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Normative age-related structural brain deviations underlying psychopathology, cognitive impairment and neurological soft signs in schizophrenia spectrum disorders.

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2 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.

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  1. [1] § Results › Association between brain deviations and cognition, neurological soft signs, and psychopathology ↔ pcntoolkit/dataio/data_factory.py, lines 219 to 273 · score 0.54 · right putamen, right pallidum, rh, lh, ventricle, brain · Report a mismatch
  2. [2] § Methods › MRI-Preprocessing › Calculating normative deviations ↔ examples/10_merge.ipynb, lines 1 to 50 · score 0.51 · hierarchical Bayesian regression, separate models, population, subset, normative model, PCNToolkit · Report a mismatch

Paper

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The authors' code

Python · 479 lines · 16 KB · GPL-3.0 · 1 match

  1. from __future__ import annotations
  3. import os
  4. import urllib.request
  5. from typing import Literal
  7. import pandas as pd
  9. from pcntoolkit.dataio.norm_data import NormData
  12. def load_fcon1000(save_path: str | None = None):
  13. """Download and save fcon dataset to specified path, or load it from there
  14. if it is already downloaded
  16. Parameters
  17. ----------
  18. save_path : str | None
  19. The path to save the dataset to, or load it from if it is already
  20. downloaded
  22. Returns
  23. -------
  24. NormData
  25. The loaded dataset as a NormData object"""
  26. if not save_path:
  27. save_path = os.path.join("pcntoolkit_resources", "data")
  28. os.makedirs(save_path, exist_ok=True)
  29. data_path = os.path.join(save_path, "fcon1000.csv")
  31. # If the dataset is not already downloaded, download it and save it to
  32. # the specified path
  33. if not os.path.exists(data_path):
  34. data = pd.read_csv(
  35. "https://raw.githubusercontent.com/predictive-clinical-neuroscience/PCNtoolkit-demo/refs/heads/main/data/fcon1000.csv"
  36. )
  37. data.to_csv(data_path, index=False)
  38. else:
  39. data = pd.read_csv(data_path)
  41. # Define the variables
  42. sex_map = {0: "F", 1: "M"}
  43. data["sex"] = data["sex"].map(sex_map)
  44. subject_ids = "sub_id"
  45. covariates = ["age"]
  46. batch_effects = ["sex", "site"]
  47. response_vars = [
  48. "lh_G&S_frontomargin_thickness",
  49. "lh_G&S_occipital_inf_thickness",
  50. "lh_G&S_paracentral_thickness",
  51. "lh_G&S_subcentral_thickness",
  52. "lh_G&S_transv_frontopol_thickness",
  53. "lh_G&S_cingul-Ant_thickness",
  54. "lh_G&S_cingul-Mid-Ant_thickness",
  55. "lh_G&S_cingul-Mid-Post_thickness",
  56. "lh_G_cingul-Post-dorsal_thickness",
  57. "lh_G_cingul-Post-ventral_thickness",
  58. "lh_G_cuneus_thickness",
  59. "lh_G_front_inf-Opercular_thickness",
  60. "lh_G_front_inf-Orbital_thickness",
  61. "lh_G_front_inf-Triangul_thickness",
  62. "lh_G_front_middle_thickness",
  63. "lh_G_front_sup_thickness",
  64. "lh_G_Ins_lg&S_cent_ins_thickness",
  65. "lh_G_insular_short_thickness",
  66. "lh_G_occipital_middle_thickness",
  67. "lh_G_occipital_sup_thickness",
  68. "lh_G_oc-temp_lat-fusifor_thickness",
  69. "lh_G_oc-temp_med-Lingual_thickness",
  70. "lh_G_oc-temp_med-Parahip_thickness",
  71. "lh_G_orbital_thickness",
  72. "lh_G_pariet_inf-Angular_thickness",
  73. "lh_G_pariet_inf-Supramar_thickness",
