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Source-space EEG alpha activity reveals brain age gaps due to neurodegeneration and disparity.

Code ↔ Paper

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  1. [1] § Methods › BAG estimation ↔ ElasticNet.ipynb, lines 163–202 · score 0.65 · squared error, absolute error, MSE, MAE, metrics, fold

Paper

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The authors' code

Jupyter notebook · 230 lines · 9.3 KB · no license · 1 match

  1. # %%
  2. path= 'C:/Users/felip/OneDrive - Universidad San Sebastian/Documentos/Brain/'
  3. #import sys
  4. #sys.path.append(path)
  5. path_= 'C:/Users/felip/OneDrive - Universidad San Sebastian/Documentos/Brain/Alpha_Brain_Clocks/Codigos_Base'
  6. import sys
  7. sys.path.append(path_)
  9. # %% [markdown]
  10. # # Regresor
  12. # %%
  13. from sklearn.linear_model import ElasticNet
  14. from base_regressor import BaseRegressor
  15. from Plotter import Plotter
  16. from skopt.space import Real, Categorical, Integer
  18. class ElasticNetRegressor(BaseRegressor):
  19. def __init__(self, save_path=None, scaler=None, params=None, params_space=None, fit_params_search=None, model_params_search=None, fit_params_train=None, model_params_train=None, name_model="ElasticNet"):
  20. super().__init__(save_path, scaler, params, params_space, fit_params_search, model_params_search, fit_params_train, model_params_train, name_model)
  22. self.model_ml = ElasticNet
  23. if params is None:
  24. self.params = {
  25. 'alpha': 0.2,
  26. 'l1_ratio': 0.5, # Proporción de L1 en la regularización
  27. 'max_iter': 10000,
  28. # 'tol': 0.001
  29. }
  30. if params_space is None:
  31. self.params_space = {
  32. 'alpha': Real(0.0001, 0.01, prior='log-uniform'),
  33. 'l1_ratio': Real( 0.9, 1.0), # Rango de 0 a 1 para la proporción de L1
  34. 'max_iter': Integer(1000, 10000),
  35. #'tol': Real(1e-5, 1e-2, prior='log-uniform')
  36. }
  39. # %% [markdown]
  40. # # Instancia de modelos
  42. # %%
  43. # from Plotter import Plotter
  44. from sklearn.preprocessing import MinMaxScaler,StandardScaler
  46. model_reg = ElasticNetRegressor()
  48. Plotters = Plotter()
  50. # Parametros de Plot
  51. colorset = 'darkorange'
  52. nameset = 'ElasticNet'
  54. #parametros de scaler
  55. #1:sin scaler 2:Zscore 3:MinMax
  56. Scaler_reg_train=2
  58. #scaler = MinMaxScaler()
  59. Scaler_reg = StandardScaler()
  61. #model_cls = XGBoostClassifier()
  64. # %%
  65. features = [ 'IAF_ORB_left', 'TF_ORB_left', 'IAF_SFG_right', 'Low_subj_spec_EPP_HPC_left', 'IAF_MFG_right', 'TF_INS_right', 'IAF_MFG_left', 'IAF_SFG_left', 'TF_ORB_right', 'Low_subj_spec_EPP_OCC_right', 'IAF_ORB_right', 'IAF_IFG_left', 'IAF_IFG_right', 'TF_IFG_right', 'TF_HPC_right', 'IAF_INS_right', 'TF_HPC_left', 'IAF_INS_left', 'IAF_HPC_left', 'IAF_HPC_right', 'IAF_OCC_left', 'Alpha2_canon_RPD_OCC_right', 'Alpha2_canon_EPP_OCC_right', 'IAF_OCC_right', 'Alpha2_canon_EPP_OCC_left', 'Alpha2_canon_RPD_OCC_left', 'Alpha2_canon_EPP_HPC_left', 'Alpha2_canon_RPD_HPC_left', 'Alpha2_canon_RPD_HPC_right', 'Alpha2_canon_EPP_HPC_right', 'Alpha2_canon_RPD_CING_right', 'High_subj_spec_RPD_OCC_right', 'High_subj_spec_EPP_OCC_right', 'High_subj_spec_RPD_HPC_left', 'High_subj_spec_EPP_HPC_left', 'High_subj_spec_EPP_OCC_left', 'High_subj_spec_RPD_OCC_left', 'High_subj_spec_RPD_CING_right', 'High_subj_spec_EPP_CING_right', 'Low_subj_spec_RPD_OCC_right', 'Low_subj_spec_RPD_OCC_left', 'Alpha2_canon_RPD_CING_left', 'Low_subj_spec_RPD_HPC_left', 