OSCR

Humanized APOE mouse brain volume increases over age irrespective of sex and APOE genotype: implications for translational validity to the human.

Code ↔ Paper

3 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 3 matches · 1 of them tie a paragraph to a whole file, not to given lines: a weak match, whose lines are not tinted
  1. [1] § Materials and methods › Image acquisition › Anatomical preprocessing ↔ iterativeN4_2.sh, lines 177–225 · score 0.80 · intensity normalized, N4BiasFieldCorrection, Brain masks, Outlier, rescaling, iterative
  2. [2] § Materials and methods › Image acquisition › Anatomical preprocessing ↔ rat-preprocessing-v2.sh, the whole file · a weak match · score 0.78 · N4BiasFieldCorrection, Brain masks, preprocessed, cropping, intensities, rescaling
  3. [3] § Materials and methods › Image acquisition › Deformation based morphometry ↔ dbm.sh, lines 394–449 · score 0.58 · deformation fields, Jacobian determinants, warp, template, log, affine

Paper

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

The paper is loaded when this pane is shown.

The authors' code

Shell · 225 lines · 14 KB · other · 1 match

  1. #!/bin/bash
  2. set -euo pipefail
  4. export ITK_GLOBAL_DEFAULT_NUMBER_OF_THREADS=${THREADS_PER_COMMAND:-$(nproc)}
  6. BEASTLIBRARY_DIR="${QUARANTINE_PATH}/resources/BEaST_libraries/combined"
  7. REGISTRATIONMODEL="${QUARANTINE_PATH}/resources/mni_icbm152_nlin_sym_09c_minc2/mni_icbm152_t1_tal_nlin_sym_09c.mnc"
  8. REGISTRATIONBRAINMASK="${QUARANTINE_PATH}/resources/mni_icbm152_nlin_sym_09c_minc2/mni_icbm152_t1_tal_nlin_sym_09c_mask.mnc"
  9. WMPRIOR="${QUARANTINE_PATH}/resources/mni_icbm152_nlin_sym_09c_minc2/mni_icbm152_wm_tal_nlin_sym_09c.mnc"
  10. GMPRIOR="${QUARANTINE_PATH}/resources/mni_icbm152_nlin_sym_09c_minc2/mni_icbm152_gm_tal_nlin_sym_09c.mnc"
  11. CSFPRIOR="${QUARANTINE_PATH}/resources/mni_icbm152_nlin_sym_09c_minc2/mni_icbm152_csf_tal_nlin_sym_09c.mnc"
  13. input=$1
  14. output=$2
  16. tmpdir=$(mktemp -d)
  18. #Calculate shrink values
  19. #Target is 4mm steps for first round, 2mm for second
  20. dx=$(mincinfo -attvalue xspace:step ${input})
  21. dy=$(mincinfo -attvalue yspace:step ${input})
  22. dz=$(mincinfo -attvalue zspace:step ${input})
  24. shrinkround1=$(python -c "import math; print max(4,int(math.ceil(4 / ( ( abs($dx) + abs($dy) + abs($dz) ) / 3.0))))")
  25. shrinkround2=$(python -c "import math; print max(2,int(math.ceil(2 / ( ( abs($dx) + abs($dy) + abs($dz) ) / 3.0))))")
  27. maxval=$(mincstats -max -quiet ${input})
  29. set -x
  31. #Generate a whole-image mask
  32. minccalc -quiet -unsigned -byte -expression 'A[0]?1:1' ${input} ${tmpdir}/initmask.mnc
  34. ################################################################################################################################################################################################################################
  35. #Round 0, N4 with Otsu Mask
  36. ################################################################################################################################################################################################################################
  37. n=0
  38. mkdir -p ${tmpdir}/${n}
  40. #Very lightly truncate image intensity and re-normalize
