Microstructural Changes in the Corpus Callosum in Different Forms of Sporadic Age-Related Cerebral Small Vessel Disease.

Code ↔ Paper

2 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 2 matches

[1] § 2. Materials and Methods ↔ features.py, lines 1–24 · score 0.80 · 20–40 %, 40–60 %, 60–80 %, forceps minor, forceps major, bundle
[2] § 2. Materials and Methods › Statistical Analysis ↔ features.py, lines 1–24 · score 0.75 · 20–40 %, 40–60 %, 60–80 %, 5–95 %, metric

Paper

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The authors' code

Python · 83 lines · 2.2 KB · no license · 2 matches

#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Sun Mar 27 01:39:18 2022
@author: miha
"""
features = (
# (list_of_metrics, list_of_bundles, *list_of_slices)
('FA AD MD', 'CST_L CST_R', (30, 50)),
('FA AD', 'CC_ForcepsMajor', (30, 70)),
('FA RD AD MD', 'CC_ForcepsMinor', (20, 40), (40, 60), (60, 80)),
('RD MD', 'CC_Mid', (5, 35), (65, 95)),
('FA', 'ILF_L ILF_R', (30, 45), (70, 85)),
('FA', 'IFOF_L IFOF_R', (40, 60)),
('FA', 'UF_L UF_R', (80, 92)),
('FA', 'MCP', (5, 20)),
('FA RD AD MD', 'CST_L CST_R CC_ForcepsMajor CC_ForcepsMinor CC_Mid', (5, 95)),
('FA', '''
ILF_L ILF_R IFOF_L IFOF_R UF_L UF_R MCP
AF_L AF_R AST_L AST_R FPT_L FPT_R MdLF_L MdLF_R PPT_L PPT_R EMC_L EMC_R
''', (5, 95)),
)
##############################################################################
import numpy as np
from collections import namedtuple
Feature = namedtuple('Feature', 'name metric bundle slice')
features_unpacked = tuple(
Feature(f'{m}_{b}_{s[0]}_{s[1]}', m, b, s)
for metrics, bundles, *slices in features
for m in metrics.split()
for b in bundles.split()
for s in slices
)
required_metrics = {f.metric for f in features_unpacked}
def extract_features(patient_data):
'''
patient_data is a dict:
{
metric: {
bundle_name: {
mean: [],
std: []
}
}
}
Example:
patient = Patient('path/to/patient/data')
features = patient.profiles_features
is equivalent to:
patient_data = {
'FA': patient.profiles_metric('data_s_DKI_fa'),
'RD': patient.profiles_metric('data_s_DKI_rd'),
'AD': patient.profiles_metric('data_s_DKI_ad'),
'MD': patient.profiles_metric('data_s_DKI_md'),
}
features = extract_features(patient_data)
'''
features = {}
for f in features_unpacked:
try:
d = patient_data[f.metric][f.bundle]
a, b = f.slice
features[f.name] = (
np.mean(d['mean'][a:b+1]), # Value = mean
np.mean(d['std'][a:b+1]), # std
)
except:
features[f.name] = (0, 0)
return features

features.py at commit 22fd2e9, no license · at the source

Overview

Authors: Elena I Kremneva¹, Larisa A Dobrynina¹, Kamila V Shamtieva¹, Anastasia A Geints¹, Mikhail S Sokolov², Maryam R Zabitova¹, Alexey S Filatov¹, Marina V Krotenkova¹

ORCID iDs: Elena I Kremneva, Larisa A Dobrynina, Anastasia A Geints, Mikhail S Sokolov, Maryam R Zabitova

Russian Center of Neurology and Neurosciences, 125367 Moscow, Russia; (E.I.K.); (L.A.D.); (K.V.S.); (A.A.G.); (A.S.F.); (M.V.K.)
Soft Matter and Physics of Fluids Centre, Bauman Moscow State Technical University, 105005 Moscow, Russia

Institutions: Bauman Moscow State Technical University (Russia)

Journal: Diagnostics (Basel, Switzerland), volume 16, issue 17, article 2861

Dates: received 15 July 2026; accepted 3 September 2026; published online 5 September 2026

Type: Research article · Language: English

License: CC BY

Identifiers: DOI 10.3390/diagnostics16172861 · PMID 42739291 · PMCID PMC13565003 · OpenAlex W7211918105

Open access: gold, a free copy (OpenAlex)

Status: code verified

Categories: structural MRI / diffusion (modality), human (organism), stroke (population), clinical / translational (subfield)

Methods: Connectivity, Statistics, Smoothing, state filtering, decompositions, Machine learning, fMRI & imaging, Physiology & signal measures