'Low_subj_spec_RPD_CING_right', 'High_subj_spec_RPD_HPC_right', 'High_subj_spec_EPP_HPC_right', 'Alpha2_canon_EPP_PARIET_left', 'Alpha2_canon_RPD_PARIET_left', 'High_subj_spec_RPD_CING_left', 'High_subj_spec_EPP_CING_left', 'Alpha2_canon_EPP_PARIET_right', 'Alpha2_canon_RPD_PARIET_right', 'Low_subj_spec_RPD_CING_left', 'Low_subj_spec_RPD_HPC_right', 'High_subj_spec_EPP_PARIET_left', 'High_subj_spec_RPD_PARIET_left', 'Low_subj_spec_RPD_PARIET_left', 'High_subj_spec_RPD_PARIET_right', 'High_subj_spec_EPP_PARIET_right', 'Low_subj_spec_RPD_PARIET_right']
  67. # %%
  68. len(features)
  70. # %% [markdown]
  71. # # Data
  73. # %%
  74. import pandas as pd
  75. import numpy as np
  76. from sklearn.preprocessing import MinMaxScaler,StandardScaler
  77. import pickle
  80. file_path_CN = f'{path}Alpha_Brain_Clocks/Data/CN_freq.xlsx'
  81. file_path_AD = f'{path}Alpha_Brain_Clocks/Data/AD_freq.xlsx'
  82. file_path_FTD = f'{path}Alpha_Brain_Clocks/Data/FTD_freq.xlsx'
  83. file_path_MCI = f'{path}Alpha_Brain_Clocks/Data/MCI_freq.xlsx'
  85. df_CN = pd.read_excel(file_path_CN)
  86. df_AD = pd.read_excel(file_path_AD)
  87. df_FTD = pd.read_excel(file_path_FTD)
  88. df_MCI = pd.read_excel(file_path_MCI)
  90. regiones = ["America", "Turquia", "Europa"]
  91. countrys = ["Chile", "Argentina", "Colombia", "Brasil", "Turquia", "Reino Unido", "Grecia", "Irlanda"]
  93. df_CN_filtrado = df_CN[(df_CN['Age'] >=50) & (df_CN['Age'] <= 90)].reset_index(drop=True)
  94. df_CN_filtrado = df_CN_filtrado[df_CN_filtrado["Region"].isin(regiones)] # Se saca Turquia
  97. df_AD_filtrado = df_AD[(df_AD['Age'] >= 50) & (df_AD['Age'] <= 90)].reset_index(drop=True)
  98. df_FTD_filtrado = df_FTD[(df_FTD['Age'] >= 50) & (df_FTD['Age'] <= 90)].reset_index(drop=True)
  99. df_MCI_filtrado = df_MCI[(df_MCI['Age'] >= 50) & (df_MCI['Age'] <= 90)].reset_index(drop=True)
  102. # %%
  103. X_CN = df_CN_filtrado[features]
  104. y_CN = df_CN_filtrado["Age"]
  105. ID_CN = df_CN_filtrado["ID_unique"]
  107. X_AD = df_AD_filtrado[features]
  108. y_AD = df_AD_filtrado["Age"]
  109. ID_AD = df_AD_filtrado["ID_unique"]
  111. X_FTD = df_FTD_filtrado[features]
  112. y_FTD = df_FTD_filtrado["Age"]
  113. ID_FTD = df_FTD_filtrado["ID_unique"]
  115. X_MCI = df_MCI_filtrado[features]
  116. y_MCI = df_MCI_filtrado["Age"]
  117. ID_MCI = df_MCI_filtrado["ID_unique"]
  119. # %%
  120. scaler = StandardScaler()
  121. scaler.fit(X_CN)
  123. X_CN_scaled = scaler.transform(X_CN)
  124. X_CN_scaled = pd.DataFrame(X_CN_scaled, columns=X_CN.columns)
  126. df_concatenado_CN = pd.concat([X_CN, y_CN, ID_CN], axis=1, ignore_index=False)
  127. df_concatenado_AD = pd.concat([X_AD, y_AD, ID_AD], axis=1, ignore_index=False)
  128. df_concatenado_FTD = pd.concat([X_FTD, y_FTD, ID_FTD], axis=1, ignore_index=False)
  129. df_concatenado_MCI = pd.concat([X_MCI, y_MCI, ID_MCI], axis=1, ignore_index=False)
  132. # %% [markdown]
  133. # # Busqueda Hiperparametros
  135. # %%
  136. opt_model, best_params = model_reg.search_best_model (X=X_CN_scaled, y=y_CN, n_iter_=50, scoring_metric='r2')
  139. # %%
  140. best_params_ = model_reg.best_hyper(num_best=10, opt_model=opt_model, num_max=50)
  141. best_params_
  143. # %% [markdown]
  144. # # Train
  146. # %%
  147. results_labels_df_CN_train, results_labels_df_CN_test, results_model, results_per_fold_CN_train, results_per_fold_CN_test, df_CN_avg_train,df_CN_avg_test= model_reg.trainer(
  148. X=X_CN,
  149. y=y_CN,
  150. ID_label='ID-unique',
  151. ID=ID_CN,