  41. #minccalc -quiet -expression "if(A[0]<${bottom}){out=${bottom};}else{if(A[0]>${top}){out=${top};}else{out=A[0];}}" ${input} ${tmpdir}/trunc.mnc
  42. ImageMath 3 ${tmpdir}/trunc.mnc TruncateImageIntensity ${input} 0.025 0.9995 256
  43. ImageMath 3 ${tmpdir}/trunc.mnc Normalize ${tmpdir}/trunc.mnc
  44. ImageMath 3 ${tmpdir}/trunc.mnc m ${tmpdir}/trunc.mnc ${maxval}
  46. ThresholdImage 3 ${tmpdir}/trunc.mnc ${tmpdir}/${n}/headmask.mnc Otsu 1
  47. ImageMath 3 ${tmpdir}/${n}/weight.mnc GetLargestComponent ${tmpdir}/${n}/headmask.mnc
  50. #Correct entire image domain
  51. N4BiasFieldCorrection -d 3 -s $shrinkround1 -x ${tmpdir}/initmask.mnc -w ${tmpdir}/${n}/weight.mnc \
  52. -b [200] -c [300x300x300x300,1e-7] --histogram-sharpening [0.05,0.01,200] -i ${input} -o [${tmpdir}/${n}/corrected.mnc,${tmpdir}/bias${n}.mnc] -r 0
  54. ImageMath 3 ${tmpdir}/${n}/corrected.mnc TruncateImageIntensity ${tmpdir}/${n}/corrected.mnc 0.025 0.9995 256
  56. #Normalize and rescale intensity
  57. ImageMath 3 ${tmpdir}/${n}/corrected.mnc Normalize ${tmpdir}/${n}/corrected.mnc
  58. ImageMath 3 ${tmpdir}/${n}/corrected.mnc m ${tmpdir}/${n}/corrected.mnc ${maxval}
  60. ################################################################################################################################################################################################################################
  61. #Round 1, N4 with brain mask intersected with Otsu mask
  62. ################################################################################################################################################################################################################################
  63. ((++n))
  64. mkdir -p ${tmpdir}/${n}
  66. #Denoise for registration and beast
  67. minc_anlm --mt $ITK_GLOBAL_DEFAULT_NUMBER_OF_THREADS --rician ${tmpdir}/$((${n} - 1))/corrected.mnc ${tmpdir}/${n}/denoise.mnc
  69. rm -rf ${tmpdir}/$((${n} - 1))
  71. #Register to MNI space
  72. bestlinreg_g -noverbose -nmi -lsq12 -target_mask ${REGISTRATIONBRAINMASK} ${tmpdir}/${n}/denoise.mnc ${REGISTRATIONMODEL} ${tmpdir}/${n}/linear.xfm ${tmpdir}/${n}/mni.mnc
  74. #Intensity normalize
  75. volume_pol --order 1 --min 0 --max 100 --noclamp ${tmpdir}/${n}/mni.mnc ${REGISTRATIONMODEL} --source_mask ${REGISTRATIONBRAINMASK} --target_mask ${REGISTRATIONBRAINMASK} ${tmpdir}/${n}/mni.norm.mnc
  77. #Run a quick beast to get a brain mask
  78. mincbeast -clobber -fill -median -same_res -flip -conf ${BEASTLIBRARY_DIR}/default.4mm.conf ${BEASTLIBRARY_DIR} ${tmpdir}/${n}/mni.norm.mnc ${tmpdir}/${n}/beastmask.mnc
  80. #Resample beast mask and MNI mask to native space
  81. itk_resample --labels --like ${tmpdir}/${n}/denoise.mnc --invert_transform --transform ${tmpdir}/${n}/linear.xfm ${REGISTRATIONBRAINMASK} ${tmpdir}/${n}/mnimask.mnc
  82. itk_resample --labels --like ${tmpdir}/${n}/denoise.mnc --invert_transform --transform ${tmpdir}/${n}/linear.xfm ${tmpdir}/${n}/beastmask.mnc ${tmpdir}/${n}/bmask.mnc
  84. #Combine the masks because sometimes beast misses badly biased cerebellum
  85. mincmath -byte -unsigned -or ${tmpdir}/${n}/mnimask.mnc ${tmpdir}/${n}/bmask.mnc ${tmpdir}/${n}/mask.mnc
  87. #Expand the mask a bit
  88. mincmorph -successive D ${tmpdir}/${n}/mask.mnc ${tmpdir}/${n}/mask_D.mnc
  90. ThresholdImage 3 ${tmpdir}/${n}/denoise.mnc ${tmpdir}/${n}/weight.mnc Otsu 1 ${tmpdir}/${n}/mask_D.mnc
