OSCR

Associations of plasma phosphorylated tau217 with cognitive impairment and brain microstructural alterations in Alzheimer's disease.

A correction to this paper has been published: the notice, 42079382, from Europe PMC.

Code ↔ Paper

6 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 6 matches · 2 of them tie a paragraph to a whole file, not to given lines: weak matches, whose lines are not tinted
  1. [1] § METHODS › MRI data analysis › Estimation of MRI relaxometry parameters ↔ example_script_run_MET2_preproc_and_recon_using_ROIs.sh, the whole file · a weak match · score 0.85 · Gibbs ringing artifacts, flip angle, MRtrix, preprocessing, mrdegibbs, denoised
  2. [2] § METHODS › MRI data analysis › Estimation of MRI relaxometry parameters ↔ example_script_run_MET2_preproc_and_recon.sh, the whole file · a weak match · score 0.80 · Gibbs ringing artifacts, MRtrix, preprocessing, mrdegibbs, denoised, Reconstruction
  3. [3] § METHODS › MRI data analysis › Estimation of MRI relaxometry parameters ↔ plot/plot_results_real_data.py, lines 51–137 · score 0.65 · myelin water fraction, free water fraction, water content, T2IE, CSF, TWC
  4. [4] § RESULTS › MRI relaxometry data ↔ plot/plot_results_real_data.py, lines 51–137 · score 0.56 · myelin water fraction, free water fraction, water content, T2IE, signals, TWC
  5. [5] § METHODS › MRI data acquisition ↔ epg/epg.py, lines 46–61 · score 0.55 · flip angle, multi echo, inversion, TR
  6. [6] § METHODS › MRI data acquisition ↔ scripts_synthetic_data_evaluation/Paper_Comparison/evaluate_all_methods_two_lobes_SNR150_300.py, lines 39–56 · score 0.54 · flip angle, multi echo, spin, TR

