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Clinicoanatomic localization of iron-rich gliosis in aphasic presentations of globular glial tauopathy.

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  1. [1] § Results ↔ process_ggt_data.ipynb, lines 212–339 · score 0.63 · cortical depth, iron stained, Pearson, correlation, intensity, DAB

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The authors' code

Jupyter notebook · 343 lines · 9.9 KB · no license · 1 match

  1. # %% [markdown]
  2. # # Install packages
  4. # %%
  5. %pip install pdnl_sana
  6. %pip install nibabel
  7. %pip install lxml
  8. %pip install pandas
  10. import os
  11. import sys
  13. import nibabel as nib
  14. from lxml import objectify
  15. import numpy as np
  16. from matplotlib import pyplot as plt
  17. import cv2
  18. import pandas as pd
  19. import scipy.stats
  21. import pdnl_sana as sana
  22. import pdnl_sana.geo
  23. import pdnl_sana.interpolate
  24. import pdnl_sana.image
  25. import pdnl_sana.color_deconvolution
  28. # %% [markdown]
  29. # # Initialize data source
  31. # %%
  33. # location of the data
  34. version = "v1"
  35. drive = os.path.join("data", version)
  36. # drive = "./for_noah/data"
  37. rois = sorted([x for x in os.listdir(drive) if x != '.DS_Store'])
  38. print("ROIs:", rois)
  40. # types of images to analyze
  41. sources = ['meguro', 'mri']
  43. # example ROI
  44. print(os.path.join(drive, rois[0]))
  45. for f in os.listdir(os.path.join(drive, rois[0])):
  46. print('\t', f)
  48. # %% [markdown]
  49. # # Functions for I/O
  51. # %%
  53. # parses the sform matrix from the header for converting annotation vector to points in the image
  54. def load_sform(f):
  55. nifti = nib.load(f)
  56. sform = np.zeros([3,3], dtype=float)
  57. for i, ax in enumerate(['x', 'y', 'z']):
  58. sform[i] = nifti.header[f'srow_{ax}'][:3]
  59. return sform
  61. # nifti I/O
  62. def load_nifti_img(f):
  63. nifti = nib.load(f)
  64. img = np.asanyarray(nifti.dataobj)
  65. if type(img.dtype) is np.dtypes.VoidDType:
  66. img = img.view((img.dtype[0], len(img.dtype.names)))
  67. img = np.squeeze(img)
  68. if len(img.shape) == 2:
  69. img = img[:,:,None]
  70. else:
  71. img = img.transpose(1,0,2)
  73. return img
  75. # parse the lxml file
  76. def load_itksnap_annotations(f):
  78. tree = objectify.fromstring(open(f, 'rb').read())
  79. annotations = tree.folder.getchildren()
  81. annos = []
  82. for annotation in annotations:
  83. if annotation.get('key') == 'ArraySize':
  84. continue
  86. # load the Position (voxel) and Offset (mm)
  87. pos, offset, label, plane = None, None, None, None
  88. for x in annotation.getchildren():
  89. if x.get('key') == 'Text':
  90. label = x.get('value')
  91. if x.get('key') == 'Plane':
  92. plane = int(x.get('value'))
  93. if x.get('key') == 'Pos':
  94. pos = np.array(list(map(float, x.get('value').split(' '))))
  95. if x.get('key') == 'Offset':
  96. offset = np.array(list(map(float, x.get('value').split(' '))))
  97. annos.append([label, plane, pos, offset])
  99. return annos
  101. # handles the coordinate changes in the 3D space
  102. def load_itksnap_ihc_annotations(f, sform):
  104. annotations = load_itksnap_annotations(f)
  105. annos = {}
  106. for label, plane, pos, offset in annotations:
  108. # grab the slice number to extract
  109. slice_num = int(np.ceil(pos[plane]))
  111. # invert direction of the anno vector
  112. offset[1] *= -1
  114. # make the offset 3D