  74. "lh_G_parietal_sup_thickness",
  75. "lh_G_postcentral_thickness",
  76. "lh_G_precentral_thickness",
  77. "lh_G_precuneus_thickness",
  78. "lh_G_rectus_thickness",
  79. "lh_G_subcallosal_thickness",
  80. "lh_G_temp_sup-G_T_transv_thickness",
  81. "lh_G_temp_sup-Lateral_thickness",
  82. "lh_G_temp_sup-Plan_polar_thickness",
  83. "lh_G_temp_sup-Plan_tempo_thickness",
  84. "lh_G_temporal_inf_thickness",
  85. "lh_G_temporal_middle_thickness",
  86. "lh_Lat_Fis-ant-Horizont_thickness",
  87. "lh_Lat_Fis-ant-Vertical_thickness",
  88. "lh_Lat_Fis-post_thickness",
  89. "lh_Pole_occipital_thickness",
  90. "lh_Pole_temporal_thickness",
  91. "lh_S_calcarine_thickness",
  92. "lh_S_central_thickness",
  93. "lh_S_cingul-Marginalis_thickness",
  94. "lh_S_circular_insula_ant_thickness",
  95. "lh_S_circular_insula_inf_thickness",
  96. "lh_S_circular_insula_sup_thickness",
  97. "lh_S_collat_transv_ant_thickness",
  98. "lh_S_collat_transv_post_thickness",
  99. "lh_S_front_inf_thickness",
  100. "lh_S_front_middle_thickness",
  101. "lh_S_front_sup_thickness",
  102. "lh_S_interm_prim-Jensen_thickness",
  103. "lh_S_intrapariet&P_trans_thickness",
  104. "lh_S_oc_middle&Lunatus_thickness",
  105. "lh_S_oc_sup&transversal_thickness",
  106. "lh_S_occipital_ant_thickness",
  107. "lh_S_oc-temp_lat_thickness",
  108. "lh_S_oc-temp_med&Lingual_thickness",
  109. "lh_S_orbital_lateral_thickness",
  110. "lh_S_orbital_med-olfact_thickness",
  111. "lh_S_orbital-H_Shaped_thickness",
  112. "lh_S_parieto_occipital_thickness",
  113. "lh_S_pericallosal_thickness",
  114. "lh_S_postcentral_thickness",
  115. "lh_S_precentral-inf-part_thickness",
  116. "lh_S_precentral-sup-part_thickness",
  117. "lh_S_suborbital_thickness",
  118. "lh_S_subparietal_thickness",
  119. "lh_S_temporal_inf_thickness",
  120. "lh_S_temporal_sup_thickness",
  121. "lh_S_temporal_transverse_thickness",
  122. "lh_MeanThickness_thickness",
  123. "BrainSegVolNotVent",
  124. "eTIV",
  125. "rh_G&S_frontomargin_thickness",
  126. "rh_G&S_occipital_inf_thickness",
  127. "rh_G&S_paracentral_thickness",
  128. "rh_G&S_subcentral_thickness",
  129. "rh_G&S_transv_frontopol_thickness",
  130. "rh_G&S_cingul-Ant_thickness",
  131. "rh_G&S_cingul-Mid-Ant_thickness",
  132. "rh_G&S_cingul-Mid-Post_thickness",
  133. "rh_G_cingul-Post-dorsal_thickness",
  134. "rh_G_cingul-Post-ventral_thickness",
  135. "rh_G_cuneus_thickness",
  136. "rh_G_front_inf-Opercular_thickness",
  137. "rh_G_front_inf-Orbital_thickness",
  138. "rh_G_front_inf-Triangul_thickness",
  139. "rh_G_front_middle_thickness",
  140. "rh_G_front_sup_thickness",
  141. "rh_G_Ins_lg&S_cent_ins_thickness",
  142. "rh_G_insular_short_thickness",
  143. "rh_G_occipital_middle_thickness",
  144. "rh_G_occipital_sup_thickness",
  145. "rh_G_oc-temp_lat-fusifor_thickness",
  146. "rh_G_oc-temp_med-Lingual_thickness",
  147. "rh_G_oc-temp_med-Parahip_thickness",
  148. "rh_G_orbital_thickness",
  149. "rh_G_pariet_inf-Angular_thickness",
  150. "rh_G_pariet_inf-Supramar_thickness",
  151. "rh_G_parietal_sup_thickness",
  152. "rh_G_postcentral_thickness",
  153. "rh_G_precentral_thickness",
  154. "rh_G_precuneus_thickness",
  155. "rh_G_rectus_thickness",
  156. "rh_G_subcallosal_thickness",
  157. "rh_G_temp_sup-G_T_transv_thickness",
  158. "rh_G_temp_sup-Lateral_thickness",
  159. "rh_G_temp_sup-Plan_polar_thickness",
  160. "rh_G_temp_sup-Plan_tempo_thickness",
  161. "rh_G_temporal_inf_thickness",
