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Resting-state brain age is associated with cognitive and sensorimotor abnormalities in schizophrenia spectrum disorders: a validation & longitudinal study.

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  1. [1] § Methods › Site harmonization & confounding ↔ neuroHarmonize/harmonizationLearn.py, lines 10–78 · score 0.61 · linear model, ComBat, harmonization model, residuals, covariates

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The authors' code

Python · 401 lines · 18 KB · MIT · 1 match

  1. import os
  2. import pickle
  3. import numpy as np
  4. import pandas as pd
  5. from statsmodels.gam.api import GLMGam, BSplines
  6. from .harmonizationApply import applyStandardizationAcrossFeatures
  7. from neuroCombat.neuroCombat import make_design_matrix, find_parametric_adjustments, adjust_data_final, aprior, bprior
  8. import copy
  10. def harmonizationLearn(data, covars, eb=True, smooth_terms=[], smooth_term_bounds=(None, None),
  11. ref_batch=None, return_s_data=False,
  12. orig_model=None, seed=None):
  13. """
  14. Wrapper for neuroCombat function that returns the harmonization model.
  16. Arguments
  17. ---------
  18. data : a numpy array
  19. data to harmonize with ComBat, dimensions are N_samples x N_features
  21. covars : a pandas DataFrame
  22. contains covariates to control for during harmonization
  23. all covariates must be encoded numerically (no categorical variables)
  24. must contain a single column "SITE" with site labels for ComBat
  25. dimensions are N_samples x (N_covariates + 1)
  27. eb : bool, default True
  28. whether to use empirical Bayes estimates of site effects
  30. smooth_terms (Optional) : a list, default []
  31. names of columns in covars to include as smooth, nonlinear terms
  32. can be any or all columns in covars, except "SITE"
  33. if empty, ComBat is applied with a linear model of covariates
  34. if not empty, Generalized Additive Models (GAMs) are used
  35. will increase computation time due to search for optimal smoothing
  37. smooth_term_bounds (Optional) : tuple of float, default (None, None)
  38. feature to support custom boundaries of the smoothing terms
  39. useful when holdout data covers different range than
  40. specify the bounds as (minimum, maximum)
  41. currently not supported for models with mutliple smooth terms
  43. ref_batch (Optional) : str or int, default None
  44. batch (site or scanner) to be used as reference for batch adjustment
  46. return_s_data (Optional) : bool, default False
  47. whether to return s_data, the standardized data array
  48. can be useful for diagnostics but will be costly to save/load if large
  50. seed (Optional) : int, default None
  51. By default, this function is non-deterministic. Setting the optional
  52. argument `seed` will make the function deterministic.
  55. Returns
  56. -------
  57. model : a dictionary of estimated model parameters
  58. design, var_pooled, B_hat, stand_mean, mod_mean,
  59. gamma_star, delta_star, info_dict (a neuroCombat invention),
  60. gamma_hat, delta_hat, gamma_bar, t2, a_prior, b_prior, smooth_model
  62. bayes_data : a numpy array
  63. harmonized data, corrected for effects of SITE
  64. dimensions are N_samples x N_features
  66. s_data (Optional) : a numpy array
  67. standardized residuals after accounting for `covars` other than `SITE`
  68. set return_s_data=True to output the variable
  70. """
  71. # set optional random seed
  72. if seed is not None:
  73. np.random.seed(seed)
  75. if orig_model is None:
  76. pass
  77. else:
  78. model = copy.deepcopy(orig_model)
  80. # bypass EB step if only one variable provided in dataset