Paper

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The authors' code

Python · 230 lines · 7.8 KB · no license · 2 matches

  1. import nibabel as nib
  2. import numpy as np
  3. import matplotlib
  4. import matplotlib.pyplot as plt
  5. import matplotlib.ticker as ticker
  6. matplotlib.rcParams['text.usetex']=True
  7. #matplotlib.rcParams['text.latex.unicode']=True
  9. from mpl_toolkits.axes_grid1.inset_locator import zoomed_inset_axes, inset_axes, mark_inset
  10. from mpl_toolkits.axes_grid1 import make_axes_locatable
  12. import warnings
  13. warnings.filterwarnings("ignore",category=FutureWarning)
  15. def colorbar(mappable):
  16. ax = mappable.axes
  17. fig = ax.figure
  18. divider = make_axes_locatable(ax)
  19. cax = divider.append_axes("right", size="5%", pad=0.05)
  20. return fig.colorbar(mappable, cax=cax)
  21. # end function
  23. def plot_real_data_slices(path_to_save_data, path_to_data, Slice, method):
  24. print('Plotting quantitative maps')
  26. data_type = 'invivo'
  27. #_______________________________________________________________________________
  28. params = {
  29. 'text.latex.preamble': r'\usepackage{gensymb}',
  30. 'image.origin': 'lower',
  31. 'image.interpolation': 'nearest',
  32. 'image.cmap': 'gray',
  33. 'axes.grid': False,
  34. 'savefig.dpi': 600, # to adjust notebook inline plot size
  35. 'axes.labelsize': 12, # fontsize for x and y labels (was 10)
  36. 'axes.titlesize': 14,
  37. 'font.size': 14, # was 10
  38. 'legend.fontsize': 12, # was 10
  39. 'xtick.labelsize': 12,
  40. 'ytick.labelsize': 12,
  41. 'text.usetex': True,
  42. 'font.family': 'serif',
  43. }
  45. #'figure.figsize': [3.39, 2.10],
  47. matplotlib.rcParams.update(params)
  49. fig1 = plt.figure('Showing all results', figsize=(11,10), constrained_layout=True)
  51. # load data
  52. img = nib.load(path_to_data)
  53. data = img.get_fdata()
  54. data = data.astype(np.float64, copy=False)
  56. img = nib.load(path_to_save_data + 'MWF.nii.gz')
  57. fM = img.get_fdata()
  58. fM = fM.astype(np.float64, copy=False)
  60. img = nib.load(path_to_save_data + 'IEWF.nii.gz')
  61. fIE = img.get_fdata()
  62. fIE = fIE.astype(np.float64, copy=False)
  64. img = nib.load(path_to_save_data + 'FWF.nii.gz')
  65. fCSF = img.get_fdata()
  66. fCSF = fCSF.astype(np.float64, copy=False)
  68. img = nib.load(path_to_save_data + 'T2_M.nii.gz')
  69. T2m = img.get_fdata()
  70. T2m = T2m.astype(np.float64, copy=False)
  72. img = nib.load(path_to_save_data + 'T2_IE.nii.gz')
  73. T2IE = img.get_fdata()
  74. T2IE = T2IE.astype(np.float64, copy=False)
  76. img = nib.load(path_to_save_data + 'TWC.nii.gz')
  77. Ktotal = img.get_fdata()
  78. Ktotal = Ktotal.astype(np.float64, copy=False)
  80. img = nib.load(path_to_save_data + 'FA.nii.gz')
  81. FA = img.get_fdata()
  82. FA = FA.astype(np.float64, copy=False)
  84. plt.subplot(3, 3, 1).set_axis_off()
  85. im0 = plt.imshow(data[:,:,Slice,0].T, cmap='gray', origin='upper')
  86. plt.title('Signal(TE=10ms)')
  87. colorbar(im0)
  89. plt.subplot(3, 3, 2).set_axis_off()
  90. im1 = plt.imshow(FA[:,:,Slice].T, cmap='plasma', origin='upper', clim=(90,180))
  91. plt.title('Flip Angle (degrees)')
  92. colorbar(im1)
  94. plt.subplot(3, 3, 4).set_axis_off()
  95. #im1 = plt.imshow(fM[:,:,Slice].T, cmap='gray', origin='lower', clim=(0,0.25))
  96. im1 = plt.imshow(fM[:,:,Slice].T, cmap='afmhot', origin='upper', clim=(0,0.25))
  97. plt.title('Myelin Water Fraction')
  98. colorbar(im1)
  100. plt.subplot(3, 3, 5).set_axis_off()
  101. #im2 = plt.imshow(fIE[:,:,Slice].T, cmap='gray', origin='lower', clim=(0,1))
  102. im2 = plt.imshow(fIE[:,:,Slice].T, cmap='magma', origin='upper', clim=(0,1))
  103. plt.title('Intra/Extra Water Fraction')
  104. colorbar(im2)
  106. plt.subplot(3, 3, 6).set_axis_off()
  107. im3 = plt.imshow(fCSF[:,:,Slice].T, cmap='hot', origin='upper', clim=(0,1))
  108. plt.title('Free Water Fraction')
  109. colorbar(im3)
  111. if data_type == 'invivo' :
  112. plt.subplot(3, 3, 7).set_axis_off()
  113. im4 = plt.imshow(T2m[:,:,Slice].T, cmap='gnuplot2', origin='upper', clim=(9,40))
  114. plt.title('T2-Myelin (ms)')
