OSCR

Regional, functional and transcriptomic decoding of multidimensional brain structure alterations in obsessive-compulsive disorder.

Code ↔ Paper

11 matches between paragraphs of the paper and lines of its authors' code, computed by the harvester (lexical-v1). Click a colored paragraph or line to see its counterpart.

The 11 matches
  1. [1] § Methods › Machine learning models for exploring brain-behavior associations ↔ mpm/machine_learning.py, lines 198–269 · score 0.89 · nested cross validation, inner folds, outer folds, machine learning, hyperparameters, tuned
  2. [2] § Methods › Quality control ↔ MIND_helpers.py, lines 7–39 · score 0.66 · median absolute deviations, Quality control
  3. [3] § Methods › Univariate statistical analyses › Permutation inference for linear models ↔ mpm/univariate.py, lines 84–114 · score 0.65 · Freedman Lane procedure, nuisance variables, response variable, permutation
  4. [4] § Methods › Structural neuroimaging phenotypes ↔ MIND.py, lines 8–49 · score 0.63 · MIND network, cortical thickness, surface area, volume, Filtering, vertex
  5. [5] § Methods › Allen Human Brain Atlas preprocessing ↔ fig_extended_2.ipynb, lines 167–260 · score 0.63 · scaled robust sigmoid, gene normalization, tolerance, mirroring, aggregated, probes
  6. [6] § Methods › Univariate statistical analyses › Permutation inference for linear models ↔ mpm/univariate.py, lines 84–114 · score 0.62 · residual forming matrix, nuisance variables, hat, permutation
  7. [7] § Methods › Allen Human Brain Atlas preprocessing ↔ code/processing.py, lines 47–196 · score 0.60 · left hemisphere, coverage, mirroring, abagen, aggregated, matched
  8. [8] § Methods › Permutation inference for machine learning ↔ mpm/machine_learning.py, lines 272–318 · score 0.60 · machine learning, cross validation, permuted, Permutation, variables
  9. [9] § Methods › Enrichment analysis › Statistics and reproducibility ↔ mpm/machine_learning.py, lines 272–318 · score 0.60 · machine learning, cross validation, reproducibility, harmonization, fold, variables
  10. [10] § Results › Probing the biological signatures of brain structure disruption ↔ code/disorders_data.py, lines 94–134 · score 0.56 · log2 fold change, logFC, axis, FDR, cortex, gene
  11. [11] § Methods › Univariate statistical analyses › Permutation inference for linear models ↔ examples/example_cca.py, lines 41–47 · score 0.51 · residual forming matrix, hat, nuisance, permutation, model