  81. if data.shape[1]==1:
  82. if eb:
  83. print('\n[neuroHarmonize]: Bypassing empirical Bayes step because only one variable to harmonize.')
  84. eb = False
  86. # transpose data as per ComBat convention
  87. data = data.T
  88. # prep covariate data
  89. covar_levels = list(covars.columns)
  90. batch_labels = np.unique(covars.SITE)
  91. batch_col = covars.columns.get_loc('SITE')
  93. if orig_model is None:
  94. pass
  95. else:
  96. isTrainSite = covars['SITE'].isin(model['SITE_labels'])
  97. isTrainSiteLabel = set(model['SITE_labels'])
  98. isTrainSiteColumns = np.where((pd.DataFrame(np.unique(covars['SITE'])).isin(model['SITE_labels']).values).flat)
  99. isTrainSiteColumnsOrig = np.where((pd.DataFrame(model['SITE_labels']).isin(np.unique(covars['SITE'])).values).flat)
  100. isTestSiteColumns = np.where((~pd.DataFrame(np.unique(covars['SITE'])).isin(model['SITE_labels']).values).flat)
  102. cat_cols = []
  103. num_cols = [covars.columns.get_loc(c) for c in covars.columns if c!='SITE']
  104. smooth_cols = [covars.columns.get_loc(c) for c in covars.columns if c in smooth_terms]
  105. # maintain a dictionary of smoothing information
  106. smooth_model = {
  107. 'perform_smoothing': len(smooth_terms)>0,
  108. 'smooth_terms': smooth_terms,
  109. 'smooth_cols': smooth_cols,
  110. 'bsplines_constructor': None,
  111. 'formula': None,
  112. 'df_gam': None
  113. }
  114. covars = np.array(covars, dtype='object')
  115. if ref_batch is None:
  116. covars[:,batch_col] = np.unique(covars[:,batch_col],return_inverse=True)[-1]
  117. ref_level=None
  118. else:
  119. ref_indices = np.argwhere((covars[:,batch_col]==ref_batch).squeeze())
  120. covars[:,batch_col] = np.unique(covars[:,batch_col],return_inverse=True)[-1]
  121. if ref_indices.shape[0]==0:
  122. ref_level=None
  123. ref_batch=None
  124. print('[neuroHarmonize] batch.ref not found. Setting to None.')
  125. else:
  126. ref_level = int(covars[ref_indices[0],batch_col])
  128. # create dictionary that stores batch info
  129. (batch_levels, sample_per_batch) = np.unique(covars[:,batch_col],return_counts=True)
  130. info_dict = {
  131. 'batch_levels': batch_levels.astype('int'),
  132. 'n_batch': len(batch_levels),
  133. 'n_sample': int(covars.shape[0]),
  134. 'sample_per_batch': sample_per_batch.astype('int'),
  135. 'batch_info': [list(np.where(covars[:,batch_col]==idx)[0]) for idx in batch_levels],
  136. 'ref_level': ref_level
  137. }
  138. ###
  139. design = make_design_matrix(covars, batch_col, cat_cols, num_cols, ref_level)
  143. ### additional setup if smoothing is performed
  144. if smooth_model['perform_smoothing']:
  145. # create cubic spline basis for smooth terms
  146. X_spline = covars[:, smooth_cols].astype(float)
  147. if orig_model is None:
  148. if len(smooth_cols)==1:
  149. bs = BSplines(X_spline, df=10, degree=3,
  150. knot_kwds=[{'lower_bound':smooth_term_bounds[0], 'upper_bound':smooth_term_bounds[1]}])
  151. else:
  152. bs = BSplines(X_spline, df=[10] * len(smooth_cols), degree=[3] * len(smooth_cols))
  153. # construct formula and dataframe required for gam
  154. formula = 'y ~ '
  155. df_gam = {}
  156. for b in batch_levels:
  157. formula = formula + 'x' + str(b) + ' + '
  158. df_gam['x' + str(b)] = design[:, b]
  159. for c in num_cols:
  160. if c not in smooth_cols:
  161. formula = formula + 'c' + str(c) + ' + '
  162. df_gam['c' + str(c)] = covars[:, c].astype(float)
  163. formula = formula[:-2] + '- 1'
  164. df_gam = pd.DataFrame(df_gam)
  165. # for matrix operations, a modified design matrix is required
  166. design = np.concatenate((df_gam, bs.basis), axis=1)
  167. # store objects in dictionary
  168. smooth_model['bsplines_constructor'] = bs
