OSCR

Association of glymphatic function with 40-Hz neural oscillations, systemic metabolic markers, and cognitive performance in healthy aging adults: An EEG and MRI study.

Code ↔ Paper

1 match 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 1 match
  1. [1] § Methods › Imaging analysis › Imaging processing ↔ src/halfpipe/tui/standards.py, lines 18–72 · score 0.60 · CSF signal, fMRI, Gaussian, smoothing, motion, width

Paper

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

The paper is loaded when this pane is shown.

The authors' code

Python · 193 lines · 6.6 KB · GPL-3.0 · 1 match

  1. from copy import deepcopy
  2. from typing import Dict, List, Union
  4. from inflection import humanize
  6. global_settings_defaults: dict[str, str] = {
  7. "dummy_scans": "0",
  8. "run_reconall": "False",
  9. "slice_timing": "False",
  10. "skull_strip_algorithm": "ants",
  11. }
  13. bandpass_filter_defaults: dict[str, dict] = {
  14. "gaussian": {"type": "gaussian", "hp_width": "125", "lp_width": None},
  15. "frequency_based": {"type": "frequency_based", "high": "0.1", "low": "0.01"},
  16. }
  18. # specify first task based defaults, for other features we will just copy it and modify what is different
  19. task_based_defaults: Dict[
  20. str,
  21. Union[
  22. Dict[str, Union[str, int, float, None, List[Union[str, bool]]]],
  23. List[Dict[str, Union[str, float]]],
  24. str,
  25. float,
  26. ],
  27. ] = {
  28. "bandpass_filter": {"type": "gaussian", "hp_width": "125", "lp_width": None},
  29. "smoothing": {"fwhm": "6"},
  30. "grand_mean_scaling": {"mean": 10000},
  31. "confounds_options": {
  32. "ICA-AROMA": ["ICA-AROMA", False],
  33. "(trans|rot)_[xyz]": ["Motion parameters", False],
  34. "(trans|rot)_[xyz]_derivative1": ["Derivatives of motion parameters", False],
  35. "(trans|rot)_[xyz]_power2": ["Motion parameters squared", False],
  36. "(trans|rot)_[xyz]_derivative1_power2": ["Derivatives of motion parameters squared", False],
  37. "motion_outlier[0-9]+": ["Motion scrubbing", False],
  38. "a_comp_cor_0[0-4]": ["aCompCor (top five components)", False],
  39. "white_matter": ["White matter signal", False],
  40. "csf": ["CSF signal", False],
  41. "global_signal": ["Global signal", False],
  42. },
  43. }
  45. seed_based_defaults = deepcopy(task_based_defaults)
  46. seed_based_defaults["minimum_coverage_label"] = "Minimum fMRI brain coverage by seed (in fraction)"
  47. seed_based_defaults["widget_header"] = "Seed images"
  48. seed_based_defaults["file_selection_widget_header"] = "Select seeds"
  49. seed_based_defaults["minimum_brain_coverage"] = 0.8
  51. dual_reg_defaults = deepcopy(task_based_defaults)
  52. dual_reg_defaults["minimum_coverage_label"] = "Minimum network template coverage by individual brain mask (in fraction)"
  53. dual_reg_defaults["widget_header"] = "Network template images"
  54. dual_reg_defaults["file_selection_widget_header"] = "Select network templates"
  55. dual_reg_defaults["minimum_brain_coverage"] = 0.8
  57. gig_ica_defaults = deepcopy(dual_reg_defaults)
  59. preproc_output_defaults = deepcopy(task_based_defaults)
  60. preproc_output_defaults["minimum_coverage_label"] = "None"
  61. preproc_output_defaults["widget_header"] = "None"
  62. preproc_output_defaults["file_selection_widget_header"] = "None"
  64. atlas_based_connectivity_defaults = deepcopy(task_based_defaults)
  65. atlas_based_connectivity_defaults["bandpass_filter"] = {"type": "frequency_based", "high": "0.1", "low": "0.01"}
  66. atlas_based_connectivity_defaults["smoothing"] = {"fwhm": None}
  67. atlas_based_connectivity_defaults["minimum_brain_coverage"] = 0.8
  68. atlas_based_connectivity_defaults["minimum_coverage_label"] = (
  69. "Minimum atlas region coverage by individual brain mask (in fraction)"
  70. )
  71. atlas_based_connectivity_defaults["widget_header"] = "Atlas images"
  72. atlas_based_connectivity_defaults["file_selection_widget_header"] = "Select atlases"