Paper

Loaded from Europe PMC by your browser, not stored by OSCR: doi.org · Europe PMC

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The authors' code

Python · 320 lines · 12 KB · MIT · 3 matches

  1. #!/usr/bin/env python3
  2. # -*- coding: utf-8 -*-
  4. import numpy as np
  5. import pandas as pd
  6. from sklearn.preprocessing import StandardScaler
  7. from sklearn.linear_model import Ridge, LogisticRegression
  8. from sklearn.metrics import matthews_corrcoef
  9. from sklearn.feature_selection import SelectPercentile, f_regression, f_classif
  10. from joblib import Parallel, delayed
  11. from neuroCombat import neuroCombat, neuroCombatFromTraining
  12. from mpm.univariate import corr, freedman_lane
  15. def is_categorical(column):
  16. """
  17. Determines if a column contains only binary (0 and 1) values.
  19. Input:
  20. column (array-like): A column of data.
  22. Returns:
  23. bool: True if the column contains only 0s and 1s, False otherwise.
  24. """
  25. unique_values = np.unique(column)
  26. return set(unique_values).issubset({0, 1})
  29. def harmonize(Y, X, Z, samples, preserve_cols):
  30. """
  31. Harmonize neuroimaging data using neuroCombat.
  33. Input:
  34. Y (pandas.DataFrame): Primary variables of interest.
  35. X (pandas.DataFrame): Neuroimaging data to be harmonized.
  36. Z (pandas.DataFrame): Covariate data.
  37. samples (pandas.Series): Sample/batch identifiers.
  38. preserve_cols (list): Columns from Z whose effect(s) should be preserved during harmonization.
  40. Returns:
  41. tuple: A tuple containing:
  42. - X_harmonized (pd.DataFrame): Harmonized neuroimaging data.
  43. - estimates (dict): Harmonization estimates from neuroCombat.
  44. """
  45. pheno = pd.concat([Y,Z[preserve_cols],samples], axis=1)
  47. cat_columns = [col for col in pheno.columns if pheno[col].dropna().isin([0, 1]).all() and col != 'sample']
  48. num_columns = [col for col in pheno.columns if not pheno[col].dropna().isin([0, 1]).all() and col != 'sample']
  50. harmonization = neuroCombat(X.T, pheno, 'sample', cat_columns, num_columns)
  52. X_harmonized = pd.DataFrame(harmonization['data']).T
  53. X_harmonized.index = X.index
  54. X_harmonized.columns = pd.MultiIndex.from_tuples(X.columns)
  56. estimates = harmonization['estimates']
  58. return X_harmonized, estimates
  61. def apply_harmonization(Y, X, Z, samples, estimates, preserve_cols):
  62. """
  63. Apply harmonization to neuroimaging data using neuroCombatFromTraining.
  65. Input:
  66. Y (pandas.DataFrame): Primary variables of interest.
  67. X (pandas.DataFrame): Neuroimaging data to be harmonized.
  68. Z (pandas.DataFrame): Covariate data.
  69. samples (pandas.Series): Sample/batch identifiers.
  70. estimates (dict): Previously estimated batch effect parameters.
  71. preserve_cols (list): Columns from Z whose effect(s) should be preserved during harmonization.
  73. Returns:
  74. pandas.DataFrame: Harmonized neuroimaging data.
  75. """
  76. pheno = pd.concat([Y,Z[preserve_cols],samples], axis=1)
  78. harmonization = neuroCombatFromTraining(X.T, pheno['sample'], estimates)
  80. X_harmonized = pd.DataFrame(harmonization['data']).T
  81. X_harmonized.index = X.index
  82. X_harmonized.columns = pd.MultiIndex.from_tuples(X.columns)
  84. return X_harmonized
  87. def prep_predictors(fold, Y, X, Z, samples, preserve_cols, select_features, percentile):
  88. """
  89. Prepares predictor variables through feature selection, harmonization, and covariate regression.
  91. Input:
  92. fold (tuple): Tuple containing train indices and test indices.
  93. Y (pandas.DataFrame): Response variable.
  94. X (pandas.DataFrame): Neuroimaging variables.
  95. Z (pandas.DataFrame): Nuisance covariate data.
  96. samples (pandas.Series): Sample/batch identifiers.
  97. preserve_cols (list): Columns from Z whose effect(s) should be preserved during harmonization.
  98. select_features (bool): Whether to perform feature selection.
  99. percentile (int): Percentile of top features to select if select_features is True.
  101. Returns:
  102. numpy.ndarray: Processed predictors after feature selection, harmonization,
  103. covariate regression, and standardization
  104. """
  105. idxtrain = fold[0]
  106. idxtest = fold[1]
  108. if select_features:
  109. if is_categorical(Y):
  110. score_func = f_classif
  111. else:
  112. score_func = f_regression
  114. selector = SelectPercentile(score_func=score_func, percentile=percentile)
  115. selector.fit(X.iloc[idxtrain,:], Y.iloc[idxtrain,:].values.ravel())
  116. X_selected = selector.transform(X)
  117. selected_mask = selector.get_support()
  118. selected_columns = X.columns[selected_mask]
  119. X_selected = pd.DataFrame(X_selected, columns=selected_columns, index=X.index)
  121. else:
  122. X_selected = X.copy()
  124. Xharm = X_selected.values.copy()
  126. Xharm[idxtrain,:], estimates = harmonize(Y.iloc[idxtrain,:],
  127. X_selected.iloc[idxtrain,:],
  128. Z.iloc[idxtrain,:],
  129. samples.iloc[idxtrain],
  130. preserve_cols)
  132. Xharm[idxtest,:] = apply_harmonization(Y.iloc[idxtest,:],
  133. X_selected.iloc[idxtest,:],
  134. Z.iloc[idxtest,:],
  135. samples.iloc[idxtest],
  136. estimates,
  137. preserve_cols)
  139. N = Xharm[idxtrain,:].shape[0]
  140. intercept = np.ones((N, 1))
  141. M = np.hstack((intercept, Z.iloc[idxtrain,:].values))
  143. predictors = np.zeros_like(Xharm)
  145. coeffs = np.linalg.lstsq(M, Xharm[idxtrain,:], rcond=None)[0]
  146. intercept = coeffs[0]
  147. b = coeffs[1:]
  149. predictors[idxtrain,:] = Xharm[idxtrain,:] - (Z.iloc[idxtrain,:].values @ b + intercept)
  150. predictors[idxtest,:] = Xharm[idxtest,:] - (Z.iloc[idxtest,:].values @ b + intercept)
  152. scaler_predictors = StandardScaler().fit(predictors[idxtrain,:])
  153. predictors[idxtrain,:] = scaler_predictors.transform(predictors[idxtrain,:])
  154. predictors[idxtest,:] = scaler_predictors.transform(predictors[idxtest,:])
