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Mapping heterogeneous brain structural subtypes in alzheimer's disease and mild cognitive impairment using normative models.

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  1. [1] § Materials and methods › Transcriptomic analysis ↔ abagen/datasets/fetchers.py, lines 72–160 · score 0.57 · Allen Human Brain, adult, abagen, transcriptomic, Ontology, Atlas

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Python · 546 lines · 19 KB · BSD-3-Clause · 1 match

  1. # -*- coding: utf-8 -*-
  2. """
  3. Functions for downloading the Allen Brain Atlas human microarray dataset.
  4. """
  6. from collections import namedtuple
  7. from functools import partial
  8. import multiprocessing as mp
  9. import os
  10. from pkg_resources import resource_filename
  12. import nibabel as nib
  13. import pandas as pd
  15. from .. import io
  16. from ..utils import load_gifti, first_entry
  17. from .utils import _get_dataset_dir, _fetch_files
  19. WELL_KNOWN_IDS = nib.volumeutils.Recoder(
  20. (('9861', 'H0351.2001', '178238387', '157722636', '157722638'),
  21. ('10021', 'H0351.2002', '178238373', '157723301', '157723303'),
  22. ('12876', 'H0351.1009', '178238359', '157722290', '157722292'),
  23. ('15496', 'H0351.1015', '178238266', '162021642', '162021644'),
  24. ('14380', 'H0351.1012', '178238316', '157721937', '157721939'),
  25. ('15697', 'H0351.1016', '178236545', '157682966', '157682968')),
  26. fields=('subj', 'uid', 'url', 't1w', 't2w')
  27. )
  28. VALID_DONORS = sorted(WELL_KNOWN_IDS.value_set('subj')
  29. | WELL_KNOWN_IDS.value_set('uid'))
  30. RESOURCE = partial(resource_filename, 'abagen')
  33. def check_donors(donors, default='12876', valid=VALID_DONORS):
  34. """
  35. Checks that provided `donors` are valid
  37. Parameters
  38. ----------
  39. donors : list of str
  40. List of donors to download; can be either donor number or UID. Can also
  41. specify 'all' to download all available donors. If 'None' is provided
  42. then `default` will be used.
  43. default : str, optional
  44. Default donor to use if `donors` is None. Default: '12876'
  45. valid : list of str, optional
  46. List of valid donnor numbers and UIDs. Default: :obj:`VALID_DONORS`
  48. Returns
  49. -------
  50. donors : list of str
  51. Donor subject IDs
  52. """
  54. if donors is None:
  55. donors = [default]
  56. elif donors == 'all':
  57. donors = valid
  58. elif isinstance(donors, str):
  59. donors = [donors]
  61. donors = list(donors).copy()
  62. for n, sub_id in enumerate(donors):
  63. if sub_id not in valid:
  64. raise ValueError('Invalid subject id: {0}. Subjects must in: {1}.'
  65. .format(sub_id, valid))
  66. donors[n] = WELL_KNOWN_IDS[sub_id] # convert to ID system
  67. donors = sorted(set(donors), key=lambda x: int(x))
  69. return donors
  72. def fetch_microarray(data_dir=None, donors=None, resume=True, verbose=1,
  73. convert=True, n_proc=1):
  74. """
  75. Downloads the Allen Human Brain Atlas microarray expression dataset
  77. Parameters
  78. ----------
  79. data_dir : str, optional
  80. Directory where data should be downloaded and unpacked. Default: $HOME/
  81. abagen-data
  82. donors : list, optional
  83. List of donors to download; can be either donor number or UID. Can also
  84. specify 'all' to download all available donors. Default: 12876
  85. resume : bool, optional
  86. Whether to resume download of a partly-downloaded file. Default: True
  87. verbose : int, optional
  88. Verbosity level (0 means no message). Default: 1
  89. convert : bool, optional
  90. Whether to convert downloaded CSV files into parquet format for faster
  91. loading in the future; only available if ``fastparquet`` and ``python-
  92. snappy`` are installed. Default: True
  93. n_proc : int, optional
  94. Number of processes to parallelize download if multiple donors are
  95. specified. Default: 1
