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Bayesian modelling demonstrates clinically relevant heterogeneity in Tau PET patterns in Alzheimer's disease.

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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 634f676, under MIT · at the source

Overview

Authors: Ye Xia1,2,3, Keith Johnson1, Georges El Fakhri4,5, Nicolas J Guehl4, Elsmarieke van de Giessen3,6, Emma C Coomans2,3, Yolande AL Pijnenburg2,3, Rik Ossenkoppele2,3, Colin Groot2,3
ORCID iDs: Colin Groot
  1. Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA USA
  2. Department of Neurology, Alzheimer Center Amsterdam, Amsterdam UMC, Amsterdam Neuroscience, Amsterdam, Netherlands
  3. Amsterdam Neuroscience, Neurodegeneration, Amsterdam, the Netherlands
  4. Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT USA
  5. Department of Biomedical Informatics and Data Science, Yale School of Medicine, New Haven, CT USA
  6. Department of Radiology and Nuclear Medicine, Amsterdam UMC, Amsterdam Neuroscience, Amsterdam, Netherlands
Institutions: Harvard University (United States); Massachusetts General Hospital (United States); Amsterdam Neuroscience (Netherlands); Amsterdam University Medical Centers (Netherlands); Yale University (United States)
Journal: European journal of nuclear medicine and molecular imaging, volume 53, issue 7, pages 4664-4676
Dates: received 4 November 2025; accepted 23 March 2026; published online 31 March 2026; in print 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1007/s00259-026-07868-5 · PMID 41915038 · PMCID PMC13197381 · OpenAlex W7143674661
Open access: hybrid, a free copy (OpenAlex)
Status: code verified (how the code is found and verified)
Categories: PET / SPECT (modality), human (organism), Alzheimer's / dementia (population), clinical / translational (subfield)
Methods: Machine learning, Statistics, Preprocessing, fMRI & imaging
Keywords: Alzheimer’s disease, Heterogeneity, Tau, PET, Latent Dirichlet Allocation
MeSH: Alzheimer Disease*, Positron-Emission Tomography*, tau Proteins*, Aged, Bayes Theorem, Female, Humans, Male (* major topic)
Topic: Dementia and Cognitive Impairment Research (Psychiatry and Mental health, Medicine), according to OpenAlex
Funding: ZonMw (10510022110010, 10510032120003); European Research Council (949570); Alzheimer Nederland (WE.03-2024-06)
Citations: not cited yet (Europe PMC); 56 references in the paper

Abstract

Background: Traditionally, subgroups have been used to explore the effects of tau heterogeneity on cognition. However, categorization into rigid, exclusive subtypes, each with their own tau pattern, may overlook the fact that individual tau patterns are complex, and most individuals express features of multiple patterns. Mapping tau patterns in all their complexity has important clinical implications, as it may enable more accurate prognostication and support the development of personalized therapeutic strategies tailored to an individual’s unique tau profile.

Methods: We applied a data-driven Bayesian model using Latent Dirichlet Allocation (LDA) to identify four covarying tau PET patterns in amyloid-positive individuals with symptomatic Alzheimer’s disease (AD) from the Amsterdam Dementia Cohort (ADC, N = 93, mean age = 65.3). The four latent tau spatial patterns identified via LDA were designated as follows: Limbic (Factor 1), characterized by predominant involvement of the limbic regions; Left TPC (Factor 2), centered on the left temporo-parietal cortex (TPC); Posterior (Factor 3), reflecting a posterior neocortical distribution; and MTL-sparing (Factor 4), showing relative sparing of limbic regions with diffuse neocortical tau deposition. Associations between individual loadings on each of these factors and cognitive domain scores were assessed using linear regression analyses. We then applied the ADC-derived model to an independent validation sample from the Alzheimer’s Disease Neuroimaging Initiative (ADNI, N = 162, mean age = 72.7) to extract individual factor loadings. Associations between factor loadings and contemporaneous cognitive performance were again tested using linear regression, and linear mixed-effects models were used to additionally explore associations with cognitive decline. All analyses were adjusted for age, sex, education, and clinical diagnosis.

