (N={n}): t={ttest_on.statistic}, p={ttest_on.pvalue:.3f}")
  458. # Prueba t pareada para UPDRS IV
  459. ttest_iv = ttest_rel(df['UPDRS IV - PRE'], df['UPDRS IV - POSTQX'])
  460. print(f"Paired t-test {group_name} UPDRS IV (N={n}): t={ttest_iv.statistic}, p={ttest_iv.pvalue:.3f}")
  462. # Guardar los resultados en el diccionario
  463. results['Group'].append(group_name)
  464. results['UPDRS III Off'].append(round(ttest_off.pvalue, 3))
  465. results['UPDRS III On'].append(round(ttest_on.pvalue, 3))
  466. results['UPDRS IV'].append(round(ttest_iv.pvalue, 3))
  468. # Realizar las pruebas para cada grupo y sexo
  469. print('OPTIMOSUB - Male')
  470. paired_t_tests(df_filtered_optimosub_male, 'OPTIMO - Male')
  472. print('OPTIMOSUB - Female')
  473. paired_t_tests(df_filtered_optimosub_female, 'OPTIMO - Female')
  475. # Crear la tabla comparativa
  476. results_df = pd.DataFrame(results)
  477. print("\nResultados Comparativos de p-valores")
  478. print(results_df)
  481. # %%
  482. # df_sex = df_exploracion but just the rows that have OPTIMO_vs_no == 1
  483. df_sex = df_exploracion[df_exploracion['OPTIMO_vs_no'] == 1]
  484. df = df_sex.rename(columns={
  485. 'UPDRS III Off - PREQX': 'UPDRS_III_Off_PREQX',
  486. 'UPDRS III Off- POSTQX': 'UPDRS_III_Off_POSTQX',
  487. 'UPDRS III On - PREQX': 'UPDRS_III_On_PREQX',
  488. 'UPDRS III On- POSTQX': 'UPDRS_III_On_POSTQX',
  489. 'UPDRS IV - PRE': 'UPDRS_IV_PRE',
  490. 'UPDRS IV - POSTQX': 'UPDRS_IV_POSTQX',
  491. 'SEXO': 'SEXO'
  492. })
  494. # Calcular las diferencias en las puntuaciones
  495. df['UPDRS_III_Off_Diff'] = df['UPDRS_III_Off_POSTQX'] - df['UPDRS_III_Off_PREQX']
  496. df['UPDRS_III_On_Diff'] = df['UPDRS_III_On_POSTQX'] - df['UPDRS_III_On_PREQX']
  497. df['UPDRS_IV_Diff'] = df['UPDRS_IV_POSTQX'] - df['UPDRS_IV_PRE']
  499. # Análisis de regresión para verificar el efecto del sexo
  500. def regress_sex_effect(df, dependent_var):
  501. X = sm.add_constant(df['SEXO'])
  502. model = sm.OLS(df[dependent_var], X).fit()
  503. print(f"Regression analysis for {dependent_var} on SEX")
  504. print(model.summary())
  506. # Verificar el efecto del sexo en las diferencias UPDRS
  507. regress_sex_effect(df, 'UPDRS_III_Off_Diff')
  508. regress_sex_effect(df, 'UPDRS_III_On_Diff')
  509. regress_sex_effect(df, 'UPDRS_IV_Diff')
  511. # %% [markdown]
  512. # ## Any difference related to disease duration?
  514. # %% [markdown]
  515. # #### Linear Regression
  517. # %%
  518. df = df_exploracion.rename(columns={
  519. 'UPDRS III Off - PREQX': 'UPDRS_III_Off_PREQX',
  520. 'UPDRS III Off- POSTQX': 'UPDRS_III_Off_POSTQX',
  521. 'UPDRS III On - PREQX': 'UPDRS_III_On_PREQX',
  522. 'UPDRS III On- POSTQX': 'UPDRS_III_On_POSTQX',
  523. 'UPDRS IV - PRE': 'UPDRS_IV_PRE',
  524. 'UPDRS IV - POSTQX': 'UPDRS_IV_POSTQX',
  525. 'DD': 'DURATION'
  526. })
  528. # Calcular las diferencias en las puntuaciones
  529. df['UPDRS_III_Off_Diff'] = df['UPDRS_III_Off_POSTQX'] - df['UPDRS_III_Off_PREQX']
  530. df['UPDRS_III_On_Diff'] = df['UPDRS_III_On_POSTQX'] - df['UPDRS_III_On_PREQX']
  531. df['UPDRS_IV_Diff'] = df['UPDRS_IV_POSTQX'] - df['UPDRS_IV_PRE']
  533. # Análisis de regresión para verificar el efecto del sexo
  534. def regress_sex_effect(df, dependent_var):
  535. X = sm.add_constant(df['DURATION'])
  536. model = sm.OLS(df[dependent_var], X).fit()
  537. print(f"Regression analysis for {dependent_var} on DURATION")
  538. print(model.summary())
  540. # Verificar el efecto del sexo en las diferencias UPDRS
  541. regress_sex_effect(df, 'UPDRS_III_Off_Diff')
  542. regress_sex_effect(df, 'UPDRS_III_On_Diff')
  543. regress_sex_effect(df, 'UPDRS_IV_Diff')