  97. Returns
  98. -------
  99. data : dict
  100. Two-level nested dictionary, where top-level keys are donor IDs and
  101. second-level keys are ['microarray', 'ontology', 'pacall', 'probes',
  102. 'annotation'], where corresponding values are lists of filepaths to
  103. downloaded CSV files.
  105. References
  106. ----------
  107. Hawrylycz, M. J., Lein, E. S., Guillozet-Bongaarts, A. L., Shen, E. H., Ng,
  108. L., Miller, J. A., ... & Abajian, C. (2012). An anatomically comprehensive
  109. atlas of the adult human brain transcriptome. Nature, 489(7416), 391.
  110. """
  112. url = "https://human.brain-map.org/api/v2/well_known_file_download/{}"
  114. dataset_name = 'microarray'
  115. data_dir = _get_dataset_dir(dataset_name, data_dir=data_dir,
  116. verbose=verbose)
  118. sub_files = ('MicroarrayExpression.csv',
  119. 'Ontology.csv', 'PACall.csv',
  120. 'Probes.csv', 'SampleAnnot.csv')
  121. n_files = len(sub_files)
  122. donors = check_donors(donors)
  124. if n_proc < 0:
  125. n_proc = mp.cpu_count() + n_proc + 1
  127. files = [
  128. [(os.path.join('normalized_microarray_donor{}'.format(sub), fname),
  129. url.format(WELL_KNOWN_IDS.url[sub]),
  130. dict(uncompress=True,
  131. move=os.path.join('normalized_microarray_donor{}'.format(sub),
  132. 'donor{}.zip'.format(sub))))
  133. for fname in sub_files]
  134. for sub in donors
  135. ]
  137. if n_proc > 1:
  138. with mp.Pool(n_proc) as pool:
  139. results = [pool.apply_async(_fetch_files,
  140. (data_dir, f),
  141. dict(resume=resume, verbose=verbose))
  142. for f in files]
  143. # flatten outputs into single list
  144. files = [fn for res in results for fn in res.get()]
  145. else:
  146. # flatten list of lists into single list
  147. files = [fn for f in files for fn in f]
  148. files = _fetch_files(data_dir, files, resume=resume, verbose=verbose)
  150. # if we want to convert files to parquet format it's good to do that now
  151. # this step is _already_ super long, so an extra 1-2 minutes is negligible
  152. if convert and io.use_parq:
  153. for fn in files[0::n_files] + files[2::n_files]:
  154. io._make_parquet(fn, convert_only=True)
  156. keys = ['microarray', 'ontology', 'pacall', 'probes', 'annotation']
  157. return {
  158. donor: dict(zip(keys, files[k:k + n_files]))
  159. for k, donor in zip(range(0, len(files), n_files), donors)
  160. }
  163. def fetch_rnaseq(data_dir=None, donors=None, resume=True, verbose=1):
  164. """
  165. Downloads RNA-sequencing data from the Allen Human Brain Atlas
  167. Parameters
  168. ----------
  169. data_dir : str, optional
  170. Directory where data should be downloaded and unpacked. Default:
  171. current directory
  172. donors : list, optional
  173. List of donors to download; can be either donor number or UID. Can also
  174. specify 'all' to download all available donors (two). Default: 9861
  175. resume : bool, optional
  176. Whether to resume download of a partly-downloaded file. Default: True
  177. verbose : int, optional
  178. Verbosity level (0 means no message). Default: 1
  180. Returns
  181. -------
  182. data : dict
  183. Two-level nested dictionary, where top-level keys are donor IDs and
  184. second-level keys are ['counts', 'tpm', 'ontology', 'genes',
  185. 'annotation'], where corresponding values are lists of filepaths to
  186. downloaded CSV files.
  188. References
  189. ----------
  190. Hawrylycz, M. J., Lein, E. S., Guillozet-Bongaarts, A. L., Shen, E. H., Ng,
  191. L., Miller, J. A., ... & Abajian, C. (2012). An anatomically comprehensive
  192. atlas of the adult human brain transcriptome. Nature, 489(7416), 391.
  193. """
  195. url = "https://human.brain-map.org/api/v2/well_known_file_download/{}"
  196. well_known_ids = {
  197. '9861': '278447594',
  198. '10021': '278448166'
  199. }
  201. dataset_name = 'rnaseq'
  202. data_dir = _get_dataset_dir(dataset_name, data_dir=data_dir,