  169. smooth_model['formula'] = formula
  170. smooth_model['df_gam'] = df_gam
  171. else:
  172. bs_basis = model['smooth_model']['bsplines_constructor'].transform(X_spline)
  173. # construct formula and dataframe required for gam
  174. formula = 'y ~ '
  175. df_gam = {}
  176. for b in batch_levels:
  177. formula = formula + 'x' + str(b) + ' + '
  178. df_gam['x' + str(b)] = design[:, b]
  179. for c in num_cols:
  180. if c not in smooth_cols:
  181. formula = formula + 'c' + str(c) + ' + '
  182. df_gam['c' + str(c)] = covars[:, c].astype(float)
  183. formula = formula[:-2] + '- 1'
  184. df_gam = pd.DataFrame(df_gam)
  185. # for matrix operations, a modified design matrix is required
  186. design = np.concatenate((df_gam, bs_basis), axis=1)
  187. ###
  188. # run steps to perform ComBat
  189. if orig_model is None:
  190. s_data, stand_mean, var_pooled, mod_mean, B_hat = standardizeAcrossFeatures(
  191. data, design, info_dict, smooth_model)
  192. LS_dict = fitLSModelAndFindPriors(s_data, design, info_dict, eb=eb)
  193. # optional: avoid EB estimates
  194. if eb:
  195. gamma_star, delta_star = find_parametric_adjustments(s_data, LS_dict, info_dict, mean_only=False)
  196. else:
  197. gamma_star = LS_dict['gamma_hat']
  198. delta_star = np.array(LS_dict['delta_hat'])
  199. bayes_data = adjust_data_final(s_data, design, gamma_star, delta_star, stand_mean, mod_mean, var_pooled, info_dict, data)
  200. # save model parameters in single object
  201. model = {'design': design, 'SITE_labels': batch_labels,
  202. 'var_pooled':var_pooled, 'B_hat':B_hat, 'stand_mean': stand_mean, 'mod_mean': mod_mean,
  203. 'gamma_star': gamma_star, 'delta_star': delta_star, 'info_dict': info_dict,
  204. 'gamma_hat': LS_dict['gamma_hat'], 'delta_hat': np.array(LS_dict['delta_hat']),
  205. 'gamma_bar': LS_dict['gamma_bar'], 't2': LS_dict['t2'],
  206. 'a_prior': LS_dict['a_prior'], 'b_prior': LS_dict['b_prior'],
  207. 'smooth_model': smooth_model, 'eb': eb,'SITE_labels_train':batch_labels,'Covariates':covar_levels,
  208. 'ref_batch': ref_batch}
  209. # transpose data to return to original shape
  210. bayes_data = bayes_data.T
  211. else:
  212. # Create train data
  213. (batch_levels, sample_per_batch) = np.unique(covars[isTrainSite,batch_col],return_counts=True)
  214. if batch_levels.size == 0:
  215. bayes_data_train = np.zeros(shape=(0,data.shape[0]))
  216. s_data_train = np.zeros(shape=(0,data.shape[0])).T
  217. else:
  218. info_dict_train = model['info_dict'].copy()
  219. info_dict_train['sample_per_batch'] = sample_per_batch.astype('int')
  220. info_dict_train['batch_info'] = [list(np.where(covars[isTrainSite,batch_col]==idx)[0]) for idx in batch_levels]
  221. tmp = np.concatenate((np.zeros(shape=(info_dict['n_sample'],len(model['SITE_labels']))), design[:,len(batch_labels):]),axis=1)
  222. s_data_train, stand_mean_train, var_pooled_train, mod_mean_train = applyStandardizationAcrossFeatures(data[:,isTrainSite], tmp[isTrainSite,:], info_dict_train, model)
  223. design2=tmp.copy()
  224. design2[:,isTrainSiteColumnsOrig[0]] = design[:,isTrainSiteColumns[0]]
  225. bayes_data_train = adjust_data_final(s_data_train, design2[isTrainSite,:], model['gamma_star'], model['delta_star'], stand_mean_train, mod_mean_train, model['var_pooled'], info_dict_train, data)
  226. # transpose data to return to original shape
  227. bayes_data_train = bayes_data_train.T
  229. # Create test data (new SITE)
  230. (batch_levels, sample_per_batch) = np.unique(covars[~isTrainSite,batch_col],return_counts=True)
  231. if batch_levels.size == 0:
  232. bayes_data_test = np.zeros(shape=(0,data.shape[0]))
  233. s_data_test = np.zeros(shape=(0,data.shape[0])).T
  234. else:
  235. info_dict_test = {
