OSCR

Transcriptional signatures of the cortical morphometric similarity network gradient in left temporal lobe epilepsy with different seizure symptoms.

Code ↔ Paper

3 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 3 matches
  1. [1] § Materials and methods › Gene expression data preprocessing ↔ abagen/cli/run.py, lines 59–123 · score 0.80 · microarray probes, right hemisphere, tissue samples, left hemisphere, boundary, filtering
  2. [2] § Materials and methods › Gene expression data preprocessing ↔ abagen/probes_.py, lines 193–256 · score 0.74 · regional variation, multiple probes, gene symbols, filtering, Abagen, intensity
  3. [3] § Materials and methods › Construction of MS gradients ↔ abagen/correct.py, lines 438–529 · score 0.64 · gray matter volume, cortical surface, Space, connectivity, correlation, mapped

Paper

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The authors' code

Python · 417 lines · 23 KB · BSD-3-Clause · 1 match

  1. # -*- coding: utf-8 -*-
  3. import argparse
  4. import logging
  5. import os
  6. from pathlib import Path
  7. from typing import Iterable
  8. import sys
  10. LGR = logging.getLogger('abagen')
  13. def isiterable(val):
  14. """ Helper function to check whether value is iterable (but not string)
  15. """
  16. return isinstance(val, Iterable) and not isinstance(val, str)
  19. def _resolve_path(path):
  20. """ Helper function for get_parser() to resolve paths
  21. """
  23. if path is not None:
  24. if isiterable(path):
  25. return [_resolve_path(p) for p in path]
  26. try:
  27. return str(Path(path).expanduser().resolve())
  28. except FileNotFoundError:
  29. return os.path.abspath(os.path.expanduser(path))
  32. def _resolve_none(inp):
  33. """ Helper function to allow 'None' as input from argparse
  34. """
  36. if inp == "None":
  37. return
  38. return inp
  41. class CheckExists(argparse.Action):
  42. """ Helper class to check that provided paths exist
  43. """
  44. def __call__(self, parser, namespace, values, option_string=None):
  45. values = self.type(values)
  46. missing = False
  47. if isiterable(values):
  48. missing = any(not os.path.exists(val) for val in values)
  49. if len(values) == 1:
  50. values = values[0]
  51. else:
  52. missing = not os.path.exists(values)
  53. if missing:
  54. parser.error('Provided value for {} does not exist: {}'
  55. .format(option_string, values))
  56. setattr(namespace, self.dest, values)
  59. def get_parser():
  60. """ Gets command-line arguments for primary get_expression_data workflow
  61. """
  63. from .. import __version__
  64. from ..correct import NORMALIZATION_METHODS
  65. from ..probes_ import SELECTION_METHODS
  67. verstr = 'abagen {}'.format(__version__)
  68. parser = argparse.ArgumentParser(
  69. formatter_class=argparse.RawDescriptionHelpFormatter,
  70. description="""
  71. Assigns microarray expression data to ROIs defined in the specified `atlas`
  73. This command aims to provide a workflow for generating pre-processed microarray
  74. expression data from the Allen Human Brain Atlas for arbitrary atlas
  75. designations. First, some basic filtering of genetic probes is performed,
  76. including:
  78. 1. Intensity-based filtering of microarray probes to remove probes that do
  79. not exceed a certain level of background noise (specified via the
  80. `--ibf_threshold` parameter),
  81. 2. Selection of a single, representative probe (or collapsing across
  82. probes) for each gene, specified via the `--probe_selection` parameter
  83. (and influenced by the `--donor_probes` parameter), and
  84. 3. Optional mirroring of the tissue samples across the left/right
  85. hemisphere boundary, as specified via the `--lr_mirror` parameter
  86. (turned off by default).
  88. Tissue samples are then matched to parcels in the defined `atlas` for each
  89. donor. If `--atlas_info` is provided then this matching is constrained by both
  90. hemisphere and tissue class designation (e.g., cortical samples from the left
  91. hemisphere are only matched to ROIs in the left cortex, subcortical samples
  92. from the right hemisphere are only matched to ROIs in the left subcortex); see
  93. the `atlas_info` parameter description for more information.
  95. Matching of microarray samples to parcels in `atlas` is done via a multi-step
  96. process:
  98. 1. Determine if the sample falls directly within a parcel,
  99. 2. Check to see if there are nearby parcels by slowly expanding the search
  100. space to include nearby voxels, up to a specified distance (specified
  101. via the `--tolerance` parameter),
  102. 3. If there are multiple nearby parcels, the sample is assigned to the
  103. closest parcel, as determined by the parcel centroid.
  105. If at any step a sample can be assigned to a parcel the matching process is
  106. terminated. When the provided atlas is not volumetric (i.e., is surface-based)
  107. the samples are simply matched to the nearest vertex, and `--tolerance` is used