  93. #Do first round of masked bias field correction, use brain mask as weight
  94. N4BiasFieldCorrection -d 3 -s $shrinkround1 -w ${tmpdir}/${n}/weight.mnc -x ${tmpdir}/initmask.mnc \
  95. -b [200] -c [300x300x300x300,1e-7] --histogram-sharpening [0.05,0.01,200] -i ${input} -o [${tmpdir}/${n}/corrected.mnc,${tmpdir}/bias${n}.mnc] -r 0
  97. ImageMath 3 ${tmpdir}/${n}/corrected.mnc TruncateImageIntensity ${tmpdir}/${n}/corrected.mnc 0.025 0.9995 256 ${tmpdir}/${n}/mask.mnc
  99. #Normalize and rescale intensity
  100. ImageMath 3 ${tmpdir}/${n}/corrected.mnc Normalize ${tmpdir}/${n}/corrected.mnc
  101. ImageMath 3 ${tmpdir}/${n}/corrected.mnc m ${tmpdir}/${n}/corrected.mnc ${maxval}
  103. itk_similarity -msq ${tmpdir}/bias$((${n} - 1)).mnc ${tmpdir}/bias${n}.mnc > ${tmpdir}/convergence.txt
  105. ################################################################################################################################################################################################################################
  106. #Round 2, N4 with nonlinearly MNI-bootstrapped WM/GM segmentation proabilities
  107. ################################################################################################################################################################################################################################
  108. ((++n))
  109. mkdir -p ${tmpdir}/${n}
  111. #Denoise for registration and beast
  112. minc_anlm --mt $ITK_GLOBAL_DEFAULT_NUMBER_OF_THREADS --rician ${tmpdir}/$((${n} - 1))/corrected.mnc ${tmpdir}/${n}/denoise.mnc
  114. #Register to MNI space
  115. bestlinreg_g -init_xfm ${tmpdir}/$((${n} - 1))/linear.xfm -close -noverbose -nmi -lsq12 -target_mask ${REGISTRATIONBRAINMASK} -source_mask ${tmpdir}/$((${n} - 1))/mask.mnc ${tmpdir}/${n}/denoise.mnc ${REGISTRATIONMODEL} ${tmpdir}/${n}/linear.xfm ${tmpdir}/${n}/mni.mnc
  117. #Intensity normalize
  118. volume_pol --order 1 --min 0 --max 100 --noclamp ${tmpdir}/${n}/mni.mnc ${REGISTRATIONMODEL} --source_mask ${tmpdir}/$((${n} - 1))/beastmask.mnc --target_mask ${REGISTRATIONBRAINMASK} ${tmpdir}/${n}/mni.norm.mnc
  120. rm -rf ${tmpdir}/$((${n} - 1))
  122. #Run a quick beast to get a brain mask
  123. mincbeast -clobber -fill -median -same_res -flip -conf ${BEASTLIBRARY_DIR}/default.4mm.conf ${BEASTLIBRARY_DIR} ${tmpdir}/${n}/mni.norm.mnc ${tmpdir}/${n}/beastmask.mnc
  125. mincmath -byte -unsigned -or ${tmpdir}/${n}/beastmask.mnc ${REGISTRATIONBRAINMASK} ${tmpdir}/${n}/mnicombinedmask.mnc
  127. minc_nuyl ${tmpdir}/${n}/mni.mnc ${REGISTRATIONMODEL} --source-mask ${tmpdir}/${n}/mnicombinedmask.mnc --target-mask ${REGISTRATIONBRAINMASK} ${tmpdir}/${n}/mni.nuyl.mnc --clobber
  129. antsRegistration --dimensionality 3 --float 1 --collapse-output-transforms 1 -a 0 --minc -u 0 \
  130. --output [${tmpdir}/${n}/nonlin,${tmpdir}/${n}/mninonlin.mnc] \
  131. --transform SyN[0.25,3,0] --metric Mattes[${REGISTRATIONMODEL},${tmpdir}/${n}/mni.nuyl.mnc,1,64] \
  132. --convergence [200x100x70x50,1e-6,10] --shrink-factors 8x4x4x2 --smoothing-sigmas 3.3973x1.6986x0.8493x0.4247mm --masks [${REGISTRATIONBRAINMASK},NULL]
  134. #Resample MNI Priors to Native space for classification
  135. antsApplyTransforms -i ${WMPRIOR} -t ${tmpdir}/${n}/linear.xfm -t ${tmpdir}/${n}/nonlin0_inverse_NL.xfm -r ${tmpdir}/${n}/denoise.mnc -o ${tmpdir}/${n}/SegmentationPrior3.mnc -d 3 -n Linear
