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Enhancing brain age estimation with structural MRI and synthesized cerebral blood volume maps.

Code ↔ Paper

2 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 2 matches
  1. [1] § Materials and methods › BrainAGE model ↔ BrainAGE Model/BrainAGE_Model.py, lines 21–64 · score 0.65 · ReLU, brain age, flattened, batch, max, block
  2. [2] § Materials and methods › BrainAGE model ↔ DeepCBV Model/DeepC_3D_Patch_test_model/TABS_Model.py, lines 57–195 · score 0.58 · ReLU, PyTorch, max, encoder, block, layer

Paper

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The authors' code

Python · 80 lines · 2.1 KB · no license · 1 match

  1. """
  2. BrainAGE 3D CNN Models
  3. ======================
  5. A VGG-style 3D convolutional architecture for brain age prediction using T1 MRI and AICBV data.
  7. This file supports:
  8. - BrainAGE_T1_Model_Weights.pkl
  9. - BrainAGE_AICBV_Model_Weights.pkl
  11. Usage:
  12. from BrainAGE_Model import AgeRegressor, load_model
  14. Author: Jordan Jomsky
  15. """
  17. import torch
  18. import torch.nn as nn
  19. import torch.optim as optim
  21. class AgeRegressor(nn.Module):
  22. """
  23. 3D Convolutional Neural Network for Brain Age estimation.
  25. Input shape:
  26. (batch, 1, 193, 229, 193)
  28. Output:
  29. Age estimate (float)
  30. """
  32. def __init__(self):
  33. super(AgeRegressor, self).__init__()
  35. self.block1 = self._make_block(1, 16)
  36. self.block2 = self._make_block(16, 32)
  37. self.block3 = self._make_block(32, 64)
  38. self.block4 = self._make_block(64, 128)
  39. self.block5 = self._make_block(128, 256)
  41. # NOTE: update this if input resolution changes
  42. self.flatten_size = 64512
  44. self.fc = nn.Linear(self.flatten_size, 1)
  46. def _make_block(self, in_c, out_c):
  47. return nn.Sequential(
  48. nn.Conv3d(in_c, out_c, 3, padding=1),
  49. nn.ReLU(),
  50. nn.Conv3d(out_c, out_c, 3, padding=1),
  51. nn.BatchNorm3d(out_c),
  52. nn.ReLU(),
  53. nn.MaxPool3d(2)
  54. )
  56. def forward(self, x):
  57. x = self.block1(x)
  58. x = self.block2(x)
  59. x = self.block3(x)
  60. x = self.block4(x)
  61. x = self.block5(x)
  62. x = x.view(x.size(0), -1)
  63. x = self.fc(x)
  64. return x.squeeze()
  66. device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
  68. def load_model(filename, map_location=device):
  69. """
  70. Load weights for either T1 or AICBV version.
  72. Examples:
  73. model = load_model("BrainAGE_T1_Model_Weights.pkl")
  74. model = load_model("BrainAGE_AICBV_Model_Weights.pkl")
  75. """
  76. model = torch.load("/mnt/data/BrainAGE_T1_Model_Weights.pkl", map_location=device)
  77. model = model.to(device)
  78. model.eval()
  79. print(f"Loaded pretrained weights from {filename}")
  80. return model

BrainAGE_Model.py at commit 586e2b5, no license · at the source

Overview

Authors: Jordan Jomsky1, Zongyu Li1, Kay C Igwe1, Yiren Zhang1, Max Lashley2, Tal Nuriel3,4, Andrew Laine1, Scott A Small3,5,6,7,8, Jia Guo2,6, for the Frontotemporal Lobar Degeneration Neuroimaging Initiative and for the Alzheimer’s Disease Neuroimaging Initiative
ORCID iDs: Jordan Jomsky, Kay C Igwe, Tal Nuriel, Andrew Laine, Scott A Small, Jia Guo
  1. Department of Biomedical Engineering, Columbia University, New York, NY 10027-7041, USA
  2. Zuckerman Institute, Columbia University, New York, NY 10027-7041, USA
  3. Taub Institute for Research on Alzheimer's Disease and the Aging Brain, Vagelos College of Physicians and Surgeons, Columbia University, New York, NY, USA
  4. Department of Pathology and Cell Biology, Columbia University, New York, NY, USA
  5. Department of Neurology, Columbia University, New York, NY 10027, USA
  6. Department of Psychiatry, Columbia University, New York, NY 10027, USA
  7. Department of Radiology, Columbia University, New York, NY, USA
  8. New York State Psychiatric Institute, New York, NY, USA
Institutions: Columbia University (United States); Mortimer B. Zuckerman Mind Brain Behavior Institute (United States); New York State Psychiatric Institute
Journal: Brain communications, volume 8, issue 5, article fcag283
Dates: received 10 December 2025; accepted 25 June 2026; published online 22 July 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1093/braincomms/fcag283 · PMID 42683072 · PMCID PMC13532576 · OpenAlex W4405035644
Open access: gold, a free copy (OpenAlex)
Preprint: arxiv:2412.01865
Status: code verified
Categories: structural MRI / diffusion (modality), Alzheimer's / dementia (population)
Methods: Connectivity, Statistics, Machine learning, fMRI & imaging
Keywords: magnetic resonance imaging, brain age, deep learning, cerebral blood volume
Topic: Brain Tumor Detection and Classification (Neurology, Neuroscience), according to OpenAlex
Funding: Hevolution; American Federation for Aging Research New Investigator Awardee; Aging Biology and Geroscience Research (PT# 24-4274 PG014621); Simons Foundation (811245); Simons Foundation International
Citations: not cited yet (Europe PMC); 69 references in the paper

