OSCR

Structure-function multilayer network integration and cognition in multiple sclerosis.

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. [1] § METHODS › Network Construction › Multilayer networks. ↔ multinetx/core/multilayer.py, lines 53–138 · score 0.67 · MultiNetX, adjacency matrices, diagonal, NetworkX, Python, multilayer
  2. [2] § METHODS › Network Construction › Multilayer networks. ↔ multinetx/__init__.py, lines 27–41 · score 0.53 · NetworkX, multiNetX, Python, multilayer

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

Python · 266 lines · 9.4 KB · no license · 1 match

  1. #! /usr/bin/env python
  2. # -*- coding: utf-8 -*-
  4. ########################################################################
  5. #
  6. # multiNetX -- a python package for general multilayer graphs
  7. #
  8. # (C) Copyright 2013-2019, Nikos E Kouvaris
  9. # multiNetX is part of the deliverables of the LASAGNE project
  10. # (multi-LAyer SpAtiotemporal Generalized NEtworks),
  11. # EU/FP7-2012-STREP-318132 (http://complex.ffn.ub.es/~lasagne/)
  12. #
  13. # multiNetX is free software: you can redistribute it and/or modify
  14. # it under the terms of the GNU General Public License as published
  15. # by the Free Software Foundation, either version 3 of the License,
  16. # or (at your option) any later version.
  17. #
  18. # multiNetX is distributed in the hope that it will be useful,
  19. # but WITHOUT ANY WARRANTY; without even the implied warranty of
  20. # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
  21. # See the GNU General Public License for more details.
  22. #
  23. # You should have received a copy of the GNU General Public License
  24. # along with this program. If not, see http://www.gnu.org/licenses/.
  25. ########################################################################
  27. """
  28. MultilayerGraph: Base class for a multi-layer network
  29. """
  30. from multinetx.core.exceptions import multinetxError
  32. # import copy
  34. try:
  35. from networkx import Graph, disjoint_union_all
  36. except ImportError:
  37. raise ImportError("NetworkX is required")
  39. try:
  40. from numpy import zeros
  41. except ImportError:
  42. raise ImportError("NumPy is required")
  44. try:
  45. from scipy.sparse import lil_matrix
  46. except ImportError:
  47. raise ImportError("SciPy is required")
  50. class MultilayerGraph(Graph):
  51. """"Define constructor of the class"""
  53. def __init__(self, list_of_layers=None, inter_adjacency_matrix=None, **attr):
  54. """Constructor of a MultilayerGraph.
  55. It creates a symmetric (undirected) MultilayerGraph object
  56. inheriting methods from networkx.Graph
  58. Parameters:
  59. -----------
  60. list_of_layers : Python list of networkx.Graph objects
  62. inter_adjacency_matrix : a lil sparse matrix (NxN) with zero
  63. diagonal elements and off-diagonal
  64. block elements defined by the
  65. inter-connectivity architecture.
  67. Return: a MultilayerGraph object
  69. Examples:
  70. ---------
  71. import multinetx as mx
  72. import numpy as np
  73. N = 10
  74. g1 = mx.erdos_renyi_graph(N,0.07,seed=218)
  75. g2 = mx.erdos_renyi_graph(N,0.07,seed=211)
  76. g3 = mx.erdos_renyi_graph(N,0.07,seed=211)