  236. 'batch_levels': batch_levels.astype('int'),
  237. 'n_batch': len(batch_levels),
  238. 'n_sample': int(covars[~isTrainSite,:].shape[0]),
  239. 'sample_per_batch': sample_per_batch.astype('int'),
  240. 'batch_info': [list(np.where(covars[~isTrainSite,batch_col]==idx)[0]) for idx in batch_levels]
  241. }
  242. design_tmp = np.concatenate((design[:,isTestSiteColumns[0]], design[:,len(batch_labels):]),axis=1)
  243. s_data_test, stand_mean_test, var_pooled_test, mod_mean_test = applyStandardizationAcrossFeatures(data[:,~isTrainSite], design_tmp[~isTrainSite,:], info_dict_test, model)
  244. LS_dict = fitLSModelAndFindPriors(s_data_test, design_tmp[~isTrainSite,:], info_dict_test, eb=eb)
  245. if eb:
  246. gamma_star, delta_star = find_parametric_adjustments(s_data_test, LS_dict, info_dict_test, mean_only=False)
  247. else:
  248. gamma_star = LS_dict['gamma_hat']
  249. delta_star = np.array(LS_dict['delta_hat'])
  250. betas = []
  251. for i in range(info_dict_test['n_batch']):
  252. diff_mean = np.mean(data[:,info_dict_test['batch_info'][i]]-np.dot(design[info_dict_test['batch_info'][i],info_dict['n_batch']:],model['B_hat'][len(model['SITE_labels']):,:]).T,axis=1)
  253. betas.append(diff_mean)
  254. new_betas = np.array(betas)
  255. model['B_hat'] = np.concatenate((model['B_hat'][:len(model['SITE_labels']),:],new_betas,model['B_hat'][len(model['SITE_labels']):,:]))
  256. model['SITE_labels'] = np.append(model['SITE_labels'],list(set(batch_labels)-isTrainSiteLabel))
  257. model['gamma_star'] = np.append(model['gamma_star'],gamma_star,axis=0)
  258. model['delta_star'] = np.append(model['delta_star'],delta_star,axis=0)
  259. model['info_dict']['n_batch'] = len(model['SITE_labels'])
  260. bayes_data_test = adjust_data_final(s_data_test, design_tmp[~isTrainSite,:], gamma_star, delta_star, stand_mean_test, mod_mean_test, model['var_pooled'], info_dict_test, data)
  261. # transpose data to return to original shape
  262. bayes_data_test = bayes_data_test.T
  263. bayes_data = np.zeros(shape=data.T.shape)
  264. bayes_data[isTrainSite,:] = bayes_data_train
  265. bayes_data[~isTrainSite,:] = bayes_data_test
  266. s_data = np.zeros(shape=data.T.shape)
  267. s_data[isTrainSite,:] = s_data_train.T
  268. s_data[~isTrainSite,:] = s_data_test.T
  270. if return_s_data:
  271. return model, bayes_data, s_data.T
  272. else:
  273. return model, bayes_data
  275. def standardizeAcrossFeatures(X, design, info_dict, smooth_model):
  276. """
  277. The original neuroCombat function standardize_across_features plus
  278. necessary modifications.
  280. This function will return all estimated parameters in addition to the
  281. standardized data.
  282. """
  283. n_batch = info_dict['n_batch']
  284. n_sample = info_dict['n_sample']
  285. sample_per_batch = info_dict['sample_per_batch']
  287. ### perform smoothing with GAMs if specified
  288. if smooth_model['perform_smoothing']:
  289. smooth_cols = smooth_model['smooth_cols']
  290. bs = smooth_model['bsplines_constructor']
  291. formula = smooth_model['formula']
  292. df_gam = smooth_model['df_gam']
  294. if X.shape[0] > 10:
  295. print('\n[neuroHarmonize]: smoothing more than 10 variables may take several minutes of computation.')
  296. # initialize penalization weight (not the final weight)
  297. alpha = np.array([1.0] * len(smooth_cols))
  298. # initialize an empty matrix for beta
  299. B_hat = np.zeros((design.shape[1], X.shape[0]))
  300. # estimate beta for each variable to be harmonized
  301. for i in range(0, X.shape[0]):
  302. df_gam.loc[:, 'y'] = X[i, :]
  303. gam_bs = GLMGam.from_formula(formula, data=df_gam, smoother=bs, alpha=alpha)
  304. res_bs = gam_bs.fit()