  108. as a standard deviation threshold. More control over the sample matching can be
  109. obtained by setting the `--missing` parameter.
  111. Once all samples have been matched to parcels for all supplied donors, the
  112. microarray expression data are optionally normalized via the provided
  113. `--sample_norm` and `--gene_norm` functions (which are influenced by the
  114. `--norm_matched` and `--norm_structures` parameters) before being aggregated
  115. across donors via the supplied `--region_agg` and `--agg_metric` parameters.
  116. """
  117. )
  119. parser.add_argument('atlas', action=CheckExists, type=_resolve_path,
  120. nargs='+',
  121. help='A NIFTI image in MNI152 space or two GIFTI '
  122. 'images in fsaverage5 space, where each parcel '
  123. 'is identified by a unique integer ID.')
  125. # because I like consistency in capitalization and punctuation...
  126. for act in parser._actions:
  127. if isinstance(act, argparse._HelpAction):
  128. act.help = act.help.capitalize() + '.'
  129. break
  131. parser.add_argument('--version', action='version', version=verstr,
  132. help='Show program version and exit.')
  133. parser.add_argument('-v', '--verbose', action='count', default=0,
  134. help='Increase verbosity of status messages to '
  135. 'display during workflow.')
  136. parser.add_argument('--debug', action='store_true', help=argparse.SUPPRESS)
  138. a_data = parser.add_argument_group('Options to specify information about '
  139. 'the atlas used')
  140. a_data.add_argument('--atlas_info', '--atlas-info', action=CheckExists,
  141. type=_resolve_path, default=None, metavar='PATH',
  142. help='Filepath to CSV file containing information '
  143. 'about `atlas`. The CSV file must have at least '
  144. 'columns ["id", "hemisphere", "structure"] which '
  145. 'contain information mapping the atlas IDs to '
  146. 'hemispheres (i.e, "L", "R", or "B") and broad '
  147. 'structural groups (i.e., "cortex", "subcortex/'
  148. 'brainstem", "cerebellum"). If provided, this '
  149. 'will constrain matching of tissue samples to '
  150. 'regions in `atlas`. If the supplied `atlas` is '
  151. 'a pair of GIFTI files with valid label tables '
  152. 'this information will be intuited.')
  154. g_data = parser.add_argument_group('Options to specify which AHBA data to '
  155. 'use during processing')
  156. g_data.add_argument('--donors', action='store', nargs='+',
  157. default='all', metavar='DONOR_ID',
  158. help='List of donors to use as sources of expression '
  159. 'data. Specified IDs can be either donor numbers '
  160. '(i.e., 9861, 10021) or UIDs (i.e., H0351.2001). '
  161. 'Can specify "all" to use all available donors. '
  162. 'Default: "all"')
  163. g_data.add_argument('--data_dir', '--data-dir', action=CheckExists,
  164. type=_resolve_path, metavar='PATH',
  165. help='Directory where expression data should be '
  166. 'downloaded to (if it does not already exist) / '
  167. 'loaded from. If not specified this will check '
  168. 'the environmental variable $ABAGEN_DATA, the '
  169. '$HOME/abagen-data directory, and the current '
  170. 'working directory. If data does not already '
  171. 'exist at one of those locations then it will be '
  172. 'downloaded to the first of these location that '
  173. 'exists and for which write access is enabled.')
  174. g_data.add_argument('--n_proc', '--n-proc', action='store', type=int,
  175. default=1,
  176. help='Number of processors to use to download AHBA '
  177. 'data. Can paralellize up to six times if all '
  178. 'donors are requested. Default: 1')
  180. w_data = parser.add_argument_group('Options to specify processing options')
  181. w_data.add_argument('--ibf_threshold', '--ibf-threshold', action='store',
  182. default=0.5, metavar='THRESHOLD',
  183. help='Threshold for intensity-based filtering of '
  184. 'probes. This number should specify the ratio of '
  185. 'samples, across all supplied donors, for which '
  186. 'a probe must have signal above background noise '
  187. 'in order to be retained. Default: 0.5')
  188. w_data.add_argument('--probe_selection', '--probe-selection',
  189. action='store', default='diff_stability',
  190. metavar='METHOD', choices=sorted(SELECTION_METHODS),
  191. help='Selection method for subsetting (or collapsing '
  192. 'across) probes that index the same gene. Must '
  193. 'be one of {"average", "mean", "max_intensity", '
  194. '"max_variance", "pc_loading", "corr_variance", '
  195. '"corr_intensity", "diff_stability", "rnaseq"}. '
  196. 'Default: "diff_stability"')
  197. w_data.add_argument('--lr_mirror', '--lr-mirror', metavar='METHOD',
  198. type=_resolve_none, default=None, choices=(
  199. None, 'bidirectional', 'leftright', 'rightleft'),
  200. help='Whether to mirror microarray expression samples '
  201. 'across hemispheres to increase spatial coverage.'
  202. ' Using "bidirectional" will mirror samples '