  152. n_splits=10,
  153. n_iterations=20,
  154. params_=best_params_[0]
  155. )
  157. # %%
  158. results_labels_df_AD_test, results_per_fold_AD_test, df_AD_avg_test = model_reg.test(X=X_AD, y=y_AD, ID= ID_AD, ID_label='ID_unique', n_splits=10, n_iterations=20,result_model=results_model)
  159. results_labels_df_FTD_test, results_per_fold_FTD_test, df_FTD_avg_test = model_reg.test(X=X_FTD, y=y_FTD, ID= ID_FTD, ID_label='ID_unique', n_splits=10, n_iterations=20,result_model=results_model)
  160. results_labels_df_MCI_test, results_per_fold_MCI_test, df_MCI_avg_test = model_reg.test(X=X_MCI, y=y_MCI, ID= ID_MCI, ID_label='ID_unique', n_splits=10, n_iterations=20,result_model=results_model)
  163. # %% [markdown]
  164. # # Metrics
  166. # %%
  167. import pandas as pd
  168. from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score
  169. import numpy as np
  171. def calculate_metrics(results_per_fold):
  172. metrics_results = []
  174. for fold_idx, fold_data in enumerate(results_per_fold):
  175. y_true = fold_data['y_labels']
  176. y_pred = fold_data['y_pred']
  177. y_pred_corrected = fold_data['y_pred_corrected']
  179. mae = mean_absolute_error(y_true, y_pred)
  180. mse = mean_squared_error(y_true, y_pred)
  181. rmse = np.sqrt(mse)
  182. r2 = r2_score(y_true, y_pred)
  184. mae_corrected = mean_absolute_error(y_true, y_pred_corrected)
  185. mse_corrected = mean_squared_error(y_true, y_pred_corrected)
  186. rmse_corrected = np.sqrt(mse_corrected)
  187. r2_corrected = r2_score(y_true, y_pred_corrected)
  189. metrics_results.append({
  190. 'fold': fold_idx,
  191. 'MAE': mae,
  192. 'R2': r2,
  193. 'MSE': mse,
  194. 'RMSE': rmse,
  195. 'MAE_corrected': mae_corrected,
  196. 'R2_corrected': r2_corrected,
  197. 'MSE_corrected': mse_corrected,
  198. 'RMSE_corrected': rmse_corrected
  199. })
  201. metrics_df = pd.DataFrame(metrics_results)
  202. return metrics_df
  204. # %%
  205. metrics_df_CN_test = calculate_metrics(results_per_fold_CN_test)
  206. metrics_df_CN_train = calculate_metrics(results_per_fold_CN_train)
  208. metrics_df_AD_test = calculate_metrics(results_per_fold_AD_test)
  209. metrics_df_FTD_test = calculate_metrics(results_per_fold_FTD_test)
  210. metrics_df_MCI_test = calculate_metrics(results_per_fold_MCI_test)
  212. # %% [markdown]
  213. # # SHAP
  215. # %%
  216. shap_values_CN, shap_values_avg_CN, shap_summary_sorted_CN, shap_per_fold_CN = model_reg.calculate_multiple_shap(
  217. df_concatenado_CN, df_concatenado_CN,'ID_unique', results_per_fold_CN_train, results_per_fold_CN_test, results_model['model'],scaler=Scaler_reg_train
  218. )
  220. shap_values_AD, shap_values_avg_AD, shap_summary_sorted_AD, shap_per_fold_AD = model_reg.calculate_multiple_shap(
  221. df_concatenado_CN, df_concatenado_AD,'ID_unique', results_per_fold_CN_train, results_per_fold_AD_test, results_model['model'],scaler=Scaler_reg_train
  222. )
  224. shap_values_FTD, shap_values_avg_FTD, shap_summary_sorted_FTD, shap_per_fold_FTD = model_reg.calculate_multiple_shap(
  225. df_concatenado_CN, df_concatenado_FTD,'ID_unique', results_per_fold_CN_train, results_per_fold_FTD_test, results_model['model'],scaler=Scaler_reg_train
  226. )
  228. shap_values_MCI, shap_values_avg_MCI, shap_summary_sorted_MCI, shap_per_fold_MCI = model_reg.calculate_multiple_shap(
  229. df_concatenado_CN, df_concatenado_MCI,'ID_unique', results_per_fold_CN_train, results_per_fold_MCI_test, results_model['model'],scaler=Scaler_reg_train
  230. )