  78. adj_block = mx.lil_matrix(np.zeros((N*3,N*3)))
  79. adj_block[0: N, N:2*N] = np.identity(N) # L_12
  80. adj_block[0: N,2*N:3*N] = np.identity(N) # L_13
  81. #adj_block[N:2*N,2*N:3*N] = np.identity(N) # L_23
  82. adj_block += adj_block.T
  84. mg = mx.MultilayerGraph(list_of_layers=[g1,g2,g3],
  85. inter_adjacency_matrix=adj_block)
  87. mg.set_edges_weights(inter_layer_edges_weight=4)
  88. mg.set_intra_edges_weights(layer=0,weight=1)
  89. mg.set_intra_edges_weights(layer=1,weight=2)
  90. mg.set_intra_edges_weights(layer=2,weight=3)
  91. """
  93. # Give an empty graph in the list_of_layers
  94. if list_of_layers is None:
  95. self.list_of_layers = [Graph()]
  96. self.num_nodes_in_layers = []
  97. else:
  98. self.list_of_layers = list_of_layers
  99. self.num_nodes_in_layers = [
  100. layer.number_of_nodes() for layer in list_of_layers
  101. ]
  103. # Number of nodes
  104. self.num_nodes = sum(self.num_nodes_in_layers)
  106. # Create the MultilayerGraph without inter-layer links.
  107. try:
  108. Graph.__init__(self, Graph(disjoint_union_all(self.list_of_layers), **attr))
  109. except multinetxError:
  110. raise multinetxError("Multiplex cannot inherit Graph properly")
  112. # Make a zero lil matrix for inter_adjacency_matrix
  113. if inter_adjacency_matrix is None:
  114. inter_adjacency_matrix = lil_matrix(zeros((self.num_nodes, self.num_nodes)))
  116. # Check if the matrix inter_adjacency_matrix is lil
  117. try:
  118. assert inter_adjacency_matrix.format == "lil"
  119. except AssertionError:
  120. raise multinetxError(
  121. "interconnecting_adjacency_matrix\
  122. is not scipy.sparse.lil"
  123. )
  125. # Lists for intra-layer and inter-layer edges
  126. if list_of_layers is None:
  127. self.intra_layer_edges = []
  128. else:
  129. self.intra_layer_edges = self.edges()
  130. self.inter_layer_edges = []
  132. # Inter-layer connection
  133. self.layers_interconnect(inter_adjacency_matrix)
  135. # MultiNetX name
  136. self.name = "multilayer"
  137. for layer in self.list_of_layers:
  138. self.name += "_" + layer.name
  140. # Constructor
  142. # Define methods of the class
  143. def add_layer(self, layer, **attr):
  144. if self.num_nodes is 0:
  145. self.list_of_layers = [layer]
  146. else:
  147. self.list_of_layers.append(layer)
  149. self.num_nodes_in_layers.append(layer.number_of_nodes())
  150. self.num_nodes = sum(self.num_nodes_in_layers)
  152. for i, j in layer.edges():
  153. self.intra_layer_edges.append(
  154. (
  155. i + (len(self.list_of_layers) - 1) * layer.number_of_nodes(),
  156. j + (len(self.list_of_layers) - 1) * layer.number_of_nodes(),
  157. )
  158. )
  159. try:
  160. Graph.__init__(
  161. self, Graph(disjoint_union_all(self.list_of_layers), **attr)
  162. )
  163. except multinetxError:
  164. raise multinetxError("Multiplex cannot inherit Graph properly")
  166. def layers_interconnect(self, inter_adjacency_matrix=None):
  167. """Parameters:
  168. -----------
  169. Examples:
  170. ---------
  171. """
  172. # Make a zero lil matrix for inter_adjacency_matrix
  173. if inter_adjacency_matrix is None:
  174. inter_adjacency_matrix = lil_matrix(zeros((self.num_nodes, self.num_nodes)))