  203. 'across both hemispheres, "leftright" will '
  204. 'mirror samples in the left hemisphere to the '
  205. 'right, and "rightleft" will mirror the right to '
  206. 'the left. Default: None')
  207. w_data.add_argument('--sim_threshold', '--sim-threshold',
  208. type=_resolve_none, default=None, metavar='THRESHOLD',
  209. help='Threshold for inter-areal similarity filtering. '
  210. 'Samples are correlated across probes and those '
  211. 'samples with a total correlation less than the '
  212. 'the provided threshold s.d. below the mean '
  213. 'across samples are excluded from futher'
  214. 'analysis. If not specified no filtering is '
  215. 'performed. Default: None')
  216. w_data.add_argument('--missing', dest='missing', metavar='METHOD',
  217. type=_resolve_none, default=None, choices=(
  218. None, 'centroids', 'interpolate'),
  219. help='How to handle regions in `atlas` that are not '
  220. 'assigned any tissue samples. If "centroids", '
  221. 'any empty regions will be assigned the '
  222. 'expression value of the nearest tissue sample '
  223. '(defined as the sample with the closest '
  224. 'Euclidean distance to the parcel centroid). If '
  225. '"interpolate", expression values will be '
  226. 'interpolated in the empty regions by assigning '
  227. 'every node in the region the expression of the '
  228. 'nearest sample and taking a weighted (inverse '
  229. 'distance) average. If not specified empty '
  230. 'regions will be returned with expression values '
  231. 'of NaN. Default: None')
  232. w_data.add_argument('--tol', '--tolerance', dest='tolerance',
  233. action='store', type=float, default=2,
  234. help='Distance (in mm) that a sample can be from a '
  235. 'parcel for it to be matched to that parcel. If '
  236. '`atlas` is GIFTI files then this measure is a '
  237. 'standard deviation threshold (i.e., samples '
  238. 'greater than `tolerance` SDs away from the mean '
  239. 'matched distance are ignored). Default: 2')
  240. w_data.add_argument('--sample_norm', '--sample-norm', action='store',
  241. default='srs', metavar='METHOD', type=_resolve_none,
  242. choices=sorted(NORMALIZATION_METHODS) + ['None', None],
  243. help='Method by which to normalize microarray '
  244. 'expression values for each sample prior to '
  245. 'collapsing into regions in `atlas`. Expression '
  246. 'values are normalized separately for each '
  247. 'sample and donor across genes. If None is '
  248. 'specified then no normalization is performed. '
  249. 'Default: "srs"')
  250. w_data.add_argument('--gene_norm', '--gene-norm', action='store',
  251. default='srs', metavar='METHOD', type=_resolve_none,
  252. choices=sorted(NORMALIZATION_METHODS) + ['None', None],
  253. help='Method by which to normalize microarray '
  254. 'expression values for each donor prior to '
  255. 'collapsing across donors. Expression values are '
  256. 'normalized separately for each gene for each '
  257. 'donor across all expression samples. If None is '
  258. 'specified then no normalization is performed. '
  259. 'Default: "srs"')
  260. w_data.add_argument('--norm_all', '--norm-all', dest='norm_matched',
  261. action='store_false', default=True,
  262. help='Whether to perform gene normalization '
  263. '(`gene_norm`) across all available samples '
  264. 'instead of only across samples that were '
  265. 'matched to regions in `atlas`. If `atlas` is '
  266. 'very small (i.e., only a few regions of '
  267. 'interest) using `--norm_all` is suggested.')
  268. w_data.add_argument('--norm_structures', '--norm-structures',
  269. action='store_true', default=False,
  270. help='Whether to perform gene normalization '
  271. '(`gene_norm`) within structural classes (i.e., '
  272. '"cortex", "subcortex/brainstem", "cerebellum") '
  273. 'instead of across all available samples.')
  274. w_data.add_argument('--region_agg', '--region-agg', action='store',
  275. default='donors', metavar='METHOD',
  276. choices=['donors', 'samples'],
  277. help='When multiple samples are identified as '
  278. 'belonging to a region in `atlas` this '
  279. 'determines how they are aggegated. If '
  280. '\'samples\', expression data from all samples '
  281. 'for all donors assigned to a given region are '
  282. 'combined. If \'donors\', expression values for '
  283. 'all samples assigned to a given region are '
  284. 'combined independently for each donor before '
  285. 'being combined across donors. See `agg_metric` '
  286. 'for mechanism by which samples are combined. '
  287. 'Default: \'donors\'')
  288. w_data.add_argument('--agg_metric', '--agg-metric', action='store',
  289. default='mean', metavar='METHOD',
  290. choices=['mean', 'median'],
  291. help='Mechanism by which to (1) reduce expression '
  292. 'data of multiple samples in the same `atlas` '
  293. 'region, and (2) reduce donor-level expression '
  294. 'data into a single "group" expression '
  295. 'dataframe. Must be one of {"mean", "median"}. '