  156. return predictors
  159. def fit(alpha, fold, predictors, Y, seed):
  160. """
  161. Fit a predictive model (classification or regression) and compute a performance metric.
  163. Input:
  164. alpha (float): Regularization parameter (C for LogisticRegression, alpha for Ridge).
  165. fold (tuple): Tuple containing train indices and test indices.
  166. predictors (numpy.ndarray): Processed predictor variables.
  167. Y (pandas.DataFrame): Response variable.
  168. seed (int): Random seed for reproducibility.
  170. Returns:
  171. float: Performance metric:
  172. - For classification: Matthews correlation coefficient.
  173. - For regression: Correlation between actual and predicted responses.
  174. """
  175. idxtrain = fold[0]
  176. idxtest = fold[1]
  178. if is_categorical(Y):
  179. model = LogisticRegression(penalty='l2',
  180. solver='lbfgs',
  181. C=alpha,
  182. max_iter=1000,
  183. class_weight='balanced',
  184. random_state=seed)
  185. model.fit(predictors[idxtrain,:], Y.iloc[idxtrain,:].values.ravel())
  186. Y_pred = model.predict(predictors[idxtest,:])
  187. r = matthews_corrcoef(Y.iloc[idxtest, :].values.ravel(), Y_pred)
  188. return r
  190. else:
  191. model = Ridge(alpha=alpha, random_state=seed)
  192. model.fit(predictors[idxtrain,:], Y.iloc[idxtrain,:].values.ravel())
  193. Y_pred = model.predict(predictors[idxtest,:])
  194. r = corr(Y.iloc[idxtest,:].values.ravel(), Y_pred)
  195. return r
  198. def compute_score(outer_fold, inner_folds, Y, X, Z, samples, preserve_cols, seed, select_features, percentile, n_jobs):
  199. """
  200. Performs nested cross-validation to tune model hyperparameters and evaluate performance.
  202. Parameters:
  203. outer_fold (tuple): Contains indices for training and test sets for final evaluation.
  204. inner_folds (list of tuples): List of tuples containing indices for inner cross-validation folds.
  205. Y (pandas.DataFrame): Response variable.
  206. X (pandas.DataFrame): Neuroimaging variables.
  207. Z (pandas.DataFrame): Nuisance covariate data.
  208. samples (pandas.Series): Sample/batch identifiers.
  209. preserve_cols (list): Columns from Z whose effect(s) should be preserved during harmonization.
  210. seed (int): Random seed for reproducibility.
  211. select_features (bool): Whether to perform feature selection.
  212. percentile (int): Percentile of top features to select if select_features is True.
  213. n_jobs (int): Number of parallel jobs to run. Use –1 to utilize all available cores.
  215. Returns:
  216. float: Performance score from the outer fold using the best hyperparameter.
  217. """
  218. idxtrain = outer_fold[0]
  220. predictors = Parallel(n_jobs=n_jobs, verbose=True)(
  221. delayed(prep_predictors)(
  222. fold,
  223. Y.iloc[idxtrain, :],
  224. X.iloc[idxtrain, :],
  225. Z.iloc[idxtrain, :],
  226. samples.iloc[idxtrain],
  227. preserve_cols,
  228. select_features,
  229. percentile
  230. )
  231. for fold in inner_folds
  232. )
  234. alphas = [1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02, 1.e+03, 1.e+04]
  236. def _alpha_score(alpha, predictors):
  237. scores = Parallel(n_jobs=1)(
  238. delayed(fit)(
  239. alpha,
  240. inner_folds[i],
  241. predictors[i],
  242. Y.iloc[idxtrain, :],
  243. seed
  244. )
  245. for i in range(len(inner_folds))
  246. )
  247. return np.mean(scores)
  249. all_rs = Parallel(n_jobs=n_jobs, verbose=True)(
  250. delayed(_alpha_score)(alpha, predictors)
  251. for alpha in alphas
  252. )
  254. best_index = int(np.argmax(all_rs))
  255. best_alpha = alphas[best_index]
  257. pred_outer = prep_predictors(
  258. outer_fold,
  259. Y,
  260. X,
  261. Z,
  262. samples,
  263. preserve_cols,
  264. select_features,
  265. percentile
  266. )
  267. r = fit(best_alpha, outer_fold, pred_outer, Y, seed)
  269. return r
  272. def run_ml(p, outer_folds, inner_folds, Y, X, Z, samples, preserve_cols, seed, select_features, percentile, n_jobs=-1):
  273. """
  274. Runs machine learning analysis with optional permutation testing.
  276. If p=0, uses original data; if p>0, creates permuted data for permutation testing
  277. while preserving relationships with covariates for continuous outcomes.
  279. Parameters:
  280. p (int): Permutation number (0 for no permutation, >0 for permutation test).
  281. outer_folds (list): List of tuples containing indices for outer cross-validation.
  282. inner_folds (list): List of lists of tuples containing indices for inner cross-validation.
  283. Y (pandas.DataFrame): Response variable.
  284. X (pandas.DataFrame): Neuroimaging variables.
  285. Z (pandas.DataFrame): Nuisance covariate data.
  286. samples (pandas.Series): Sample/batch identifiers.
  287. preserve_cols (list): Columns from Z whose effect(s) should be preserved during harmonization.
  288. seed (int): Random seed for reproducibility.
  289. select_features (bool): Whether to perform feature selection.
  290. percentile (int): Percentile of top features to select if select_features is True.
  292. Returns:
  293. list: Performance scores from the outer folds.
  294. """
  295. if p == 0:
  296. Yshuf = Y.copy()
  297. else:
  298. np.random.seed(p)
  299. N = Y.shape[0]
  300. idy = np.random.permutation(N)
  302. if is_categorical(Y):
  303. P = np.eye(N)[idy]
  304. Yshuf = P @ Y.values
  305. else:
  306. Z_centered = np.column_stack([np.ones((N, 1)), Z.values - np.mean(Z.values, axis=0)])
  307. Hz = Z_centered @ np.linalg.pinv(Z_centered)
  308. Rz = np.eye(N) - Hz
  309. Yshuf = freedman_lane(Y.values, Z_centered, idy, Rz=Rz, Hz=Hz)
  311. Yshuf = pd.DataFrame(Yshuf, columns=Y.columns, index=Y.index)
  313. rs = []
  314. for outer_fold, inner_fold in zip(outer_folds, inner_folds):
  315. r = compute_score(outer_fold, inner_fold, Yshuf, X, Z, samples, preserve_cols, seed, select_features, percentile, n_jobs)
  316. rs.append(r)
  318. return rs