  115. colorbar(im4)
  117. plt.subplot(3, 3, 8).set_axis_off()
  118. #im5 = plt.imshow(T2IE[:,:,Slice].T, origin='lower', clim=(50,100))
  119. im5 = plt.imshow(T2IE[:,:,Slice].T, cmap='gnuplot2', origin='upper', clim=(50,90))
  120. plt.title('T2-Intra/Extra (ms)')
  121. colorbar(im5)
  122. elif data_type == 'exvivo' :
  123. plt.subplot(3, 3, 7).set_axis_off()
  124. im4 = plt.imshow(T2m[:,:,Slice].T, origin='upper', clim=(5,25))
  125. plt.title('T2-Myelin (ms)')
  126. colorbar(im4)
  128. plt.subplot(3, 3, 8).set_axis_off()
  129. im5 = plt.imshow(T2IE[:,:,Slice].T, origin='upper', clim=(30,60))
  130. plt.title('T2-Intra/Extra (ms)')
  131. colorbar(im5)
  132. #end if
  134. plt.subplot(3, 3, 9).set_axis_off()
  135. im6 = plt.imshow(Ktotal[:,:,Slice].T, cmap='gray', origin='upper')
  136. plt.title('Total Water Content')
  137. colorbar(im6)
  139. #plt.tight_layout()
  140. fig1.set_constrained_layout_pads(w_pad=0.05, h_pad=0.05, hspace=0.07, wspace=-0.3)
  141. #plt.savefig(path_to_save_data + 'MET2_' + method + '.png', bbox_inches='tight', dpi=600)
  142. plt.savefig(path_to_save_data + 'MET2_' + method + '.png', dpi=600)
  143. #plt.show()
  144. #fig1.show()
  146. #_______________________________________________________________________________
  147. fig2, axes = plt.subplots(nrows=2, ncols=4, figsize=(18,8.0), constrained_layout=True)
  148. ax0, ax1, ax2, ax3, ax4, ax5, ax6, ax7 = axes.flatten()
  150. im1 = ax0.imshow(FA[:,:,Slice].T, cmap='gray', origin='upper', clim=(90,180))
  151. ax0.set_title('Flip Angle')
  152. colorbar(im1)
  154. x = FA[:,:,Slice].flatten()
  155. x = x[x>0]
  156. ax4.hist(x, 50, density=1, facecolor='lime', alpha=1.0)
  157. ax4.set_title('Histogram of FA')
  158. ax4.set_xlabel('FA')
  159. ax4.set_ylabel('Probability')
  160. ax4.grid(True)
  162. im1 = ax1.imshow(fM[:,:,Slice].T, cmap='gray', origin='upper', clim=(0,0.25))
  163. ax1.set_title('MWF')
  164. colorbar(im1)
  166. x = fM[:,:,Slice].flatten()
  167. x = x[x>0]
  168. ax5.hist(x, 50, density=1, facecolor='SkyBlue', alpha=1.0, range=[0, 0.4])
  169. ax5.set_title('Histogram of MWF')
  170. ax5.set_xlabel('MWF')
  171. ax5.set_ylabel('Probability')
  172. ax5.grid(True)
  174. if data_type == 'invivo' :
  175. im1 = ax2.imshow(T2m[:,:,Slice].T, cmap='gray', origin='upper', clim=(10,40))
  176. ax2.set_title('T2m')
  177. colorbar(im1)
  179. x = T2m[:,:,Slice].flatten()
  180. x = x[x>0]
  181. ax6.hist(x, 50, density=1, facecolor='IndianRed', alpha=1.0, range=[10, 40])
  182. ax6.set_title('Histogram of T2m')
  183. ax6.set_xlabel('T2m')
  184. ax6.set_ylabel('Probability')
  185. ax6.grid(True)
  187. im1 = ax3.imshow(T2IE[:,:,Slice].T, cmap='gray', origin='upper', clim=(50,100))
  188. ax3.set_title('T2IE')
  189. colorbar(im1)
  191. x = T2IE[:,:,Slice].flatten()
  192. x = x[x>0]
  193. ax7.hist(x, 50, density=1, facecolor='tan', alpha=1.0, range=[40, 110])
  194. ax7.set_title('Histogram of T2IE')
  195. ax7.set_xlabel('T2IE')
  196. ax7.set_ylabel('Probability')
  197. ax7.grid(True)
  198. elif data_type == 'exvivo' :
  199. im1 = ax2.imshow(T2m[:,:,Slice].T, cmap='gray', origin='upper', clim=(5,25))
  200. ax2.set_title('T2m')
  201. colorbar(im1)
  203. x = T2m[:,:,Slice].flatten()
  204. x = x[x>0]
  205. ax6.hist(x, 50, density=1, facecolor='IndianRed', alpha=1.0)
  206. ax6.set_title('Histogram of T2m')
  207. ax6.set_xlabel('T2m')
  208. ax6.set_ylabel('Probability')
  209. ax6.grid(True)
  211. im1 = ax3.imshow(T2IE[:,:,Slice].T, cmap='gray', origin='upper', clim=(30,60))
  212. ax3.set_title('T2IE')
  213. colorbar(im1)
  215. x = T2IE[:,:,Slice].flatten()
  216. x = x[x>0]
  217. ax7.hist(x, 50, density=1, facecolor='tan', alpha=1.0)
  218. ax7.set_title('Histogram of T2IE')
  219. ax7.set_xlabel('T2IE')
  220. ax7.set_ylabel('Probability')
  221. ax7.grid(True)
  222. #end if
  224. #plt.tight_layout()
  225. #plt.savefig(path_to_save_data + 'MET2_histograms' + method + '.png', bbox_inches='tight', dpi=600)
  226. plt.savefig(path_to_save_data + 'MET2_histograms_' + method + '.png', dpi=600)
  227. #plt.show()
  228. #fig2.show()
  229. #plt.close('all')
  230. #end main function

