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Understanding Glymphatic's Role In Proteinopathies (GRIP): A single centre case-control study protocol.

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The authors' code

Python · 205 lines · 6.1 KB · MIT

  1. import os
  2. import click
  4. import simple_mri as sm
  5. import numpy as np
  6. import matplotlib.pyplot as plt
  8. from gmri2fem import segment_tools as segtools
  9. from gmri2fem.csf_segmentation import create_csf_seg
  10. from gmri2fem.visualization import slice_volume
  11. from gmri2fem.segment_tools import find_description_label, find_label_description
  14. @click.command("csf-segmentation")
  15. @click.argument("subject", type=str)
  16. def main(subject):
  17. aparc_mri = sm.load_mri(
  18. f"mri_processed_data/freesurfer/{subject}/mri/aparc+aseg.mgz",
  19. dtype=np.int16,
  20. )
  21. mask_base_mri = sm.load_mri(
  22. f"mri_processed_data/{subject}/registered/{subject}_ses-01_acq-mixed_SE-modulus_registered.nii.gz",
  23. dtype=np.single,
  24. )
  25. csf_seg_mri = create_csf_seg(aparc_mri, mask_base_mri)
  26. sm.save_mri(
  27. csf_seg_mri,
  28. f"mri_processed_data/{subject}/segmentations/{subject}_seg-csf_mask.nii.gz",
  29. dtype=np.uint8,
  30. )
  32. def to_cortex_labels(description_list):
  33. """Map a cortical region-description to it's corresponding FreeSurfer name
  34. for both left and right hemisphere."""
  35. return [f"ctx-lh-{d.lower().replace(' ', '')}" for d in description_list] + [
  36. f"ctx-rh-{d.lower().replace(' ', '')}" for d in description_list
  37. ]
  39. ventricles = [
  40. "3rd-Ventricle",
  41. "4th-Ventricle",
  42. "Left-Lateral-Ventricle",
  43. "Right-Lateral-Ventricle",
  44. "Left-Inf-Lat-Vent",
  45. "Right-Inf-Lat-Vent",
  46. ]
  48. infratentorial = [
  49. "Brain-Stem",
  50. "Left-Cerebellum-Cortex",
  51. "Right-Cerebellum-Cortex",
  52. ]
  54. basal = [
  55. *to_cortex_labels(
  56. [
  57. "medial orbitofrontal",
  58. "fusiform",
  59. "frontal pole",
  60. ]
  61. ),
  62. "Optic-Chiasm",
  63. "Left-VentralDC",
  64. "Right-VentralDC",
  65. "Left-Hippocampus",
  66. "Right-Hippocampus",
  67. ]
  69. medial = to_cortex_labels(
  70. [
  71. "Posterior cingulate",
  72. "Rostral anterior cingulate",
  73. "Caudal anterior cingulate",
  74. "Superiorfrontal",
  75. "Precuneus",
  76. "Isthmus cingulate",
  77. "Paracentral",
  78. "Cuneus",
  79. "Lingual",
  80. "Pericalcarine",
  81. "Entorhinal",
  82. "Parahippocampal",
  83. ]
  84. )
  86. insula = to_cortex_labels(
  87. [
  88. "Insula",
  89. ]
  90. )
  92. groups = {
  93. "ventricles": ventricles,
  94. "infratentorial": infratentorial,
  95. "basal": basal,
  96. "medial": medial,
  97. "insula": insula,
  98. }
  100. lut_table = segtools.read_lut(
  101. f"{os.environ['FREESURFER_HOME']}/FreeSurferColorLUT.txt"
  102. )
  103. labels = np.unique(csf_seg_mri.data[csf_seg_mri.data > 0])
  104. grouped_descriptions = sum(groups.values(), start=[])
  105. grouped_labels = [
  106. find_description_label(x, lut_table) for x in grouped_descriptions
  107. ]
  108. groups["lateral"] = [
  109. find_label_description(x, lut_table)
  110. for x in labels[~np.isin(labels, grouped_labels)]
  111. ]
  112. relabeling = {
  113. group: [