machine_learning.py at commit 2d85fbd, under MIT · at the source

Overview

Authors: Leonardo Cardoso Saraiva1,2, João R Sato3, Isaac Sebenius4, Nadza Dzinalija5,6, Carla del Río-Torné7,8, Fábio Godinho9, Antonio C Lopes2, Thomas V Fernandez10, Monicke O Lima2, Vanessa R Ramos2, Ricardo Iglesio9, Yoshinari Abe11,12, Pino Alonso7,8,13, Stephanie H Ameis14,15, Alan Anticevic16, Ana Araújo17,18, Paul D Arnold19,20, Srinivas Balachander21, Nerisa Banaj22, Marcelo C Batistuzzo2,23
and 83 other authorsFrancesco Benedetti24,25, Irene Bollettini25, Beatrice Bravi25, Brian Brennan26,27, Jan Buitelaar28,29, Miguel Castelo-Branco17,18, Sunah Choi30, Ana D Costa31,32, Sara Dallaspezia25, Damiaan Denys33,34, Isabel C Duarte17,35, Marco A N Echevarria2, Goi Khia Eng36,37, Afonso Fernandes31,32, Jamie D Feusner38,39,40, Martijn Figee41, Sophie M D D Fitzsimmons5,6, Leonardo F Fontenelle42,43,44, Rachael Grazioplene1, Minji Ha30, Alejandro Hinojosa45, Marcelo Q Hoexter2, Chaim Huijser46,47, Hao Hu48, Anthony James49, Minah Kim50,51, Jun Soo Kwon52,53,54, Luisa Lazaro13,55,56, Christine Lochner57, Mafalda Machado-Sousa31,32, Hein van Marle33,34,58, Ignacio Martínez-Zalacaín7,59, David Mataix-Cols60,61, José M Menchón7,8,13, Luciano Minuzzi62,63, Pedro Morgado31,32, Emma Muñoz-Moreno45, Tomohiro Nakao64, Janardhanan C Narayanaswamy21,65, Erika L Nurmi66, Joseph O’Neill66,67, Inkyung Park30, Mary L Phillips68, John C Piacentini66, Maria Picó-Pérez31,69, Fabrizio Piras22, Federica Piras22, Tjardo S Postma5,6, Chiang-shan R Li1,70, Janardhan Y C Reddy21, Daan van Rooij28,71, Yuki Sakai11,72,73, Juliana B de Salles Andrade42, Freda Scheffler74, Venkataram Shivakumar75, Noam Soreni63,76, Emily R Stern36,37,77, Anouk van der Straten33,34,78, Sophia I Thomopoulos79, Hirofumi Tomiyama64, Fernanda Tovar-Moll42, Daniela Vecchio22, Dick J Veltman5,6, Ganesan Venkatasubramanian21, Chris Vriend5,6, Zhen Wang80, Ysbrand D van der Werf5,6, Guido van Wingen33,34, Qing Zhao80, ENIGMA-OCD Working Group81,82,83,84,85,86,87, Alexander W Charney41,88,89, Youngsun T Cho1,10, Roseli G Shavitt2, Helen Pushkarskaya1, Carles Soriano-Mas7,8,13, Rafael Romero-Garcia90,91, Paul M Thompson79, Dan J Stein92, Odile A van den Heuvel5,6, Anderson M Winkler1,93, Euripedes C Miguel Filho2, Christopher Pittenger1,10,70,94,95,96, Carolina Cappi97
ORCID iDs: Leonardo Cardoso Saraiva, Isaac Sebenius, Carla del Río-Torné, Thomas V Fernandez, Ricardo Iglesio, Yoshinari Abe, Pino Alonso, Stephanie H Ameis, Ana Araújo, Paul D Arnold, Nerisa Banaj, Marcelo C Batistuzzo, Francesco Benedetti, Jan Buitelaar, Miguel Castelo-Branco, Damiaan Denys, Isabel C Duarte, Afonso Fernandes, Jamie D Feusner, Martijn Figee, Alejandro Hinojosa, Marcelo Q Hoexter, Chaim Huijser, Anthony James, Minah Kim, Jun Soo Kwon, Luisa Lazaro, Christine Lochner, Ignacio Martínez-Zalacaín, David Mataix-Cols, José M Menchón, Pedro Morgado, Mary L Phillips, John C Piacentini, Fabrizio Piras, Federica Piras, Chiang-shan R Li, Yuki Sakai, Freda Scheffler, Emily R Stern, Sophia I Thomopoulos, Hirofumi Tomiyama, Fernanda Tovar-Moll, Ganesan Venkatasubramanian, Zhen Wang, Ysbrand D van der Werf, Alexander W Charney, Youngsun T Cho, Carles Soriano-Mas, Paul M Thompson, Odile A van den Heuvel, Anderson M Winkler, Euripedes C Miguel Filho, Christopher Pittenger
97 affiliations
  1. Department of Psychiatry, Yale University School of Medicine, New Haven, CT USA
  2. Department of Psychiatry, University of São Paulo, São Paulo, Brazil
  3. Center of Mathematics, Computing and Cognition, Universidade Federal do ABC, Santo André, Brazil
  4. Department of Psychiatry, University of Cambridge, Cambridge, UK
  5. Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Psychiatry, Department of Anatomy & Neurosciences, Amsterdam, The Netherlands
  6. Amsterdam Neuroscience, Compulsivity Impulsivity & Attention, Amsterdam, The Netherlands
  7. Psychiatry and Mental Health Group, Neuroscience Program, Bellvitge Biomedical Research Institute (IDIBELL), L’Hospitalet de Llobregat, Spain
  8. Department of Clinical Sciences, School of Medicine, University of Barcelona, L’Hospitalet de Llobregat, Spain
  9. Neurosurgery Division, Department of Neurology, Hospital das Clínicas, University of São Paulo Medicine School, São Paulo, SP Brazil
  10. Yale Child Study Center, Yale University, New Haven, CT USA
  11. Department of Psychiatry, Graduate School of Medical Science, Kyoto Prefectural University of Medicine, Kyoto, Japan
  12. Sugimoto Psychiatric Clinic, Kyoto, Japan
  13. Network Center for Biomedical Research on Mental Health (CIBERSAM), Carlos III Health Institute (ISCIII), Madrid, Spain
  14. Cundill Centre for Child and Youth Depression, Margaret and Wallace McCain Centre for Child, Youth and Family Mental Health, Centre for Addiction and Mental Health, Toronto, ON Canada
  15. Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, ON Canada
  16. Janssen Research & Development, LLC, a Johnson & Johnson Company, Titusville, NJ USA
  17. CIBIT, ICNAS, University of Coimbra, Coimbra, Portugal
  18. Faculty of Medicine, University of Coimbra, Coimbra, Portugal
  19. The Mathison Centre for Mental Health Research & Education, Hotchkiss Brain Institute, University of Calgary, Calgary, AB Canada
  20. Departments of Psychiatry and Medical Genetics, Cumming School of Medicine, University of Calgary, Calgary, AB Canada
  21. OCD Clinic, Department of Psychiatry, National Institute of Mental Health and Neurosciences (NIMHANS), Bangalore, India
  22. Laboratory of Neuropsychiatry, Department of Clinical Neuroscience and Neurorehabilitation, IRCCS Santa Lucia Foundation, Rome, Italy
  23. Department of Methods and Techniques in Psychology, Pontifical Catholic University, São Paulo, SP Brazil
  24. Vita-Salute San Raffaele University, Milano, Italy
  25. Psychiatry and Clinical Psychobiology, Division of Neuroscience, IRCCS Ospedale San Raffaele, Milano, Italy
  26. McLean Hospital, Belmont, MA USA
  27. Harvard Medical School, Boston, MA USA
  28. Radboudumc, Department of Medical Neuroscience, Nijmegen, The Netherlands
  29. Karakter Child and Adolescent Psychiatry University Centre, Nijmegen, The Netherlands
  30. Department of Brain and Cognitive Sciences, Seoul National University College of Natural Sciences, Seoul, Republic of Korea
  31. Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Braga, Portugal
  32. Clinical Academic Center – Braga, Braga, Portugal
  33. Amsterdam UMC, location University of Amsterdam, Department of Psychiatry, Amsterdam, The Netherlands
  34. Amsterdam Neuroscience, Amsterdam, The Netherlands
  35. ICNAS, University of Coimbra, Coimbra, Portugal
  36. Department of Psychiatry, New York University Grossman School of Medicine, New York, NY USA
  37. Nathan Kline Institute for Psychiatric Research, Orangeburg, NY USA
  38. Division of Neurosciences & Clinical Translation, Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, ON Canada
  39. Centre for Addiction and Mental Health, Toronto, ON Canada
  40. Department of Women’s and Children’s Health, Karolinska Institutet, Stockholm, Sweden
  41. Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY USA
  42. D’Or Institute for Research and Education (IDOR), Rio de Janeiro, RJ Brazil
  43. Institute of Psychiatry, Federal University of Rio de Janeiro (IPUB/UFRJ), Rio de Janeiro, RJ Brazil
  44. Department of Psychiatry, School of Clinical Sciences, Monash University, Clayton, VIC Australia
  45. MRI Core Facility, IDIBAPS, Barcelona, Spain
  46. Levvel, Amsterdam, The Netherlands
  47. Amsterdam UMC, Department of Child and Adolescent Psychiatry, Amsterdam, The Netherlands
  48. Department of Psychiatry, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, P.R. China
  49. Department of Psychiatry, University of Oxford, Warneford Hospital, Oxford, OX3 7JX UK
  50. Department of Psychiatry, Seoul National University College of Medicine, Seoul, Republic of Korea
  51. Department of Neuropsychiatry, Seoul National University Hospital, Seoul, Republic of Korea
  52. Department of Psychiatry, Hanyang University College of Medicine, Seoul, Republic of Korea
  53. Department of Neuropsychiatry, Hanyang University Hospital, Seoul, Republic of Korea
  54. Institute of Human Behavioral Medicine, SNU-MRC, Seoul, Republic of Korea
  55. Department of Child and Adolescent Psychiatry and Psychology, Hospital Clínic, Barcelona, Institut d’Investigacions August Pi i Sunyer (IDIBAPS), Barcelona, Spain
  56. Department of Medicine, University of Barcelona, Barcelona, Spain
  57. SAMRC Unit on Risk & Resilience in Mental Disorders, Department of Psychiatry, Stellenbosch University, Stellenbosch, South Africa
  58. GGZ inGeest Mental Health Care, Amsterdam, The Netherlands