  203. verbose=verbose)
  205. sub_files = ('Genes.csv', 'Ontology.csv',
  206. 'RNAseqCounts.csv', 'RNAseqTPM.csv', 'SampleAnnot.csv')
  207. n_files = len(sub_files)
  208. valid = ['9861', '10021', 'H0351.2001', 'H0351.2002']
  209. donors = sorted(set(check_donors(donors, default=valid[0])) & set(valid),
  210. key=lambda x: int(x))
  212. files = [
  213. [(os.path.join('rnaseq_donor{}'.format(sub), fname),
  214. url.format(well_known_ids[sub]),
  215. dict(uncompress=True,
  216. move=os.path.join('rnaseq_donor{}'.format(sub),
  217. 'donor{}.zip'.format(sub))))
  218. for fname in sub_files]
  219. for sub in donors
  220. ]
  222. files = [fn for f in files for fn in f]
  223. files = _fetch_files(data_dir, files, resume=resume, verbose=verbose)
  225. keys = ['genes', 'ontology', 'counts', 'tpm', 'annotation']
  226. return {
  227. donor: dict(zip(keys, files[k:k + n_files]))
  228. for k, donor in zip(range(0, len(files), n_files), donors)
  229. }
  232. def fetch_raw_mri(data_dir=None, donors=None, resume=True, verbose=1):
  233. """
  234. Downloads the "raw" Allen Human Brain Atlas T1w/T2w MRI images
  236. Parameters
  237. ----------
  238. data_dir : str, optional
  239. Directory where data should be downloaded and unpacked. Default: $HOME/
  240. abagen-data
  241. donors : list, optional
  242. List of donors to download; can be either donor number or UID. Can also
  243. specify 'all' to download all available donors. Default: 12876
  244. resume : bool, optional
  245. Whether to resume download of a partly-downloaded file. Default: True
  246. verbose : int, optional
  247. Verbosity level (0 means no message). Default: 1
  249. Returns
  250. -------
  251. mris : dict
  252. Two-level nested dictionary, where top-level keys are donor IDs and
  253. second-level keys are ['t1w', 't2w'], where corresponding values are
  254. lists of filepaths to downloaded Nifti files
  255. """
  257. url = "https://human.brain-map.org/api/v2/well_known_file_download/{}"
  259. dataset_name = 'mri'
  260. data_dir = _get_dataset_dir(dataset_name, data_dir=data_dir,
  261. verbose=verbose)
  263. sub_files = dict(t1w='T1.nii.gz', t2w='T2.nii.gz')
  264. n_files = len(sub_files)
  265. donors = check_donors(donors)
  267. files = [
  268. (os.path.join('mri_donor{}'.format(sub), fname),
  269. url.format(getattr(WELL_KNOWN_IDS, img)[sub]),
  270. dict(move=os.path.join('mri_donor{}'.format(sub),
  271. fname)))
  272. for sub in donors
  273. for img, fname in sub_files.items()
  274. ]
  276. files = _fetch_files(data_dir, files, resume=resume, verbose=verbose)
  278. return {
  279. donor: dict(zip(sub_files.keys(), files[k:k + n_files]))
  280. for k, donor in zip(range(0, len(files), n_files), donors)
  281. }
  284. def fetch_freesurfer(data_dir=None, donors=None, resume=True, verbose=1):
  285. """
  286. Downloads FreeSurfer reconstructions of the Allen Human Brain Atlas MRIs
  288. Parameters
  289. ----------
  290. data_dir : str, optional
  291. Directory where data should be downloaded and unpacked. Default: $HOME/
  292. abagen-data
  293. donors : list, optional
  294. List of donors to download; can be either donor number or UID. Can also
  295. specify 'all' to download all available donors. Default: 12876
  296. resume : bool, optional
  297. Whether to resume download of a partly-downloaded file. Default: True
  298. verbose : int, optional
  299. Verbosity level (0 means no message). Default: 1
  301. Returns
  302. -------
  303. freesurfer : dict
  304. Dictionary where keys are donor IDs and values are paths to FreeSurfer
  305. directories for requested `donors`
  307. References
  308. ----------
  309. Romero-Garcia, R., Whitaker, K., Vasa, F., Seidlitz, J., Shinn, M., Fonagy,
  310. P., Jones, P., et al. (2017). Data supporting NSPN publication "Structural
  311. covariance networks are coupled to expression of genes enriched in