  162. "rh_G_temporal_middle_thickness",
  163. "rh_Lat_Fis-ant-Horizont_thickness",
  164. "rh_Lat_Fis-ant-Vertical_thickness",
  165. "rh_Lat_Fis-post_thickness",
  166. "rh_Pole_occipital_thickness",
  167. "rh_Pole_temporal_thickness",
  168. "rh_S_calcarine_thickness",
  169. "rh_S_central_thickness",
  170. "rh_S_cingul-Marginalis_thickness",
  171. "rh_S_circular_insula_ant_thickness",
  172. "rh_S_circular_insula_inf_thickness",
  173. "rh_S_circular_insula_sup_thickness",
  174. "rh_S_collat_transv_ant_thickness",
  175. "rh_S_collat_transv_post_thickness",
  176. "rh_S_front_inf_thickness",
  177. "rh_S_front_middle_thickness",
  178. "rh_S_front_sup_thickness",
  179. "rh_S_interm_prim-Jensen_thickness",
  180. "rh_S_intrapariet&P_trans_thickness",
  181. "rh_S_oc_middle&Lunatus_thickness",
  182. "rh_S_oc_sup&transversal_thickness",
  183. "rh_S_occipital_ant_thickness",
  184. "rh_S_oc-temp_lat_thickness",
  185. "rh_S_oc-temp_med&Lingual_thickness",
  186. "rh_S_orbital_lateral_thickness",
  187. "rh_S_orbital_med-olfact_thickness",
  188. "rh_S_orbital-H_Shaped_thickness",
  189. "rh_S_parieto_occipital_thickness",
  190. "rh_S_pericallosal_thickness",
  191. "rh_S_postcentral_thickness",
  192. "rh_S_precentral-inf-part_thickness",
  193. "rh_S_precentral-sup-part_thickness",
  194. "rh_S_suborbital_thickness",
  195. "rh_S_subparietal_thickness",
  196. "rh_S_temporal_inf_thickness",
  197. "rh_S_temporal_sup_thickness",
  198. "rh_S_temporal_transverse_thickness",
  199. "rh_MeanThickness_thickness",
  200. "Left-Lateral-Ventricle",
  201. "Left-Inf-Lat-Vent",
  202. "Left-Cerebellum-White-Matter",
  203. "Left-Cerebellum-Cortex",
  204. "Left-Thalamus-Proper",
  205. "Left-Caudate",
  206. "Left-Putamen",
  207. "Left-Pallidum",
  208. "3rd-Ventricle",
  209. "4th-Ventricle",
  210. "Brain-Stem",
  211. "Left-Hippocampus",
  212. "Left-Amygdala",
  213. "CSF",
  214. "Left-Accumbens-area",
  215. "Left-VentralDC",
  216. "Left-vessel",
  217. "Left-choroid-plexus",
  218. "Right-Lateral-Ventricle",
  219. "Right-Inf-Lat-Vent",
  220. "Right-Cerebellum-White-Matter",
  221. "Right-Cerebellum-Cortex",
  222. "Right-Thalamus-Proper",
  223. "Right-Caudate",
  224. "Right-Putamen",
  225. "Right-Pallidum",
  226. "Right-Hippocampus",
  227. "Right-Amygdala",
  228. "Right-Accumbens-area",
  229. "Right-VentralDC",
  230. "Right-vessel",
  231. "Right-choroid-plexus",
  232. "5th-Ventricle",
  233. "WM-hypointensities",
  234. "Left-WM-hypointensities",
  235. "Right-WM-hypointensities",
  236. "non-WM-hypointensities",
  237. "Left-non-WM-hypointensities",
  238. "Right-non-WM-hypointensities",
  239. "Optic-Chiasm",
  240. "CC_Posterior",
  241. "CC_Mid_Posterior",
  242. "CC_Central",
  243. "CC_Mid_Anterior",
  244. "CC_Anterior",
  245. "BrainSegVol",
  246. "BrainSegVolNotVentSurf",
  247. "lhCortexVol",
  248. "rhCortexVol",
  249. "CortexVol",
  250. "lhCerebralWhiteMatterVol",
  251. "rhCerebralWhiteMatterVol",
  252. "CerebralWhiteMatterVol",
  253. "SubCortGrayVol",
  254. "TotalGrayVol",
  255. "SupraTentorialVol",
  256. "SupraTentorialVolNotVent",
  257. "SupraTentorialVolNotVentVox",
  258. "MaskVol",
  259. "BrainSegVol-to-eTIV",
  260. "MaskVol-to-eTIV",
  261. "lhSurfaceHoles",
  262. "rhSurfaceHoles",
  263. "SurfaceHoles",
  264. "EstimatedTotalIntraCranialVol",
  265. ]
  266. norm_data = NormData.from_dataframe(
  267. name="fcon1000",
  268. dataframe=data,
  269. covariates=covariates,