  59. Department of Radiology, Bellvitge University Hospital, Barcelona, Spain
  60. Centre for Psychiatry Research, Department of Clinical Neuroscience, Karolinska Institutet & Stockholm Health Care Services, Region Stockholm, Stockholm, Sweden
  61. Department of Clinical Sciences, Lund University, Lund, Sweden
  62. Anxiety Treatment and Research Clinic, Department of Psychiatry and Behavioral Neuroscience, McMaster University, Hamilton, Ontario, Canada
  63. Department of Psychiatry and Behavioral Neuroscience, McMaster University, Hamilton, Ontario, Canada
  64. Department of Neuropsychiatry, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
  65. Monash Health & Department of Psychiatry, School of Clinical Sciences, Monash University, Melbourne, VIC Australia
  66. Division of Child and Adolescent Psychiatry, Jane & Terry Semel Institute for Neurosciences, University of California, Los Angeles, CA USA
  67. UCLA Brain Research Institute, Los Angeles, CA USA
  68. Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA USA
  69. Departamento de Psicología Básica, Clínica y Psicobiología, Universitat Jaume I, Castellón de la Plana, Spain
  70. Department of Neuroscience, Yale University, New Haven, CT USA
  71. Department of Experimental Psychology, Utrecht University, Utrecht, The Netherlands
  72. ATR Brain Information Communication Research Laboratory Group, Kyoto, Japan
  73. XNef, Inc, Kyoto, Japan
  74. Department of Psychiatry and Mental Health, University of Cape Town, and Neuroscience Institute, University of Cape Town, Cape Town, South Africa
  75. Department of Integrative Medicine, National Institute of Mental Health and Neurosciences (NIMHANS), Bangalore, India
  76. Pediatric OCD Consultation Service, Anxiety Treatment and Research Clinic, St. Joseph’s Healthcare, Hamilton, Ontario, Canada
  77. Neuroscience Institute, New York University Grossman School of Medicine, New York, NY USA
  78. Levvel, Academic Center for Child and Adolescent Psychiatry and Specialized Youth Care, Amsterdam, The Netherlands
  79. Imaging Genetics Center, Stevens Institute for Neuroimaging & Informatics, Keck School of Medicine, University of Southern California, Marina del Rey, CA USA
  80. Department of Clinical Psychology, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, P.R. China
  81. LASI — Intelligent Systems Associate Laboratory, Guimarães, Portugal
  82. CFisUC, Department of Physics, University of Coimbra, Coimbra, Portugal
  83. Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea
  84. Mental Health Neuroscience Department, Division of Psychiatry and Max Planck UCL Centre for Computational Psychiatry and Ageing Research, Queen Square Institute of Neurology, UCL, London, UK
  85. Arkin Mental Health Care, Department of Psychiatry, Amsterdam, The Netherlands
  86. School of Psychology, University of Minho, Braga, Portugal
  87. Department of Psychiatry, Zhejiang University School of Medicine, Hangzhou, China
  88. Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY USA
  89. Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY USA
  90. Department of Medical Physiology and Biophysics, Instituto de Biomedicina de Sevilla (IBiS), HUVR/CSIC/Universidad de Sevilla/CIBERSAM, ISCIII, 41013 Sevilla, Spain
  91. Department of Psychiatry, University of Cambridge, Herchel Smith Bldg, Robinson Way, Cambridge, CB2 0SZ UK
  92. SAMRC Unit on Risk & Resilience in Mental Disorders, Department of Psychiatry and Neuroscience Institute, University of Cape Town, Cape Town, South Africa
  93. Division of Human Genetics, School of Medicine, The University of Texas Rio Grande Valley, Brownsville, TX USA
  94. Department of Psychology, Yale University, New Haven, CT USA
  95. Center for Brain and Mind Health, Yale University School of Medicine, New Haven, CT USA
  96. Wu-Tsai Institute, Yale University, New Haven, CT USA
  97. Rutgers University, Robert Wood Johnson Medical School, New Brunswick, NJ USA
Institutions: Universidade de São Paulo (Brazil); Yale University (United States); Universidade Federal do ABC (Brazil); University of Cambridge (United Kingdom); Amsterdam Neuroscience (Netherlands); Amsterdam University Medical Centers (Netherlands); Vrije Universiteit Amsterdam (Netherlands); Institut d'Investigació Biomédica de Bellvitge (Spain); Universitat de Barcelona (Spain); Hospital das Clínicas da Faculdade de Medicina da Universidade de São Paulo (Brazil); Kyoto Prefectural University of Medicine (Japan); Instituto de Salud Carlos III (Spain); Centro de Investigación Biomédica en Red de Salud Mental (Spain); Centre for Addiction and Mental Health (Canada); University of Toronto (Canada); Margaret and Wallace McCain Centre for Child, Youth, and Family Mental Health (Canada); Johnson & Johnson (United States) (United States); Janssen Research & Development (United States) (United States); University of Coimbra (Portugal); University of Calgary (Canada); Hotchkiss Brain Institute (Canada); Mathison Centre for Mental Health Research and Education (Canada); National Institute of Mental Health and Neurosciences (India); Fondazione Santa Lucia (Italy); Pontifícia Universidade Católica de São Paulo (Brazil); Vita-Salute San Raffaele University (Italy); IRCCS Ospedale San Raffaele (Italy); Harvard University (United States); McLean Hospital (United States); Radboud University Nijmegen (Netherlands); Radboud University Medical Center (Netherlands); Karakter (Netherlands); Seoul National University (South Korea); Maastricht University (Netherlands); Clinical Academic Center of Braga (Portugal); University of Minho (Portugal); Amsterdam UMC Location University of Amsterdam (Netherlands); Nathan Kline Institute for Psychiatric Research (United States); New York University (United States); Karolinska Institutet (Sweden); Icahn School of Medicine at Mount Sinai (United States); Universidade Federal do Rio de Janeiro (Brazil); D’Or Institute for Research and Education (Brazil); Monash University (Australia); Consorci Institut D'Investigacions Biomediques August Pi I Sunyer (Spain); Levvel (Netherlands); Shanghai Jiao Tong University (China); Shanghai Mental Health Center (China); Warneford Hospital (United Kingdom); University of Oxford (United Kingdom); Seoul National University Hospital (South Korea); Hanyang University Seoul Hospital (South Korea); Hanyang University (South Korea); Hospital Clínic de Barcelona (Spain); South African Medical Research Council (South Africa); Stellenbosch University (South Africa); GGZ inGeest (Netherlands); University of Cape Town (South Africa); Bellvitge University Hospital (Spain); Lund University (Sweden); Region Stockholm (Sweden); Stockholm Health Care Services (Sweden); Korea Advanced Institute of Science and Technology (South Korea); McMaster University (Canada); University of Amsterdam (Netherlands); Kyushu University (Japan); Monash Health (Australia); University of California, Los Angeles (United States); University of Pittsburgh (United States); Universitat Jaume I (Spain); Utrecht University (Netherlands); Advanced Telecommunications Research Institute International (Japan); Deakin University (Australia); St. Joseph’s Healthcare Hamilton (Canada); University of Southern California (United States); Instituto de Biomedicina de Sevilla (Spain); Universidad de Sevilla (Spain); The University of Texas Rio Grande Valley (United States); Rutgers, The State University of New Jersey (United States)
Journal: Nature communications, volume 17, issue 1, article 8480
Dates: received 27 December 2025; accepted 27 May 2026; published online 24 June 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1038/s41467-026-74153-2 · PMID 42343090 · PMCID PMC13478458 · OpenAlex W4411702003
Open access: gold, a free copy (OpenAlex)
Preprint: doi:10.21203/rs.3.rs-6900305/v1
Status: code verified
Categories: genetics / omics (modality), human (organism), other condition (population), cellular / molecular (subfield)
Methods: Statistics, Machine learning, Connectivity, Preprocessing
Keywords: Computational neuroscience, Machine learning, Obsessive compulsive disorder, Gene expression
MeSH: Brain*, Obsessive-Compulsive Disorder*, Transcriptome*, Adult, Female, Humans, Magnetic Resonance Imaging, Male, Phenotype (* major topic)
Topic: Obsessive-Compulsive Spectrum Disorders (Clinical Psychology, Psychology), according to OpenAlex
Funding: Fundação de Amparo à Pesquisa do Estado de São Paulo (São Paulo Research Foundation) (2022/10207-0); NIMH NIH HHS (K99 MH128540, R00 MH128540); International OCD Foundation (2025 Michael A. Jenike Young Investigator Award)
Citations: not cited yet (Europe PMC); 118 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.