UPDRS.ipynb at commit 451a9ca, no license · at the source

Overview

Authors: Malen Razkin1,2, Edurne Ruiz de Gopegui3, Beatriz Tijero4,5,6, Tamara Fernández del Valle4,5, Gaizka Bilbao3, Ainara Dolado7,8, Imanol Lambarri9, Juan Carlos Gómez-Estéban4,5,6,10, Asier Erramuzpe2,11, Marta Ruiz-Lopez4,5
  1. Biomedical Research Doctorate Program, University of the Basque Country, Leioa, Spain
  2. Computational Neuroimaging Lab, Biobizkaia Health Research Institute, Barakaldo, Spain
  3. Department of Neurosurgery, Cruces University Hospital, Barakaldo, Spain
  4. Neurodegenerative Diseases Group, Biobizkaia Bizkaia Health Research Institute, Barakaldo, Spain
  5. Department of Neurology, Cruces University Hospital, Barakaldo, Spain
  6. CIBERNED-CIBER, Institute Carlos III, Madrid, Spain
  7. Radiology Department, Cruces University Hospital, Barakaldo, Spain
  8. Department of Surgery and Radiology and Physical Medicine, University of the Basque Country UPV/EHU, Leioa, Spain
  9. Neurophysiology Department, Cruces University Hospital, Barakaldo, Spain
  10. Department of Neurosciences, University of the Basque Country UPV/EHU, Leioa, Spain
  11. Ikerbasque: The Basque Foundation for Science, Bilbao, Spain
Institutions: University of the Basque Country (Spain); Hospital de Cruces (Spain); Biomedical Research Networking Center on Neurodegenerative Diseases (Spain); Ikerbasque (Spain)
Journal: Clinical neurophysiology practice, volume 11, pages 766-773
Dates: received 3 February 2026; accepted 23 August 2026; published online 27 August 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1016/j.cnp.2026.08.008 · PMID 42733699 · PMCID PMC13571461 · OpenAlex W7204472157
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: extracellular electrophysiology (units, LFP) (modality), clinical / translational (subfield)
Methods: Statistics
Keywords: Deep brain stimulation, Subthalamic nucleus, Local field potentials, Neuroimaging, UPDRS
Topic: Neurological disorders and treatments (Neurology, Medicine), according to OpenAlex
Funding: Ikerbasque, Basque Foundation for Science; Ministerio de Ciencia e Innovación (RYC2021-032390-I, RYC2021); Instituto de Salud Carlos III (PI23/01270)
Citations: not cited yet (Europe PMC); 31 references in the paper

Abstract

The abstract is not reproduced here: the paper's license (CC BY-NC-ND) does not allow it. Read it in the paper, at the publisher or on Europe PMC.