  305. # Optimal penalization weights alpha can be obtained through gcv/kfold
  306. # Note: kfold is faster, gcv is more robust
  307. gam_bs.alpha = gam_bs.select_penweight_kfold()[0]
  308. res_bs_optim = gam_bs.fit()
  309. B_hat[:, i] = res_bs_optim.params
  310. ###
  311. else:
  312. B_hat = np.dot(np.dot(np.linalg.inv(np.dot(design.T, design)), design.T), X.T)
  314. batch_info = info_dict['batch_info']
  315. ref_level = info_dict['ref_level']
  316. if ref_level is not None:
  317. grand_mean = np.transpose(B_hat[ref_level,:])
  318. X_ref = X[:,batch_info[ref_level]]
  319. design_ref = design[batch_info[ref_level],:]
  320. n_sample_ref = sample_per_batch[ref_level]
  321. var_pooled = np.dot(((X_ref - np.dot(design_ref, B_hat).T)**2), np.ones((n_sample_ref, 1)) / float(n_sample_ref))
  322. else:
  323. grand_mean = np.dot((sample_per_batch/ float(n_sample)).T, B_hat[:n_batch,:])
  324. var_pooled = np.dot(((X - np.dot(design, B_hat).T)**2), np.ones((n_sample, 1)) / float(n_sample))
  326. stand_mean = np.dot(grand_mean.T.reshape((len(grand_mean), 1)), np.ones((1, n_sample))) # nothing but grand mean
  328. # new code in neuroCombat to compute model mean
  329. if design is not None:
  330. tmp = copy.deepcopy(design)
  331. tmp[:,range(0,n_batch)] = 0
  332. mod_mean = np.transpose(np.dot(tmp, B_hat))
  334. s_data = ((X- stand_mean - mod_mean) / np.dot(np.sqrt(var_pooled), np.ones((1, n_sample))))
  336. return s_data, stand_mean, var_pooled, mod_mean, B_hat
  338. def fitLSModelAndFindPriors(s_data, design, info_dict, eb=True):
  339. """
  340. The original neuroCombat function fit_LS_model_and_find_priors plus
  341. necessary modifications.
  343. This function will return no EB information if eb=False
  344. """
  345. n_batch = info_dict['n_batch']
  346. batch_info = info_dict['batch_info']
  348. batch_design = design[:,:n_batch]
  349. gamma_hat = np.array(np.dot(np.dot(np.linalg.inv(np.matrix(np.dot(batch_design.T, batch_design))), batch_design.T), s_data.T))
  351. delta_hat = []
  352. for i, batch_idxs in enumerate(batch_info):
  353. delta_hat.append(np.var(s_data[:,batch_idxs],axis=1,ddof=1))
  355. if eb:
  356. gamma_bar = np.mean(gamma_hat, axis=1)
  357. t2 = np.var(gamma_hat,axis=1, ddof=1)
  359. a_prior = list(map(aprior, delta_hat))
  360. b_prior = list(map(bprior, delta_hat))
  362. LS_dict = {}
  363. LS_dict['gamma_hat'] = gamma_hat
  364. LS_dict['delta_hat'] = delta_hat
  365. LS_dict['gamma_bar'] = gamma_bar
  366. LS_dict['t2'] = t2
  367. LS_dict['a_prior'] = a_prior
  368. LS_dict['b_prior'] = b_prior
  369. return LS_dict
  370. else:
  371. LS_dict = {}
  372. LS_dict['gamma_hat'] = gamma_hat
  373. LS_dict['delta_hat'] = delta_hat
  374. LS_dict['gamma_bar'] = None
  375. LS_dict['t2'] = None
  376. LS_dict['a_prior'] = None
  377. LS_dict['b_prior'] = None
  378. return LS_dict
  381. def saveHarmonizationModel(model, file_name):
  382. """
  383. Save a harmonization model from harmonizationLearn().
  385. For saving model contents, this function will create a new file specified
  386. by file_name, and store the model using the pickle package.
  388. """
  389. if os.path.exists(file_name):
  390. raise ValueError('Model file already exists: %s. Change name or delete to save.' % file_name)
  391. # estimate size of out_file
  392. est_size = 0
  393. for key in ['design', 'B_hat', 'stand_mean', 'mod_mean', 'var_pooled',
  394. 'gamma_star', 'delta_star', 'gamma_hat', 'delta_hat']:
  395. est_size += model[key].nbytes / 1e6
  396. print('\n[neuroHarmonize]: Saving model object, estimated size in MB: %4.2f' % est_size)
  397. out_file = open(file_name, 'wb')
  398. pickle.dump(model, out_file)
  399. out_file.close()
  401. return None