Keywords: cerebral small vessel disease, diffusion MRI, white matter microstructure, MC-SMT, corpus callosum, cognitive impairment, heterogeneity

Topic: Advanced Neuroimaging Techniques and Applications (Radiology, Nuclear Medicine and Imaging, Medicine), according to OpenAlex

Funding: Russian Science Foundation (22-15-00183-P)

Citations: not cited yet (Europe PMC); 41 references in the paper

Abstract

Background/Objectives: Cerebral small vessel disease (SVD) is a heterogeneous condition in which similar conventional MRI findings may be associated with different clinical manifestations and pathogenetic mechanisms. Previously, hierarchical clustering of structural MRI features in patients with severe white matter hyperintensities (Fazekas 3) identified two MRI phenotypes, designated MRI Type 1 and MRI Type 2. Diffusion MRI (dMRI) may provide additional information about the microstructural differences between these phenotypes. To compare white matter microstructure between MRI Type 1 and MRI Type 2 of sporadic age-related SVD using signal-based and biophysical dMRI models. Methods: This cross-sectional study included 75 patients with SVD and 36 age- and sex-matched healthy controls. Among the patients with SVD, 43 had MRI Type 1 and 32 had MRI Type 2. All participants underwent structural and multi-shell dMRI on a 3 Tesla MRI scanner. Diffusion metrics were derived using multiple models: Diffusion Tensor Imaging (DTI), Diffusion Kurtosis Imaging (DKI), Neurite Orientation Dispersion and Density Imaging (NODDI), White Matter Tract Integrity (WMTI), and the Multi-compartment Spherical Mean Technique (MC-SMT). Tract-profile analysis was performed in three corpus callosum segments: the forceps major, forceps minor, and body. Group differences were assessed using age- and sex-adjusted general linear models with correction for multiple comparisons. The combined discriminative value of dMRI metrics was evaluated using regularized Elastic Net logistic regression with repeated nested five-fold cross-validation. Results: After adjustment for age and sex, the overall group effect remained significant for 45 of 48 global dMRI measures following Benjamini–Hochberg correction. Compared with MRI Type 2, MRI Type 1 showed lower fractional anisotropy (FA), neurite density index (NDI), intra-axonal volume fraction (INTRA), axonal water fraction (AWF), mean kurtosis (MK), axial kurtosis (AK), and radial kurtosis (RK), and higher mean diffusivity (MD), radial diffusivity (RD), extra-axonal mean diffusivity (EXTRA_MD), extra-axonal transverse diffusivity (EXTRA_TRANS), and extra-axonal radial diffusivity (radEAD). These differences were generally most pronounced in the body of the corpus callosum. In the segmental analysis, 131 of 144 values showed a significant overall group effect after correction, and 108 demonstrated significant differences between MRI Type 1 and MRI Type 2. The largest effects were observed in the 60–80% interval of the corpus callosum body, particularly for AWF, MK, INTRA, EXTRA_TRANS, RK, FA, RD, radEAD, and MD. An Elastic Net model combining age, sex, and 48 global dMRI measures discriminated MRI Type 1 from MRI Type 2 with an internally validated area under the curve of 0.866 (95% CI, 0.762–0.953), accuracy of 86.7%, sensitivity of 75.0%, and specificity of 95.3%. Ten dMRI features showed a selection frequency of at least 70% across repeated model construction. Conclusions: MRI Type 1 is characterized by more severe and spatially extensive corpus callosum microstructural abnormalities than MRI Type 2, despite broadly similar vascular risk-factor profiles. The findings support the heterogeneity of sporadic age-related SVD and indicate that combined signal-based and biophysical dMRI metrics may improve MRI phenotyping. The observed associations should be interpreted as indirect markers of tissue microstructure and require confirmation in larger, independent, and longitudinal cohorts.

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 2 matches between paragraphs and lines of code.

mikhail-matrosov/classibundler

License: none: the authors keep all their rights

State: the link answers, verified on 26 September 2026

Evidence: files inventoried

Commit: 22fd2e919fd1e538baf1451d3986fd8977d479a2, 9 July 2022

Languages: Python (11), Jupyter (3)

Size: 25 files, 14 scripts

Software Heritage: archived

Found in: the text, “2. Materials and Methods”

Holds: README, environment (requirements-train.txt, requirements.txt), documentation, 3 notebooks

Not found: license file, CITATION.cff, tests, continuous integration

Tools: NumPy (12 files), DIPY (6 files), Matplotlib (5 files), SciPy (2 files), NiBabel (1 file), Numba (1 file), OpenCV (1 file), scikit-image (1 file), scikit-learn (1 file)