  74. # reho and falff have same configuration
  75. reho_defaults = deepcopy(atlas_based_connectivity_defaults)
  76. # pop unused keys
  77. reho_defaults.pop("minimum_coverage_label")
  78. reho_defaults.pop("widget_header")
  79. reho_defaults.pop("file_selection_widget_header")
  80. # bring back smoothing because it was None at Atlas
  81. reho_defaults["smoothing"] = {"fwhm": "6"}
  82. reho_defaults["zscore"] = True
  84. falff_defaults = deepcopy(reho_defaults)
  86. group_level_modesl_defaults: dict[str, list[dict[str, str]]] = {
  87. "cutoffs": [
  88. {
  89. "type": "cutoff",
  90. "action": "exclude",
  91. "field": "fd_mean",
  92. "cutoff": "0.5",
  93. },
  94. {
  95. "type": "cutoff",
  96. "action": "exclude",
  97. "field": "fd_perc",
  98. "cutoff": "10.0",
  99. },
  100. ]
  101. }
  103. # this maps how feature labels are viewed in the UI by the use, change value to change the label
  104. feature_label_map: dict[str, str] = {
  105. "task_based": "Task-based",
  106. "seed_based_connectivity": "Seed-based connectivity",
  107. "dual_regression": "Network Template Regression\n'Dual regression'",
  108. "gig_ica": "Network Template Regression\n'Neuromark'",
  109. "atlas_based_connectivity": "Atlas-based Connectivity",
  110. "reho": "ReHo",
  111. "falff": "fALFF",
  112. "preprocessed_image": "Output preprocessed image",
  113. }
  115. # same as above, but for group level models
  116. group_level_model_label_map: dict[str, str] = {"me": "Intercept-only", "lme": "Linear model"}
  118. # colors of the entity highlight in the path pattern builder
  119. entity_colors: dict[str, str] = {
  120. "sub": "red",
  121. "ses": "green",
  122. "run": "magenta",
  123. "task": "cyan",
  124. "dir": "yellow",
  125. "condition": "orange",
  126. "acq": "purple", # Changed to purple for uniqueness
  127. "echo": "brown", # Changed to brown for uniqueness
  128. "desc": "red", # there is only one entity when desc is used
  129. }
  131. # to avoid confusion between textual and rich color definitions, we declare the colors in hex
  132. color_hex_map = {
  133. "green": "#008000",
  134. "red": "#ff0000",
  135. "cyan": "#58d1eb",
  136. "magenta": "#f4005f",
  137. "yellow": "#ffff00",
  138. "brown": "#a52a2a",
  139. "orange": "#ffa500",
  140. "purple": "#af00ff",
  141. }
  143. hex_color_map = {v: k for k, v in color_hex_map.items()}
  145. # same as above but for field maps
  146. field_map_group_labels: dict[str, str] = {
  147. "epi": "EPI (blip-up blip-down)",
  148. "siemens": "Phase difference and magnitude (used by Siemens scanners)",
  149. "philips": "Scanner-computed field map and magnitude (used by GE / Philips scanners)",
  150. }
  152. field_map_labels: dict[str, str] = {
  153. "magnitude1": "first set of magnitude image",
  154. "magnitude2": "second set of magnitude image",
  155. "phase1": "first set of phase image",
  156. "phase2": "second set of phase image",
  157. "phasediff": "phase difference image",
  158. "fieldmap": "field map image",
  159. }
  162. # function for displaying some labels in the meta_data_steps
  163. def display_str(x):
  164. """
  165. Formats a string for display, handling specific cases.
  167. Parameters
  168. ----------
  169. x : str
  170. The input string to format.
  172. Returns
  173. -------
  174. str
  175. The formatted string.
  176. """
  177. if x == "MNI152NLin6Asym":
  178. return "MNI ICBM 152 non-linear 6th Generation Asymmetric (FSL)"
  179. elif x == "MNI152NLin2009cAsym":
  180. return "MNI ICBM 2009c Nonlinear Asymmetric"
  181. elif x == "slice_encoding_direction":
  182. return "slice acquisition direction"
  183. return humanize(x)
  186. aggregate_order = ["dir", "run", "ses", "task"]
  188. entity_display_aliases = {
  189. "ses": "session",
  190. "sub": "subject",
  191. "dir": "direction",
  192. "acq": "acquisition",
  193. }