ElasticNet.ipynb at commit fffe71c, no license · at the source

Overview

Authors: Mónica Otero1, Felipe I. Carriel-Rubilar2, Hernan Hernandez3, Jhosmary Cuadros3, Jorge G. Condado4, Agustin Sainz-Ballesteros3, Hernando Santamaria-Garcia5,6,7, Agustina Legaz3,8, Agustina Birba3, Sol Fittipaldi3,9,10,11, Francisco Lopera12, John Ochoa-Gómez12, David Aguillon12, Alfredis González-Hernández13, Jasmin Bonilla-Santos14, Rodrigo A. Gonzalez-Montealegre15, Görsev G. Yener16,17,18, Bahar Güntekin19,20, İlayda Kıyı21, Tuba Aktürk22,23
and 22 other authorsEbru Yıldırım24,25, Lütfü Hanoğlu26, Renato Anghinah27, Pedro A. Valdes-Sosa28,29, Ronaldo Garcia-Reyes28,29, Javier Escudero30, Susanna Lopez31, Robert Whelan9,11, Alberto Fernández32, Adolfo M. García8,10,11,33, David Huepe34, Marcio Soto-Añari35, Eduar Herrera36, Daniel Abasolo37, Nicolás Rubido38, Ruaridh A. Clark39, Wael El-Deredy40, Jesús M. Cortes4, Mario A. Parra41, Claudio Babiloni31,42, Agustin Ibanez3,8,10,11,19,43,44, Pavel Prado45
ORCID iDs: David Aguillon, Rodrigo A. Gonzalez-Montealegre, Tuba Aktürk, Pedro A. Valdes-Sosa, Javier Escudero, Marcio Soto-Añari, Eduar Herrera, Nicolás Rubido, Ruaridh A. Clark, Wael El-Deredy, Jesús M. Cortes, Agustin Ibanez, Pavel Prado
45 affiliations
  1. Facultad de Ingeniería, Universidad San Sebastián,Santiago, Chile
  2. Doctorado en Biología Computacional, Universidad San Sebastián,Santiago, Chile
  3. Latin American Brain Health Institute (BrainLat), Universidad Adolfo Ibañez,Santiago, Chile
  4. Computational Neuroimaging Lab, Biobizkaia Health Research Institute, Barakaldo, Spain
  5. Pontificia Universidad Javeriana, PhD Program of Neuroscience,Bogotá, Colombia
  6. Center for Brain and Cognition, Intellectus, Bogotá, Colombia
  7. Hospital Universitario San Ignacio, Bogotá, Colombia and Universidad Nacional de Colombia,Bogotá, Colombia
  8. Cognitive Neuroscience Center, Universidad de San Andrés,Buenos Aires, Argentina
  9. School of Psychology, Trinity College Dublin,Dublin, Ireland
  10. Global Brain Health Institute (GBHI), University of California San Francisco,San Francisco, USA
  11. Global Brain Health Institute (GBHI), Trinity College Dublin,Dublin, Ireland
  12. Grupo de Neurociencias de Antioquia (GNA), University of Antioquia,Medellín, Colombia
  13. Department of Psychology, Master Program of Clinical Neuropsychology, Universidad Surcolombiana Neiva,Huila, Colombia
  14. Universidad Cooperativa de Colombia,Arauca, Colombia
  15. AG is with the Laboratorio de Neurocognición y Psicofisología, Universidad Surcolombiana,Neiva, Colombia
  16. Dokuz Eylül University, Medical School, Neurology Department,Izmir, Turkey
  17. Dokuz Eylül University, Health Sciences Institute, Neuroscience Department,Izmir, Turkey
  18. Izmir Biomedicine and Genome Center,Izmir, Turkey
  19. Department of Biophysics, School of Medicine, Istanbul Medipol University,Istanbul, Turkey
  20. Neuroscience Research Center, Research Institute for Health Sciences and Technologies, SABITA, Istanbul Medipol University,Istanbul, Turkey
  21. Dokuz Eylül University, Health Sciences Institute, Neuroscience Department,Izmir, Türkiye
  22. TA is with the Neuroscience Research Center, Research Institute for Health Sciences and Technologies (SABITA), Istanbul Medipol University,Istanbul, Turkey
  23. Section Brain Stimulation and Cognition, Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University,Maastricht, The Netherlands
  24. Neuroscience and Neurotechnology Center of Excellence (NÖROM), Gazi University,Ankara, Türkiye
  25. Istanbul Medipol University,Istanbul, Turkey
  26. Department of Neurology, Istanbul Medipol University,Istanbul, Turkey
  27. Reference Center of Behavioural Disturbances and Dementia, School of Medicine, University of Sao Paulo,Sao Paulo, Brazil
  28. Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China,Chengdu, China
  29. Human Brain Mapping Department, Cuban Neuroscience Center,Habana, Cuba
  30. Institute for Imaging, Data and Communications, School of Engineering, University of Edinburgh,Edinburgh, UK
  31. Department of Physiology and Pharmacology “V. Erspamer”, Sapienza University of Rome,Rome, Italy
  32. Department of Legal Medicine, Psychiatry and Pathology, Complutense University,Madrid, Spain