  175. # Check if the matrix inter_adjacency_matrix is lil
  176. try:
  177. assert inter_adjacency_matrix.format == "lil"
  178. except AssertionError:
  179. raise multinetxError(
  180. "interconnecting_adjacency_matrix\
  181. is not scipy.sparse.lil"
  182. )
  183. for i, row in enumerate(inter_adjacency_matrix.rows):
  184. for pos, j in enumerate(row):
  185. if i > j:
  186. self.inter_layer_edges.append((i, j))
  187. self.add_edges_from(self.inter_layer_edges)
  189. def get_number_of_layers(self):
  190. """Return the number of graphs"""
  191. return len(self.list_of_layers)
  193. def get_number_of_nodes_in_layers(self):
  194. """Return the number of nodes in each graph"""
  195. return self.num_nodes_in_layers
  197. def get_number_of_nodes(self):
  198. return self.num_nodes
  200. def get_intra_layer_edges(self):
  201. """Return a list with the intra-layer edges"""
  202. return self.intra_layer_edges
  204. def get_intra_layer_edges_of_layer(self, layer=0):
  205. """Return a list with the intra-layer edges of layer"""
  206. edge_list = self.get_layer(layer).edges()
  207. # liste of previous layer
  208. past = [self.num_nodes_in_layers[i] for i in range(layer)]
  209. # number of previous nodes
  210. num_past = sum(past)
  211. elist = []
  212. for i, j in edge_list:
  213. elist.append((num_past + i, num_past + j))
  214. return elist
  216. def get_inter_layer_edges(self):
  217. """Return a list with the inter-layer edges"""
  218. return self.inter_layer_edges
  220. def get_list_of_layers(self):
  221. """Return a list with the graphs of the layers"""
  222. return self.list_of_layers
  224. def get_layer(self, layer_number):
  225. """Return the networkx graph of the layer layer_number"""
  226. return self.list_of_layers[layer_number]
  228. def set_edges_weights(
  229. self, intra_layer_edges_weight=None, inter_layer_edges_weight=None
  230. ):
  231. """Set the weights of the MultilayerGraph edges
  232. ---
  233. intra_layer_edges_weight
  234. inter_layer_edges_weight
  235. ---
  236. Set the "intra_layer_edges_weight" and "inter_layer_edges_weight"
  237. as an edge attribute with the name "weight"
  238. """
  239. if intra_layer_edges_weight is not None:
  240. self.add_edges_from(self.intra_layer_edges, weight=intra_layer_edges_weight)
  241. if inter_layer_edges_weight is not None:
  242. self.add_edges_from(self.inter_layer_edges, weight=inter_layer_edges_weight)
  244. def set_intra_edges_weights(self, layer=0, weight=None):
  245. """Set the weights of the MultilayerGraph edges
  246. ---
  247. intra_layer_edges_weight
  248. ---
  249. Set the "intra_layer_edges_weight" and "intra_layer_edges_weight"
  250. as an edge attribute with the name "weight"
  251. """
  252. elist = self.get_intra_layer_edges_of_layer(layer=layer)
  253. self.add_edges_from(elist, weight=weight)
  255. def info(self):
  256. """Returns some information of the object MultilayerGraph"""
  257. info = "{}-layer graph,\
  258. intra_layer_edges:{},\
  259. inter_layer_edges:{},\
  260. number_of_nodes_in_layer:{} ".format(
  261. len(self.list_of_layers),
  262. len(self.intra_layer_edges),
  263. len(self.inter_layer_edges),
  264. self.num_nodes_in_layers,
  265. )
  266. return info

