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Contrastive and Transfer Learning for Aligned Multimodal Neuroimaging Classification of Autism Spectrum Disorder.

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Paper

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

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The authors' code

Python · 59 lines · 2.3 KB · Apache-2.0

  1. import os
  2. import torch
  3. import nibabel as nib
  4. from torch.utils.data import Dataset
  5. from torchvision import transforms
  6. import torch.nn.functional as F
  7. import scipy
  8. import numpy as np
  9. import pandas as pd
  10. class ABIDE_Data(Dataset):
  11. def __init__(self, root_dir):
  12. self.img_root_dir = root_dir+'/MRI'
  13. self.fc_root_dir = root_dir+'/fMRI'
  14. self.labels = pd.read_csv(root_dir+'/label.csv')['DX_GROUP']
  15. self.names = pd.read_csv(root_dir+'/label.csv')['SUB_ID']
  16. self.sub_names = [int(name) for name in os.listdir(self.fc_root_dir) if os.path.isdir(os.path.join(self.fc_root_dir, name))]
  17. self.transform = transforms.Compose([transforms.ToTensor()])
  19. self.img_file_paths = self._get_img_file_paths()
  20. self.fc_file_paths = self._get_fc_file_paths()
  22. def __len__(self):
  23. return len(self.img_file_paths)
  25. def __getitem__(self, idx):
  26. img_file_path = self.img_file_paths[idx]
  27. fc_file_path = self.fc_file_paths[idx]
  29. # Load NIfTI file using nibabel
  30. img = nib.load(img_file_path)
  31. img_data = img.get_fdata()
  32. img_data = (img_data - 224) / 224
  33. # Apply transformations (if needed)
  34. img_data = self.transform(img_data).permute(1,0,2)
  35. fc_data=scipy.io.loadmat(fc_file_path)['connectivity']
  36. fc_data=self._fc_feature(fc_data)
  37. label=torch.tensor([0,1]) if np.array(self.labels.iloc[self.names.tolist().index(self.sub_names[idx])] == 2) else torch.tensor([1,0])
  38. return img_data,fc_data,label
  40. def _get_img_file_paths(self):
  41. img_file_paths = []
  42. for subdir, dirs, files in os.walk(self.img_root_dir):
  43. for file in files:
  44. if file.endswith(".nii"):
  45. img_file_paths.append(os.path.join(subdir, file))
  46. return img_file_paths
  47. def _get_fc_file_paths(self):
  48. fc_file_paths = []
  49. for subdir, dirs, files in os.walk(self.fc_root_dir):
  50. for file in files:
  51. if file.endswith(".mat"):
  52. fc_file_paths.append(os.path.join(subdir, file))
  53. return fc_file_paths
  54. def _fc_feature(self, fc):
  55. # 提取上三角部分
  56. upper_triangular = np.triu(fc, k=1)
  57. # 转换为张量
  58. fc_tensor = torch.tensor(upper_triangular[np.nonzero(upper_triangular)])
  59. return fc_tensor

dataset.py at commit b1791e5, under Apache-2.0 · at the source

Overview

Authors: Raja Vavekanand1, Ganesh Kumar2, Muhammad Moazzam Jawaid3, Shafiya Qadeer Memon4, Teerath Kumar5
ORCID iDs: Raja Vavekanand, Ganesh Kumar, Muhammad Moazzam Jawaid, Teerath Kumar
  1. Department of Information Technology, Benazir Bhutto Shaheed University Lyari, Karachi 75660, Sindh, Pakistan
  2. Department of Computing, Universiti Teknologi PETRONAS, Seri Iskandar 32610, Malaysia
  3. School of Computer Science and Informatics, De Montfort University, Leicester LE1 9BH, UK
  4. Department of Software Engineering, Mehran University of Engineering and Technology, Jamshoro 76062, Sindh, Pakistan
  5. School of Computing, Atlantic Technological University, F94 DV52 Letterkenny, Ireland
Institutions: Benazir Bhutto Shaheed University Lyari (Pakistan); Universiti Teknologi Petronas (Malaysia); De Montfort University (United Kingdom); Mehran University of Engineering and Technology (Pakistan); Atlantic Technological University (Ireland)
Journal: Journal of imaging, volume 12, issue 7, article 328
Dates: received 20 June 2026; accepted 17 July 2026; published online 20 July 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.3390/jimaging12070328 · PMID 42506174 · PMCID PMC13412821 · OpenAlex W7169769381
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: human (organism), autism (population)
Methods: Spectral & time-frequency, Connectivity, Machine learning, fMRI & imaging
Keywords: Autism Spectrum Disorder, multimodal classification, contrastive learning, transfer learning, neuroimaging
Topic: Autism Spectrum Disorder Research (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Citations: not cited yet (Europe PMC); 38 references in the paper