  296. 'Default: "mean"')
  298. p_data = parser.add_argument_group('Options to modify the AHBA data used')
  299. p_data.add_argument('--no-reannotated', '--no_reannotated',
  300. dest='reannotated', action='store_false', default=True,
  301. help='Whether to use the original probe information '
  302. 'from the AHBA dataset instead of the '
  303. 'reannotated probe information from '
  304. 'Arnatkevic̆iūtė et al., 2019. Using reannotated '
  305. 'probe information discards probes that could '
  306. 'not be reliably matched to genes. Default: '
  307. 'False (i.e., use reannotations)')
  308. p_data.add_argument('--no-corrected-mni', '--no_corrected_mni',
  309. dest='corrected_mni', action='store_false',
  310. default=True,
  311. help='Whether to use the original MNI coordinates '
  312. 'provided with the AHBA data instead of the '
  313. '"corrected" MNI coordinates shipped with the '
  314. '`alleninf` package when matching tissue samples '
  315. 'to anatomical regions. Default: False (i.e., '
  316. 'use corrected coordinates)')
  318. o_data = parser.add_argument_group('Options to modify how data are output')
  319. o_data.add_argument('--stdout', action='store_true',
  320. help='Generated region x gene dataframes will be '
  321. 'printed to stdout for piping to other things. '
  322. 'You should REALLY consider just using --output-'
  323. 'file instead and working with the generated '
  324. 'CSV file(s). Incompatible with `--save-counts` '
  325. 'and `--save-donors` (i.e., this will override '
  326. 'those options). Default: False')
  327. o_data.add_argument('--output-file', '--output_file', action='store',
  328. type=_resolve_path, metavar='PATH',
  329. default='abagen_expression.csv',
  330. help='Path to desired output file. The generated '
  331. 'region x gene dataframe will be saved here. '
  332. 'Default: $PWD/abagen_expression.csv')
  333. o_data.add_argument('--save-counts', '--save_counts', action='store_true',
  334. help='Whether to save dataframe containing number of '
  335. 'samples from each donor that were assigned '
  336. 'to each region in `atlas`. If specified, will '
  337. 'be saved to the path specified by '
  338. '`output-file`, appending "counts" to the end of '
  339. 'the filename. Default: False')
  340. o_data.add_argument('--save-donors', '--save_donors', action='store_true',
  341. help='Whether to save donor-level expression '
  342. 'dataframes instead of aggregating expression '
  343. 'across donors with provided `agg_metric`. If '
  344. 'specified, dataframes will be saved to path '
  345. 'specified by `output-file`, appending donor IDs '
  346. 'to the end of the filename. Default: False')
  348. return parser
  351. def main(args=None):
  352. """ Runs primary get_expression_data workflow
  353. """
  355. from ..allen import get_expression_data
  357. opts = get_parser().parse_args(args)
  359. # debugging is fun
  360. if opts.debug:
  361. print(opts)
  362. return
  364. # run the workflow
  365. expression = get_expression_data(atlas=opts.atlas,
  366. atlas_info=opts.atlas_info,
  367. ibf_threshold=opts.ibf_threshold,
  368. probe_selection=opts.probe_selection,
  369. sim_threshold=opts.sim_threshold,
  370. lr_mirror=opts.lr_mirror,
  371. missing=opts.missing,
  372. tolerance=opts.tolerance,
  373. sample_norm=opts.sample_norm,
  374. gene_norm=opts.gene_norm,
  375. norm_matched=opts.norm_matched,
  376. norm_structures=opts.norm_structures,
  377. region_agg=opts.region_agg,
  378. agg_metric=opts.agg_metric,
  379. corrected_mni=opts.corrected_mni,
  380. reannotated=opts.reannotated,
  381. return_counts=opts.save_counts,
  382. return_donors=opts.save_donors,
  383. donors=opts.donors,
  384. data_dir=opts.data_dir,
  385. verbose=opts.verbose,
  386. n_proc=opts.n_proc)
  388. output_path = os.path.dirname(opts.output_file)
  389. fname_pref = os.path.splitext(os.path.basename(opts.output_file))[0]
  391. # WHY?!?
  392. if opts.stdout and not (opts.save_counts or opts.save_donors):
  393. expression.to_csv(sys.stdout)
  394. return
  396. # expand the tuple, if needed
  397. if opts.save_counts:
  398. expression, counts = expression
  399. counts_fname = os.path.join(output_path, fname_pref + '_counts.csv')
  400. LGR.info('Saving samples counts to {}'.format(counts_fname))
  401. counts.to_csv(counts_fname)
  403. # determine how best to save expression output files
  404. if opts.save_donors:
  405. # save each donor dataframe as a separate file
  406. for donor, exp in expression.items():
  407. exp_fname = os.path.join(output_path,
  408. fname_pref + '_{}.csv'.format(donor))
  409. LGR.info('Saving donor {} info to {}'.format(donor, exp_fname))
  410. exp.to_csv(exp_fname)
  411. else:
  412. expression.to_csv(opts.output_file)
  415. if __name__ == '__main__':
  416. raise RuntimeError('abagen/cli/run.py should not be run directly.\nPlease '
  417. '`pip install` abagen and use the `abagen` command.')