multilayer.py at commit 0f1d808, no license · at the source

Overview

Authors: Lucas C Breedt1, Giuseppe Pontillo2,3,4,5, Fernando A N Santos1,6, Chris Vriend1,7, Ferran Prados2,8,9, Alle Meije Wink2,4, Alvino Bisecco10, Alessandro Cagol11,12,13,14, Massimiliano Calabrese15, Marco Castellaro15,16, Sara Collorone3, Rosa Cortese17, Nicola De Stefano17, Christian Enzinger18, Massimo Filippi19,20,21,22,23, Michael A Foster3, Antonio Gallo10, Gabriel Gonzalez-Escamilla24, Cristina Granziera11,12,13, Sergiu Groppa24
and 18 other authorsEinar A Høgestøl25,26,27, Sara Llufriu28, Eloy Martinez-Heras28, Elisabeth Solana28, Silvia Messina29, Marcello Moccia3,30, Gro O Nygaard25,27, Jacqueline Palace29, Daniela Pinter18, Mara A Rocca19,20,23, Ahmed Toosy3, Paola Valsasina19, Olga Ciccarelli3, Eva M Strijbis2,31, Frederik Barkhof2,3,4,8,32, Menno M Schoonheim1,2, Linda Douw1, MAGNIMS study group
ORCID iDs: Lucas C Breedt, Michael A Foster, Sergiu Groppa, Eloy Martinez-Heras, Jacqueline Palace, Ahmed Toosy, Frederik Barkhof
32 affiliations
  1. Department of Anatomy and Neurosciences, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
  2. MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
  3. Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
  4. Department of Radiology and Nuclear Medicine, MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
  5. Departments of Advanced Biomedical Sciences and Electrical Engineering and Information Technology, University of Naples “Federico II,” Naples, Italy
  6. Dutch Institute for Emergent Phenomena (DIEP), Institute for Advanced Studies, University of Amsterdam, Amsterdam, The Netherlands
  7. Department of Psychiatry, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
  8. Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom
  9. E-Health Center, Universitat Oberta de Catalunya, Barcelona, Spain
  10. Department of Advanced Medical and Surgical Sciences, University of Campania “Luigi Vanvitelli,” Naples, Italy
  11. Translational Imaging in Neurology (ThINK) Basel, Department of Biomedical Engineering, Faculty of Medicine, University Hospital Basel and University of Basel, Basel, Switzerland
  12. Department of Neurology, University Hospital Basel, Basel, Switzerland
  13. Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Basel, Switzerland
  14. Dipartimento di Scienze della Salute, Università degli Studi di Genova
  15. Department of Neurosciences, Biomedicine and Movement Sciences, University of Verona, Verona, Italy
  16. Department of Information Engineering, University of Padova, Padova, Italy
  17. Department of Medicine, Surgery and Neuroscience, University of Siena, Siena, Italy
  18. Department of Neurology, Medical University of Graz, Graz, Austria
  19. Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy
  20. Neurology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy
  21. Neurorehabilitation Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy
  22. Neurophysiology Service, IRCCS San Raffaele Scientific Institute, Milan, Italy
  23. Vita-Salute San Raffaele University, Milan, Italy
  24. Movement Disorders, Neurostimulation and Neuroimaging, University Medicine Mainz, Mainz, Germany
  25. Department of Neurology, Oslo University Hospital, Oslo, Norway
  26. Department of Psychology, University of Oslo, Oslo, Norway
  27. Institute of Clinical Medicine, University of Oslo, Oslo, Norway
  28. Neuroimmunology and Multiple Sclerosis Unit and Laboratory of Advanced Imaging in Neuroimmunological Diseases (ImaginEM), Hospital Clinic and Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), University of Barcelona, Barcelona, Spain
  29. Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
  30. Department of Molecular Medicine and Medical Biotechnology, University of Naples “Federico II,” Naples, Italy
  31. Department of Neurology, MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
  32. Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
Institutions: Amsterdam Neuroscience (Netherlands); Amsterdam University Medical Centers (Netherlands); Vrije Universiteit Amsterdam (Netherlands); Amsterdam UMC Location Vrije Universiteit Amsterdam (Netherlands); National Hospital for Neurology and Neurosurgery (United Kingdom); UCL Queen Square Institute of Neurology (United Kingdom); University College London (United Kingdom); University of Naples Federico II (Italy); University of Amsterdam (Netherlands); Universitat Oberta de Catalunya (Spain); University of Campania "Luigi Vanvitelli" (Italy); University of Basel (Switzerland); University Hospital of Basel (Switzerland); University of Genoa (Italy); University of Verona (Italy); University of Padua (Italy); University of Siena (Italy); Medical University of Graz (Austria); Vita-Salute San Raffaele University (Italy); IRCCS Ospedale San Raffaele (Italy); Istituto di Ricovero e Cura a Carattere Scientifico San Raffaele (Italy); University Medical Center of the Johannes Gutenberg University Mainz (Germany); Oslo University Hospital (Norway); University of Oslo (Norway); Hospital Clínic de Barcelona (Spain); Consorci Institut D'Investigacions Biomediques August Pi I Sunyer (Spain); Universitat de Barcelona (Spain); University of Oxford (United Kingdom)
Journal: Network neuroscience (Cambridge, Mass.), volume 10, issue 2, pages 400-417
Dates: received 31 March 2025; accepted 22 December 2025; published online 22 April 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1162/netn.a.545 · PMID 42039096 · PMCID PMC13108497 · OpenAlex W7124219763
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: structural MRI / diffusion (modality), fMRI (modality), multiple sclerosis (population), cognitive (subfield)
Methods: Spectral & time-frequency, Connectivity, Smoothing, state filtering, decompositions, Machine learning, Preprocessing, Statistics, fMRI & imaging
Keywords: Executive functioning, Graph theory, Functional connectivity, Structural connectivity, Multilayer networks
Topic: Functional Brain Connectivity Studies (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Citations: not cited yet (Europe PMC); 71 references in the paper