  115. offset = np.insert(offset, 2, 0)
  117. # calculate the start and end points of the annotation
  118. p0 = np.linalg.inv(sform) @ (sform @ pos)
  119. p1 = (np.linalg.inv(sform) @ ((sform @ pos) - offset))
  121. # remove the z axis
  122. p0 = np.delete(p0, plane)
  123. p1 = np.delete(p1, plane)
  125. # store the (x,y) annotation
  126. annos[label] = sana.geo.Curve([p0[0], p1[0]], [p0[1], p1[1]])
  128. # parse the annotation labels into a common label set
  129. segments = {
  130. 'CSF': annos['gm'],
  131. 'GM': annos['wm'],
  132. 'L': annos['left'],
  133. 'R': annos['right'],
  134. }
  136. return segments
  138. # handles the coordinate changes in the 2D space
  139. def load_itksnap_mri_annotations(f, sform, version):
  141. annotations = load_itksnap_annotations(f)
  142. annos = {}
  143. for label, plane, pos, offset in annotations:
  145. # grab the slice number to extract
  146. slice_num = int(np.ceil(pos[plane]))
  148. # invert direction of the anno vector
  149. offset[1] *= -1
  151. # make the offset 3D
  152. if pos[1] < 1 and version == "v1":
  153. offset = np.insert(offset, 0, 0)
  154. else:
  155. offset = np.insert(offset, 1, 0)
  157. # calculate the start and end points of the annotation
  158. p0 = np.linalg.inv(sform) @ (sform @ pos)
  159. p1 = (np.linalg.inv(sform) @ ((sform @ pos) - offset))
  161. # remove the z axis
  162. p0 = np.delete(pos, plane)
  163. p1 = np.delete(p1, plane)
  165. # store the (y,x) annotation
  166. annos[label] = sana.geo.Curve([p0[0], p1[0]], [p0[1], p1[1]])
  168. # convert to (x,y) annotation
  169. annos[label] = annos[label][:, ::-1]
  171. # parse the annotation labels into a common label set
  172. segments = {
  173. 'CSF': annos['gm'],
  174. 'GM': annos['wm'],
  175. 'L': annos['left'],
  176. 'R': annos['right'],
  177. }
  179. # NOTE: we're just assuming that all annotations are in the same plane/slice_num
  180. return segments
  182. # load the images and annotations for a given ROI
  183. def load_data(version, roi, source, ax=None):
  184. drive = os.path.join("data", version)
  186. anno_f = os.path.join(drive, roi, f'{source}.annot')
  187. img_f = os.path.join(drive, roi, f'{source}.nii.gz')
  189. sform = load_sform(img_f)
  190. if source == 'mri':
  191. annos = load_itksnap_mri_annotations(anno_f, sform, version)
  192. else:
  193. annos = load_itksnap_ihc_annotations(anno_f, sform)
  194. img = load_nifti_img(img_f)
  196. if not ax is None:
  197. _ = ax.imshow(img, cmap='gray')
  198. _ = [ax.plot(*annos[label].T, label=label) for label in annos]
  199. _ = ax.legend()
  200. _ = ax.set_title(f'{roi}\n{source}')
  202. return img, annos
  204. # test the I/O
  205. version = "v2"
  206. fig, axs = plt.subplots(1,2)
  207. roi = 'A. Patient #1 naPPA, BA8'
  208. img, annos = load_data(version, roi, source='meguro', ax=axs[0])
  209. img, annos = load_data(version, roi, source='mri', ax=axs[1])
  212. # %% [markdown]
  213. # # Correlation Analysis
  215. # %%
  217. aspect_ratio = 0.66
  218. h = 40
  219. w = int(40*aspect_ratio)
  221. def resample_rois(version):
  222. drive = os.path.join("data", version)
  224. rois = sorted([x for x in os.listdir(drive) if x != '.DS_Store'])
  226. data = {}
  227. for i, roi in enumerate(rois):
  228. data[roi] = {}
  230. # load the data
  231. fig, axs = plt.subplots(1, len(sources), figsize=(20,10))
  232. for j, source in enumerate(sources):
  233. img, annos = load_data(version, roi, source, ax=axs[j])
  234. data[roi][source] = [img, annos]