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

Overview

Authors: Kanlin Lin1,2, Taipeng Zeng1,2, Pan Zhang2, Hui Li2, Pengfan Yang2, Zhifeng Huang2, Nuozhen Chen2, Xiaoyang Wang1,2, Liyuan Fu1,2, Shangwen Xu1,2
  1. Fuzong Teaching Hospital of Fujian University of Traditional Chinese Medicine, Fuzhou, China
  2. 900th Hospital of PLA Joint Logistic Support Force, Fuzhou, China
Institutions: Fujian University of Traditional Chinese Medicine (China)
Journal: Frontiers in neuroscience, volume 20, article 1833695
Dates: received 18 March 2026; accepted 22 May 2026; published online 3 June 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.3389/fnins.2026.1833695 · PMID 42318194 · PMCID PMC13273452 · OpenAlex W7163324514
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: structural MRI / diffusion (modality), human (organism), epilepsy (population)
Methods: Connectivity, Statistics, Smoothing, state filtering, decompositions, Preprocessing, fMRI & imaging
Keywords: focal to bilateral tonic–clonic seizures, gene expression, gradient, morphometric similarity network, temporal lobe epilepsy
Topic: Functional Brain Connectivity Studies (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Citations: not cited yet (Europe PMC); 64 references in the paper

Abstract

Background: Temporal lobe epilepsy (TLE) manifests with diverse seizure symptoms, including focal to bilateral tonic—clonic seizures (FBTCS), linked to widespread brain network disruptions. The role of cortical morphometric similarity (MS) network gradients and their relationship with gene expression in TLE remains unclear.

Methods: We studied MS network gradient abnormalities through group comparisons among 87 left TLE patients (48 FBTCS−, 39 FBTCS+) and 63 healthy controls (HC). In addition, partial least squares (PLS) regression analysis was performed to investigate the association between gradient changes and whole-brain gene expression in left FBTCS+ TLE patients.