Abstract

People with multiple sclerosis (MS) often present with cognitive deficits that cannot fully be attributed to focal brain alterations. Whole-brain network changes show stronger relations, but MS network insights have mostly focused on either structural or functional (single-layer) networks, while recent work has shown the importance of multilayer frontoparietal network integration for cognition. Here, we explored the cognitive relevance of multilayer integration of the frontoparietal network in relapsing–remitting MS (n = 780) using diffusion and resting-state fMRI. Cognitive relations were first assessed for nodal multilayer eigenvector centrality, averaged over frontoparietal network nodes as a measure of integration, and post hoc for mean eccentricity for both single layer and multilayers. Higher multilayer frontoparietal network centrality was associated with worse Symbol Digit Modalities Test (SDMT) performance (β = −.117, p = .005). Mean eccentricity of single-layer diffusion (β = −.123, p < .001) and multilayer networks (β = .085, p = .018) were associated with SDMT performance. However, results could not be replicated using a different anatomical parcellation. This study showed that cognition in MS is related to multilayer network parameters. Nevertheless, correlations were weak and atlas specific, suggesting that a binary structure–function multilayer network approach is not particularly relevant as a correlate of cognition in MS.

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 2 matches between paragraphs and lines of code.

python.org

License: none: the authors keep all their rights
State: the link answers, verified on 28 September 2026
Evidence: the link answers
Software Heritage: not checked
Found in: the text, “Multilayer networks.”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Availability: 1 check, the latest on 28 September 2026: the link answers (HTTP 200)
  • 28 September 2026: the link answers (HTTP 200)
At the source: python.org/

networkx

License: none: the authors keep all their rights
State: the link answers, verified on 29 September 2026
Evidence: the link answers
Software Heritage: not checked
Found in: the text, “Multilayer networks.”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Availability: 1 check, the latest on 29 September 2026: the link answers (HTTP 200)
  • 29 September 2026: the link answers (HTTP 200)
At the source: github.com/networkx

nkoub/multinetx

License: none: the authors keep all their rights
State: the link answers, verified on 29 September 2026
Evidence: files inventoried
Commit: 0f1d80869cb5ba8138988bd778ec480b9a099268, 5 August 2024
Languages: Jupyter (10), Python (7)
Size: 54 files, 17 scripts
Software Heritage: archived
Found in: the text, “Multilayer networks.”
Holds: README, environment (requirements.txt, setup.cfg, setup.py), documentation, 5 notebooks
Not found: license file, CITATION.cff, tests, continuous integration
Tools: NumPy (9 files), Matplotlib (7 files), NetworkX (5 files), SciPy (4 files)
Availability: 1 check, the latest on 29 September 2026: the link answers
  • 29 September 2026: the link answers
18 files
At the source: github.com/nkoub/multinetx

multinetlab-amsterdam

License: none: the authors keep all their rights
State: the link answers, verified on 29 September 2026
Evidence: the link answers
Software Heritage: not checked
Found in: the text, “Multilayer networks.”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Availability: 1 check, the latest on 29 September 2026: the link answers (HTTP 200)
  • 29 September 2026: the link answers (HTTP 200)
At the source: github.com/multinetlab-amsterdam

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:

  • 4 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 17 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

The code used for the analyses in this study are available upon request; please contact L.D.

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, 29 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 38 authors, 5 keywords, 68 references.