plot_results_real_data.py at commit 1bc36b0, no license · at the source

Overview

Authors: Miguel Ángel Rivas‐Fernández1, Sara Basanta‐Torres2,3,4, Mónica Lindín2,3,4, Montserrat Zurrón2,3,4, Fernando Díaz2,3,4, Arturo Xosé Pereiro4,5, Cristina Lojo‐Seoane4,5, Ana Isabel Rodríguez‐Pérez4,6,7, José Luis Labandeira4,6,7, Santiago Galdo‐Álvarez2,3,4
ORCID iDs: Miguel Ángel Rivas‐Fernández, Sara Basanta‐Torres, Santiago Galdo‐Álvarez
  1. Department of Psychology, Sociology and Philosophy, University of León, León, Spain
  2. Department of Clinical Psychology and Psychobiology, University of Santiago de Compostela (USC), Santiago de Compostela, Spain
  3. Cognitive Neuroscience Research and Psychogerontology Group (NeuCogA‐Aging), Institute of Psychology (IPsiUS), USC, Santiago de Compostela, Spain
  4. Health Research Institute of Santiago de Compostela (IDIS), Santiago de Compostela, Spain
  5. Department of Developmental Psychology, University of Santiago de Compostela (USC), Santiago de Compostela, Spain
  6. Cellular and Molecular Neurobiology of Parkinson's Disease, Research Center for Molecular Medicine and Chronic Diseases (CIMUS), University of Santiago de Compostela (USC), Santiago de Compostela, Spain
  7. Networking Research Center on Neurodegenerative Diseases (CIBERNED), Madrid, Spain
Institutions: Universidad de León (Spain); Universidade de Santiago de Compostela (Spain); Instituto de Investigación Sanitaria de Santiago (Spain); Biomedical Research Networking Center on Neurodegenerative Diseases (Spain); Center for Research in Molecular Medicine and Chronic Diseases (Spain)
Journal: Alzheimer's & dementia (Amsterdam, Netherlands), volume 18, issue 2, article e70333
Dates: received 8 January 2026; accepted 17 March 2026; published online 19 April 2026
Type: Research article · Language: English
License: CC BY-NC
Identifiers: DOI 10.1002/dad2.70333 · PMID 42016779 · PMCID PMC13092427 · OpenAlex W7154894008
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: structural MRI / diffusion (modality), human (organism), Alzheimer's / dementia (population), cellular / molecular (subfield)
Methods: Statistics, fMRI & imaging
Keywords: Alzheimer's disease, brain microstructure, free water fraction, intra‐/extracellular water fraction, multicomponent T2‐relaxometry, myelin water fraction, plasma phosphorylated tau217
Topic: Dementia and Cognitive Impairment Research (Psychiatry and Mental health, Medicine), according to OpenAlex
Funding: Ministerio de Economía y Competitividad (PID2020‐114521RB‐C21/C22, PSI2017‐89389‐C2‐1/2‐R, PID2023‐151659OB‐C21/C22); Ministerio de Ciencia Innovación y Universidades (PID2021‐126848NB‐I00, PID2023‐150743OB‐I00)
Citations: not cited yet (Europe PMC); 50 references in the paper
Notices: A correction to this paper has been published (42079382, from Europe PMC)