Results: FBTCS+ patients showed significant reductions in the principal MS network gradient within default mode network (DMN) regions compared to healthy controls, while FBTCS− patients exhibited no such abnormalities. Gradient alterations in FBTCS+ were linked to whole-brain expression of genes involved in neurobiological pathways, cell types, and cortical layers.

Conclusion: FBTCS+ TLE is associated with distinct MS network gradient alterations, which may reflect underlying molecular mechanisms contributing to structural changes linked to severe seizure symptoms.

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

Repository

Its files are read in the Code ↔ Paper reader above, with 3 matches 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
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

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.

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Data

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

Data availability statement

The original contributions presented in the study are included in the article/Supplementary material. Further inquiries can be directed to the corresponding authors.

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 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, pages, dates, 10 authors, 5 keywords, 64 references.

Cite

This paper

Lin, K., Zeng, T., Zhang, P., Li, H., Yang, P., Huang, Z., Chen, N., Wang, X., Fu, L., & Xu, S. (2026). Transcriptional signatures of the cortical morphometric similarity network gradient in left temporal lobe epilepsy with different seizure symptoms. Frontiers in neuroscience, 20, 1833695. https://doi.org/10.3389/fnins.2026.1833695

BibTeX

@article{lin2026transcriptional,
author = {Lin, Kanlin and Zeng, Taipeng and Zhang, Pan and Li, Hui and Yang, Pengfan and Huang, Zhifeng and Chen, Nuozhen and Wang, Xiaoyang and Fu, Liyuan and Xu, Shangwen},
title = {{Transcriptional signatures of the cortical morphometric similarity network gradient in left temporal lobe epilepsy with different seizure symptoms}},
journal = {Frontiers in neuroscience},
year = {2026},
month = jun,
volume = {20},
pages = {1833695},
publisher = {Frontiers Media SA},
issn = {1662-4548},
doi = {10.3389/fnins.2026.1833695},
url = {https://doi.org/10.3389/fnins.2026.1833695},
pmid = {42318194},
pmcid = {PMC13273452}
}

RIS

TY - JOUR
AU - Lin, Kanlin
AU - Zeng, Taipeng
AU - Zhang, Pan
AU - Li, Hui
AU - Yang, Pengfan
AU - Huang, Zhifeng
AU - Chen, Nuozhen
AU - Wang, Xiaoyang
AU - Fu, Liyuan
AU - Xu, Shangwen
TI - Transcriptional signatures of the cortical morphometric similarity network gradient in left temporal lobe epilepsy with different seizure symptoms
T2 - Frontiers in neuroscience
J2 - Front Neurosci
PY - 2026
DA - 2026/06/03
VL - 20
SP - 1833695
SN - 1662-4548
PB - Frontiers Media SA
DO - 10.3389/fnins.2026.1833695
UR - https://doi.org/10.3389/fnins.2026.1833695
LA - en
ER -

CSL-JSON

{
"id": "10.3389/fnins.2026.1833695",
"type": "article-journal",
"title": "Transcriptional signatures of the cortical morphometric similarity network gradient in left temporal lobe epilepsy with different seizure symptoms",
"container-title": "Frontiers in neuroscience",
"author": [
{
"family": "Lin",
"given": "Kanlin"
},
{
"family": "Zeng",
"given": "Taipeng"
},
{
"family": "Zhang",
"given": "Pan"
},
{
"family": "Li",
"given": "Hui"
},
{
"family": "Yang",
"given": "Pengfan"
},
{
"family": "Huang",
"given": "Zhifeng"
},
{
"family": "Chen",
"given": "Nuozhen"
},
{
"family": "Wang",
"given": "Xiaoyang"
},
{
"family": "Fu",
"given": "Liyuan"
},
{
"family": "Xu",
"given": "Shangwen"
}
],
"container-title-short": "Front Neurosci",
"volume": "20",
"page": "1833695",
"DOI": "10.3389/fnins.2026.1833695",
"PMID": "42318194",
"PMCID": "PMC13273452",
"ISSN": "1662-4548",
"publisher": "Frontiers Media SA",
"URL": "https://doi.org/10.3389/fnins.2026.1833695",
"language": "en",
"issued": {
"date-parts": [
[
2026,
6,
3
]
]
}
}

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