  312. supragranular layers of the human cortex " [Dataset].
  313. https://doi.org/10.17863/CAM.11392
  314. """
  316. url = "https://www.repository.cam.ac.uk/bitstream/handle/1810/265272/" \
  317. "donor{}.zip"
  318. dataset_name = 'freesurfer'
  319. data_dir = _get_dataset_dir(dataset_name, data_dir=data_dir,
  320. verbose=verbose)
  322. donors = check_donors(donors)
  323. files = [
  324. ('donor{}'.format(sub),
  325. url.format(sub),
  326. dict(uncompress=True,
  327. move=os.path.join('freesurfer.tar.gz')))
  328. for sub in donors
  329. ]
  331. files = _fetch_files(data_dir, files, resume=resume, verbose=verbose)
  333. return {
  334. donor: files[k]
  335. for k, donor in enumerate(donors)
  336. }
  339. def fetch_desikan_killiany(native=False, surface=False, *args, **kwargs):
  340. """
  341. Fetches Desikan-Killiany atlas shipped with `abagen`
  343. Parameters
  344. ----------
  345. native : bool, optional
  346. Whether to return individualized atlases in donor native space.
  347. Default: False
  348. surface : bool, optional
  349. Whether to return surface instead of volumetric parcellation. This
  350. option is currently incompatible with ``native=True``; instead, refer
  351. to :func:`abagen.datasets.fetch_freesurfer` for donor-specific surface
  352. atlases. Default: False
  354. Returns
  355. -------
  356. atlas : dict
  357. Dictionary with keys ['image', 'info'] pointing to atlas image and
  358. information files. If ``native`` then 'image' is a dictionary where
  359. keys are donor IDs and values are image paths. If ``surface`` then
  360. 'image' is a tuple of GIFTI files (.label.gii.gz)
  362. References
  363. ----------
  364. Desikan, R. S., Ségonne, F., Fischl, B., Quinn, B. T., Dickerson, B. C.,
  365. Blacker, D., ... & Albert, M. S. (2006). An automated labeling system
  366. for subdividing the human cerebral cortex on MRI scans into gyral based
  367. regions of interest. Neuroimage, 31(3), 968-980.
  369. Examples
  370. --------
  371. >>> import abagen
  372. >>> atlas = abagen.fetch_desikan_killiany()
  373. >>> print(atlas['image']) # doctest: +ELLIPSIS
  374. /.../abagen/data/atlas-desikankilliany.nii.gz
  375. >>> print(atlas['info']) # doctest: +ELLIPSIS
  376. /.../abagen/data/atlas-desikankilliany.csv
  378. When fetching native-space atlases, `atlas['image']` will be a dictionary
  379. where the keys are donor IDs and the values are paths to the donor-specific
  380. atlases:
  382. >>> atlas = abagen.fetch_desikan_killiany(native=True)
  383. >>> print(atlas['image'].keys())
  384. dict_keys(['9861', '10021', '12876', '14380', '15496', '15697'])
  385. >>> print(atlas['image']['9861']) # doctest: +ELLIPSIS
  386. /.../abagen/data/native_dk/9861/atlas-desikankilliany.nii.gz
  387. """
  389. # grab resource filenames
  390. img = dict()
  391. for donor in check_donors('all'):
  392. fp = 'data' if not native else os.path.join('data', 'native_dk', donor)
  393. if surface:
  394. impath = tuple([
  395. RESOURCE(
  396. os.path.join(fp, f'atlas-desikankilliany-{h}.label.gii.gz')
  397. )
  398. for h in ('lh', 'rh')
  399. ])
  400. else:
  401. impath = RESOURCE(os.path.join(fp, 'atlas-desikankilliany.nii.gz'))
  402. img[donor] = impath
  403. if not native:
  404. img = first_entry(img)
  405. info = RESOURCE('data/atlas-desikankilliany.csv')
  407. return dict(image=img, info=info)
  410. def fetch_gene_group(group):
  411. """
  412. Return list of gene acronyms belonging to provided `group`
  414. Groups are defined as in [DS1]_
  416. Parameters
  417. ----------
  418. group : {'brain', 'neuron', 'oligodendrocyte', 'synaptome', 'layers'}
  419. Desired gene group
  421. Returns
  422. -------
  423. genes : list of str
  424. List of gene acronyms
  426. References
  427. ----------
  428. .. [DS1] Burt, J. B., Demirtaş, M., Eckner, W. J., Navejar, N. M., Ji, J.