Abstract

Introduction: Plasma phosphorylated tau217 (p‐tau217) is a promising biomarker for Alzheimer's disease (AD) risk detection. Its relationship with brain microstructure and cognitive impairment remains unclear. Multi‐component T2‐relaxometry is an MRI technique sensitive to myelin content, axonal degeneration, and neuroinflammation.

Methods: A total of 229 participants classified by p‐tau217 levels into p‐tau217– (n = 176), p‐tau217+ (n = 26), and intermediate (n = 27) underwent neuropsychological testing and MRI. Voxel‐wise general linear models controlling for age, sex, education, apolipoprotein E (APOE, and white matter lesions were performed for total water content (TWC), myelin water fraction (MWF), intra‐/extracellular water fraction (IEWF), geometric mean of intra‐/extracellular water (T2IE), and free/quasi‐free water fraction (FQFWF).

Results: The p‐tau217+ participants showed poorer cognition, increases in FQFWF and TWC, and reductions in IEWF and T2IE across cortical and subcortical regions and white matter tracts.

Discussion: High p‐tau217 level associates with brain microstructure alterations and poorer cognition, supporting it as a biomarker of AD‐related neuropathology and the utility of T2‐relaxometry for detecting tissue integrity.

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

Repository

Its files are read in the Code ↔ Paper reader above, with 6 matches between paragraphs and lines of code.

ejcanalesr/multicomponent-T2-toolbox

License: none: the authors keep all their rights
State: the link answers, verified on 29 September 2026
Evidence: files inventoried
Commit: 1bc36b02708028f2b99a669ebb923cca17ae0828, 7 September 2023
Languages: Python (26), Shell (2)
Size: 43 files, 28 scripts
Software Heritage: not archived
Found in: “DATA AVAILABILITY STATEMENT”
Holds: README, environment (requirements.txt)
Not found: license file, CITATION.cff, tests, continuous integration, documentation
Tools: NumPy (21 files), SciPy (16 files), Matplotlib (14 files), NiBabel (11 files), pandas (4 files), Numba (3 files), seaborn (3 files), FSL (2 files), MRtrix3 (2 files), scikit-image (2 files)
Availability: 1 check, the latest on 29 September 2026: the link answers
  • 29 September 2026: the link answers
29 files
At the source: github.com/ejcanalesr/multicomponent-T2-toolbox

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:

  • 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 28 scripts, each with its path and the digest of its content;
  • 6 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 data that support the findings of this study are available from the corresponding author, upon reasonable request. The code used in this work is publicly available at https://github.com/ejcanalesr/multicomponent‐T2‐toolbox (https://github.com/ejcanalesr/multicomponent-T2-toolbox).

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, 29 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 10 authors, 7 keywords, 2 funders, 47 references, 1 integrity notice.

Cite

This paper

Rivas‐Fernández, M. Á., Basanta‐Torres, S., Lindín, M., Zurrón, M., Díaz, F., Pereiro, A. X., Lojo‐Seoane, C., Rodríguez‐Pérez, A. I., Labandeira, J. L., & Galdo‐Álvarez, S. (2026). Associations of plasma phosphorylated tau217 with cognitive impairment and brain microstructural alterations in Alzheimer's disease. Alzheimer's & dementia (Amsterdam, Netherlands), 18(2), e70333. https://doi.org/10.1002/dad2.70333