standards.py at commit b65f53d, under GPL-3.0 · at the source

Overview

Authors: Shiwei Lin1, Xueting Lu1, Qunjun Liang2, Hongyan Huang1, Shengli Chen1, Qianyun Chen1, Hongyue Wei1, Yingwei Qiu1
ORCID iDs: Qunjun Liang, Qianyun Chen, Hongyue Wei, Yingwei Qiu
  1. Department of Radiology, Nanshan People’s Hospital (NSPH), Shenzhen University,Taoyuan AVE 89, Nanshan district, Shenzhen, 518000 P R China
  2. School of Marxism, Guangxi University of Science and Technology,Liuzhou, 545000 P. R. China
Institutions: Shenzhen University (China); Guangxi University of Science and Technology (China)
Journal: Translational psychiatry, volume 16, issue 1, article 420
Dates: received 22 September 2025; accepted 3 June 2026; published online 13 June 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1038/s41398-026-04157-5 · PMID 42285933 · PMCID PMC13487156 · OpenAlex W7164539542
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: structural MRI / diffusion (modality), EEG (modality), fMRI (modality), human (organism), healthy (population), clinical / translational (subfield)
Methods: Spectral & time-frequency, Connectivity, Statistics, Smoothing, state filtering, decompositions, Machine learning, Preprocessing, fMRI & imaging, Physiology & signal measures
Keywords: Physiology, Diseases, Neuroscience
MeSH: Brain*, Cognition*, Glymphatic System*, Healthy Aging*, Adult, Aged, Biomarkers, Diffusion Tensor Imaging, Electroencephalography, Female, Humans, Magnetic Resonance Imaging, Male, Middle Aged, Young Adult (* major topic)
Topic: Cerebrospinal fluid and hydrocephalus (Cellular and Molecular Neuroscience, Neuroscience), according to OpenAlex
Funding: This work has received funding by grants from the Natural Science Foundation of Guangdong Province (grants 2024A1515013203 and 2022A1515012503), the Nature and Science Basic Research Foundation of Shenzhen (JCYJ20230807115916035), the Medicine Plus Program of Shenzhen University (2024YG008) and the Scientific and Technological Project of Nanshan (NS2022007, NSZD2023025)
Citations: not cited yet (Europe PMC); 71 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.

Repository

Its files are read in the Code ↔ Paper reader above, with 1 match between paragraphs and lines of code.

HALFpipe/HALFpipe

License: GPL-3.0
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: b65f53d305936a2660ba420bbdff6c156416b3be, 18 August 2026
Languages: Python (455), Shell (5)
Size: 611 files, 460 scripts
Software Heritage: not archived
Found in: the text, “Imaging processing”
Holds: README, license file, environment (Dockerfile, pyproject.toml, recipes/conda_build_config.yaml), tests, continuous integration
Not found: CITATION.cff, documentation
Tools: NumPy (90 files), Nipype (86 files), NiBabel (51 files), pandas (38 files), Nilearn (17 files), FSL (14 files), SciPy (14 files), fMRIPrep (13 files), TemplateFlow (9 files), AFNI (6 files), NetworkX (4 files), Numba (4 files), PyBIDS (4 files), ANTs (3 files), Matplotlib (3 files), DataLad (2 files), FreeSurfer (1 file), seaborn (1 file), statsmodels (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
462 files
At the source: github.com/HALFpipe/HALFpipe

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;
  • 460 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.

