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Variations of global brain asymmetry are associated with aging and related diseases.

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Paper

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The authors' code

Shell · 179 lines · 5.9 KB · MIT

  1. #!/usr/bin/env bash
  2. # Written by Wu Jianxiao and CBIG under MIT license: https://github.com/ThomasYeoLab/CBIG/blob/master/LICENSE.md
  3. # This function warps an input volumes to a target volume space with specified ANTs warp or inverse warp files
  4. ###########################################
  5. # Main commands
  6. ###########################################
  7. main(){
  8. # Set up warp files
  9. warp=${warp_dir}/${warp_prefix}1Warp.nii.gz
  10. inverse=${warp_dir}/${warp_prefix}1InverseWarp.nii.gz
  11. affine=${warp_dir}/${warp_prefix}0GenericAffine.mat
  12. # Set up fmri_reg command
  13. if [ ! -z $fmri_reg ]; then
  14. fmri_warp="-t $fmri_reg"
  15. fi
  16. # Set up time_series command
  17. if [ $time_series -eq 1 ]; then
  18. vol4d="-e 3"
  19. fi
  20. # Warp input volume to target's T1 space
  21. output=$output_dir/${output_prefix}.nii.gz
  22. if [ ! -e $output ]; then
  23. case $warp_setting in
  24. forward)
  25. cmd="${ANTs_dir}/antsApplyTransforms -d 3 $vol4d -i $input -r $target -n $interp -t $warp -t $affine $fmri_warp"
  26. cmd="$cmd -o $output"
  27. echo $cmd
  28. eval $cmd
  29. ;;
  30. inverse)
  31. cmd="${ANTs_dir}/antsApplyTransforms -d 3 $vol4d -i $input -r $target -n $interp $fmri_warp -t [$affine, 1]"
  32. cmd="$cmd -t $inverse -o $output"
  33. echo $cmd
  34. eval $cmd
  35. ;;
  36. *)
  37. echo "Invalid warp setting. Use either 'forward' or 'inverse'"
  38. esac
  39. else
  40. echo "The input volume have already been projected into ${target}'s space by ANTs"
  41. fi
  42. }
  43. ##################################################################
  44. # Function usage
  45. ##################################################################
  46. # Usage
  47. usage() { echo "
  48. Usage: CBIG_antsApplyReg_vol2vol.sh -i input -r target -w warp_prefix -p output_prefix
  49. This script applies existing warps prepared using ANTs registration to map an input volume to a target volume.
  50. REQUIRED ARGUMENTS:
  51. -i <input> absolute path to input volume to map from
  52. -r <target> absolute path to target volume to be mapped to
  53. -w <warp_prefix> prefix of the warp files. Specifically, the affine, forward and inverse warps should have the
  54. following names:
  55. warp_prefix0GenericAffine.mat
  56. warp_prefix1Warp.nii.gz
  57. warp_prefix1InverseWarp.nii.gz
  58. -p <output_prefix> prefix for output volume
  59. OPTIONAL ARGUMENTS:
  60. -f <fmri_reg> absolute path to native-to-T1 registration file for fMRI input. The file must have been
  61. converted to format used by ANTs (e.g. .txt or .mat) if it was generated using other tools.
  62. [ default: unset ]
  63. -e <time_series> set this to 1 if the input volume is 4D (time-series)
  64. [ default: 0 ]
  65. -d <warp_dir> absolute path to the warps
  66. [ default: $(pwd)/results ]
  67. -o <output_dir> absolute path to output directory
  68. [ default: $(pwd)/results ]
  69. -s <warp setting> warping direction ('forward' for forward warp, 'inverse' for inverse warp). For example, if
  70. the target space was registered to the input space during ANTs registration, then inverse
  71. warp should be used to now map the input to the target.
  72. [ default: forward ]
  73. -t <interp> interpolation (Linear, NearestNeighbor, etc.)
  74. [ default: Linear ]
  75. -a <ANTs_dir> directory where ANTs is installed
  76. [ default: $CBIG_ANTS_DIR ]
  77. -h display help message
  78. OUTPUTS:
  79. $0 will create 1 output file in the output directory, corresponding to the input warped into target's space.
  80. For example:
  81. output_prefix.nii.gz
  82. EXAMPLE:
  83. $0 -i /path/to/my/data.nii.gz -r /path/to/my/template.nii.gz -w subject_moving_template_fixed -p data_to_template
  84. -f data_to_T1.txt -s forward
  85. " 1>&2; exit 1; }
  86. # Display help message if no argument is supplied
  87. if [ $# -eq 0 ]; then
  88. usage; 1>&2; exit 1
  89. fi
  90. ##################################################################
  91. # Assign input variables
  92. ##################################################################
  93. # Default parameter
  94. warp_dir=$(pwd)/results
  95. output_dir=$(pwd)/results
  96. warp_setting=forward
  97. interp=Linear
  98. ANTs_dir=$CBIG_ANTS_DIR
  99. time_series=0
  100. # Assign parameter
  101. while getopts "i:r:w:p:f:e:d:o:s:t:a:h" opt; do
  102. case $opt in
  103. i) input=${OPTARG} ;;
  104. r) target=${OPTARG} ;;
  105. w) warp_prefix=${OPTARG} ;;
  106. p) output_prefix=${OPTARG} ;;
  107. f) fmri_reg=${OPTARG} ;;
  108. e) time_series=${OPTARG} ;;
  109. d) warp_dir=${OPTARG} ;;
  110. o) output_dir=${OPTARG} ;;
  111. s) warp_setting=${OPTARG} ;;
  112. t) interp=${OPTARG} ;;
  113. a) ANTs_dir=${OPTARG} ;;
  114. h) usage; exit ;;
  115. *) usage; 1>&2; exit 1 ;;
  116. esac
  117. done
  118. ##################################################################
  119. # Check parameter
  120. ##################################################################
  121. if [ -z $target ]; then
  122. echo "Reference volume not defined."; 1>&2; exit 1
  123. fi
  124. if [ -z $input ]; then
  125. echo "Input volume not defined."; 1>&2; exit 1
  126. fi
  127. if [ -z $warp_prefix ]; then
  128. echo "Warp prefix not defined."; 1>&2; exit 1
  129. fi
  130. if [ -z $output_prefix ]; then
  131. echo "Output prefix not defined."; 1>&2; exit 1
  132. fi
  133. ##################################################################
  134. # Disable multi-threading
  135. ##################################################################
  136. ITK_GLOBAL_DEFAULT_NUMBER_OF_THREADS=1
  137. export ITK_GLOBAL_DEFAULT_NUMBER_OF_THREADS
  138. ##################################################################
  139. # Set up output directory
  140. ##################################################################
  141. if [ ! -d "$output_dir" ]; then
  142. echo "Output directory does not exist. Making directory now..."
  143. mkdir -p $output_dir
  144. fi
  145. ###########################################
  146. # Implementation
  147. ###########################################
  148. main