  236. fig.tight_layout()
  237. plt.savefig(os.path.join('output', version, f'{roi}_01_thumbnails.png'))
  238. plt.close()
  240. # apply the resampling
  241. fig, axs = plt.subplots(1, len(sources), figsize=(20,10))
  242. for j, source in enumerate(sources):
  243. img, annos = data[roi][source][:2]
  245. sample_grid, _ = sana.interpolate.fan_sample(annos['CSF'], annos['R'], annos['GM'], annos['L'])
  246. resampled = sana.interpolate.grid_sample(sana.image.Frame(img.copy(), level=0), sample_grid)
  247. resampled.resize(sana.geo.Point(w, h, level=0), interpolation=cv2.INTER_LINEAR)
  249. data[roi][source] = (img, annos, resampled)
  251. axs[j].imshow(resampled.img, cmap='gray')
  252. axs[j].set_title(f'{source}')
  254. fig.suptitle(f'{roi}')
  256. fig.tight_layout()
  257. plt.savefig(os.path.join('output', version, f'{roi}_02_resampled.png'))
  258. plt.close()
  260. return data
  262. def find_correlation(version):
  263. data = {}
  264. data = resample_rois(version)
  266. df = []
  268. fig, axs = plt.subplots(len(data),len(sources)+1, figsize=(8,20), sharey=True)
  270. for i, roi in enumerate(rois):
  271. ax_signals = axs[i,2]
  273. signals = []
  274. for j, source in enumerate(sources):
  275. img, annos, resampled = data[roi][source]
  277. if source == 'mri':
  278. target = resampled.img[:,:,0]
  280. # convert the RGB IHC data to grayscale using color deconvolution
  281. else:
  282. if source == 'lfb':
  283. ss = sana.color_deconvolution.StainSeparator('LFB-CV')
  284. stains = ss.separate(resampled.img)
  285. target = stains[:,:,0]
  286. else:
  287. ss = sana.color_deconvolution.StainSeparator('H-DAB')
  288. stains = ss.separate(resampled.img)
  289. target = stains[:,:,1]
  291. axs[i][j].imshow(target, cmap='gray', extent=(0, 2.64, -2, 2))
  292. axs[i][j].set_title(source.replace('meguro', 'Iron Stain').replace('mri', 'MRI T2*w'))
  293. axs[i][j].axis('off')
  295. # signal standardization
  296. target = (target - np.mean(target)) / np.std(target)
  297. signal = np.median(target, axis=1)[5:]
  298. signals.append(signal)
  300. # ax_signals.plot(np.linspace(0, 1, len(signal)), signal, label=source.replace('meguro', 'Iron Stain').replace('mri', 'MRI T2*w'), linewidth=3)
  301. ax_signals.legend(loc='center left', bbox_to_anchor=(.8, 0.5))
  302. ax_signals.set_xlabel('Cortical Depth')
  303. ax_signals.set_ylabel('Standardized Intensity')
  304. ax_signals.set_xticks([0, 0.25, 0.5, 0.75, 1.0])
  305. ax_signals.axhline(0, color='black')
  306. ax_signals.set_ylim([-2,2])
  307. ax_signals.set_xlim([0,1])
  309. # calculate and store the correlation between IHC and MRI
  310. X_signal = signals[-1]
  311. Y_signal = signals[0]
  312. ax_signals.plot(X_signal, Y_signal, '.')
  314. res = scipy.stats.pearsonr(X_signal, Y_signal)
  315. R = res.statistic
  316. pvalue = res.pvalue
  317. ax_signals.set_title(roi+' | '+r"$R=%.2f$" % (R), loc="left")
  318. ax_signals.set_yticks([-2, -1, 0, 1, 2])
  319. ax_signals.set_xticks([0, 0.25, 0.50, 0.75, 1.0])
  321. df.append({
  322. 'ROI': roi,
  323. f'R_{version}': R,
  324. f'p_{version}': pvalue
  325. })
  327. fig.tight_layout()
  328. plt.savefig(os.path.join('output', f'final_figure_{version}.png'))
  329. plt.close()
  331. df = pd.DataFrame(df)
  333. return df
  335. df_v1 = find_correlation("v1")
  336. df_v2 = find_correlation("v2")
  337. df = df_v1.merge(df_v2, on='ROI')
  338. df.to_csv(os.path.join('output', 'correlations.csv'), index=False)
  339. print(df)
  342. # %%