Abstract

Brain age gap estimation (BrainAGE) is a promising imaging-derived biomarker of neurobiological ageing and disease risk, yet current approaches rely predominantly on T1-weighted structural MRI, overlooking functional vascular changes that may precede tissue damage and cognitive decline. Deep learning-derived cerebral blood volume (DeepCBV) maps, synthesized from non-contrast MRI, offer a scalable alternative to contrast-enhanced perfusion imaging by capturing vascular information relevant to early neurodegeneration. We developed a multimodal BrainAGE framework that combines predictions from two separate three-dimensional convolutional neural networks: one trained only on structural MRI scans and another trained only on DeepCBV maps generated by a pre-trained three-dimensional patch-based deep learning model. Each model was trained and validated on 2851 scans (1507 females) from 13 open-source datasets and was evaluated for concordance with mild cognitive impairment (MCI) and Alzheimer’s disease (AD) using 1233 subjects. The combined model achieved the most accurate brain age gap for cognitively normal (CN) controls, with a mean absolute error of 3.95 years (R2 = 0.943), outperforming models trained on MRI (mean absolute error = 4.10) or DeepCBV alone (mean absolute error = 4.49). Saliency maps revealed complementary modality contributions: MRI emphasized white matter and cortical atrophy, while DeepCBV highlighted vascular-rich and periventricular regions implicated in hypoperfusion and early cerebrovascular dysfunction, consistent with known patterns of normal ageing. Next, we observed that BrainAGE increased stepwise across diagnostic strata (CN < MCI < AD) and correlated with cognitive impairment (Clinical Dementia Rating Sum of Boxes ⍴ = 0.403; Mini-Mental State Examination ⍴ = −0.310). DeepCBV-based BrainAGE showed a particularly strong separation between stable versus progressive MCI (Mann–Whitney U = 2.177 × 104, P = 4.43 × 10−8), suggesting sensitivity to prodromal vascular changes that precede overt atrophy. Integrating structural MRI with deep learning-derived vascular measures substantially enhances BrainAGE estimation and improves sensitivity to MCI and Alzheimer’s disease progression, supporting its potential role in risk stratification, early detection and monitoring of therapeutic response. By enabling a functional-like assessment from routine MRI, this approach lowers barriers to multimodal evaluation and provides a clinically actionable biomarker for large-scale ageing and dementia studies.

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 2 matches between paragraphs and lines of code.

jzjomsky/DeepCBV-BrainAGE

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 586e2b5a46b8a5326357707f837b4f1e6d16b002, 1 September 2026
Languages: Python (6)
Size: 13 files, 6 scripts
Software Heritage: not archived
Found in: the text, “BrainAGE model”
Holds: README, environment (DeepCBV Model/requirements.txt)
Not found: license file, CITATION.cff, tests, continuous integration, documentation
Tools: PyTorch (5 files), NiBabel (1 file), NumPy (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
7 files
At the source: github.com/jzjomsky/DeepCBV-BrainAGE

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;
  • 6 scripts, each with its path and the digest of its content;
  • 2 matches 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

Data were provided in part by IXI, accessed from http://brain-development.org/ixi-dataset.

Data were provided in part by OASIS. OASIS Cross-Sectional: Principal Investigators: D. Marcus, R, Buckner, J, Csernansky J. Morris; P50 AG05681, P01 AG03991, P01 AG026276, R01 AG021910, P20 MH071616, U24 RR021382 OASIS: Longitudinal: Principal Investigators: D. Marcus, R, Buckner, J. Csernansky, J. Morris; P50 AG05681, P01 AG03991, P01 AG026276, R01 AG021910, P20 MH071616, U24 RR021382.

Data were provided in part by the Brain Genomics Superstruct Project of Harvard University and the Massachusetts General Hospital (Principal Investigators: Randy Buckner, Joshua Roffman, and Jordan Smoller), with support from the Center for Brain Science Neuroinformatics Research Group, the Athinoula A. Martinos Center for Biomedical Imaging, and the Center for Human Genetic Research. 20 individual investigators at Harvard and MGH generously contributed data to the overall project.