Abstract

Autism Spectrum Disorder (ASD) assessment remains challenging because behavioural instruments are partly observer-dependent and neuroimaging data are heterogeneous. This paper presents FAA (Fuse After Aligned), which is a multimodal classification framework that combines transfer learning for structural MRI (sMRI) representation learning with a contrastive objective for the pre-fusion alignment of sMRI and resting-state functional MRI-derived functional connectivity (FC) features. Evaluation was restricted to the single-site ABIDE-I New York University subset comprising 75 participants with ASD and 98 typically developing controls. Under the reported five-fold internal cross-validation protocol, FAA achieved a mean accuracy of 92.6% compared with 87.4% for naive fusion and 90.9% for the sMRI-only baseline. Ablation analyses indicate that adding the contrastive objective is associated with improved classification performance and that ResNet-18 outperforms the evaluated ViT-16 configurations in this small-sample setting. These findings support the methodological value of pre-fusion feature alignment within the evaluated cohort. The framework offers a robust, computationally efficient, and clinically viable approach for objective ASD diagnosis with strong potential for generalisation to multi-site neuroimaging applications.

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

Repository

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

rajavavek/FAA-ASD-Neuroimaging

License: Apache-2.0
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: b1791e5ece3dcc044d146d7a7462027c057aa701, 18 June 2026
Languages: Python (44), Shell (3)
Size: 89 files, 47 scripts
Software Heritage: not archived
Found in: “Data Availability Statement”
Holds: README, license file
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: PyTorch (36 files), NumPy (16 files), pandas (8 files), NiBabel (4 files), scikit-learn (4 files), SciPy (4 files)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
49 files
At the source: github.com/rajavavek/FAA-ASD-Neuroimaging

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;
  • 47 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 Availability Statement

The source code used in this paper is publicly available at https://github.com/rajavavek/FAA-ASD-Neuroimaging (accessed on 16 July 2026).

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, issue, pages, dates, 5 authors, 5 keywords, 37 references.

Cite

This paper

Vavekanand, R., Kumar, G., Jawaid, M. M., Memon, S. Q., & Kumar, T. (2026). Contrastive and Transfer Learning for Aligned Multimodal Neuroimaging Classification of Autism Spectrum Disorder. Journal of imaging, 12(7), 328. https://doi.org/10.3390/jimaging12070328

BibTeX

@article{vavekanand2026contrastive,
author = {Vavekanand, Raja and Kumar, Ganesh and Jawaid, Muhammad Moazzam and Memon, Shafiya Qadeer and Kumar, Teerath},
title = {{Contrastive and Transfer Learning for Aligned Multimodal Neuroimaging Classification of Autism Spectrum Disorder}},
journal = {Journal of imaging},
year = {2026},
month = jul,
volume = {12},
number = {7},
pages = {328},
publisher = {Multidisciplinary Digital Publishing Institute (MDPI)},
issn = {2313-433X},
doi = {10.3390/jimaging12070328},
url = {https://doi.org/10.3390/jimaging12070328},
pmid = {42506174},
pmcid = {PMC13412821}
}

RIS

TY - JOUR
AU - Vavekanand, Raja
AU - Kumar, Ganesh
AU - Jawaid, Muhammad Moazzam
AU - Memon, Shafiya Qadeer
AU - Kumar, Teerath
TI - Contrastive and Transfer Learning for Aligned Multimodal Neuroimaging Classification of Autism Spectrum Disorder
T2 - Journal of imaging
J2 - J Imaging
PY - 2026
DA - 2026/07/20
VL - 12
IS - 7
SP - 328
SN - 2313-433X
PB - Multidisciplinary Digital Publishing Institute (MDPI)
DO - 10.3390/jimaging12070328
UR - https://doi.org/10.3390/jimaging12070328
LA - en
ER -

CSL-JSON

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"container-title-short": "J Imaging",
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