  136. antsApplyTransforms -i ${GMPRIOR} -t ${tmpdir}/${n}/linear.xfm -t ${tmpdir}/${n}/nonlin0_inverse_NL.xfm -r ${tmpdir}/${n}/denoise.mnc -o ${tmpdir}/${n}/SegmentationPrior2.mnc -d 3 -n Linear
  137. antsApplyTransforms -i ${CSFPRIOR} -t ${tmpdir}/${n}/linear.xfm -t ${tmpdir}/${n}/nonlin0_inverse_NL.xfm -r ${tmpdir}/${n}/denoise.mnc -o ${tmpdir}/${n}/SegmentationPrior1.mnc -d 3 -n Linear
  138. #Masks
  139. antsApplyTransforms -i ${REGISTRATIONBRAINMASK} -t ${tmpdir}/${n}/linear.xfm -r ${tmpdir}/${n}/denoise.mnc -o ${tmpdir}/${n}/mnimask.mnc -d 3 -n NearestNeighbor
  140. antsApplyTransforms -i ${tmpdir}/${n}/beastmask.mnc -t ${tmpdir}/${n}/linear.xfm -r ${tmpdir}/${n}/denoise.mnc -o ${tmpdir}/${n}/bmask.mnc -d 3 -n NearestNeighbor
  142. #Combine the masks because sometimes beast misses badly biased cerebellum
  143. mincmath -byte -unsigned -or ${tmpdir}/${n}/mnimask.mnc ${tmpdir}/${n}/bmask.mnc ${tmpdir}/${n}/mask.mnc
  145. #Expand the mask a bit
  146. mincmorph -successive D ${tmpdir}/${n}/mask.mnc ${tmpdir}/${n}/mask_D.mnc
  148. #Do an initial classification using the MNI priors, remove outliers
  149. Atropos -d 3 -x ${tmpdir}/${n}/mask_D.mnc -c [25,0.0] -a ${tmpdir}/${n}/denoise.mnc -i PriorProbabilityImages[3,${tmpdir}/${n}/SegmentationPrior%d.mnc,0] -k Gaussian -m [0.1,1x1x1] \
  150. -o [${tmpdir}/${n}/classify.mnc,${tmpdir}/${n}/SegmentationPosteriors%d.mnc] -r 1 -p Socrates[0,1,0.1,0.1] -w GrubbsRosner
  152. #Combine GM and WM probably images into a N4 mask,
  153. ImageMath 3 ${tmpdir}/${n}/weight.mnc PureTissueN4WeightMask ${tmpdir}/${n}/SegmentationPosteriors2.mnc ${tmpdir}/${n}/SegmentationPosteriors3.mnc
  155. #Perform bias field correction with weight mask
  156. N4BiasFieldCorrection -d 3 -s $shrinkround1 -w ${tmpdir}/${n}/weight.mnc -x ${tmpdir}/initmask.mnc \
  157. -b [200] -c [300x300x300x300,1e-7] --histogram-sharpening [0.05,0.01,200] -i ${input} -o [${tmpdir}/${n}/corrected.mnc,${tmpdir}/bias${n}.mnc] -r 0
  159. ImageMath 3 ${tmpdir}/${n}/corrected.mnc TruncateImageIntensity ${tmpdir}/${n}/corrected.mnc 0.025 0.9995 256 ${tmpdir}/${n}/mask.mnc
  161. #Normalize and rescale intensity
  162. ImageMath 3 ${tmpdir}/${n}/corrected.mnc Normalize ${tmpdir}/${n}/corrected.mnc
  163. ImageMath 3 ${tmpdir}/${n}/corrected.mnc m ${tmpdir}/${n}/corrected.mnc ${maxval}
  165. itk_similarity -msq ${tmpdir}/bias$((${n} - 1)).mnc ${tmpdir}/bias${n}.mnc >> ${tmpdir}/convergence.txt
  167. ################################################################################################################################################################################################################################
  168. #Remaining rounds, N4 with segmentations bootstrapped from prior run
  169. #Stop when max iterations or when normalized sum of squares of difference of bias
  170. #Fields less than 1e-4
  171. ################################################################################################################################################################################################################################
  172. while true
  173. do
  174. ((++n))
  175. mkdir -p ${tmpdir}/${n}
  177. #Denoise for registration and beast