process_ggt_data.ipynb at commit 7d82f74, no license · at the source

Overview

Authors: David J Irwin1, Sheina Emrani1, Daniel T Ohm1, Winifred Trotman1, Alejandra Bahena1, Eric Teunissen-Bermeo1, Philip Sabatini1, Sandhitsu R Das1, Gabor Mizsei2, Karthik Prabhakaran1, Ranjit Ittyerah1, H Branch Coslett1, Lauren Massimo1, David A Wolk1, John A Detre1,2, James C Gee2, Edward B Lee3, Paul Yushkevich2, Corey T McMillan1, M Dylan Tisdall2
ORCID iDs: David J Irwin, Daniel T Ohm, Edward B Lee, Paul Yushkevich, M Dylan Tisdall
  1. Department Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
  2. Department Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
  3. Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
Institutions: University of Pennsylvania (United States)
Journal: Brain communications, volume 8, issue 3, article fcag169
Dates: received 14 November 2025; accepted 27 May 2026; published online 2 June 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1093/braincomms/fcag169 · PMID 42238478 · PMCID PMC13227947 · OpenAlex W7163220754
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: Connectivity
Keywords: globular glial tauopathy, iron, neuroinflammation, MRI, primary progressive aphasia
Topic: Epilepsy research and treatment (Psychiatry and Mental health, Medicine), according to OpenAlex
Funding: NIA NIH HHS (R01 AG056014, R01 AG069474, P30 AG072979)
Citations: not cited yet (Europe PMC); 30 references in the paper
Research resources: RRID:SCR_022398

Abstract

Globular glial tauopathy is a 4-repeat tauopathy associated with heterogenous clinical syndromes, including primary progressive aphasia. Iron-reactive gliosis in mid-to-deep cortical layers has previously been reported in this disorder, but detailed anatomic localization and its relationship to clinical symptoms is understudied, particularly within the anatomic framework of primary progressive aphasia. In a series of five autopsy-confirmed patients with globular glial tauopathy and one healthy control, we utilize ultra-high-resolution whole-hemisphere ex vivo 7 Telsa MRI and digital pathology to study whole-hemisphere and local laminar/cellular patterns of pathology within affected cortex. We find signature laminar patterns of iron-rich gliosis localized to brain regions implicated in distinct clinical aphasia syndromes between patients: patients who presented with non-fluent aphasia had iron-rich gliosis pathology localized to inferior and superior frontal and motor regions while iron-rich pathology was largely localized to the anterior temporal lobe in a patient with the semantic variant. Moreover, in one patient with non-fluent aphasia and additional iron-sensitive 7 Telsa MRI during life, we find evidence of antemortem iron-rich pathology in the same frontal regions observed post-mortem. These data suggest that focal neuroinflammation and iron dysregulation may contribute to the clinical expression of tauopathies and be detectable during life to improve diagnosis.

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

Repository

Its files are read in the Code ↔ Paper reader above, with 1 match between paragraphs and lines of code.

picsl-ftdc-computational-pathology/ftdc_ggt_paper

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 7d82f7436f6d773d65d0367e580bfc795d54efa2, 13 January 2026
Languages: Jupyter (1)
Size: 153 files, 1 script
Software Heritage: not archived
Found in: “Data availability”
Holds: README, 1 notebook
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Matplotlib (1 file), NiBabel (1 file), NumPy (1 file), OpenCV (1 file), pandas (1 file), SciPy (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
2 files
At the source: github.com/PICSL-FTDC-Computational-Pathology/FTDC_GGT_paper/tree/main

The paper's code and data availability statement is in the Data section.

Tracing map

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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;
  • 1 script, each with its path and the digest of its content;
  • 1 match 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

Datasets cited

Data availability

All MRI imaging data that support this study are openly available via Dryad at the following link: https://doi.org/10.5061/dryad.dbrv15fgk. All histopathology data that supports this study and tissue are available upon reasonable request from the authors, conditional on establishing a formal data sharing agreement with the University of Pennsylvania. Code for figure generation and laminar analysis and corresponding images are available at GithHub at the following link: https://github.com/PICSL-FTDC-Computational-Pathology/FTDC_GGT_paper/tree/main.

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 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 20 authors, 5 keywords, 1 funder, 29 references, 1 RRID.

Cite

This paper

Irwin, D. J., Emrani, S., Ohm, D. T., Trotman, W., Bahena, A., Teunissen-Bermeo, E., Sabatini, P., Das, S. R., Mizsei, G., Prabhakaran, K., Ittyerah, R., Coslett, H. B., Massimo, L., Wolk, D. A., Detre, J. A., Gee, J. C., Lee, E. B., Yushkevich, P., McMillan, C. T., & Tisdall, M. D. (2026). Clinicoanatomic localization of iron-rich gliosis in aphasic presentations of globular glial tauopathy. Brain communications, 8(3), fcag169. https://doi.org/10.1093/braincomms/fcag169