  270. batch_effects=batch_effects,
  271. response_vars=response_vars,
  272. subject_ids=subject_ids,
  273. remove_Nan=True,
  274. )
  275. return norm_data
  278. _LNM_DATA_URL = "https://raw.githubusercontent.com/predictive-clinical-neuroscience/pu25_code/main/data/LNM_data.csv"
  279. _LNM_METADATA_COLUMNS = frozenset({"sub_id", "age", "sex", "site", "visit", "group"})
  282. def load_lnm(
  283. save_path: str | None = None,
  284. group: Literal["control", "patient"] | None = None,
  285. ) -> NormData:
  286. """Download and save the LNM longitudinal dataset, or load it from disk.
  288. The dataset has two visits per subject (controls and patients).
  290. Parameters
  291. ----------
  292. save_path : str | None
  293. Folder to save or load ``LNM_data.csv`` from. Defaults to
  294. ``pcntoolkit_resources/data``.
  295. group : {"control", "patient"} | None
  296. Keep only the subjects of this group. Defaults to ``None``, which
  297. keeps everyone.
  299. Returns
  300. -------
  301. NormData
  302. Longitudinal dataset with ``visits="visit"``.
  304. Raises
  305. ------
  306. ValueError
  307. If ``group`` is not ``"control"``, ``"patient"`` or ``None``.
  308. """
  309. if group not in (None, "control", "patient"):
  310. raise ValueError(f"group must be 'control', 'patient' or None, not {group!r}.")
  312. if not save_path:
  313. save_path = os.path.join("pcntoolkit_resources", "data")
  314. os.makedirs(save_path, exist_ok=True)
  315. data_path = os.path.join(save_path, "LNM_data.csv")
  317. if not os.path.exists(data_path):
  318. # Download the LNM dataset if it does not exist locally
  319. data = pd.read_csv(_LNM_DATA_URL)
  320. data.to_csv(data_path, index=False)
  321. else:
  322. data = pd.read_csv(data_path)
  324. if group is not None:
  325. data = data[data["group"] == group]
  327. subject_ids = "sub_id"
  328. covariates = ["age"]
  329. batch_effects = ["sex", "site"]
  330. # Response variables are all columns that are not metadata.
  331. response_vars = [
  332. column for column in data.columns if column not in _LNM_METADATA_COLUMNS
  333. ]
  335. norm_data = NormData.from_dataframe(
  336. name=f"lnm_{group}" if group else "lnm",
  337. dataframe=data,
  338. covariates=covariates,
  339. batch_effects=batch_effects,
  340. response_vars=response_vars,
  341. subject_ids=subject_ids,
  342. visits="visit", # specify the visit columm
  343. remove_Nan=True,
  344. )
  345. return norm_data
  348. CORRELATION_MATRIX_URL = (
  349. "https://raw.githubusercontent.com/CharFraza/CPC_ML_tutorial/"
  350. "master/data/cmat_rh_G_front_middle_thickness.csv"
  351. )
  352. CORRELATION_MATRIX_RESPONSE_VAR = "rh_G_front_middle_thickness"
  355. def fetch_example_correlation_matrix(save_path: str | None = None) -> str:
  356. """Download an example correlation matrix and return the path to its CSV.
  358. Parameters
  359. ----------
  360. save_path : str | None
  361. Folder to save the matrix to. Defaults to
  362. ``pcntoolkit_resources/data/correlation_matrix``.
  364. Returns
  365. -------
  366. str
  367. Path to the CSV, named ``<response_var>.csv`` as
  368. ``CorrelationMatrix.load`` expects.
  369. """
  370. if not save_path:
  371. save_path = os.path.join("pcntoolkit_resources", "data", "correlation_matrix")
  372. os.makedirs(save_path, exist_ok=True)
  373. # Name the file after the response variable.
  374. data_path = os.path.join(save_path, f"{CORRELATION_MATRIX_RESPONSE_VAR}.csv")