  178. minc_anlm --mt $ITK_GLOBAL_DEFAULT_NUMBER_OF_THREADS --rician ${tmpdir}/$((${n} - 1))/corrected.mnc ${tmpdir}/${n}/denoise.mnc
  180. #Register to MNI space
  181. bestlinreg_g -init_xfm ${tmpdir}/$((${n} - 1))/linear.xfm -close -noverbose -nmi -lsq12 -target_mask ${REGISTRATIONBRAINMASK} -source_mask ${tmpdir}/$((${n} - 1))/mask.mnc ${tmpdir}/${n}/denoise.mnc ${REGISTRATIONMODEL} ${tmpdir}/${n}/linear.xfm ${tmpdir}/${n}/mni.mnc
  183. #Intensity normalize
  184. volume_pol --order 1 --min 0 --max 100 --noclamp ${tmpdir}/${n}/mni.mnc ${REGISTRATIONMODEL} --source_mask ${tmpdir}/$((${n} - 1))/beastmask.mnc --target_mask ${REGISTRATIONBRAINMASK} ${tmpdir}/${n}/mni.norm.mnc
  186. #Run a quick beast to get a brain mask
  187. mincbeast -clobber -fill -median -same_res -flip -conf ${BEASTLIBRARY_DIR}/default.2mm.conf ${BEASTLIBRARY_DIR} ${tmpdir}/${n}/mni.norm.mnc ${tmpdir}/${n}/beastmask.mnc
  189. #Resample beast mask and MNI mask to native space
  190. itk_resample --labels --like ${tmpdir}/${n}/denoise.mnc --invert_transform --transform ${tmpdir}/${n}/linear.xfm ${REGISTRATIONBRAINMASK} ${tmpdir}/${n}/mnimask.mnc
  191. itk_resample --labels --like ${tmpdir}/${n}/denoise.mnc --invert_transform --transform ${tmpdir}/${n}/linear.xfm ${tmpdir}/${n}/beastmask.mnc ${tmpdir}/${n}/bmask.mnc
  193. #Combine the masks because sometimes beast misses badly biased cerebellum
  194. mincmath -unsigned -byte -or ${tmpdir}/${n}/mnimask.mnc ${tmpdir}/${n}/bmask.mnc ${tmpdir}/${n}/mask.mnc
  196. #Do an initial classification using the MNI priors, remove outliers
  197. Atropos -d 3 -x ${tmpdir}/${n}/mask.mnc -c [25,0.0] -a ${tmpdir}/${n}/denoise.mnc -i PriorProbabilityImages[3,${tmpdir}/$((${n} - 1))/SegmentationPosteriors%d.mnc,0.25] -k Gaussian -m [0.1,1x1x1] \
  198. -o [${tmpdir}/${n}/classify.mnc,${tmpdir}/${n}/SegmentationPosteriors%d.mnc] -r 1 -p Socrates[1] -w GrubbsRosner
  200. rm -rf ${tmpdir}/$((${n} - 1))
  202. #Combine GM and WM probably images into a N4 mask,
  203. ImageMath 3 ${tmpdir}/${n}/weight.mnc PureTissueN4WeightMask ${tmpdir}/${n}/SegmentationPosteriors2.mnc ${tmpdir}/${n}/SegmentationPosteriors3.mnc
  205. #Perform bias field correction with weight mask
  206. N4BiasFieldCorrection -d 3 -s $shrinkround2 -w ${tmpdir}/${n}/weight.mnc -x ${tmpdir}/initmask.mnc \
  207. -b [200] -c [300x300x300x300,1e-7] --histogram-sharpening [0.05,0.01,200] -i ${input} -o [${tmpdir}/${n}/corrected.mnc,${tmpdir}/bias${n}.mnc] -r 0
  209. ImageMath 3 ${tmpdir}/${n}/corrected.mnc TruncateImageIntensity ${tmpdir}/${n}/corrected.mnc 0.025 0.9995 256 ${tmpdir}/${n}/mask.mnc
  211. #Normalize and rescale intensity
  212. ImageMath 3 ${tmpdir}/${n}/corrected.mnc Normalize ${tmpdir}/${n}/corrected.mnc
  213. ImageMath 3 ${tmpdir}/${n}/corrected.mnc m ${tmpdir}/${n}/corrected.mnc ${maxval}
  215. itk_similarity -msq ${tmpdir}/bias$((${n} - 1)).mnc ${tmpdir}/bias${n}.mnc >> ${tmpdir}/convergence.txt
  217. [[ ( ${n} -lt 4 ) || ( (${n} -lt 10) && ( $(python -c "print $(itk_similarity -msq ${tmpdir}/bias$((${n} - 1)).mnc ${tmpdir}/bias${n}.mnc) > 0.0001") == "True" ) ) ]] || break
  219. done
  221. cp -f ${tmpdir}/${n}/corrected.mnc ${output}
  222. #cp -f ${tmpdir}/${n}/mask.mnc $(dirname $output)/$(basename $output .mnc).mask.mnc
  223. #cp -f ${tmpdir}/${n}/classify.mnc $(dirname $output)/$(basename $output .mnc).classify.mnc
  225. rm -rf ${tmpdir}