Data availability statement

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

  • no repository, dataset or request procedure was recognized in it

Read it in the paper: doi.org/10.1038/s41398-026-04157-5.

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, issue, pages, dates, 8 authors, 3 keywords, 15 MeSH terms, 1 funder, 69 references.

Cite

This paper

Lin, S., Lu, X., Liang, Q., Huang, H., Chen, S., Chen, Q., Wei, H., & Qiu, Y. (2026). Association of glymphatic function with 40-Hz neural oscillations, systemic metabolic markers, and cognitive performance in healthy aging adults: An EEG and MRI study. Translational psychiatry, 16(1), 420. https://doi.org/10.1038/s41398-026-04157-5

BibTeX

@article{lin2026association,
author = {Lin, Shiwei and Lu, Xueting and Liang, Qunjun and Huang, Hongyan and Chen, Shengli and Chen, Qianyun and Wei, Hongyue and Qiu, Yingwei},
title = {{Association of glymphatic function with 40-Hz neural oscillations, systemic metabolic markers, and cognitive performance in healthy aging adults: An EEG and MRI study}},
journal = {Translational psychiatry},
year = {2026},
month = jun,
volume = {16},
number = {1},
pages = {420},
publisher = {Nature Publishing Group},
issn = {2158-3188},
doi = {10.1038/s41398-026-04157-5},
url = {https://doi.org/10.1038/s41398-026-04157-5},
pmid = {42285933},
pmcid = {PMC13487156}
}

RIS

TY - JOUR
AU - Lin, Shiwei
AU - Lu, Xueting
AU - Liang, Qunjun
AU - Huang, Hongyan
AU - Chen, Shengli
AU - Chen, Qianyun
AU - Wei, Hongyue
AU - Qiu, Yingwei
TI - Association of glymphatic function with 40-Hz neural oscillations, systemic metabolic markers, and cognitive performance in healthy aging adults: An EEG and MRI study
T2 - Translational psychiatry
J2 - Transl Psychiatry
PY - 2026
DA - 2026/06/13
VL - 16
IS - 1
SP - 420
SN - 2158-3188
PB - Nature Publishing Group
DO - 10.1038/s41398-026-04157-5
UR - https://doi.org/10.1038/s41398-026-04157-5
LA - en
ER -

CSL-JSON

{
"id": "10.1038/s41398-026-04157-5",
"type": "article-journal",
"title": "Association of glymphatic function with 40-Hz neural oscillations, systemic metabolic markers, and cognitive performance in healthy aging adults: An EEG and MRI study",
"container-title": "Translational psychiatry",
"author": [
{
"family": "Lin",
"given": "Shiwei"
},
{
"family": "Lu",
"given": "Xueting"
},
{
"family": "Liang",
"given": "Qunjun"
},
{
"family": "Huang",
"given": "Hongyan"
},
{
"family": "Chen",
"given": "Shengli"
},
{
"family": "Chen",
"given": "Qianyun"
},
{
"family": "Wei",
"given": "Hongyue"
},
{
"family": "Qiu",
"given": "Yingwei"
}
],
"container-title-short": "Transl Psychiatry",
"volume": "16",
"issue": "1",
"page": "420",
"DOI": "10.1038/s41398-026-04157-5",
"PMID": "42285933",
"PMCID": "PMC13487156",
"ISSN": "2158-3188",
"publisher": "Nature Publishing Group",
"URL": "https://doi.org/10.1038/s41398-026-04157-5",
"language": "en",
"issued": {
"date-parts": [
[
2026,
6,
13
]
]
}
}

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

Similar papers

The papers with a page that share the most with this one: the tools found in their code, their categories, datasets, cited references and authors, the rarest counting most.