CBIG_antsApplyReg_vol2vol.sh at commit 35b5664, under MIT · at the source

Overview

Authors: Haoyu Hu1,2, Duxiao Guo1,2, Yi Pu3, Yilamujiang Abuduaini1,2, Xichunwang Wang1,2, Clyde Francks4,5,6, Paul M. Thompson7, Xiang-Zhen Kong1,2,8,9
  1. Department of Psychology and Behavioral Sciences, Zhejiang University, Hangzhou, China
  2. The State Key Lab of Brain-Machine Intelligence, Zhejiang University, Hangzhou, China
  3. Shanghai Key Laboratory of Brain Functional Genomics (Ministry of Education), School of Psychology and Cognitive Science, East China Normal University, Shanghai, China
  4. Language and Genetics Department, Max Planck Institute for Psycholinguistics, Nijmegen, Netherlands
  5. Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, Netherlands
  6. Department of Medical Neuroscience, Radboud University Medical Center, Nijmegen, Netherlands
  7. Imaging Genetics Center, Mark & Mary Stevens Neuroimaging & Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
  8. Department of Psychiatry of Sir Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
  9. Zhejiang Key Laboratory of Neurocognitive Development and Mental Health, Zhejiang University, Hangzhou, Zhejiang
Journal: Science advances, volume 12, issue 27, article eadu9309
Dates: received 4 December 2024; accepted 18 May 2026; published online 1 July 2026; in print July 2026
Type: Research article · Language: English
License: CC BY-NC
Identifiers: DOI 10.1126/sciadv.adu9309 · PMID 42384796 · PMCID PMC13322246 · OpenAlex W7166891294
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: human (organism), Alzheimer's / dementia (population), Parkinson's (population), cognitive (subfield)
Methods: Statistics, Smoothing, state filtering, decompositions, Machine learning, Connectivity, fMRI & imaging, Preprocessing
MeSH: Aging*, Alzheimer Disease*, Brain*, Functional Laterality*, Parkinson Disease*, Aged, Cognition, Female, Humans, Machine Learning, Magnetic Resonance Imaging, Male (* major topic)
Journal subjects: Neuroscience, Cognitive Neuroscience
Topic: Hemispheric Asymmetry in Neuroscience (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: NIH (R01MH134962, U01AG068057); National Natural Science Foundation of China (National Science Foundation of China) (32571219, 32400882, 32171031); Max Planck Society; Brain Science and Brain-like Intelligence Technology - National Science and Technology Major Project (2021ZD0200409, 2025ZD0218801); Alzheimer’s Association; Fundamental Research Funds for the Central Universities (226-2025-00144); Zhejiang Key Laboratory of Neurocognitive Development and Mental Health (2025E10037)
Citations: not cited yet (Europe PMC); 93 references in the paper

Abstract

Lateralization is a hallmark of brain organization, yet the structural basis underlying this phenomenon remains a critical, unresolved question in cognitive and systems neuroscience. In this study, we applied multivariate machine learning techniques to investigate variations of global brain asymmetry and their associations with cognitive functions, aging, and aging-related diseases, using large-scale datasets. Our findings revealed substantial and previously unknown structural differences between the hemispheres, and established key associations between structural asymmetries and lateralized functions. At the population level, we identified unique aging trajectories of hemispheric differences and uncovered diagnosis-specific variations in patients with Alzheimer’s and Parkinson’s disease, and in APOE ε4 carriers at genetic risk. Notably, we identified a “left hemi-aging” pattern that challenges the conventional “right hemi-aging” model. Together, these results advance our understanding of functional lateralization in the human brain and highlight the potential of global brain asymmetry as a biomarker for brain aging and related diseases.