harmonizationLearn.py at commit 89ef77f, under MIT · at the source

Overview

Authors: Sebastian Volkmer1,2,3, Stefan Fritze1,3, Dilsa Cemre Akkoc Altinok1,3, Geva Brandt1, Ersoy Kocak1,2, Julius Wiegert1,2, Oksana Berhe1,3, Yuchen Lin1,3, Heike Tost1,3, Andreas Meyer-Lindenberg1,3, Dusan Hirjak1,3, Emanuel Schwarz1,2,3
ORCID iDs: Sebastian Volkmer, Dilsa Cemre Akkoc Altinok, Julius Wiegert, Heike Tost, Dusan Hirjak, Emanuel Schwarz
  1. Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg,Mannheim, Germany
  2. Hector Institute for Artificial Intelligence in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University,Mannheim, Germany
  3. German Centre for Mental Health (DZPG), Partner site Mannheim-Heidelberg-Ulm, Mannheim, Germany
Institutions: Central Institute of Mental Health (Germany); Heidelberg University (Germany)
Journal: Translational psychiatry, volume 16, issue 1, article 451
Dates: received 11 March 2026; accepted 25 August 2026; published online 4 September 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1038/s41398-026-04426-3 · PMID 42697895 · PMCID PMC13545253 · OpenAlex W7208788794
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: structural MRI / diffusion (modality), human (organism), schizophrenia / psychosis (population)
Methods: Spectral & time-frequency, Statistics, Machine learning, Preprocessing, Connectivity, fMRI & imaging
Keywords: Diagnostic markers, Schizophrenia
MeSH: Aging*, Brain*, Schizophrenia*, Adolescent, Adult, Case-Control Studies, Cognition, Female, Humans, Longitudinal Studies, Magnetic Resonance Imaging, Male, Middle Aged, Young Adult (* major topic)
Topic: Functional Brain Connectivity Studies (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: Deutsche Forschungsgemeinschaft (DFG HI 1928/2-1, HI 1928/6-1, 1928/5-1, HI 1928/2-1, SCHW 1768/8-1, HI 1928/14-1, /14-1, HI 1928/5‐1); Hector Stiftung; Hector Stiftung II
Citations: not cited yet (Europe PMC); 80 references in the paper