  33. Departamento de Lingüística y Literatura, Facultad de Humanidades, Universidad de Santiago,Santiago, Chile
  34. Center for Social and Cognitive Neuroscience (CSCN), School of Psychology, Universidad Adolfo Ibáñez, Penalolen,Santiago, Chile
  35. Universidad Católica San Pablo,Arequipa, Perú
  36. Departamento de Estudios Psicológicos, Universidad Icesi,Cali, Colombia
  37. Centre for Biomedical Engineering, School of Engineering, Faculty of Engineering and Physical Sciences, University of Surrey,Guildford, UK
  38. Institute for Complex Systems and Mathematical Biology, University of Aberdeen,Aberdeen, UK
  39. Centre for Signal and Image Processing, Department of Electronic and Electrical Engineering, University of Strathclyde,Strathclyde, UK
  40. Center of Interdisciplinary Biomedical and Engineering Research for Health, Universidad de Valparaíso,Valparaíso, Chile
  41. Department of Psychological Sciences and Health, University of Strathclyde,Glasgow, UK
  42. Hospital San Raffaele Cassino, Cassino, Italy
  43. Trinity College Dublin, The University of Dublin,Dublin, Ireland
  44. Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation,Barcelona, Spain
  45. Escuela de Fonoaudiología, Facultad de Ciencias de la Rehabilitación y Calidad de Vida, Universidad San Sebastián,Santiago, Chile
Institutions: San Sebastián University (Chile); Adolfo Ibáñez University (Chile); Pontificia Universidad Javeriana (Colombia); Universidad Nacional de Colombia (Colombia); University of San Andrés (Argentina); Trinity College Dublin (Ireland); University of California, San Francisco (United States); Universidad de Antioquia (Colombia); Universidad Surcolombiana (Colombia); Universidad Cooperativa de Colombia (Colombia); Dokuz Eylül University (Türkiye); Izmir Biomedicine and Genome Center (Türkiye); Istanbul Medipol University (Türkiye); Maastricht University (Netherlands); Gazi University (Türkiye); Universidade de São Paulo (Brazil); University of Electronic Science and Technology of China (China); Cuban Neuroscience Center (Cuba); University of Edinburgh (United Kingdom); Sapienza University of Rome (Italy); Universidad Complutense de Madrid (Spain); Universidad de Santiago de Chile (Chile); Universidad Católica San Pablo (Peru); Icesi University (Colombia); University of Surrey (United Kingdom); University of Aberdeen (United Kingdom); University of Strathclyde (United Kingdom); University of Valparaíso (Chile); Barcelonaβeta Brain Research Center (Spain)
Journal: Communications biology, volume 9, issue 1, article 740
Dates: received 8 May 2025; accepted 24 April 2026; published online 21 May 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1038/s42003-026-10205-z · PMID 42162258 · PMCID PMC13226700 · OpenAlex W7161794754
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: structural MRI / diffusion (modality), EEG (modality), human (organism), other condition (population), Alzheimer's / dementia (population)
Methods: Spectral & time-frequency, Statistics, Machine learning, Preprocessing, fMRI & imaging, Physiology & signal measures
Keywords: Cognitive ageing, Dynamical systems
MeSH: Aging*, Alpha Rhythm*, Alzheimer Disease*, Brain*, Electroencephalography*, Neurodegenerative Diseases*, Aged, Aged, 80 and over, Cognitive Dysfunction, Female, Humans, Male, Middle Aged, Socioeconomic Disparities in Health (* major topic)
Topic: Functional Brain Connectivity Studies (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: ANID FONDECYT Regular 1260530; ANID BASAL FB210008, ANID FONDECYT Iniciación 11241484, ANID EXPLORACION 13240042; Davos Alzheimer’s Collaborative; Atlantic Fellowship from the Global Brain Health Institute (GBHI); Universidad Cooperativa de Colombia—CONADI; Consejo Nacional de Ciencia, Tecnología e Innovación Tecnológica de Peru (CONCYTEC) and Programa Nacional de Investigación Científica y Estudios Avanzados (PROCIENCIA), contrato N° PE501082737-2023-PROCIENCIA; Interfaculty Grant (Grant No. CA03130123, Universidad Icesi, Cali, Colombia); Ikerbasque: The Basque Foundation for Science, and from Spanish Ministry of Science (PID2023-148008OB-I00), Spanish Ministry of Health (PI22/01118), Basque Ministry of Health (2025111091, 2023111002, 2022111031); M.A.P. is supported by BrainLat Seed Grant - BL-SRGP2020-02
Citations: not cited yet (Europe PMC); 104 references in the paper