iterativeN4_2.sh at commit 7400e90, under other · at the source

Overview

Authors: Adam C Raikes1, Avnish Bhattrai1, Tian Wang1,2, Jean-Paul Wiegand1, Roberta Diaz Brinton1,2,3
ORCID iDs: Adam C Raikes, Avnish Bhattrai, Tian Wang, Jean-Paul Wiegand, Roberta Diaz Brinton
  1. Center for Innovation in Brain Science, University of Arizona, Tucson, AZ, United States
  2. Department of Neurology, University of Arizona, Tucson, AZ, United States
  3. Department of Pharmacology, College of Medicine Tucson, University of Arizona, Tucson, AZ, United States
Institutions: University of Arizona (United States)
Journal: Frontiers in neuroscience, volume 20, article 1843319
Dates: received 31 March 2026; accepted 19 June 2026; published online 14 July 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.3389/fnins.2026.1843319 · PMID 42524115 · PMCID PMC13408376 · OpenAlex W4415625883
Open access: gold, a free copy (OpenAlex)
Preprint: doi:10.1101/2025.10.27.684892
Status: code verified
Categories: genetics / omics (modality), structural MRI / diffusion (modality), human (organism), mouse (organism), Alzheimer's / dementia (population), clinical / translational (subfield)
Methods: Connectivity, Statistics, Preprocessing
Keywords: aging, Alzheimer’s disease, APOE, brain volume, mouse model, MRI
Topic: Alzheimer's disease research and treatments (Physiology, Medicine), according to OpenAlex
Funding: NIA NIH HHS (R01 AG057931, P01 AG026572)
Citations: not cited yet (Europe PMC); 70 references in the paper

Abstract

Background: Humanized APOE mouse models are widely used to study late-onset Alzheimer’s disease (LOAD) risk, yet it remains unclear whether they reproduce the macrostructural brain changes observed in human aging and disease.

Methods: We performed ex vivo magnetic resonance imaging to quantify total and voxelwise brain volumes in male and female mice across APOE genotypes (ε3/ε3, ε3/ε4, ε4/ε4) and ages spanning 6–25 months.

Results: Total brain volume increased with age (cross-sectional estimate: 2.12 mm3/month) and was greater in APOE-ε4 carriers, with no effect of sex. Voxelwise analyses revealed regionally specific changes independent of total volume, characterized by cortical volume decreases and subcortical preservation or increases, as well as sex-dependent spatial patterns. No localized volumetric effects of APOE genotype were detected.

Conclusion: These findings indicate that, despite incorporating a major genetic risk factor for LOAD, this model does not reproduce the atrophy phenotype characteristic of human aging and Alzheimer’s disease. Instead, the observed pattern is more consistent with non-pathological or vulnerable aging, suggesting that humanized APOE alone is insufficient to induce MRI-detectable macrostructural atrophy within this cross-sectional comparison, though this finding does not preclude other APOE-dependent pathological mechanisms.

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

Repositories

Its files are read in the Code ↔ Paper reader above, with 3 matches between paragraphs and lines of code.

