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Surface-based brain age index reflects hematological impacts on cognitive development in children with beta-thalassemia major.

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Paper

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The authors' code

Python · 52 lines · 1.7 KB · no license

  3. import torch
  4. from torch_geometric.data import InMemoryDataset, Data
  5. from os.path import join, isfile
  6. from os import listdir
  7. import numpy as np
  8. import os.path as osp
  9. from imports.read_abide_stats_parall import read_data
  12. class AGEDataset(InMemoryDataset):
  13. def __init__(self, root, name, num_nodes, transform=None, pre_transform=None):
  14. self.root = root
  15. self.name = name
  16. self.num_nodes = num_nodes
  17. super(AGEDataset, self).__init__(root, transform, pre_transform)
  18. self.data, self.slices = torch.load(self.processed_paths[0])
  21. @property
  22. def raw_file_names(self):
  23. data_dir = osp.join(self.root, 'raw')
  24. onlyfiles = [f for f in listdir(data_dir) if osp.isfile(osp.join(data_dir, f))]
  25. onlyfiles.sort()
  26. return onlyfiles
  27. @property
  28. def processed_file_names(self):
  29. return 'data.pt'
  31. def download(self):
  32. # Download to `self.raw_dir`.
  33. return
  35. def process(self):
  36. # Read data into huge `Data` list.
  37. self.data, self.slices = read_data(self.raw_dir, self.num_nodes)
  39. if self.pre_filter is not None:
  40. data_list = [self.get(idx) for idx in range(len(self))]
  41. data_list = [data for data in data_list if self.pre_filter(data)]
  42. self.data, self.slices = self.collate(data_list)
  44. if self.pre_transform is not None:
  45. data_list = [self.get(idx) for idx in range(len(self))]
  46. data_list = [self.pre_transform(data) for data in data_list]
  47. self.data, self.slices = self.collate(data_list)
  49. torch.save((self.data, self.slices), self.processed_paths[0])
  51. def __repr__(self):
  52. return '{}({})'.format(self.name, len(self))

AGEDataset.py at commit 49d32dd, no license · at the source

Overview

Authors: Shumin Xu1, Zhuoshuo Li2, Xinyi Liu1, Sixi Liu1, Xiaodong Wang1, Pengjie Yang2, Xuan Cai2, Hongwu Zeng1, Mengting Liu2, Yaowen Li1
ORCID iDs: Yaowen Li
  1. Department of Radiology, Shenzhen Children’s Hospital, Shenzhen, China
  2. School of Biomedical Engineering, Sun Yat-sen University, Shenzhen 518107, China
Institutions: Shenzhen Children's Hospital (China); Sun Yat-sen University (China)
Journal: iScience, volume 29, issue 6, article 116072
Dates: received 3 June 2025; accepted 6 May 2026; published online 22 May 2026
Type: Research article · Language: English
License: CC BY-NC
Identifiers: DOI 10.1016/j.isci.2026.116072 · PMID 42231960 · PMCID PMC13224037 · OpenAlex W7162137140
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: structural MRI / diffusion (modality), human (organism), developmental (subfield)
Methods: Connectivity, Statistics, Machine learning, fMRI & imaging, Preprocessing
Keywords: Health sciences, Medicine, Hematology, Neurology, Psychiatry
Topic: Hemoglobinopathies and Related Disorders (Genetics, Medicine), according to OpenAlex
Funding: Sanming Project of Medicine in Shenzen Municipality (SZSM202011005); Shenzhen Science and Technology Innovation Program (JCYJ20240813112459030); National Natural Science Foundation of China (U24A20340)
Citations: not cited yet (Europe PMC); 74 references in the paper

Abstract

Children with beta-thalassemia major (β-TM) are at risk of neurodevelopmental or cognitive impairment. In this study, we developed SurfGNN, a surface-based graph neural network model, to estimate brain age from cortical morphological features extracted via structural MRI, to a cross-sectional cohort of 25 β-TM patients and 40 age- and sex-matched healthy controls (ages 6–15 years). A brain age index (BAI)—the difference between predicted and chronological age—was computed and analyzed in relation to cognitive performance (Wechsler Intelligence Scale scores) and hematological parameters. Results showed that β-TM children had significantly lower cognitive scores and more delayed brain age (lower BAI) compared to healthy controls. Furthermore, BAI significantly correlated with hemoglobin and ferritin levels and mediated their association with cognitive performance, as demonstrated by mediation analysis. The SurfGNN model outperformed conventional machine learning baselines and provided interpretable insights into regional cortical alterations. Our findings suggest that BAI may serve as a sensitive biomarker for detecting early brain developmental delays in β-TM.

