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Accelerated brain aging in Veterans with posttraumatic stress symptoms and mild traumatic brain injury.

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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 · 100 lines · 1.9 KB · no license

  3. import keras
  4. from keras.applications.resnet50 import ResNet50
  5. from keras.preprocessing import image
  6. from keras import models, optimizers
  7. from keras.preprocessing.image import ImageDataGenerator
  8. from keras import regularizers
  9. from keras.layers import Conv2D, MaxPooling2D, Flatten, Dense
  10. from keras import models
  11. from keras import layers
  12. from keras import optimizers
  14. import numpy as np
  15. import pandas as pd
  16. import math
  18. #import h5py
  19. from keras.models import load_model
  20. import sys
  21. import os
  22. from os import listdir
  24. model = load_model(sys.argv[3])
  25. print(model.summary())
  27. test_dir = sys.argv[1]
  29. batch_size = 80
  31. datagen_test=ImageDataGenerator(rescale=1./255, horizontal_flip= False, vertical_flip = False,
  32. featurewise_center = False, featurewise_std_normalization = False)
  34. print(test_dir)
  36. test_generator=datagen_test.flow_from_directory(
  37. directory=str(test_dir),
  38. batch_size=batch_size,
  39. seed=42,
  40. shuffle=False,
  41. class_mode=None)
  43. labels_test = []
  44. sitelist = []
  45. IDlist = []
  46. sex_test = []
  47. slice_test = []
  48. deplist = []
  49. test_generator.reset()
  50. i = 0
  52. for x in test_generator.filenames:
  53. i = i+1
  54. sl = x.split('-')[1].split('.')[0]
  55. x = x.split('_T1')[0]
  57. IDlist.append(x)
  61. test_generator.reset()
  62. predicty = model.predict_generator(test_generator,verbose=1, steps = test_generator.n/batch_size)
  71. prediction_data = pd.DataFrame()
  72. prediction_data['ID'] = IDlist
  74. prediction_data['Prediction'] = predicty
  76. IDset = set(prediction_data['ID'].values)
  77. IDset = list(IDset)
  80. final_prediction = []
  81. final_labels = []
  82. final_site = []
  84. for x in IDset:
  87. check_predictions = prediction_data[prediction_data['ID']==x]['Prediction']
  88. predicty = check_predictions.reset_index(drop = True)
  90. final_prediction.append(np.median(predicty))
  95. predicty1 = final_prediction
  97. out_data = pd.DataFrame()
  98. out_data['ID'] = IDset
  99. out_data['Pred_Age'] = predicty1
  100. out_data.to_csv(sys.argv[2], index=False)

Model_Test.py at commit 505f4e4, no license · at the source

Overview

Authors: Ruth Klaming1,2, Andrea D Spadoni1,2, Jessica Bomyea1,3,2, Wesley K Thompson4,5, Alan N Simmons1,3,2
ORCID iDs: Ruth Klaming, Andrea D Spadoni
  1. VA San Diego Healthcare System, 3350 La Jolla Village Dr, San Diego, CA, 92161, USA
  2. University of California, San Diego, Department of Psychiatry, 9500 Gilman Drive, La Jolla, CA, 92093, USA
  3. VA San Diego Healthcare System, Center of Excellence for Stress and Mental Health, 3350 La Jolla Village Dr, San Diego, CA, 92161, USA
  4. Center for Population Neuroscience and Genetics, Laureate Institute for Brain Research, 6655 S Yale Ave, Tulsa, OK, 74136, USA
  5. University of California, San Diego, Department of Radiology, 9500 Gilman Drive, La Jolla, CA, 92093, USA
Institutions: University of California San Diego (United States); VA San Diego Healthcare System (United States); Center of Excellence for Stress and Mental Health (United States); Laureate Institute for Brain Research (United States)
Journal: Neuroimage. Reports, volume 6, issue 3, article 100377
Dates: received 10 October 2025; accepted 27 June 2026; published online 2 July 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1016/j.ynirp.2026.100377 · PMID 42434106 · PMCID PMC13352378 · OpenAlex W7167061845
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: structural MRI / diffusion (modality), human (organism), other condition (population), traumatic brain injury (population), clinical / translational (subfield)
Methods: Spectral & time-frequency, Statistics
Keywords: Brain aging, Deep learning method, Posttraumatic stress disorder, Mild traumatic brain injury, Magnetic resonance imaging
Topic: Traumatic Brain Injury Research (Epidemiology, Medicine), according to OpenAlex
Funding: RRD VA (IK2 RX004777)
Citations: not cited yet (Europe PMC); 84 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.

vishnubashyam/DeepBrainNet

License: none: the authors keep all their rights
State: the link answers, verified on 26 September 2026
Evidence: files inventoried
Commit: 505f4e4fdda6a2f5773bec0c98ef567a7a4ae36e, 4 September 2020
Languages: Python (2), Shell (1)
Size: 11 files, 3 scripts
Software Heritage: not archived
Found in: the text, “Neuroimaging and brain age computation”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: NumPy (2 files), pandas (2 files), Keras (1 file), NiBabel (1 file), Pillow (1 file), scikit-learn (1 file)
Availability: 1 check, the latest on 26 September 2026: the link answers
  • 26 September 2026: the link answers
4 files
At the source: github.com/vishnubashyam/DeepBrainNet

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;
  • 3 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

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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:

  • it says that the data are available on request

Read it in the paper: doi.org/10.1016/j.ynirp.2026.100377.

Versions

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Version 2, 28 September 2026

  • Authors: added Andrea D Spadoni (0000-0002-8367-6406); removed Andrea D Spadoni

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 5 authors, 5 keywords, 1 funder, 74 references.

Cite

This paper

Klaming, R., Spadoni, A. D., Bomyea, J., Thompson, W. K., & Simmons, A. N. (2026). Accelerated brain aging in Veterans with posttraumatic stress symptoms and mild traumatic brain injury. Neuroimage. Reports, 6(3), 100377. https://doi.org/10.1016/j.ynirp.2026.100377

BibTeX

@article{klaming2026accelerated,
author = {Klaming, Ruth and Spadoni, Andrea D and Bomyea, Jessica and Thompson, Wesley K and Simmons, Alan N},
title = {{Accelerated brain aging in Veterans with posttraumatic stress symptoms and mild traumatic brain injury}},
journal = {Neuroimage. Reports},
year = {2026},
month = jul,
volume = {6},
number = {3},
pages = {100377},
publisher = {Elsevier},
issn = {2666-9560},
doi = {10.1016/j.ynirp.2026.100377},
url = {https://doi.org/10.1016/j.ynirp.2026.100377},
pmid = {42434106},
pmcid = {PMC13352378}
}

RIS

TY - JOUR
AU - Klaming, Ruth
AU - Spadoni, Andrea D
AU - Bomyea, Jessica
AU - Thompson, Wesley K
AU - Simmons, Alan N
TI - Accelerated brain aging in Veterans with posttraumatic stress symptoms and mild traumatic brain injury
T2 - Neuroimage. Reports
J2 - Neuroimage Rep
PY - 2026
DA - 2026/07/02
VL - 6
IS - 3
SP - 100377
SN - 2666-9560
PB - Elsevier
DO - 10.1016/j.ynirp.2026.100377
UR - https://doi.org/10.1016/j.ynirp.2026.100377
LA - en
ER -

CSL-JSON

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"PMCID": "PMC13352378",
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