CoBrALab/minc-toolkit-extras

License: other
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 7400e90a9a259b4652658a30392864c8d6bcbced, 19 June 2026
Languages: Shell (37), Python (5), Perl (1)
Size: 58 files, 43 scripts
Software Heritage: not archived
Found in: the text
Holds: README, license file
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: ANTs (30 files), NumPy (4 files), SciPy (2 files), SimpleITK (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
44 files
At the source: github.com/CoBrALab/minc-toolkit-extras

CoBrALab/optimized_antsMultivariateTemplateConstruction

License: other
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 477735b38736f9ba91f7e6372ab8eb77d8629bed, 21 September 2026
Languages: Shell (10), Python (4)
Size: 25 files, 14 scripts
Software Heritage: not archived
Found in: the text
Holds: README, license file, CITATION.cff, documentation
Not found: environment file, tests, continuous integration
Tools: SimpleITK (4 files), NumPy (3 files), ANTs (2 files), SciPy (2 files), statsmodels (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
16 files
At the source: github.com/CoBrALab/optimized_antsMultivariateTemplateConstruction

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;
  • 56 scripts, each with its path and the digest of its content;
  • 3 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 statement

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

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 2, 28 September 2026

  • Authors: added Avnish Bhattrai (0000-0002-8376-123X); Tian Wang (0000-0003-1867-5850); Jean-Paul Wiegand (0000-0002-0385-1527); Roberta Diaz Brinton (0000-0001-8450-772X); removed Avnish Bhattrai; Tian Wang; Jean-Paul Wiegand; Roberta Diaz Brinton

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, pages, dates, 5 authors, 6 keywords, 1 funder, 65 references.

Cite

This paper

Raikes, A. C., Bhattrai, A., Wang, T., Wiegand, J.-P., & Brinton, R. D. (2026). Humanized APOE mouse brain volume increases over age irrespective of sex and APOE genotype: implications for translational validity to the human. Frontiers in neuroscience, 20, 1843319. https://doi.org/10.3389/fnins.2026.1843319

BibTeX

@article{raikes2026humanized,
author = {Raikes, Adam C and Bhattrai, Avnish and Wang, Tian and Wiegand, Jean-Paul and Brinton, Roberta Diaz},
title = {{Humanized APOE mouse brain volume increases over age irrespective of sex and APOE genotype: implications for translational validity to the human}},
journal = {Frontiers in neuroscience},
year = {2026},
month = jul,
volume = {20},
pages = {1843319},
publisher = {Frontiers Media SA},
issn = {1662-4548},
doi = {10.3389/fnins.2026.1843319},
url = {https://doi.org/10.3389/fnins.2026.1843319},
pmid = {42524115},
pmcid = {PMC13408376}
}

RIS

TY - JOUR
AU - Raikes, Adam C
AU - Bhattrai, Avnish
AU - Wang, Tian
AU - Wiegand, Jean-Paul
AU - Brinton, Roberta Diaz
TI - Humanized APOE mouse brain volume increases over age irrespective of sex and APOE genotype: implications for translational validity to the human
T2 - Frontiers in neuroscience
J2 - Front Neurosci
PY - 2026
DA - 2026/07/14
VL - 20
SP - 1843319
SN - 1662-4548
PB - Frontiers Media SA
DO - 10.3389/fnins.2026.1843319
UR - https://doi.org/10.3389/fnins.2026.1843319
LA - en
ER -

CSL-JSON

{
"id": "10.3389/fnins.2026.1843319",
"type": "article-journal",
"title": "Humanized APOE mouse brain volume increases over age irrespective of sex and APOE genotype: implications for translational validity to the human",
"container-title": "Frontiers in neuroscience",
"author": [
{
"family": "Raikes",
"given": "Adam C"
},
{
"family": "Bhattrai",
"given": "Avnish"
},
{
"family": "Wang",
"given": "Tian"
},
{
"family": "Wiegand",
"given": "Jean-Paul"
},
{
"family": "Brinton",
"given": "Roberta Diaz"
}
],
"container-title-short": "Front Neurosci",
"volume": "20",
"page": "1843319",
"DOI": "10.3389/fnins.2026.1843319",
"PMID": "42524115",
"PMCID": "PMC13408376",
"ISSN": "1662-4548",
"publisher": "Frontiers Media SA",
"URL": "https://doi.org/10.3389/fnins.2026.1843319",
"language": "en",
"issued": {
"date-parts": [
[
2026,
7,
14
]
]
}
}

The tracing map gets a citation of its own once an author has validated it and it has a DOI.