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

Repository

Its files are read in the Code ↔ Paper reader above.

ZhuoshL/SurfGNN

License: none: the authors keep all their rights
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Commit: 49d32dd109521b27001127baa4649f47b1e450cc, 16 October 2024
Languages: Python (12)
Size: 30 files, 12 scripts
Software Heritage: not archived
Found in: the resources table
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: PyTorch (10 files), PyTorch Geometric (9 files), NumPy (5 files), SciPy (3 files), Matplotlib (1 file), pandas (1 file), scikit-learn (1 file)
Availability: 1 check, the latest on 28 September 2026: the link answers
  • 28 September 2026: the link answers
13 files
At the source: github.com/ZhuoshL/SurfGNN

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:

  • 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 12 scripts, each with its path and the digest of its content;
  • no match between paragraphs and code yet;
  • 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 and code availability

Data reported in this paper will be shared by the lead contact upon request.

This paper does not report original code.

Any additional information required to reanalyze the data reported in this paper is available from the lead contact upon request.

Reproduced under the paper's license (CC BY-NC), 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 2, 28 September 2026

  • Authors: added Yaowen Li (0009-0000-0014-1397); removed Yaowen Li

Version 1, 28 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 10 authors, 5 keywords, 3 funders, 65 references.

Cite

This paper

Xu, S., Li, Z., Liu, X., Liu, S., Wang, X., Yang, P., Cai, X., Zeng, H., Liu, M., & Li, Y. (2026). Surface-based brain age index reflects hematological impacts on cognitive development in children with beta-thalassemia major. iScience, 29(6), 116072. https://doi.org/10.1016/j.isci.2026.116072

BibTeX

@article{xu2026surface,
author = {Xu, Shumin and Li, Zhuoshuo and Liu, Xinyi and Liu, Sixi and Wang, Xiaodong and Yang, Pengjie and Cai, Xuan and Zeng, Hongwu and Liu, Mengting and Li, Yaowen},
title = {{Surface-based brain age index reflects hematological impacts on cognitive development in children with beta-thalassemia major}},
journal = {iScience},
year = {2026},
month = may,
volume = {29},
number = {6},
pages = {116072},
publisher = {Elsevier},
issn = {2589-0042},
doi = {10.1016/j.isci.2026.116072},
url = {https://doi.org/10.1016/j.isci.2026.116072},
pmid = {42231960},
pmcid = {PMC13224037}
}

RIS

TY - JOUR
AU - Xu, Shumin
AU - Li, Zhuoshuo
AU - Liu, Xinyi
AU - Liu, Sixi
AU - Wang, Xiaodong
AU - Yang, Pengjie
AU - Cai, Xuan
AU - Zeng, Hongwu
AU - Liu, Mengting
AU - Li, Yaowen
TI - Surface-based brain age index reflects hematological impacts on cognitive development in children with beta-thalassemia major
T2 - iScience
J2 - iScience
PY - 2026
DA - 2026/05/22
VL - 29
IS - 6
SP - 116072
SN - 2589-0042
PB - Elsevier
DO - 10.1016/j.isci.2026.116072
UR - https://doi.org/10.1016/j.isci.2026.116072
LA - en
ER -

CSL-JSON

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"container-title": "iScience",
"author": [
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"given": "Shumin"
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{
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"PMCID": "PMC13224037",
"ISSN": "2589-0042",
"publisher": "Elsevier",
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"language": "en",
"issued": {
"date-parts": [
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