  429. L., Martin, W. J., ... & Murray, J. D. (2018). Hierarchy of
  430. transcriptomic specialization across human cortex captured by
  431. structural neuroimaging topography. Nature neuroscience, 21(9), 1251.
  432. """
  434. groups = ['brain', 'neuron', 'oligodendrocyte', 'synaptome', 'layers']
  435. if group.lower() not in groups:
  436. raise ValueError('Provided group {} not one of the available gene '
  437. 'groups: {}'.format(group, groups))
  439. group = group.lower()
  440. fn = RESOURCE(os.path.join('data', 'burt2018_natneuro.csv.gz'))
  441. genes = pd.read_csv(fn).query('group == "{}"'.format(group))['acronym']
  443. return sorted(list(genes))
  446. def fetch_donor_info():
  447. """
  448. Returns dataframe with donor demographic information
  450. Returns
  451. -------
  452. info : pandas.DataFrame
  453. With columns ['donor', 'age', 'sex', 'ethnicity', 'medical_conditions',
  454. 'post_mortem_interval_hours'] detailing basic demographic info about
  455. donors
  456. """
  458. fn = RESOURCE(os.path.join('data', 'donor_info.csv'))
  459. donors = pd.read_csv(fn)
  461. return donors
  464. Brain = namedtuple('Brain', ('lh', 'rh'))
  465. Surface = namedtuple('Surface', ('vertices', 'faces'))
  468. def fetch_fsaverage5(load=True):
  469. """
  470. Fetches and optionally loads fsaverage5 surface
  472. Parameters
  473. ----------
  474. load : bool, optional
  475. Whether to pre-load files. Default: True
  477. Returns
  478. -------
  479. brain : namedtuple ('lh', 'rh')
  480. If `load` is True, a namedtuple where each entry in the tuple is a
  481. hemisphere, represented as a namedtuple with fields ('vertices',
  482. 'faces'). If `load` is False, a namedtuple where entries are filepaths.
  483. """
  485. hemispheres = []
  486. for hemi in ('lh', 'rh'):
  487. fn = RESOURCE(
  488. os.path.join('data', f'fsaverage5-pial-{hemi}.surf.gii.gz')
  489. )
  490. if load:
  491. hemispheres.append(Surface(*load_gifti(fn).agg_data()))
  492. else:
  493. hemispheres.append(fn)
  495. return Brain(*hemispheres)
  498. def fetch_fsnative(donors, surf='pial', load=True, data_dir=None, resume=True,
  499. verbose=1):
  500. """
  501. Fetches and optionally loads fsnative surface of `donor`
  503. Parameters
  504. ----------
  505. donors : str or list-of-str
  506. Donor(s) to download; can be either donor number or UID. Can also
  507. specify 'all' to download all available donors.
  508. surf : {'orig', 'white', 'pial', 'inflated', 'sphere'}, optional
  509. Which surface to load. Default: 'pial'
  510. load : bool, optional
  511. Whether to pre-load files. Default: True
  512. data_dir : str, optional
  513. Directory where data should be downloaded and unpacked. Default: $HOME/
  514. abagen-data
  515. resume : bool, optional
  516. Whether to resume download of a partly-downloaded file. Default: True
  517. verbose : int, optional
  518. Verbosity level (0 means no message). Default: 1
  520. Returns
  521. -------
  522. brain : namedtuple ('lh', 'rh')
  523. If `load` is True, a namedtuple where each entry in the tuple is a
  524. hemisphere, represented as a namedtuple with fields ('vertices',
  525. 'faces'). If `load` is False, a namedtuple where entries are filepaths.
  526. If multiple donors are requested a dictionary is returned where keys
  527. are donor IDs.
  528. """
  530. donors = check_donors(donors)
  531. if len(donors) > 1:
  532. return {donor: fetch_fsnative(donor, surf, data_dir, resume, verbose)
  533. for donor in donors}
  535. donors = donors[0]
  536. fpath = fetch_freesurfer(donors=donors, data_dir=data_dir, resume=resume,
  537. verbose=verbose)[donors]
  538. hemispheres = []
  539. for hemi in ('lh', 'rh'):
  540. fn = os.path.join(fpath, 'surf', f'{hemi}.{surf}')
  541. if load:
  542. hemispheres.append(Surface(*nib.freesurfer.read_geometry(fn)))
  543. else:
  544. hemispheres.append(fn)
  546. return Brain(*hemispheres)