Abstract

Brain-age models use neuroimaging features to predict chronological age. The resulting brain-age gap (BAG), defined as predicted brain age minus chronological age, quantifies deviations from age-expected brain characteristics. Structural brain-age acceleration is well established in schizophrenia spectrum disorders (SSD), but the utility of resting-state functional connectivity (rs-FC)–based brain age remains unclear. Here, we trained rs-FC brain-age models on aggregated lifespan data from healthy controls (n ≈ 2200) and evaluated them in four independent SSD case–control cohorts. Across independent cohorts and two brain atlases, SSD showed higher FC-based BAG than healthy controls (β ≈ 0.4–0.6), indicating modest functional brain-age elevation at the group level. However, within SSD, more negative (younger appearing) BAG was associated with poorer cognitive performance, longer duration of illness, and higher neurological soft signs (NSS). Over 12–24 weeks, increases in BAG were associated with reductions in NSS motor coordination and hard signs. Together, these findings suggest that rs-FC brain age captures both a small case–control shift and a clinically relevant dimension within SSD that is not well described by uniform “acceleration”. FC-based BAG may therefore reflect heterogeneity in network-level development and reorganization, with younger-appearing functional profiles potentially indexing greater neurodevelopmental burden.

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

Repository

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rpomponio/neuroHarmonize

License: MIT
State: the link answers, verified on 26 September 2026
Evidence: files inventoried
Commit: 89ef77fa44c7c5f11113e22f766cc66a9776af71, 13 September 2026
Languages: Python (10)
Size: 24 files, 10 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README, license file, environment (pyproject.toml, setup.py), tests, continuous integration
Not found: CITATION.cff, documentation
Tools: NumPy (8 files), pandas (7 files), neuroHarmonize (4 files), NiBabel (4 files), neuroCombat (3 files), statsmodels (3 files)
Availability: 1 check, the latest on 26 September 2026: the link answers
  • 26 September 2026: the link answers
12 files
At the source: github.com/rpomponio/neuroHarmonize

Code availability

Only publicly available software was used for the preprocessing and analysis of this analysis. Fmriprep was used for preprocessing of the MRI data and is available here https://fmriprep.org/en/23.0.1/, connectivity matrices were calculated with nilearn https://nilearn.github.io/stable/index.html. Data was harmonized with https://github.com/rpomponio/neuroHarmonize. Machine learning age regression was calculated with scikit learn https://scikit-learn.org/stable/index.html. Optuna was used for gridsearch https://hub.optuna.org/samplers/grid_search/ statsmodels were used for statistical testing https://www.statsmodels.org/stable/index.html.

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

Tracing map

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

What the map holds:

  • 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 10 scripts, each with its path and the digest of its content;
  • 1 match between paragraphs of the paper and lines of the code (method lexical-v1);
  • neither the text of the paper nor the code itself.

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

Data

Datasets cited

Data availability

The inhouse data is not publicly available but can be shared upon reasonable request. The public available datasets can be accessed as follows: CamCAN dataset is available here https://opendata.mrc-cbu.cam.ac.uk/projects/camcan/, AOMIC PIOP1: https://openneuro.org/datasets/ds002785/versions/2.0.0, AOMIC PIOP2: https://openneuro.org/datasets/ds002790/versions/2.0.0, Dallas Lifespan: https://openneuro.org/datasets/ds004856/versions/1.3.0, OASIS-3: https://www.nitrc.org/projects/oasis/, SALD: https://fcon_1000.projects.nitrc.org/indi/retro/sald.html.

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

Versions

The history of this record: each version stored by the harvester or made by a correction of its authors or of the maintainers of its code, and what changed in its facts. The texts of the paper (its abstract, its availability statements) are not part of it; versions that changed only those are not listed.

Version 3, 28 September 2026

  • Funding: added Deutsche Forschungsgemeinschaft: DFG HI 1928/2-1, HI 1928/6-1, 1928/5-1, HI 1928/2-1, SCHW 1768/8-1, HI 1928/14-1, /14-1, HI 1928/5‐1; Hector Stiftung; Hector Stiftung II

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 12 authors, 2 keywords, 14 MeSH terms, 72 references.