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CompNeuroLabUSS/Alpha_Brain_Clocks

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State: the link answers, verified on 28 September 2026
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Commit: fffe71c5bf3595f3fb044eae2ce3d1e04f020cda, 27 March 2026
Languages: Jupyter (1), Python (1)
Size: 3 files, 2 scripts
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Found in: “Code availability”
Holds: 1 notebook
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Tools: NumPy (2 files), pandas (2 files), scikit-learn (2 files), Matplotlib (1 file), Nilearn (1 file), SciPy (1 file), seaborn (1 file), SHAP (1 file), statsmodels (1 file)
Availability: 1 check, the latest on 28 September 2026: the link answers
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At the source: github.com/CompNeuroLabUSS/Alpha_Brain_Clocks

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Otero, M., Carriel-Rubilar, F. I., Hernandez, H., Cuadros, J., Condado, J. G., Sainz-Ballesteros, A., Santamaria-Garcia, H., Legaz, A., Birba, A., Fittipaldi, S., Lopera, F., Ochoa-Gómez, J., Aguillon, D., González-Hernández, A., Bonilla-Santos, J., Gonzalez-Montealegre, R. A., Yener, G. G., Güntekin, B., Kıyı, İ., . . . Prado, P. (2026). Source-space EEG alpha activity reveals brain age gaps due to neurodegeneration and disparity. Communications biology, 9(1), 740. https://doi.org/10.1038/s42003-026-10205-z