fetchers.py at commit dc4a007, under BSD-3-Clause · at the source

Overview

Authors: Xiaotong Wei1, Tingting Zhang1, Ronglong Xiong1, Qiuzhu Zhang1, Junjun Zhang1, Zhenlan Jin1, Ling Li1
ORCID iDs: Ling Li
  1. The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054 China
Institutions: University of Electronic Science and Technology of China (China)
Journal: Translational psychiatry, volume 16, issue 1, article 168
Dates: received 7 August 2025; accepted 1 February 2026; published online 2 March 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1038/s41398-026-03902-0 · PMID 41771832 · PMCID PMC13022452 · OpenAlex W7133209647
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: structural MRI / diffusion (modality), human (organism), Alzheimer's / dementia (population)
Methods: Statistics, Machine learning, Smoothing, state filtering, decompositions, Preprocessing
Keywords: Diseases, Neuroscience
MeSH: Alzheimer Disease*, Brain*, Cognitive Dysfunction*, Gray Matter*, Aged, Aged, 80 and over, Amygdala, Brain Mapping, Cluster Analysis, Clustering Algorithms, Disease Progression, Female, Hippocampus, Humans, Magnetic Resonance Imaging, Male (* major topic)
Topic: Dementia and Cognitive Impairment Research (Psychiatry and Mental health, Medicine), according to OpenAlex
Funding: Sichuan Provincial Department of Science and Technology | Sichuan Province Science and Technology Support Program (2025ZNSFSC0453); National Natural Science Foundation of China (62571096); Sichuan Province Innovative Talent Funding Project for Postdoctoral Fellows
Citations: cited by 2 papers (Europe PMC); 74 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 1 match between paragraphs and lines of code.

rmarkello/abagen

License: BSD-3-Clause
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: dc4a007e4e902e51f97251390c8d1bbf7e58c6d3, 29 September 2023
Languages: Python (52), Shell (1)
Size: 130 files, 53 scripts
Software Heritage: archived
Found in: the text, “Transcriptomic analysis”
Holds: README, license file, environment (requirements.txt, setup.cfg, setup.py, docs/requirements.txt), tests, continuous integration, documentation
Not found: CITATION.cff
Tools: NumPy (23 files), pandas (22 files), abagen (20 files), NiBabel (11 files), SciPy (7 files)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
55 files
At the source: github.com/rmarkello/abagen

amarquand/PCNtoolkit

License: GPL-3.0
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Commit: 73b19a0f900138281d3a29b9514f2cfc631662af, 25 September 2026
Languages: Python (88), Jupyter (33)
Size: 275 files, 121 scripts
Software Heritage: not archived
Found in: “Data availability”
Holds: README, license file, CITATION.cff, environment (pyproject.toml, doc/requirements.txt), tests, continuous integration, documentation, 33 notebooks
Tools: NumPy (66 files), pandas (41 files), Matplotlib (34 files), seaborn (28 files), xarray (20 files), SciPy (13 files), PyMC (10 files), scikit-learn (9 files), ArviZ (5 files), NiBabel (1 file), Connectome Workbench (1 file)
Availability: 1 check, the latest on 28 September 2026: the link answers
  • 28 September 2026: the link answers
123 files
At the source: github.com/amarquand/PCNtoolkit