  376. # Download the CSV if it does not exist locally.
  377. if not os.path.exists(data_path):
  378. urllib.request.urlretrieve(CORRELATION_MATRIX_URL, data_path)
  380. return data_path
  383. # NOTE: This dataset is not public
  384. def load_lifespan_big(
  385. n_response_vars: int | None = None,
  386. n_largest_sites: int | None = None,
  387. n_subjects: int | None = None,
  388. ) -> NormData:
  389. """
  390. Load the lifespan_big dataset, which is a large lifespan dataset with many sites.
  392. Parameters
  393. ----------
  394. n_response_vars : int | None
  395. If specified, only use the first n_response_vars response
  396. variables.
  397. n_largest_sites : int | None
  398. If specified, only keep data from the n_largest_sites largest
  399. sites.
  400. n_subjects : int | None
  401. If specified, randomly sample n_subjects subjects.
  403. Returns
  404. -------
  405. NormData
  406. The loaded dataset as a NormData object.
  407. """
  408. # Define the variables in the dataset
  409. subject_ids = ["participant_id"]
  410. covariates = ["age"]
  411. batch_effects = ["sex", "site"]
  413. # Define the dtypes for loading the dataset, to ensure that categorical
  414. # variables are loaded as strings and numerical variables as floats
  415. dtypes = {"participant_id": str, "group": str, "group2": str}
  416. for col in batch_effects:
  417. dtypes[col] = str
  418. for col in covariates:
  419. dtypes[col] = float
  421. # Load the lifespan dataset with 57116 subjects from the Braicharts paper:
  422. # https://doi.org/10.7554/eLife.72904
  423. data = pd.read_csv(
  424. "/project_cephfs/3022017.06/projects/stijdboe/Data/sairut_data/"
  425. "lifespan_big.csv",
  426. dtype=dtypes,
  427. )
  429. # Drop rows where all values are NaN
  430. data = data.dropna(axis=0, how="all", inplace=False)
  431. # Drop columns where even if 1 value is NaN
  432. data = data.dropna(axis=1, how="any", inplace=False)
  434. data["sex"] = data["sex"].map({"0.0": "Female", "1.0": "Male", "2.0": "Female"})
  435. data["site"] = data["site_ID"]
  437. # If requested, take only the n largest sites
  438. if n_largest_sites is not None:
  439. data = data[
  440. data["site_ID"].isin(
  441. data["site_ID"].value_counts().head(n_largest_sites).index
  442. )
  443. ]
  445. # If requested, take only n subjects
  446. if n_subjects is not None:
  447. data = data.sample(n=n_subjects, replace=False)
  449. # Define response variables as all variables that
  450. # are not in subject_ids, covariates, batch_effects,
  451. # and that have variance > 0
  452. def is_response_var(col_name: str) -> bool:
  453. return (
  454. col_name not in subject_ids
  455. and col_name not in covariates
  456. and col_name not in batch_effects
  457. and not col_name.startswith("site_")
  458. and not col_name.startswith("group")
  459. and not col_name.startswith("race")
  460. and data[col_name].var() > 0
  461. )
  463. response_vars = [col for col in data.columns if is_response_var(col)]
  465. # If requested, take only n response variables
  466. if n_response_vars is not None:
  467. response_vars = response_vars[:n_response_vars]
  469. # Create NormData object
  470. norm_data = NormData.from_dataframe(
  471. name="lifespan_big",
  472. dataframe=data,
  473. covariates=covariates,
  474. batch_effects=batch_effects,
  475. response_vars=response_vars,
  476. subject_ids=subject_ids,
  477. )
  479. return norm_data