Results: In both the discovery and validation cohort, we identified distinct associations between tau factor loadings and cognitive performance. Higher loading on Factor 1: limbic tau was generally associated with relatively better baseline cognition and slower cognitive decline. Factor 2: left TPC tau was linked to worse baseline scores and faster decline in memory, MMSE and language scores but only in the ADNI cohort. Factor 3: posterior tau was associated with worse baseline MMSE in the ADC cohort and to worse visuospatial performance both cross-sectionally and longitudinally in the ADNI cohort. Factor 4: MTL-sparing tau was related to better longitudinal memory in the ADNI cohort.

Conclusion: This data-driven approach identified overlapping tau patterns that relate to distinct cognitive domains. The findings highlight the value of continuous factor modeling in understanding heterogeneity in Alzheimer’s disease. Such a refinement of quantifying tau heterogeneity may improve patient stratification, refine prognostic accuracy, and ultimately guide the development of individualized treatment strategies.

Supplementary Information: The online version contains supplementary material available at 10.1007/s00259-026-07868-5.

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

Repository

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

thomasyeolab/cbig

License: MIT
State: the link answers, verified on 27 October 2026
Evidence: files inventoried
Commit: 634f676630929a71297852d01dd92a287103e861, 1 October 2026
Languages: MATLAB (2110), Shell (652), Python (589), C (113), C++ (65), C/C++ (62), R (25), Jupyter (5)
Size: 9,316 files, 3,621 scripts
Software Heritage: not archived
Found in: “Data availability”
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 (94 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: 2 checks, the latest on 27 October 2026: the link answers
  • 27 October 2026: the link answers
  • 27 September 2026: the link answers
2,000 files

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.

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  • 1 repository 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;
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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

The full code and documentation for the Bayesian modeling approach used here is publicly available at https://github.com/ThomasYeoLab/CBIG/tree/master/stable_projects/disorder_subtypes/Zhang2016_ADFactors. Anonymized data used in the present study may be available upon request to the corresponding author.

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

Versions

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Version 1, 30 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 9 authors, 5 keywords, 8 MeSH terms, 3 funders, 49 references.

Cite

This paper

Xia, Y., Johnson, K., Fakhri, G. E., Guehl, N. J., van de Giessen, E., Coomans, E. C., Pijnenburg, Y. A., Ossenkoppele, R., & Groot, C. (2026). Bayesian modelling demonstrates clinically relevant heterogeneity in Tau PET patterns in Alzheimer's disease. European journal of nuclear medicine and molecular imaging, 53(7), 4664-4676. https://doi.org/10.1007/s00259-026-07868-5

BibTeX

@article{xia2026bayesian,
author = {Xia, Ye and Johnson, Keith and Fakhri, Georges El and Guehl, Nicolas J and van de Giessen, Elsmarieke and Coomans, Emma C and Pijnenburg, Yolande AL and Ossenkoppele, Rik and Groot, Colin},
title = {{Bayesian modelling demonstrates clinically relevant heterogeneity in Tau PET patterns in Alzheimer's disease}},
journal = {European journal of nuclear medicine and molecular imaging},
year = {2026},
month = mar,
volume = {53},
number = {7},
pages = {4664--4676},
publisher = {Springer Science+Business Media},
issn = {1619-7070},
doi = {10.1007/s00259-026-07868-5},
url = {https://doi.org/10.1007/s00259-026-07868-5},
pmid = {41915038},
pmcid = {PMC13197381}
}

RIS

TY - JOUR
AU - Xia, Ye
AU - Johnson, Keith
AU - Fakhri, Georges El
AU - Guehl, Nicolas J
AU - van de Giessen, Elsmarieke
AU - Coomans, Emma C
AU - Pijnenburg, Yolande AL
AU - Ossenkoppele, Rik
AU - Groot, Colin
TI - Bayesian modelling demonstrates clinically relevant heterogeneity in Tau PET patterns in Alzheimer's disease
T2 - European journal of nuclear medicine and molecular imaging
J2 - Eur J Nucl Med Mol Imaging
PY - 2026
DA - 2026/03/31
VL - 53
IS - 7
SP - 4664
EP - 4676
SN - 1619-7070
PB - Springer Science+Business Media
DO - 10.1007/s00259-026-07868-5
UR - https://doi.org/10.1007/s00259-026-07868-5
LA - en
ER -

CSL-JSON

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