Cite

This paper

Volkmer, S., Fritze, S., Altinok, D. C. A., Brandt, G., Kocak, E., Wiegert, J., Berhe, O., Lin, Y., Tost, H., Meyer-Lindenberg, A., Hirjak, D., & Schwarz, E. (2026). Resting-state brain age is associated with cognitive and sensorimotor abnormalities in schizophrenia spectrum disorders: a validation & longitudinal study. Translational psychiatry, 16(1), 451. https://doi.org/10.1038/s41398-026-04426-3

BibTeX

@article{volkmer2026resting,
author = {Volkmer, Sebastian and Fritze, Stefan and Altinok, Dilsa Cemre Akkoc and Brandt, Geva and Kocak, Ersoy and Wiegert, Julius and Berhe, Oksana and Lin, Yuchen and Tost, Heike and Meyer-Lindenberg, Andreas and Hirjak, Dusan and Schwarz, Emanuel},
title = {{Resting-state brain age is associated with cognitive and sensorimotor abnormalities in schizophrenia spectrum disorders: a validation \& longitudinal study}},
journal = {Translational psychiatry},
year = {2026},
month = sep,
volume = {16},
number = {1},
pages = {451},
publisher = {Nature Publishing Group},
issn = {2158-3188},
doi = {10.1038/s41398-026-04426-3},
url = {https://doi.org/10.1038/s41398-026-04426-3},
pmid = {42697895},
pmcid = {PMC13545253}
}

RIS

TY - JOUR
AU - Volkmer, Sebastian
AU - Fritze, Stefan
AU - Altinok, Dilsa Cemre Akkoc
AU - Brandt, Geva
AU - Kocak, Ersoy
AU - Wiegert, Julius
AU - Berhe, Oksana
AU - Lin, Yuchen
AU - Tost, Heike
AU - Meyer-Lindenberg, Andreas
AU - Hirjak, Dusan
AU - Schwarz, Emanuel
TI - Resting-state brain age is associated with cognitive and sensorimotor abnormalities in schizophrenia spectrum disorders: a validation & longitudinal study
T2 - Translational psychiatry
J2 - Transl Psychiatry
PY - 2026
DA - 2026/09/04
VL - 16
IS - 1
SP - 451
SN - 2158-3188
PB - Nature Publishing Group
DO - 10.1038/s41398-026-04426-3
UR - https://doi.org/10.1038/s41398-026-04426-3
LA - en
ER -

CSL-JSON

{
"id": "10.1038/s41398-026-04426-3",
"type": "article-journal",
"title": "Resting-state brain age is associated with cognitive and sensorimotor abnormalities in schizophrenia spectrum disorders: a validation & longitudinal study",
"container-title": "Translational psychiatry",
"author": [
{
"family": "Volkmer",
"given": "Sebastian"
},
{
"family": "Fritze",
"given": "Stefan"
},
{
"family": "Altinok",
"given": "Dilsa Cemre Akkoc"
},
{
"family": "Brandt",
"given": "Geva"
},
{
"family": "Kocak",
"given": "Ersoy"
},
{
"family": "Wiegert",
"given": "Julius"
},
{
"family": "Berhe",
"given": "Oksana"
},
{
"family": "Lin",
"given": "Yuchen"
},
{
"family": "Tost",
"given": "Heike"
},
{
"family": "Meyer-Lindenberg",
"given": "Andreas"
},
{
"family": "Hirjak",
"given": "Dusan"
},
{
"family": "Schwarz",
"given": "Emanuel"
}
],
"container-title-short": "Transl Psychiatry",
"volume": "16",
"issue": "1",
"page": "451",
"DOI": "10.1038/s41398-026-04426-3",
"PMID": "42697895",
"PMCID": "PMC13545253",
"ISSN": "2158-3188",
"publisher": "Nature Publishing Group",
"URL": "https://doi.org/10.1038/s41398-026-04426-3",
"language": "en",
"issued": {
"date-parts": [
[
2026,
9,
4
]
]
}
}

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

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