data_factory.py at commit 20a95b6, under GPL-3.0 · at the source

Overview

Authors: Sebastian Volkmer1,2,3, Katharina M Kubera4, Stefan Fritze1,3, Geva Brandt1, Dilsa Cemre Akkoc Altinok1,3, Jonas Daub1,3, Jacqueline Kukovic1,3, Kent-Tjorben Böttcher1,3, Oksana Berhe1,3, Yuchen Lin1,3, Heike Tost1,3, Andre F Marquand5,6, Andreas Meyer-Lindenberg1,3, Emanuel Schwarz1,2,3, Dusan Hirjak1,3
ORCID iDs: Sebastian Volkmer, Dilsa Cemre Akkoc Altinok, Jonas Daub, Heike Tost, Andre F Marquand, Emanuel Schwarz, Dusan Hirjak
  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), Partnersite Mannheim-Heidelberg-Ulm, Mannheim, Germany
  4. Center for Psychosocial Medicine, Department of General Psychiatry, University of Heidelberg, Heidelberg, Germany
  5. Donders Centre for Cognitive Neuroimaging, Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, the Netherlands
  6. Department of Cognitive Neuroscience, Radboud University Medical Centre, Nijmegen, the Netherlands
Institutions: Heidelberg University (Germany); Central Institute of Mental Health (Germany); Medizinische Fakultät Mannheim (Germany); Deutsches Zentrum für Psychische Gesundheit (Germany); Radboud University Nijmegen (Netherlands); Radboud University Medical Center (Netherlands); Donders Centre for Cognitive Neuroimaging (Netherlands); Donders Institute for Brain, Cognition and Behaviour (Netherlands)
Journal: Translational psychiatry, volume 16, issue 1, article 197
Dates: received 19 August 2025; accepted 6 March 2026; published online 20 March 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1038/s41398-026-03956-0 · PMID 41862444 · PMCID PMC13039878 · OpenAlex W7139956584
Open access: gold, a free copy (OpenAlex)
Status: code verified (how the code is found and verified)
Categories: structural MRI / diffusion (modality), human (organism), schizophrenia / psychosis (population)
Methods: Statistics, Connectivity, Machine learning
Keywords: Pathogenesis, Neuroscience
MeSH: Brain*, Cognitive Dysfunction*, Schizophrenia*, Schizophrenic Psychology*, Adult, Brain Cortical Thickness, Female, Gray Matter, Humans, Magnetic Resonance Imaging, Male, Middle Aged, Young Adult (* major topic)
Topic: Schizophrenia research and treatment (Psychiatry and Mental health, Medicine), according to OpenAlex
Funding: Bundesministerium für Bildung und Forschung (Federal Ministry of Education and Research) (01GQ1102); Deutsche Forschungsgemeinschaft (SCHW 1768/8-1, HI 1928/14-1, HI 1928/5-1)
Citations: cited by 2 papers (Europe PMC); 93 references in the paper

Abstract

Schizophrenia spectrum disorders (SSD) are marked by widespread structural brain abnormalities. Neuroanatomical normative modeling (NM) can quantify person-specific deviations from healthy variability, yet it remains unknown whether pre-trained, large-scale NM features support site-held-out classification and mechanistic brain–behavior mapping in SSD. Here, we applied a publicly available PCNtoolkit model (trained on ~57,000 healthy controls from 82 sites) to six independent cohorts (N = 831) to derive individual deviations in cortical thickness (CT) and subcortical volumes from T1-weighted MRI. Employing a random forest classifier with leave-site-out cross-validation, we achieved a balanced accuracy of 65%, which underscores the inherent complexity of SSD. Feature importance analysis identified total gray matter volume, mean CT, and CT changes in limbic and sensorimotor regions as key predictive features. Relative to healthy controls, SSD participants showed a higher burden of extreme negative deviations, which related to reduced attention and processing speed and to elevated neurological soft signs (NSS). Finally, canonical correlation analysis revealed a robust multivariate relationship linking structural deviationsparticularly CT changes in limbic and sensorimotor cortices, to cognition and NSS. Together, these results demonstrate that NM features transferred from a large external reference can generalize across sites and elucidate clinically relevant brain–behavior associations in SSD, supporting the integration of multimodal, large-scale datasets to advance biomarker discovery and inform earlier, more targeted interventions.