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

Repositories

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

ThomasYeoLab/CBIG

License: MIT
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 35b5664bec8822e2f77da5e090e96f91d0095be6, 31 August 2026
Languages: MATLAB (2100), Shell (651), Python (571), C (113), C++ (65), C/C++ (62), R (25), Jupyter (5)
Size: 9,187 files, 3,592 scripts
Software Heritage: not archived
Found in: the text, “Multivariate brain asymmetry analyses in young a”
Holds: README, license file, environment (external_packages/python/mapalign-master/requirements.txt, external_packages/python/mapalign-master/setup.py, external_packages/python/yapf-master/setup.cfg, external_packages/python/yapf-master/setup.py), tests, documentation, 2 notebooks
Not found: CITATION.cff, continuous integration
Tools: NumPy (130 files), PyTorch (122 files), FreeSurfer (93 files), SciPy (61 files), FieldTrip (60 files), Statistics and Machine Learning Toolbox (50 files), FSL (44 files), SPM (40 files), GIfTI library for MATLAB (10 files), Image Processing Toolbox (8 files), Connectome Workbench (8 files), scikit-learn (5 files), AFNI (2 files), Matplotlib (2 files), Tools for NIfTI and ANALYZE image (MATLAB) (2 files), ANTs (1 file), NiBabel (1 file), Nilearn (1 file), tedana (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
2,000 files

Zenodo 15561190

License: CC-BY-4.0
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Size: 1 file
Software Heritage: not checked
Found in: “Data, code, and materials availability:”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)
  • 27 September 2026: the link answers (HTTP 200)

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:

  • 2 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 1,998 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, code, and materials availability

All data and code needed to evaluate and reproduce the results in the paper are present in the paper and/or the Supplementary Materials. This study did not generate new materials. The UK Biobank data used in this study are available to researchers upon application. Researchers must register and submit an application via the UK Biobank Access Management System (www.ukbiobank.ac.uk/). Detailed instructions and requirements for access are available on the UK Biobank website. The HCP data are publicly available via the Connectome Coordination Facility (www.humanconnectome.org/). Researchers must create an account and agree to the HCP Open Access Data Use Terms to download the datasets. All derived data are available in the main text or the Supplementary Materials. All analyses were carried out as described in the Materials and Methods. Software versions and relevant parameters are included in the corresponding Materials and Methods sections. Scripts are available from open-access repository at Zenodo (https://zenodo.org/records/15561190).

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 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 8 authors, 12 MeSH terms, 7 funders, 87 references.

Cite

This paper

Hu, H., Guo, D., Pu, Y., Abuduaini, Y., Wang, X., Francks, C., Thompson, P. M., & Kong, X.-Z. (2026). Variations of global brain asymmetry are associated with aging and related diseases. Science advances, 12(27), eadu9309. https://doi.org/10.1126/sciadv.adu9309

BibTeX

@article{hu2026variations,
author = {Hu, Haoyu and Guo, Duxiao and Pu, Yi and Abuduaini, Yilamujiang and Wang, Xichunwang and Francks, Clyde and Thompson, Paul M. and Kong, Xiang-Zhen},
title = {{Variations of global brain asymmetry are associated with aging and related diseases}},
journal = {Science advances},
year = {2026},
month = jul,
volume = {12},
number = {27},
pages = {eadu9309},
publisher = {American Association for the Advancement of Science},
issn = {2375-2548},
doi = {10.1126/sciadv.adu9309},
url = {https://doi.org/10.1126/sciadv.adu9309},
pmid = {42384796},
pmcid = {PMC13322246}
}

RIS

TY - JOUR
AU - Hu, Haoyu
AU - Guo, Duxiao
AU - Pu, Yi
AU - Abuduaini, Yilamujiang
AU - Wang, Xichunwang
AU - Francks, Clyde
AU - Thompson, Paul M.
AU - Kong, Xiang-Zhen
TI - Variations of global brain asymmetry are associated with aging and related diseases
T2 - Science advances
J2 - Sci Adv
PY - 2026
DA - 2026/07/01
VL - 12
IS - 27
SP - eadu9309
SN - 2375-2548
PB - American Association for the Advancement of Science
DO - 10.1126/sciadv.adu9309
UR - https://doi.org/10.1126/sciadv.adu9309
LA - en
ER -

CSL-JSON

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