Similar papers

The papers with a page that share the most with this one: the tools found in their code, their categories, datasets, cited references and authors, the rarest counting most.

[1] doi:10.1126/sciadv.adq6577 [code]
Autism-like phenotypes and increased NMDAR2D expression in mice with KDM5B histone lysine demethylase deficiency.
Journal: Science advances
In common: SciPy, NumPy, mouse, 6 references
[2] doi:10.1162/imag.a.1310 [code]
Experimental quality control induces changes in Allen mouse brain connectomes.
Journal: Imaging neuroscience (Cambridge, Mass.)
In common: ANTs, SciPy, NumPy, mouse, 5 references
[3] doi:10.1162/imag.a.1235 [code]
Intracranial volume: To adjust or not to adjust? It is not a matter of if, but how.
Journal: Imaging neuroscience (Cambridge, Mass.)
In common: ANTs, SciPy, NumPy, structural MRI / diffusion, 4 references
[4] doi:10.3390/cancers18162636 [code]
Exploring the Impact of T2-Weighted MRI Fat Saturation on Radiomics Stability for Brain Radionecrosis Prediction After Skull-Base Proton Therapy: A Pilot Study.
Journal: Cancers
In common: SimpleITK, ANTs, statsmodels, 2 other tools, structural MRI / diffusion, clinical / translational, 2 references
[5] doi:10.1126/sciadv.aeb5842 [code]
Regulation of autism-related self-injurious behavior by electrical stimulation of corticostriatal circuits in mice and humans.
Journal: Science advances
In common: SimpleITK, ANTs, statsmodels, 2 other tools, mouse, 1 reference
[6] doi:10.1093/radadv/umag025 [code]
OpenMAP-BrainAge: generalizable and interpretable brain age predictor from MRI.
Journal: Radiology advances
In common: SimpleITK, ANTs, SciPy, 1 other tool, Alzheimer's / dementia, structural MRI / diffusion, 1 reference
[7] doi:10.1002/alz.71649 [code]
Postmortem brain MRI reveals differential associations of subcortical and limbic volumes with cortical thinning and neurodegenerative pathologies.
Journal: Alzheimer's & dementia : the journal of the Alzheimer's Association
In common: SimpleITK, ANTs, statsmodels, 2 other tools, Alzheimer's / dementia, structural MRI / diffusion
[8] doi:10.1002/mrm.70488 [code]
Advancing the Volumetric Analysis of Ultra-Low-Field Brain MRI Using Image-to-Image Translation.
Journal: Magnetic resonance in medicine
In common: SimpleITK, SciPy, NumPy, structural MRI / diffusion, 3 references
[9] doi:10.3389/fmed.2026.1875760 [code]
Adaptive multi-stage domain unlearning for white-matter lesion segmentation.
Journal: Frontiers in medicine
In common: SimpleITK, SciPy, NumPy, structural MRI / diffusion, 3 references
[10] doi:10.1093/braincomms/fcag129 [code]
Longitudinal changes of choroid plexus volumes and MRI ratios in multiple sclerosis.
Journal: Brain communications
In common: SimpleITK, ANTs, SciPy, 1 other tool, structural MRI / diffusion, clinical / translational, 1 reference

Contribute

The authors of this paper can claim it, correct its record and validate its tracing map, and the maintainers of its code (its owner, or a public member of its organization) correct what it says of their repository; anyone signed in can ask for its removal. Every request goes to OSCR's own machine, which answers it; your account page follows them.

Sign in with ORCID to claim this paper as one of its authors, correct its record or validate its tracing map: when the paper's metadata lists your ORCID iD, you are recognized at once. Maintainers of its code: sign in with GitHub, then claim the repository on your account page.

Request its removal

To ask OSCR to remove this record, the copies of its authors' scripts or its tracing map, use the removal request page: signed in, you say who you are, what to remove and why, then review and confirm the request. Published rules decide every request (how).

Discussion, reproductions, activity

Discussion: questions and error reports about this paper and its code, from signed-in readers and its authors. It opens with sign-in.

Reproductions: reports from readers who ran the authors' code: what they reproduced, with which environment, commit and data. It opens with sign-in.

Activity: what happens around this paper: new versions of its record, its map's validation, discussions and reproductions. It opens with sign-in.