BibTeX

@article{irwin2026clinicoanatomic,
author = {Irwin, David J and Emrani, Sheina and Ohm, Daniel T and Trotman, Winifred and Bahena, Alejandra and Teunissen-Bermeo, Eric and Sabatini, Philip and Das, Sandhitsu R and Mizsei, Gabor and Prabhakaran, Karthik and Ittyerah, Ranjit and Coslett, H Branch and Massimo, Lauren and Wolk, David A and Detre, John A and Gee, James C and Lee, Edward B and Yushkevich, Paul and McMillan, Corey T and Tisdall, M Dylan},
title = {{Clinicoanatomic localization of iron-rich gliosis in aphasic presentations of globular glial tauopathy}},
journal = {Brain communications},
year = {2026},
month = jun,
volume = {8},
number = {3},
pages = {fcag169},
publisher = {Oxford University Press},
issn = {2632-1297},
doi = {10.1093/braincomms/fcag169},
url = {https://doi.org/10.1093/braincomms/fcag169},
pmid = {42238478},
pmcid = {PMC13227947}
}

RIS

TY - JOUR
AU - Irwin, David J
AU - Emrani, Sheina
AU - Ohm, Daniel T
AU - Trotman, Winifred
AU - Bahena, Alejandra
AU - Teunissen-Bermeo, Eric
AU - Sabatini, Philip
AU - Das, Sandhitsu R
AU - Mizsei, Gabor
AU - Prabhakaran, Karthik
AU - Ittyerah, Ranjit
AU - Coslett, H Branch
AU - Massimo, Lauren
AU - Wolk, David A
AU - Detre, John A
AU - Gee, James C
AU - Lee, Edward B
AU - Yushkevich, Paul
AU - McMillan, Corey T
AU - Tisdall, M Dylan
TI - Clinicoanatomic localization of iron-rich gliosis in aphasic presentations of globular glial tauopathy
T2 - Brain communications
J2 - Brain Commun
PY - 2026
DA - 2026/06/02
VL - 8
IS - 3
SP - fcag169
SN - 2632-1297
PB - Oxford University Press
DO - 10.1093/braincomms/fcag169
UR - https://doi.org/10.1093/braincomms/fcag169
LA - en
ER -

CSL-JSON

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"id": "10.1093/braincomms/fcag169",
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"title": "Clinicoanatomic localization of iron-rich gliosis in aphasic presentations of globular glial tauopathy",
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In common: OpenCV, NiBabel, pandas, 3 other tools, Alzheimer's / dementia, structural MRI / diffusion
[5] doi:10.1162/imag.a.1358
Spatial covariance of mGluR5 density and structural degeneration in behavioral variant frontotemporal degeneration.
Journal: Imaging neuroscience (Cambridge, Mass.)
In common: Alzheimer's / dementia, cellular / molecular, author David J. Irwin
[6] doi:10.1093/jnen/nlaf152 [code]
Clinical and pathologic correlations of machine learning quantification of Aβ deposits across 3 brain regions of decedents with Alzheimer disease.
Journal: Journal of neuropathology and experimental neurology
In common: OpenCV, pandas, SciPy, 2 other tools, Alzheimer's / dementia, 1 reference
[7] doi:10.1093/braincomms/fcag176 [code]
Tau topography subtypes account for clinical heterogeneity and longitudinal trajectories in early-onset Alzheimer's disease.
Journal: Brain communications
In common: pandas, SciPy, Matplotlib, 1 other tool, Alzheimer's / dementia, 2 references
[8] doi:10.1371/journal.pone.0344600 [code]
Robust disease prognosis via diagnostic knowledge preservation: A sequential learning approach.
Journal: PloS one
In common: OpenCV, NiBabel, pandas, 3 other tools, Alzheimer's / dementia
[9] doi:10.1002/epi.70296 [code]
Fully automated three-dimensional deep learning-based magnetic resonance imaging segmentation of brain cavities in epilepsy surgery.
Journal: Epilepsia
In common: OpenCV, NiBabel, pandas, 3 other tools, structural MRI / diffusion
[10] doi:10.1186/s12880-026-02481-2 [code]
Deep learning-based neuroanatomical profiling reveals population-specific brain changes in multiple sclerosis: a large-scale Middle Eastern study.
Journal: BMC medical imaging
In common: OpenCV, NiBabel, pandas, 3 other tools, structural MRI / diffusion

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