Repositories

Its files are read in the Code ↔ Paper reader above, with 11 matches between paragraphs and lines of code.

biorender.com/bnwqaux

License: none: the authors keep all their rights
State: the link is dead, verified on 27 September 2026
Evidence: found in the paper
Software Heritage: not checked
Found in: the text, “Sample and structural neuroimaging phenotypes”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Availability: 1 check, the latest on 27 September 2026: the link is dead (HTTP 404)
  • 27 September 2026: the link is dead (HTTP 404)
At the source: BioRender.com/bnwqaux

ColeLab/ColeAnticevicNetPartition

License: other
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: e4ea9fd709ead8616843924b717c6abce62ea05c, 23 June 2026
Languages: MATLAB (28), Python (2), C (2), Shell (1)
Size: 124 files, 33 scripts
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Holds: README, license file
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Tools: Connectome Workbench (3 files), cifti-matlab (2 files), Matplotlib (2 files), NiBabel (2 files), NumPy (2 files), GIfTI library for MATLAB (1 file), SPM (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
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35 files
At the source: github.com/ColeLab/ColeAnticevicNetPartition

richardajdear/AHBA_gradients

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Commit: ab7939cf1811cba6296b882e35e60b09fed7d653, 23 January 2025
Languages: Python (13), Jupyter (12), R (6)
Size: 87 files, 31 scripts
Software Heritage: not archived
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Holds: README, environment (docker/Dockerfile), 12 notebooks
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Tools: NumPy (13 files), pandas (13 files), ggseg (6 files), patchwork (6 files), neuromaps (5 files), statsmodels (5 files), tidyverse (5 files), NiBabel (4 files), scikit-learn (4 files), abagen (3 files), SciPy (3 files), BrainSMASH (2 files), Brain Connectivity Toolbox (1 file), BrainSpace (1 file), ggpubr (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
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At the source: github.com/richardajdear/AHBA_gradients

csleo95/Multi-phenotype-morphometry

License: MIT
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Evidence: files inventoried
Commit: 2d85fbd8301808ae40163a476749400792719f35, 24 April 2026
Languages: Python (12)
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Software Heritage: not archived
Found in: “Code availability”
Holds: README, license file, environment (pyproject.toml, requirements.txt)
Not found: CITATION.cff, tests, continuous integration, documentation
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Availability: 1 check, the latest on 27 September 2026: the link answers
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At the source: github.com/csleo95/Multi-phenotype-morphometry

isebenius/MIND

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 0d334454eaac62e49a197801446ce7750256c882, 12 March 2024
Languages: Python (4), Jupyter (2)
Size: 15 files, 6 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README, environment (requirements.txt)
Not found: license file, CITATION.cff, tests, continuous integration, documentation
Tools: NumPy (5 files), pandas (5 files), NiBabel (3 files), SciPy (3 files), Matplotlib (2 files), AFNI (1 file), FreeSurfer (1 file), Nipype (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
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7 files
At the source: github.com/isebenius/MIND

enigma.ini.usc.edu/ongoing/enigma-shape-analysis

License: none: the authors keep all their rights
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Found in: “Code availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)
  • 27 September 2026: the link answers (HTTP 200)
At the source: enigma.ini.usc.edu/ongoing/enigma-shape-analysis/