The paper's code and data availability statement is in the Data section.

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:

  • 2 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 174 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

No dataset and no data link were found in the paper.

Code and data availability statement

The paper has a code and data availability statement. Its license (CC BY-NC-ND) does not allow reproducing it here; in short, from what the harvester recognized in it:

Read it in the paper: doi.org/10.1038/s41398-026-03902-0.

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 1, 30 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 7 authors, 2 keywords, 16 MeSH terms, 3 funders, 70 references.

Cite

This paper

Wei, X., Zhang, T., Xiong, R., Zhang, Q., Zhang, J., Jin, Z., & Li, L. (2026). Mapping heterogeneous brain structural subtypes in alzheimer's disease and mild cognitive impairment using normative models. Translational psychiatry, 16(1), 168. https://doi.org/10.1038/s41398-026-03902-0

BibTeX

@article{wei2026mapping,
author = {Wei, Xiaotong and Zhang, Tingting and Xiong, Ronglong and Zhang, Qiuzhu and Zhang, Junjun and Jin, Zhenlan and Li, Ling},
title = {{Mapping heterogeneous brain structural subtypes in alzheimer's disease and mild cognitive impairment using normative models}},
journal = {Translational psychiatry},
year = {2026},
month = mar,
volume = {16},
number = {1},
pages = {168},
publisher = {Nature Publishing Group},
issn = {2158-3188},
doi = {10.1038/s41398-026-03902-0},
url = {https://doi.org/10.1038/s41398-026-03902-0},
pmid = {41771832},
pmcid = {PMC13022452}
}

RIS

TY - JOUR
AU - Wei, Xiaotong
AU - Zhang, Tingting
AU - Xiong, Ronglong
AU - Zhang, Qiuzhu
AU - Zhang, Junjun
AU - Jin, Zhenlan
AU - Li, Ling
TI - Mapping heterogeneous brain structural subtypes in alzheimer's disease and mild cognitive impairment using normative models
T2 - Translational psychiatry
J2 - Transl Psychiatry
PY - 2026
DA - 2026/03/02
VL - 16
IS - 1
SP - 168
SN - 2158-3188
PB - Nature Publishing Group
DO - 10.1038/s41398-026-03902-0
UR - https://doi.org/10.1038/s41398-026-03902-0
LA - en
ER -

CSL-JSON

{
"id": "10.1038/s41398-026-03902-0",
"type": "article-journal",
"title": "Mapping heterogeneous brain structural subtypes in alzheimer's disease and mild cognitive impairment using normative models",
"container-title": "Translational psychiatry",
"author": [
{
"family": "Wei",
"given": "Xiaotong"
},
{
"family": "Zhang",
"given": "Tingting"
},
{
"family": "Xiong",
"given": "Ronglong"
},
{
"family": "Zhang",
"given": "Qiuzhu"
},
{
"family": "Zhang",
"given": "Junjun"
},
{
"family": "Jin",
"given": "Zhenlan"
},
{
"family": "Li",
"given": "Ling"
}
],
"container-title-short": "Transl Psychiatry",
"volume": "16",
"issue": "1",
"page": "168",
"DOI": "10.1038/s41398-026-03902-0",
"PMID": "41771832",
"PMCID": "PMC13022452",
"ISSN": "2158-3188",
"publisher": "Nature Publishing Group",
"URL": "https://doi.org/10.1038/s41398-026-03902-0",
"language": "en",
"issued": {
"date-parts": [
[
2026,
3,
2
]
]
}
}

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

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