Availability: 1 check, the latest on 26 September 2026: the link answers

26 September 2026: the link answers

15 files

Check classifier.ipynb, Jupyter, 297 lines
atlas.py, Python, 74 lines
classibundler.py, Python, 90 lines
config.py, Python, 49 lines
draw_groups.py, Python, 312 lines
draw_profiles.py, Python, 212 lines
drawing_utils.py, Python, 32 lines
features.py, Python, 83 lines, 2 matches
generate_centroids.ipynb, Jupyter, 134 lines
lessions.py, Python, 134 lines
make new atlas.ipynb, Jupyter, 179 lines
patient.py, Python, 174 lines
processing.py, Python, 202 lines
rendering.py, Python, 268 lines
README.md, Text, 166 lines

At the source: github.com/mikhail-matrosov/classibundler

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;
14 scripts, each with its path and the digest of its content;
2 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

All data relevant to this study are included in the article. Additional information may be obtained from the corresponding author upon reasonable request, subject to patient confidentiality and applicable data protection regulations.

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, 8 authors, 7 keywords, 1 funder, 40 references.

Cite

This paper

Kremneva, E. I., Dobrynina, L. A., Shamtieva, K. V., Geints, A. A., Sokolov, M. S., Zabitova, M. R., Filatov, A. S., & Krotenkova, M. V. (2026). Microstructural Changes in the Corpus Callosum in Different Forms of Sporadic Age-Related Cerebral Small Vessel Disease. Diagnostics (Basel, Switzerland), 16(17), 2861. https://doi.org/10.3390/diagnostics16172861

BibTeX

@article{kremneva2026microstructural, author = {Kremneva, Elena I and Dobrynina, Larisa A and Shamtieva, Kamila V and Geints, Anastasia A and Sokolov, Mikhail S and Zabitova, Maryam R and Filatov, Alexey S and Krotenkova, Marina V}, title = {{Microstructural Changes in the Corpus Callosum in Different Forms of Sporadic Age-Related Cerebral Small Vessel Disease}}, journal = {Diagnostics (Basel, Switzerland)}, year = {2026}, month = sep, volume = {16}, number = {17}, pages = {2861}, publisher = {Multidisciplinary Digital Publishing Institute (MDPI)}, issn = {2075-4418}, doi = {10.3390/diagnostics16172861}, url = {https://doi.org/10.3390/diagnostics16172861}, pmid = {42739291}, pmcid = {PMC13565003} }

RIS

TY - JOUR AU - Kremneva, Elena I AU - Dobrynina, Larisa A AU - Shamtieva, Kamila V AU - Geints, Anastasia A AU - Sokolov, Mikhail S AU - Zabitova, Maryam R AU - Filatov, Alexey S AU - Krotenkova, Marina V TI - Microstructural Changes in the Corpus Callosum in Different Forms of Sporadic Age-Related Cerebral Small Vessel Disease T2 - Diagnostics (Basel, Switzerland) J2 - Diagnostics (Basel) PY - 2026 DA - 2026/09/05 VL - 16 IS - 17 SP - 2861 SN - 2075-4418 PB - Multidisciplinary Digital Publishing Institute (MDPI) DO - 10.3390/diagnostics16172861 UR - https://doi.org/10.3390/diagnostics16172861 LA - en ER -

CSL-JSON

{ "id": "10.3390/diagnostics16172861", "type": "article-journal", "title": "Microstructural Changes in the Corpus Callosum in Different Forms of Sporadic Age-Related Cerebral Small Vessel Disease", "container-title": "Diagnostics (Basel, Switzerland)", "author": [ { "family": "Kremneva", "given": "Elena I" }, { "family": "Dobrynina", "given": "Larisa A" }, { "family": "Shamtieva", "given": "Kamila V" }, { "family": "Geints", "given": "Anastasia A" }, { "family": "Sokolov", "given": "Mikhail S" }, { "family": "Zabitova", "given": "Maryam R" }, { "family": "Filatov", "given": "Alexey S" }, { "family": "Krotenkova", "given": "Marina V" } ], "container-title-short": "Diagnostics (Basel)", "volume": "16", "issue": "17", "page": "2861", "DOI": "10.3390/diagnostics16172861", "PMID": "42739291", "PMCID": "PMC13565003", "ISSN": "2075-4418", "publisher": "Multidisciplinary Digital Publishing Institute (MDPI)", "URL": "https://doi.org/10.3390/diagnostics16172861", "language": "en", "issued": { "date-parts": [ [ 2026, 9, 5 ] ] } }

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