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

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amarquand/PCNtoolkit

License: GPL-3.0
State: the link answers, verified on 29 October 2026
Evidence: files inventoried
Commit: 20a95b639190dfb9eabc31e53bd31fdb818a7141, 28 October 2026
Languages: Python (90), Jupyter (34)
Size: 280 files, 124 scripts
Software Heritage: not archived
Found in: the text, “Introduction”
Holds: README, license file, CITATION.cff, environment (pyproject.toml, doc/requirements.txt), tests, continuous integration, documentation, 34 notebooks
Tools: NumPy (62 files), pandas (44 files), Matplotlib (31 files), seaborn (27 files), xarray (19 files), SciPy (15 files), PyMC (8 files), scikit-learn (7 files), ArviZ (3 files), NiBabel (1 file), Connectome Workbench (1 file)
Availability: 2 checks, the latest on 29 October 2026: the link answers
  • 29 October 2026: the link answers
  • 28 September 2026: the link answers
126 files
At the source: github.com/amarquand/PCNtoolkit

Tracing map

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Data

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The data that support the findings of this study are available from the corresponding author, upon reasonable request.

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

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Version 1, 30 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 15 authors, 2 keywords, 13 MeSH terms, 2 funders, 88 references.

Cite

This paper

Volkmer, S., Kubera, K. M., Fritze, S., Brandt, G., Akkoc Altinok, D. C., Daub, J., Kukovic, J., Böttcher, K.-T., Berhe, O., Lin, Y., Tost, H., Marquand, A. F., Meyer-Lindenberg, A., Schwarz, E., & Hirjak, D. (2026). Normative age-related structural brain deviations underlying psychopathology, cognitive impairment and neurological soft signs in schizophrenia spectrum disorders. Translational psychiatry, 16(1), 197. https://doi.org/10.1038/s41398-026-03956-0

BibTeX

@article{volkmer2026normative,
author = {Volkmer, Sebastian and Kubera, Katharina M and Fritze, Stefan and Brandt, Geva and Akkoc Altinok, Dilsa Cemre and Daub, Jonas and Kukovic, Jacqueline and Böttcher, Kent-Tjorben and Berhe, Oksana and Lin, Yuchen and Tost, Heike and Marquand, Andre F and Meyer-Lindenberg, Andreas and Schwarz, Emanuel and Hirjak, Dusan},
title = {{Normative age-related structural brain deviations underlying psychopathology, cognitive impairment and neurological soft signs in schizophrenia spectrum disorders}},
journal = {Translational psychiatry},
year = {2026},
month = mar,
volume = {16},
number = {1},
pages = {197},
publisher = {Nature Publishing Group},
issn = {2158-3188},
doi = {10.1038/s41398-026-03956-0},
url = {https://doi.org/10.1038/s41398-026-03956-0},
pmid = {41862444},
pmcid = {PMC13039878}
}

RIS

TY - JOUR
AU - Volkmer, Sebastian
AU - Kubera, Katharina M
AU - Fritze, Stefan
AU - Brandt, Geva
AU - Akkoc Altinok, Dilsa Cemre
AU - Daub, Jonas
AU - Kukovic, Jacqueline
AU - Böttcher, Kent-Tjorben
AU - Berhe, Oksana
AU - Lin, Yuchen
AU - Tost, Heike
AU - Marquand, Andre F
AU - Meyer-Lindenberg, Andreas
AU - Schwarz, Emanuel
AU - Hirjak, Dusan
TI - Normative age-related structural brain deviations underlying psychopathology, cognitive impairment and neurological soft signs in schizophrenia spectrum disorders
T2 - Translational psychiatry
J2 - Transl Psychiatry
PY - 2026
DA - 2026/03/20
VL - 16
IS - 1
SP - 197
SN - 2158-3188
PB - Nature Publishing Group
DO - 10.1038/s41398-026-03956-0
UR - https://doi.org/10.1038/s41398-026-03956-0
LA - en
ER -

CSL-JSON

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