BibTeX

@article{otero2026source,
author = {Otero, Mónica and Carriel-Rubilar, Felipe I. and Hernandez, Hernan and Cuadros, Jhosmary and Condado, Jorge G. and Sainz-Ballesteros, Agustin and Santamaria-Garcia, Hernando and Legaz, Agustina and Birba, Agustina and Fittipaldi, Sol and Lopera, Francisco and Ochoa-Gómez, John and Aguillon, David and González-Hernández, Alfredis and Bonilla-Santos, Jasmin and Gonzalez-Montealegre, Rodrigo A. and Yener, Görsev G. and Güntekin, Bahar and Kıyı, İlayda and Aktürk, Tuba and Yıldırım, Ebru and Hanoğlu, Lütfü and Anghinah, Renato and Valdes-Sosa, Pedro A. and Garcia-Reyes, Ronaldo and Escudero, Javier and Lopez, Susanna and Whelan, Robert and Fernández, Alberto and García, Adolfo M. and Huepe, David and Soto-Añari, Marcio and Herrera, Eduar and Abasolo, Daniel and Rubido, Nicolás and Clark, Ruaridh A. and El-Deredy, Wael and Cortes, Jesús M. and Parra, Mario A. and Babiloni, Claudio and Ibanez, Agustin and Prado, Pavel},
title = {{Source-space EEG alpha activity reveals brain age gaps due to neurodegeneration and disparity}},
journal = {Communications biology},
year = {2026},
month = may,
volume = {9},
number = {1},
pages = {740},
publisher = {Nature Publishing Group},
issn = {2399-3642},
doi = {10.1038/s42003-026-10205-z},
url = {https://doi.org/10.1038/s42003-026-10205-z},
pmid = {42162258},
pmcid = {PMC13226700}
}

RIS

TY - JOUR
AU - Otero, Mónica
AU - Carriel-Rubilar, Felipe I.
AU - Hernandez, Hernan
AU - Cuadros, Jhosmary
AU - Condado, Jorge G.
AU - Sainz-Ballesteros, Agustin
AU - Santamaria-Garcia, Hernando
AU - Legaz, Agustina
AU - Birba, Agustina
AU - Fittipaldi, Sol
AU - Lopera, Francisco
AU - Ochoa-Gómez, John
AU - Aguillon, David
AU - González-Hernández, Alfredis
AU - Bonilla-Santos, Jasmin
AU - Gonzalez-Montealegre, Rodrigo A.
AU - Yener, Görsev G.
AU - Güntekin, Bahar
AU - Kıyı, İlayda
AU - Aktürk, Tuba
AU - Yıldırım, Ebru
AU - Hanoğlu, Lütfü
AU - Anghinah, Renato
AU - Valdes-Sosa, Pedro A.
AU - Garcia-Reyes, Ronaldo
AU - Escudero, Javier
AU - Lopez, Susanna
AU - Whelan, Robert
AU - Fernández, Alberto
AU - García, Adolfo M.
AU - Huepe, David
AU - Soto-Añari, Marcio
AU - Herrera, Eduar
AU - Abasolo, Daniel
AU - Rubido, Nicolás
AU - Clark, Ruaridh A.
AU - El-Deredy, Wael
AU - Cortes, Jesús M.
AU - Parra, Mario A.
AU - Babiloni, Claudio
AU - Ibanez, Agustin
AU - Prado, Pavel
TI - Source-space EEG alpha activity reveals brain age gaps due to neurodegeneration and disparity
T2 - Communications biology
J2 - Commun Biol
PY - 2026
DA - 2026/05/21
VL - 9
IS - 1
SP - 740
SN - 2399-3642
PB - Nature Publishing Group
DO - 10.1038/s42003-026-10205-z
UR - https://doi.org/10.1038/s42003-026-10205-z
LA - en
ER -

CSL-JSON

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"language": "en",
"issued": {
"date-parts": [
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2026,
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21
]
]
}
}

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