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compneurobilbao/stn-beta-analysis

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 451a9ca2349c7c74a04ec071a4e16f8d1f5fb343, 26 November 2025
Languages: Jupyter (4)
Size: 7 files, 4 scripts
Software Heritage: not archived
Found in: the text, “Statistics”
Holds: README, environment (requirements.txt), 4 notebooks
Not found: license file, CITATION.cff, tests, continuous integration, documentation
Tools: Matplotlib (4 files), NumPy (4 files), pandas (4 files), SciPy (4 files), seaborn (4 files), statsmodels (4 files)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
5 files
At the source: github.com/compneurobilbao/stn-beta-analysis

Tracing map

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What the map holds:

  • 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
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  • no match between paragraphs and code yet;
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Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

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Version 2, 28 September 2026

  • Funding: added Ikerbasque, Basque Foundation for Science; Ministerio de Ciencia e Innovación: RYC2021-032390-I, RYC2021; Instituto de Salud Carlos III: PI23/01270

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, pages, dates, 10 authors, 5 keywords, 31 references.

Cite

This paper

Razkin, M., de Gopegui, E. R., Tijero, B., del Valle, T. F., Bilbao, G., Dolado, A., Lambarri, I., Gómez-Estéban, J. C., Erramuzpe, A., & Ruiz-Lopez, M. (2026). Subthalamic beta activity and neuroimaging concordance in deep brain stimulation: electrode placement and clinical outcomes. Clinical neurophysiology practice, 11, 766-773. https://doi.org/10.1016/j.cnp.2026.08.008

BibTeX

@article{razkin2026subthalamic,
author = {Razkin, Malen and de Gopegui, Edurne Ruiz and Tijero, Beatriz and del Valle, Tamara Fernández and Bilbao, Gaizka and Dolado, Ainara and Lambarri, Imanol and Gómez-Estéban, Juan Carlos and Erramuzpe, Asier and Ruiz-Lopez, Marta},
title = {{Subthalamic beta activity and neuroimaging concordance in deep brain stimulation: electrode placement and clinical outcomes}},
journal = {Clinical neurophysiology practice},
year = {2026},
month = aug,
volume = {11},
pages = {766--773},
publisher = {Elsevier},
issn = {2467-981X},
doi = {10.1016/j.cnp.2026.08.008},
url = {https://doi.org/10.1016/j.cnp.2026.08.008},
pmid = {42733699},
pmcid = {PMC13571461}
}

RIS

TY - JOUR
AU - Razkin, Malen
AU - de Gopegui, Edurne Ruiz
AU - Tijero, Beatriz
AU - del Valle, Tamara Fernández
AU - Bilbao, Gaizka
AU - Dolado, Ainara
AU - Lambarri, Imanol
AU - Gómez-Estéban, Juan Carlos
AU - Erramuzpe, Asier
AU - Ruiz-Lopez, Marta
TI - Subthalamic beta activity and neuroimaging concordance in deep brain stimulation: electrode placement and clinical outcomes
T2 - Clinical neurophysiology practice
J2 - Clin Neurophysiol Pract
PY - 2026
DA - 2026/08/27
VL - 11
SP - 766
EP - 773
SN - 2467-981X
PB - Elsevier
DO - 10.1016/j.cnp.2026.08.008
UR - https://doi.org/10.1016/j.cnp.2026.08.008
LA - en
ER -

CSL-JSON

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"id": "10.1016/j.cnp.2026.08.008",
"type": "article-journal",
"title": "Subthalamic beta activity and neuroimaging concordance in deep brain stimulation: electrode placement and clinical outcomes",
"container-title": "Clinical neurophysiology practice",
"author": [
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"family": "Razkin",
"given": "Malen"
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{
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{
"family": "Tijero",
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{
"family": "del Valle",
"given": "Tamara Fernández"
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{
"family": "Bilbao",
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"page": "766-773",
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"PMID": "42733699",
"PMCID": "PMC13571461",
"ISSN": "2467-981X",
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"date-parts": [
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27
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}
}

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