  114. segtools.find_description_label(x, lut_table) for x in group_descriptions
  115. ]
  116. for group, group_descriptions in groups.items()
  117. }
  119. grouped_seg = segtools.collapse(csf_seg_mri.data, relabeling)
  120. grouped_seg_mri = sm.SimpleMRI(grouped_seg, csf_seg_mri.affine)
  121. sm.save_mri(
  122. grouped_seg_mri,
  123. f"mri_processed_data/{subject}/segmentations/{subject}_seg-csf_grouped.nii.gz",
  124. np.int16,
  125. )
  127. # Also create a lookup-table for identifying regions
  128. grouped_lut = segtools.canonical_lut(relabeling, "jet", permute_colors=None)
  129. grouped_cmap = segtools.listed_colormap(grouped_lut)
  130. segtools.write_lut(
  131. f"mri_processed_data/{subject}/segmentations/{subject}_seg-csf_grouped_LUT.txt",
  132. grouped_lut,
  133. )
  135. cerebellum = [
  136. "Left-Cerebellum-Cortex",
  137. "Right-Cerebellum-Cortex",
  138. ]
  139. labels = np.unique(csf_seg_mri.data[csf_seg_mri.data > 0])
  140. relabeling = {
  141. "cerebellum": [find_description_label(x, lut_table) for x in cerebellum],
  142. "ventricles": [find_description_label(x, lut_table) for x in ventricles],
  143. }
  144. relabeling["SAS"] = list(
  145. labels[~np.isin(labels, relabeling["cerebellum"] + relabeling["ventricles"])]
  146. )
  148. groups = {
  149. desc: [find_label_description(x, lut_table) for x in group_labels]
  150. for desc, group_labels in relabeling.items()
  151. }
  153. newlut = segtools.canonical_lut(relabeling, "viridis", permute_colors=None)
  154. newseg_cmap = segtools.listed_colormap(newlut)
  155. newseg = segtools.collapse(csf_seg_mri.data, relabeling)
  156. newseg_mri = sm.SimpleMRI(newseg, csf_seg_mri.affine)
  157. sm.save_mri(
  158. newseg_mri,
  159. f"mri_processed_data/{subject}/segmentations/{subject}_seg-csf_sas-ventricle-cerebellum.nii.gz",
  160. np.int16,
  161. )
  162. segtools.write_lut(
  163. f"mri_processed_data/{subject}/segmentations/{subject}_seg-csf_sas-ventricle-cerebellum-LUT.txt",
  164. newlut,
  165. )
  167. lut_table = segtools.read_lut(
  168. f"{os.environ['FREESURFER_HOME']}/FreeSurferColorLUT.txt"
  169. )
  170. fs_cmap = segtools.listed_colormap(lut_table)
  172. slice_ = ("sagittal", 184)
  174. t1w_mri = sm.load_mri(
  175. f"mri_processed_data/{subject}/registered/{subject}_ses-01_T1w_registered.nii.gz",
  176. dtype=np.single,
  177. )
  178. t1w = t1w_mri.data
  179. im_ref = slice_volume(t1w, *slice_)
  180. im_segmentations = [
  181. slice_volume(mri.data, *slice_)
  182. for mri in [csf_seg_mri, grouped_seg_mri, newseg_mri]
  183. ]
  185. fig, axes = plt.subplots(1, 3, figsize=(12, 6))
  186. for ax in axes:
  187. ax.imshow(im_ref, cmap="gray", vmin=0, vmax=np.quantile(t1w[t1w > 0], 0.95))
  188. ax.axis("off")
  190. for ax, seg, cmap in zip(
  191. axes, im_segmentations, [fs_cmap, grouped_cmap, newseg_cmap]
  192. ):
  193. ax.imshow(np.where(seg > 0, seg, np.nan), **cmap, interpolation="nearest")
  195. axes[0].set_title("FreeSurfer-based segmentation")
  196. axes[1].set_title("Grouped segmentation 1")
  197. axes[2].set_title("Grouped segmentation 2")
  199. plt.tight_layout()
  200. plt.savefig("figures/segmentations.png", bbox_inches="tight")
  201. plt.show()
  204. if __name__ == "__main__":
  205. main()