BibTeX

@article{rivasfernandez2026associations,
author = {Rivas‐Fernández, Miguel Ángel and Basanta‐Torres, Sara and Lindín, Mónica and Zurrón, Montserrat and Díaz, Fernando and Pereiro, Arturo Xosé and Lojo‐Seoane, Cristina and Rodríguez‐Pérez, Ana Isabel and Labandeira, José Luis and Galdo‐Álvarez, Santiago},
title = {{Associations of plasma phosphorylated tau217 with cognitive impairment and brain microstructural alterations in Alzheimer's disease}},
journal = {Alzheimer's \& dementia (Amsterdam, Netherlands)},
year = {2026},
month = apr,
volume = {18},
number = {2},
pages = {e70333},
publisher = {Wiley},
issn = {2352-8729},
doi = {10.1002/dad2.70333},
url = {https://doi.org/10.1002/dad2.70333},
pmid = {42016779},
pmcid = {PMC13092427}
}

RIS

TY - JOUR
AU - Rivas‐Fernández, Miguel Ángel
AU - Basanta‐Torres, Sara
AU - Lindín, Mónica
AU - Zurrón, Montserrat
AU - Díaz, Fernando
AU - Pereiro, Arturo Xosé
AU - Lojo‐Seoane, Cristina
AU - Rodríguez‐Pérez, Ana Isabel
AU - Labandeira, José Luis
AU - Galdo‐Álvarez, Santiago
TI - Associations of plasma phosphorylated tau217 with cognitive impairment and brain microstructural alterations in Alzheimer's disease
T2 - Alzheimer's & dementia (Amsterdam, Netherlands)
J2 - Alzheimers Dement (Amst)
PY - 2026
DA - 2026/04/19
VL - 18
IS - 2
SP - e70333
SN - 2352-8729
PB - Wiley
DO - 10.1002/dad2.70333
UR - https://doi.org/10.1002/dad2.70333
LA - en
ER -

CSL-JSON

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"id": "10.1002/dad2.70333",
"type": "article-journal",
"title": "Associations of plasma phosphorylated tau217 with cognitive impairment and brain microstructural alterations in Alzheimer's disease",
"container-title": "Alzheimer's & dementia (Amsterdam, Netherlands)",
"author": [
{
"family": "Rivas‐Fernández",
"given": "Miguel Ángel"
},
{
"family": "Basanta‐Torres",
"given": "Sara"
},
{
"family": "Lindín",
"given": "Mónica"
},
{
"family": "Zurrón",
"given": "Montserrat"
},
{
"family": "Díaz",
"given": "Fernando"
},
{
"family": "Pereiro",
"given": "Arturo Xosé"
},
{
"family": "Lojo‐Seoane",
"given": "Cristina"
},
{
"family": "Rodríguez‐Pérez",
"given": "Ana Isabel"
},
{
"family": "Labandeira",
"given": "José Luis"
},
{
"family": "Galdo‐Álvarez",
"given": "Santiago"
}
],
"container-title-short": "Alzheimers Dement (Amst)",
"volume": "18",
"issue": "2",
"page": "e70333",
"DOI": "10.1002/dad2.70333",
"PMID": "42016779",
"PMCID": "PMC13092427",
"ISSN": "2352-8729",
"publisher": "Wiley",
"URL": "https://doi.org/10.1002/dad2.70333",
"language": "en",
"issued": {
"date-parts": [
[
2026,
4,
19
]
]
}
}

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Journal: Nature communications
In common: MRtrix3, scikit-image, NiBabel, 5 other tools, 1 reference
[8] doi:10.1038/s41598-026-51531-w [code]
Multimodal age-dependent diffusion-MRI analysis of the neocortex in a rat model of cortical dysplasia.
Journal: Scientific reports
In common: MRtrix3, FSL, scikit-image, 5 other tools, structural MRI / diffusion
[9] doi:10.1002/nbm.70331 [code]
Dipolar Order Mapping Based on Spin-Lock Magnetic Resonance Imaging.
Journal: NMR in biomedicine
In common: MRtrix3, FSL, NiBabel, 5 other tools, structural MRI / diffusion
[10] 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: FSL, scikit-image, NiBabel, 5 other tools, Alzheimer's / dementia, structural MRI / diffusion, cellular / molecular

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