Zenodo 19770466

License: MIT
State: the link answers, verified on 27 September 2026
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Software Heritage: not checked
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Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: NumPy (10 files), pandas (7 files), SciPy (7 files), scikit-learn (6 files), statsmodels (6 files), neuroCombat (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)
  • 27 September 2026: the link answers (HTTP 200)
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At the source:

codeocean:7065197

License: none: the authors keep all their rights
State: cannot be verified, verified on 27 September 2026
Evidence: found in the paper
Software Heritage: not checked
Found in: “Code availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Availability: 1 check, the latest on 27 September 2026: cannot be verified
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At the source:

Code availability statement

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Data

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Recorded: type, language, journal, volume, issue, pages, dates, 103 authors, 4 keywords, 9 MeSH terms, 3 funders, 109 references.

Cite

This paper

Cardoso Saraiva, L., Sato, J. R., Sebenius, I., Dzinalija, N., del Río-Torné, C., Godinho, F., Lopes, A. C., Fernandez, T. V., Lima, M. O., Ramos, V. R., Iglesio, R., Abe, Y., Alonso, P., Ameis, S. H., Anticevic, A., Araújo, A., Arnold, P. D., Balachander, S., Banaj, N., . . . Cappi, C. (2026). Regional, functional and transcriptomic decoding of multidimensional brain structure alterations in obsessive-compulsive disorder. Nature communications, 17(1), 8480. https://doi.org/10.1038/s41467-026-74153-2

BibTeX

@article{cardososaraiva2026regional,
author = {Cardoso Saraiva, Leonardo and Sato, João R and Sebenius, Isaac and Dzinalija, Nadza and del Río-Torné, Carla and Godinho, Fábio and Lopes, Antonio C and Fernandez, Thomas V and Lima, Monicke O and Ramos, Vanessa R and Iglesio, Ricardo and Abe, Yoshinari and Alonso, Pino and Ameis, Stephanie H and Anticevic, Alan and Araújo, Ana and Arnold, Paul D and Balachander, Srinivas and Banaj, Nerisa and Batistuzzo, Marcelo C and Benedetti, Francesco and Bollettini, Irene and Bravi, Beatrice and Brennan, Brian and Buitelaar, Jan and Castelo-Branco, Miguel and Choi, Sunah and Costa, Ana D and Dallaspezia, Sara and Denys, Damiaan and Duarte, Isabel C and Echevarria, Marco A N and Eng, Goi Khia and Fernandes, Afonso and Feusner, Jamie D and Figee, Martijn and Fitzsimmons, Sophie M D D and Fontenelle, Leonardo F and Grazioplene, Rachael and Ha, Minji and Hinojosa, Alejandro and Hoexter, Marcelo Q and Huijser, Chaim and Hu, Hao and James, Anthony and Kim, Minah and Kwon, Jun Soo and Lazaro, Luisa and Lochner, Christine and Machado-Sousa, Mafalda and van Marle, Hein and Martínez-Zalacaín, Ignacio and Mataix-Cols, David and Menchón, José M and Minuzzi, Luciano and Morgado, Pedro and Muñoz-Moreno, Emma and Nakao, Tomohiro and Narayanaswamy, Janardhanan C and Nurmi, Erika L and O’Neill, Joseph and Park, Inkyung and Phillips, Mary L and Piacentini, John C and Picó-Pérez, Maria and Piras, Fabrizio and Piras, Federica and Postma, Tjardo S and Li, Chiang-shan R and Reddy, Janardhan Y C and van Rooij, Daan and Sakai, Yuki and de Salles Andrade, Juliana B and Scheffler, Freda and Shivakumar, Venkataram and Soreni, Noam and Stern, Emily R and van der Straten, Anouk and Thomopoulos, Sophia I and Tomiyama, Hirofumi and Tovar-Moll, Fernanda and Vecchio, Daniela and Veltman, Dick J and Venkatasubramanian, Ganesan and Vriend, Chris and Wang, Zhen and van der Werf, Ysbrand D and van Wingen, Guido and Zhao, Qing and {ENIGMA-OCD Working Group} and Charney, Alexander W and Cho, Youngsun T and Shavitt, Roseli G and Pushkarskaya, Helen and Soriano-Mas, Carles and Romero-Garcia, Rafael and Thompson, Paul M and Stein, Dan J and van den Heuvel, Odile A and Winkler, Anderson M and Miguel Filho, Euripedes C and Pittenger, Christopher and Cappi, Carolina},
title = {{Regional, functional and transcriptomic decoding of multidimensional brain structure alterations in obsessive-compulsive disorder}},
journal = {Nature communications},
year = {2026},
month = jun,
volume = {17},
number = {1},
pages = {8480},
publisher = {Nature Publishing Group},
issn = {2041-1723},
doi = {10.1038/s41467-026-74153-2},
url = {https://doi.org/10.1038/s41467-026-74153-2},
pmid = {42343090},
pmcid = {PMC13478458}
}