main.py at commit 20ee998, under MIT · at the source

Overview

Authors: Sofie Lysholm Lian1,2, Tryggve Holck Storås3,4,5, Ebba Müller Gløersen6, Grethe Løvland3, Einar Hopp3,4,7, Ingrid Mossige3,4,5, Markus Hovd5,8,9, Hilde Thelle Uggerud10, Kristin Reimers Kardel11, Heidi A Bye12, Anne Lise Buset12, Kirsten Valdmanis12, Anne Brita Knapskog2,13, Anne Rita Øksengård14, Tormod Fladby2,15, Kristian Bernhard Nilsen2,16, Kent-André Mardal5,17,18, Geir Ringstad5,6, Per Kristian Eide2,5,19, Mathias Toft1,2, Kaja Nordengen1,8
ORCID iDs: Einar Hopp, Ingrid Mossige, Markus Hovd, Anne Rita Øksengård, Kaja Nordengen
19 affiliations
  1. Department of Neurology, Oslo University Hospital, Oslo, Norway
  2. Institute of Clinical Medicine, University of Oslo, Oslo, Norway
  3. The Intervention Centre, Oslo University Hospital, Oslo, Norway
  4. Department of Physics and Computational Radiology, Oslo University Hospital, Oslo, Norway
  5. K.G. Jebsen Centre for Brain Fluid Research, Oslo, Norway
  6. Division of Radiology and Nuclear Medicine, Oslo University Hospital, Oslo, Norway
  7. Department of Psychology, Oslo University Hospital, Oslo, Norway
  8. Department of Pharmacy, University of Oslo, Oslo, Norway
  9. Department of Transplantation medicine, Oslo University Hospital, Oslo, Norway
  10. Norwegian Climate and Environmental Research Institute (NILU), Kjeller, Norway
  11. User representative, Norwegian Health Association, Oslo, Norway
  12. User representative, Norwegian Parkinson Association, Oslo, Norway
  13. Department of Geriatric Medicine, Oslo University Hospital, Oslo, Norway
  14. Norwegian Health Association, Oslo, Norway
  15. Department of Neurology, Akershus University Hospital, Lørenskog, Norway
  16. Department of Neurophysiology, Oslo University Hospital, Oslo, Norway
  17. Department of Numerical Analysis and Scientific Computing, Simula Research Laboratory, Oslo, Norway
  18. Department of Mathematics, University of Oslo, Oslo, Norway
  19. Department of Neurosurgery, Oslo University Hospital, Oslo, Norway
Institutions: Oslo University Hospital (Norway); University of Oslo (Norway); NILU (Norway); Norwegian Pharmacy Association (Norway); Norwegian Medical Association (Norway); Akershus University Hospital (Norway); Simula Research Laboratory (Norway)
Journal: PloS one, volume 21, issue 10, article e0360617
Dates: received 29 July 2026; accepted 28 September 2026; published online 29 October 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1371/journal.pone.0360617 · PMID 42956044 · PMCID PMC13718934 · OpenAlex W7224446357
Open access: gold, a free copy (OpenAlex)
Status: code verified (how the code is found and verified)
Categories: structural MRI / diffusion (modality), human (organism), Alzheimer's / dementia (population), Parkinson's (population)
Methods: Statistics, Connectivity, Single-unit activity, calcium imaging, Physiology & signal measures, fMRI & imaging
MeSH: Alzheimer Disease*, Glymphatic System*, Lewy Body Disease*, Parkinson Disease*, Aged, alpha-Synuclein, Amyloid beta-Peptides, Biomarkers, Case-Control Studies, Female, Humans, Magnetic Resonance Imaging, Male, tau Proteins (* major topic)
Topic: Cerebrospinal fluid and hydrocephalus (Clinical Neurology, Medicine), according to OpenAlex
Citations: not cited yet (Europe PMC); 62 references in the paper