Data used in preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at: http://adni.loni.usc.edu/wp-content/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf

Data collection and sharing for this project was funded by the Alzheimer’s Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Department of Defense award number W81XWH-12–2-0012). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: AbbVie, Alzheimer’s Association; Alzheimer’s Drug Discovery Foundation; Araclon Biotech; BioClinica, Inc.; Biogen; Bristol-Myers Squibb Company; CereSpir, Inc.; Cogstate; Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; EuroImmun; F. Hoffmann-La Roche Ltd and its affiliated company Genentech, Inc.; Fujirebio; GE Healthcare; IXICO Ltd.; Janssen Alzheimer Immunotherapy Research & Development, LLC.; Johnson & Johnson Pharmaceutical Research & Development LLC.; Lumosity; Lundbeck; Merck & Co., Inc.; Meso Scale Diagnostics, LLC.; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging; Servier; Takeda Pharmaceutical Company; and Transition Therapeutics. The Canadian Institutes of Health Research is providing funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health (www.fnih.org). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer’s Therapeutic Research Institute at the University of Southern California. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California.

Data used in the preparation of this article was obtained from the Australian Imaging Biomarkers and Lifestyle flagship study of ageing (AIBL) funded by the Commonwealth Scientific and Industrial Research Organisation (CSIRO) which was made available at the ADNI database (adni.loni.usc.edu). The AIBL researchers contributed data but did not participate in analysis or writing of this report. AIBL researchers are listed at www.aibl.csiro.au (https://aibl.org.au/).

Data used in preparation of this article were obtained from the Frontotemporal Lobar Degeneration Neuroimaging Initiative (FTLDNI) database (https://ida.loni.usc.edu/login.jsp?project=NIFD). The investigators at NIFD/FTLDNI contributed to the design and implementation of FTLDNI and/or provided data, but did not participate in analysis or writing of this report (unless otherwise listed).

Data collection and sharing for this project was funded by the Frontotemporal Lobar Degeneration Neuroimaging Initiative (National Institutes of Health Grant R01 AG032306). The study is coordinated through the University of California, San Francisco, Memory and Aging Center. FTLDNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California.

Data used in the preparation of this article were obtained from the Parkinson’s Progression Markers Initiative (PPMI) database (https://www.ppmi-info.org/access-data-specimens/data). For up-to-date information on the study, visit www.ppmi-info.org. PPMI—a public–private partnership—is funded by the Michael J. Fox Foundation for Parkinson’s Research and funding partners, the full names of all of the PPMI funding partners can found at https://www.ppmi-info.org/about-ppmi/who-we-are/study-sponsors.

Data used in preparation of this article were obtained from the SchizConnect database (http://schizconnect.org) As such, the investigators within SchizConnect contributed to the design and implementation of SchizConnect and/or provided data but did not participate in analysis or writing of this report. Data collection and sharing for this project was funded by NIMH cooperative agreement 1U01MH097435.

All data used during the study are available in the International Data-sharing Initiative (INDI, http://fcon_1000.projects.nitrc.org/) provided by the Southwest University Longitudinal Imaging Multimodal (SLIM) study.

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

Versions

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Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 10 authors, 4 keywords, 5 funders, 64 references.

Cite

This paper

Jomsky, J., Li, Z., Igwe, K. C., Zhang, Y., Lashley, M., Nuriel, T., Laine, A., Small, S. A., Guo, J., & for the Frontotemporal Lobar Degeneration Neuroimaging Initiative and for the Alzheimer’s Disease Neuroimaging Initiative. (2026). Enhancing brain age estimation with structural MRI and synthesized cerebral blood volume maps. Brain communications, 8(5), fcag283. https://doi.org/10.1093/braincomms/fcag283

BibTeX

@article{jomsky2026enhancing,
author = {Jomsky, Jordan and Li, Zongyu and Igwe, Kay C and Zhang, Yiren and Lashley, Max and Nuriel, Tal and Laine, Andrew and Small, Scott A and Guo, Jia and {for the Frontotemporal Lobar Degeneration Neuroimaging Initiative and for the Alzheimer’s Disease Neuroimaging Initiative}},
title = {{Enhancing brain age estimation with structural MRI and synthesized cerebral blood volume maps}},
journal = {Brain communications},
year = {2026},
month = jul,
volume = {8},
number = {5},
pages = {fcag283},
publisher = {Oxford University Press},
issn = {2632-1297},
doi = {10.1093/braincomms/fcag283},
url = {https://doi.org/10.1093/braincomms/fcag283},
pmid = {42683072},
pmcid = {PMC13532576}
}

RIS

TY - JOUR
AU - Jomsky, Jordan
AU - Li, Zongyu
AU - Igwe, Kay C
AU - Zhang, Yiren
AU - Lashley, Max
AU - Nuriel, Tal
AU - Laine, Andrew
AU - Small, Scott A
AU - Guo, Jia
AU - for the Frontotemporal Lobar Degeneration Neuroimaging Initiative and for the Alzheimer’s Disease Neuroimaging Initiative
TI - Enhancing brain age estimation with structural MRI and synthesized cerebral blood volume maps
T2 - Brain communications
J2 - Brain Commun
PY - 2026
DA - 2026/07/22
VL - 8
IS - 5
SP - fcag283
SN - 2632-1297
PB - Oxford University Press
DO - 10.1093/braincomms/fcag283
UR - https://doi.org/10.1093/braincomms/fcag283
LA - en
ER -

CSL-JSON

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