RIS

TY - JOUR
AU - Cardoso Saraiva, Leonardo
AU - Sato, João R
AU - Sebenius, Isaac
AU - Dzinalija, Nadza
AU - del Río-Torné, Carla
AU - Godinho, Fábio
AU - Lopes, Antonio C
AU - Fernandez, Thomas V
AU - Lima, Monicke O
AU - Ramos, Vanessa R
AU - Iglesio, Ricardo
AU - Abe, Yoshinari
AU - Alonso, Pino
AU - Ameis, Stephanie H
AU - Anticevic, Alan
AU - Araújo, Ana
AU - Arnold, Paul D
AU - Balachander, Srinivas
AU - Banaj, Nerisa
AU - Batistuzzo, Marcelo C
AU - Benedetti, Francesco
AU - Bollettini, Irene
AU - Bravi, Beatrice
AU - Brennan, Brian
AU - Buitelaar, Jan
AU - Castelo-Branco, Miguel
AU - Choi, Sunah
AU - Costa, Ana D
AU - Dallaspezia, Sara
AU - Denys, Damiaan
AU - Duarte, Isabel C
AU - Echevarria, Marco A N
AU - Eng, Goi Khia
AU - Fernandes, Afonso
AU - Feusner, Jamie D
AU - Figee, Martijn
AU - Fitzsimmons, Sophie M D D
AU - Fontenelle, Leonardo F
AU - Grazioplene, Rachael
AU - Ha, Minji
AU - Hinojosa, Alejandro
AU - Hoexter, Marcelo Q
AU - Huijser, Chaim
AU - Hu, Hao
AU - James, Anthony
AU - Kim, Minah
AU - Kwon, Jun Soo
AU - Lazaro, Luisa
AU - Lochner, Christine
AU - Machado-Sousa, Mafalda
AU - van Marle, Hein
AU - Martínez-Zalacaín, Ignacio
AU - Mataix-Cols, David
AU - Menchón, José M
AU - Minuzzi, Luciano
AU - Morgado, Pedro
AU - Muñoz-Moreno, Emma
AU - Nakao, Tomohiro
AU - Narayanaswamy, Janardhanan C
AU - Nurmi, Erika L
AU - O’Neill, Joseph
AU - Park, Inkyung
AU - Phillips, Mary L
AU - Piacentini, John C
AU - Picó-Pérez, Maria
AU - Piras, Fabrizio
AU - Piras, Federica
AU - Postma, Tjardo S
AU - Li, Chiang-shan R
AU - Reddy, Janardhan Y C
AU - van Rooij, Daan
AU - Sakai, Yuki
AU - de Salles Andrade, Juliana B
AU - Scheffler, Freda
AU - Shivakumar, Venkataram
AU - Soreni, Noam
AU - Stern, Emily R
AU - van der Straten, Anouk
AU - Thomopoulos, Sophia I
AU - Tomiyama, Hirofumi
AU - Tovar-Moll, Fernanda
AU - Vecchio, Daniela
AU - Veltman, Dick J
AU - Venkatasubramanian, Ganesan
AU - Vriend, Chris
AU - Wang, Zhen
AU - van der Werf, Ysbrand D
AU - van Wingen, Guido
AU - Zhao, Qing
AU - ENIGMA-OCD Working Group
AU - Charney, Alexander W
AU - Cho, Youngsun T
AU - Shavitt, Roseli G
AU - Pushkarskaya, Helen
AU - Soriano-Mas, Carles
AU - Romero-Garcia, Rafael
AU - Thompson, Paul M
AU - Stein, Dan J
AU - van den Heuvel, Odile A
AU - Winkler, Anderson M
AU - Miguel Filho, Euripedes C
AU - Pittenger, Christopher
AU - Cappi, Carolina
TI - Regional, functional and transcriptomic decoding of multidimensional brain structure alterations in obsessive-compulsive disorder
T2 - Nature communications
J2 - Nat Commun
PY - 2026
DA - 2026/06/24
VL - 17
IS - 1
SP - 8480
SN - 2041-1723
PB - Nature Publishing Group
DO - 10.1038/s41467-026-74153-2
UR - https://doi.org/10.1038/s41467-026-74153-2
LA - en
ER -

CSL-JSON

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"given": "Federica"
},
{
"family": "Postma",
"given": "Tjardo S"
},
{
"family": "Li",
"given": "Chiang-shan R"
},
{
"family": "Reddy",
"given": "Janardhan Y C"
},
{
"family": "van Rooij",
"given": "Daan"
},
{
"family": "Sakai",
"given": "Yuki"
},
{
"family": "de Salles Andrade",
"given": "Juliana B"
},
{
"family": "Scheffler",
"given": "Freda"
},
{
"family": "Shivakumar",
"given": "Venkataram"
},
{
"family": "Soreni",
"given": "Noam"
},
{
"family": "Stern",
"given": "Emily R"
},
{
"family": "van der Straten",
"given": "Anouk"
},
{
"family": "Thomopoulos",
"given": "Sophia I"
},
{
"family": "Tomiyama",
"given": "Hirofumi"
},
{
"family": "Tovar-Moll",
"given": "Fernanda"
},
{
"family": "Vecchio",
"given": "Daniela"
},
{
"family": "Veltman",
"given": "Dick J"
},
{
"family": "Venkatasubramanian",
"given": "Ganesan"
},
{
"family": "Vriend",
"given": "Chris"
},
{
"family": "Wang",
"given": "Zhen"
},
{
"family": "van der Werf",
"given": "Ysbrand D"
},
{
"family": "van Wingen",
"given": "Guido"
},
{
"family": "Zhao",
"given": "Qing"
},
{
"literal": "ENIGMA-OCD Working Group"
},
{
"family": "Charney",
"given": "Alexander W"
},
{
"family": "Cho",
"given": "Youngsun T"
},
{
"family": "Shavitt",
"given": "Roseli G"
},
{
"family": "Pushkarskaya",
"given": "Helen"
},
{
"family": "Soriano-Mas",
"given": "Carles"
},
{
"family": "Romero-Garcia",
"given": "Rafael"
},
{
"family": "Thompson",
"given": "Paul M"
},
{
"family": "Stein",
"given": "Dan J"
},
{
"family": "van den Heuvel",
"given": "Odile A"
},
{
"family": "Winkler",
"given": "Anderson M"
},
{
"family": "Miguel Filho",
"given": "Euripedes C"
},
{
"family": "Pittenger",
"given": "Christopher"
},
{
"family": "Cappi",
"given": "Carolina"
}
],
"container-title-short": "Nat Commun",
"volume": "17",
"issue": "1",
"page": "8480",
"DOI": "10.1038/s41467-026-74153-2",
"PMID": "42343090",
"PMCID": "PMC13478458",
"ISSN": "2041-1723",
"publisher": "Nature Publishing Group",
"URL": "https://doi.org/10.1038/s41467-026-74153-2",
"language": "en",
"issued": {
"date-parts": [
[
2026,
6,
24
]
]
}
}

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