Abstract

Alzheimer’s disease (AD), Parkinson’s disease (PD), and dementia with Lewy bodies (DLB) are proteinopathies characterized by pathological accumulation of amyloid beta, phosphorylated-tau, and alpha-synuclein. Impaired clearance of these proteins is thought to contribute to disease pathogenesis, whereas preserved or enhanced clearance may be compensatory. Beyond intracellular degradation within neurons and glial cells, an extracellular clearance pathway, the glymphatic system, facilitates cerebrospinal fluid flow into perivascular spaces, exchange with interstitial fluid, and subsequent clearance from the brain. Glymphatic transport may therefore be critical for removal of excess proteins and metabolic waste, and its dysfunction could promote protein aggregation and neurodegeneration. The Glymphatic’s Role In Proteinopathies (GRIP) study is an observational case-control imaging and biomarker study comparing glymphatic clearance in patients with AD, PD, and DLB versus healthy controls. We aim to recruit 70 participants: PD (n = 20), DLB (n = 10), AD (n = 20), and age- and sex-matched healthy controls (n = 20). The AD group will be stratified by pathology, with half amyloid-positive/tau-negative (A + T-) and half amyloid- and tau-positive (A + T+). The PD group will be stratified by disease duration. Participants will undergo baseline MRI followed by intrathecal injection of gadobutrol (0.25 mmol) and serial MRI at 5, 24, 48, and 72 hours. Influx and efflux of tracer will be modeled from T1 signal changes. Blood will be sampled repeatedly from 1 to 72 hours; cerebrospinal fluid will be collected at baseline. Individual glymphatic clearance will be estimated using population pharmacokinetic methods, where gadobutrol serves as a tracer. Sleep will be monitored with triaxial accelerometry and sleep diaries. The primary outcome is group differences in glymphatic clearance metrics between each disease group and controls; secondary analyses will explore associations with cognition, disease severity and duration, sleep quality, and cerebrospinal fluid protein levels.

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

Repository

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

jorgenriseth/grip-mixed

License: MIT
State: the link answers, verified on 13 November 2026
Evidence: files inventoried
Commit: 20ee9980ca659a71667dd2843fa903dbb58fd594, 3 November 2025
Languages: Python (1), Jupyter (1)
Size: 13 files, 2 scripts
Software Heritage: not archived
Found in: the text, “Image analysis and postprocessing”
Holds: README, license file, environment (pixi.lock, pyproject.toml, uv.lock, docker/Dockerfile), 1 notebook
Not found: CITATION.cff, tests, continuous integration, documentation
Tools: FreeSurfer (2 files), Matplotlib (2 files), NumPy (2 files)
Availability: 2 checks, the latest on 13 November 2026: the link answers
  • 13 November 2026: the link answers
  • 2 October 2026: the link answers
4 files
At the source: github.com/jorgenriseth/grip-mixed.git

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;
  • 2 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 Availability

The study generates data of a sensitive nature, including detailed clinical information and imaging data. All such data are stored and managed within the Services for Sensitive Data (TSD) platform, which provides secure, access-controlled storage in accordance with institutional and regulatory requirements. Access to the dataset is restricted and can be granted upon reasonable request to the project leader, subject to applicable ethical and legal approvals. We have, however, been able to make a full imaging series publicly available from one control(60), which can be downloaded from Zenodo: https://zenodo.org/records/14266867.

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

Versions

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Version 1, 31 October 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 21 authors, 14 MeSH terms, 58 references.

Cite

This paper

Lian, S. L., Storås, T. H., Gløersen, E. M., Løvland, G., Hopp, E., Mossige, I., Hovd, M., Uggerud, H. T., Kardel, K. R., Bye, H. A., Buset, A. L., Valdmanis, K., Knapskog, A. B., Øksengård, A. R., Fladby, T., Nilsen, K. B., Mardal, K.-A., Ringstad, G., Eide, P. K., . . . Nordengen, K. (2026). Understanding Glymphatic's Role In Proteinopathies (GRIP): A single centre case-control study protocol. PloS one, 21(10), e0360617. https://doi.org/10.1371/journal.pone.0360617