[1] doi:10.1162/imag.a.1198 [code]
MEPrep: A robust pipeline for multi-echo fMRI denoising and preprocessing.
Journal: Imaging neuroscience (Cambridge, Mass.)
In common: TemplateFlow, fMRIPrep, PyBIDS, 11 other tools, fMRI, 1 reference
[2] doi:10.1186/s13293-026-00891-z [code]
Sex differences in dynamic and static measures of brain integration derived from resting-state functional magnetic resonance imaging.
Journal: Biology of sex differences
In common: DataLad, PyBIDS, Nipype, 12 other tools, fMRI
[3] doi:10.1038/s41467-026-71151-2 [code]
Common and distinct neural correlates of social interaction processing and theory of mind in narratives.
Journal: Nature communications
In common: fMRIPrep, Nipype, AFNI, 12 other tools
[4] doi:10.1162/imag.a.1347 [code]
Neural and behavioural correlates of theory of mind reasoning in five-year-old children born preterm.
Journal: Imaging neuroscience (Cambridge, Mass.)
In common: fMRIPrep, PyBIDS, Nipype, 9 other tools, fMRI
[5] doi:10.1016/j.dcn.2026.101765 [code]
Fusiform face area development correlates with development in higher-order social brain regions.
Journal: Developmental cognitive neuroscience
In common: fMRIPrep, PyBIDS, Nipype, 9 other tools, fMRI
[6] doi:10.1162/netn.a.547 [code]
An evaluation of the efficacy of single-echo and multi-echo fMRI denoising strategies.
Journal: Network neuroscience (Cambridge, Mass.)
In common: fMRIPrep, Nipype, ANTs, 10 other tools, fMRI
[7] doi:10.1162/imag.a.1245 [code]
Towards precision EEG connectomics: Evaluating the benefits of dense sampling.
Journal: Imaging neuroscience (Cambridge, Mass.)
In common: Nipype, AFNI, ANTs, 10 other tools, EEG
[8] doi:10.1038/s41597-026-06869-1 [code]
Individual Brain Charting: fifth release of high-resolution fMRI data for cognitive mapping.
Journal: Scientific data
In common: PyBIDS, Nipype, ANTs, 9 other tools, fMRI
[9] doi:10.1038/s41467-026-71568-9 [code]
Convergent and selective representations of pain, appetitive processes, aversive processes, and cognitive control in the insula.
Journal: Nature communications
In common: fMRIPrep, Nipype, ANTs, 9 other tools
[10] doi:10.1162/imag.a.1362 [code]
Human fMRI at 11.7T: Assessing feasibility, stability, and reliability on the Iseult scanner.
Journal: Imaging neuroscience (Cambridge, Mass.)
In common: fMRIPrep, Nipype, ANTs, 9 other tools, fMRI

Contribute

The authors of this paper can claim it, correct its record and validate its tracing map, and the maintainers of its code (its owner, or a public member of its organization) correct what it says of their repository; anyone signed in can ask for its removal. Every request goes to OSCR's own machine, which answers it; your account page follows them.

Sign in with ORCID to claim this paper as one of its authors, correct its record or validate its tracing map: when the paper's metadata lists your ORCID iD, you are recognized at once. Maintainers of its code: sign in with GitHub, then claim the repository on your account page.

Request its removal

To ask OSCR to remove this record, the copies of its authors' scripts or its tracing map, use the removal request page: signed in, you say who you are, what to remove and why, then review and confirm the request. Published rules decide every request (how).

Discussion, reproductions, activity

Discussion: questions and error reports about this paper and its code, from signed-in readers and its authors. It opens with sign-in.

Reproductions: reports from readers who ran the authors' code: what they reproduced, with which environment, commit and data. It opens with sign-in.

Activity: what happens around this paper: new versions of its record, its map's validation, discussions and reproductions. It opens with sign-in.