BibTeX

@article{lian2026understanding,
author = {Lian, Sofie Lysholm and Storås, Tryggve Holck and Gløersen, Ebba Müller and Løvland, Grethe and Hopp, Einar and Mossige, Ingrid and Hovd, Markus and Uggerud, Hilde Thelle and Kardel, Kristin Reimers and Bye, Heidi A and Buset, Anne Lise and Valdmanis, Kirsten and Knapskog, Anne Brita and Øksengård, Anne Rita and Fladby, Tormod and Nilsen, Kristian Bernhard and Mardal, Kent-André and Ringstad, Geir and Eide, Per Kristian and Toft, Mathias and Nordengen, Kaja},
title = {{Understanding Glymphatic's Role In Proteinopathies (GRIP): A single centre case-control study protocol}},
journal = {PloS one},
year = {2026},
month = oct,
volume = {21},
number = {10},
pages = {e0360617},
publisher = {PLOS},
issn = {1932-6203},
doi = {10.1371/journal.pone.0360617},
url = {https://doi.org/10.1371/journal.pone.0360617},
pmid = {42956044},
pmcid = {PMC13718934}
}

RIS

TY - JOUR
AU - Lian, Sofie Lysholm
AU - Storås, Tryggve Holck
AU - Gløersen, Ebba Müller
AU - Løvland, Grethe
AU - Hopp, Einar
AU - Mossige, Ingrid
AU - Hovd, Markus
AU - Uggerud, Hilde Thelle
AU - Kardel, Kristin Reimers
AU - Bye, Heidi A
AU - Buset, Anne Lise
AU - Valdmanis, Kirsten
AU - Knapskog, Anne Brita
AU - Øksengård, Anne Rita
AU - Fladby, Tormod
AU - Nilsen, Kristian Bernhard
AU - Mardal, Kent-André
AU - Ringstad, Geir
AU - Eide, Per Kristian
AU - Toft, Mathias
AU - Nordengen, Kaja
TI - Understanding Glymphatic's Role In Proteinopathies (GRIP): A single centre case-control study protocol
T2 - PloS one
J2 - PLoS One
PY - 2026
DA - 2026/10/29
VL - 21
IS - 10
SP - e0360617
SN - 1932-6203
PB - PLOS
DO - 10.1371/journal.pone.0360617
UR - https://doi.org/10.1371/journal.pone.0360617
LA - en
ER -

CSL-JSON

{
"id": "10.1371/journal.pone.0360617",
"type": "article-journal",
"title": "Understanding Glymphatic's Role In Proteinopathies (GRIP): A single centre case-control study protocol",
"container-title": "PloS one",
"author": [
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"family": "Lian",
"given": "Sofie Lysholm"
},
{
"family": "Storås",
"given": "Tryggve Holck"
},
{
"family": "Gløersen",
"given": "Ebba Müller"
},
{
"family": "Løvland",
"given": "Grethe"
},
{
"family": "Hopp",
"given": "Einar"
},
{
"family": "Mossige",
"given": "Ingrid"
},
{
"family": "Hovd",
"given": "Markus"
},
{
"family": "Uggerud",
"given": "Hilde Thelle"
},
{
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"given": "Kristin Reimers"
},
{
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},
{
"family": "Buset",
"given": "Anne Lise"
},
{
"family": "Valdmanis",
"given": "Kirsten"
},
{
"family": "Knapskog",
"given": "Anne Brita"
},
{
"family": "Øksengård",
"given": "Anne Rita"
},
{
"family": "Fladby",
"given": "Tormod"
},
{
"family": "Nilsen",
"given": "Kristian Bernhard"
},
{
"family": "Mardal",
"given": "Kent-André"
},
{
"family": "Ringstad",
"given": "Geir"
},
{
"family": "Eide",
"given": "Per Kristian"
},
{
"family": "Toft",
"given": "Mathias"
},
{
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"given": "Kaja"
}
],
"container-title-short": "PLoS One",
"volume": "21",
"issue": "10",
"page": "e0360617",
"DOI": "10.1371/journal.pone.0360617",
"PMID": "42956044",
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"publisher": "PLOS",
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"language": "en",
"issued": {
"date-parts": [
[
2026,
10,
29
]
]
}
}

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