Cite

This paper

Breedt, L. C., Pontillo, G., Santos, F. A. N., Vriend, C., Prados, F., Wink, A. M., Bisecco, A., Cagol, A., Calabrese, M., Castellaro, M., Collorone, S., Cortese, R., De Stefano, N., Enzinger, C., Filippi, M., Foster, M. A., Gallo, A., Gonzalez-Escamilla, G., Granziera, C., . . . MAGNIMS study group. (2026). Structure-function multilayer network integration and cognition in multiple sclerosis. Network neuroscience (Cambridge, Mass.), 10(2), 400-417. https://doi.org/10.1162/netn.a.545

BibTeX

@article{breedt2026structure,
author = {Breedt, Lucas C and Pontillo, Giuseppe and Santos, Fernando A N and Vriend, Chris and Prados, Ferran and Wink, Alle Meije and Bisecco, Alvino and Cagol, Alessandro and Calabrese, Massimiliano and Castellaro, Marco and Collorone, Sara and Cortese, Rosa and De Stefano, Nicola and Enzinger, Christian and Filippi, Massimo and Foster, Michael A and Gallo, Antonio and Gonzalez-Escamilla, Gabriel and Granziera, Cristina and Groppa, Sergiu and Høgestøl, Einar A and Llufriu, Sara and Martinez-Heras, Eloy and Solana, Elisabeth and Messina, Silvia and Moccia, Marcello and Nygaard, Gro O and Palace, Jacqueline and Pinter, Daniela and Rocca, Mara A and Toosy, Ahmed and Valsasina, Paola and Ciccarelli, Olga and Strijbis, Eva M and Barkhof, Frederik and Schoonheim, Menno M and Douw, Linda and {MAGNIMS study group}},
title = {{Structure-function multilayer network integration and cognition in multiple sclerosis}},
journal = {Network neuroscience (Cambridge, Mass.)},
year = {2026},
month = apr,
volume = {10},
number = {2},
pages = {400--417},
publisher = {MIT Press},
issn = {2472-1751},
doi = {10.1162/netn.a.545},
url = {https://doi.org/10.1162/netn.a.545},
pmid = {42039096},
pmcid = {PMC13108497}
}

RIS

TY - JOUR
AU - Breedt, Lucas C
AU - Pontillo, Giuseppe
AU - Santos, Fernando A N
AU - Vriend, Chris
AU - Prados, Ferran
AU - Wink, Alle Meije
AU - Bisecco, Alvino
AU - Cagol, Alessandro
AU - Calabrese, Massimiliano
AU - Castellaro, Marco
AU - Collorone, Sara
AU - Cortese, Rosa
AU - De Stefano, Nicola
AU - Enzinger, Christian
AU - Filippi, Massimo
AU - Foster, Michael A
AU - Gallo, Antonio
AU - Gonzalez-Escamilla, Gabriel
AU - Granziera, Cristina
AU - Groppa, Sergiu
AU - Høgestøl, Einar A
AU - Llufriu, Sara
AU - Martinez-Heras, Eloy
AU - Solana, Elisabeth
AU - Messina, Silvia
AU - Moccia, Marcello
AU - Nygaard, Gro O
AU - Palace, Jacqueline
AU - Pinter, Daniela
AU - Rocca, Mara A
AU - Toosy, Ahmed
AU - Valsasina, Paola
AU - Ciccarelli, Olga
AU - Strijbis, Eva M
AU - Barkhof, Frederik
AU - Schoonheim, Menno M
AU - Douw, Linda
AU - MAGNIMS study group
TI - Structure-function multilayer network integration and cognition in multiple sclerosis
T2 - Network neuroscience (Cambridge, Mass.)
J2 - Netw Neurosci
PY - 2026
DA - 2026/04/22
VL - 10
IS - 2
SP - 400
EP - 417
SN - 2472-1751
PB - MIT Press
DO - 10.1162/netn.a.545
UR - https://doi.org/10.1162/netn.a.545
LA - en
ER -

CSL-JSON

{
"id": "10.1162/netn.a.545",
"type": "article-journal",
"title": "Structure-function multilayer network integration and cognition in multiple sclerosis",
"container-title": "Network neuroscience (Cambridge, Mass.)",
"author": [
{
"family": "Breedt",
"given": "Lucas C"
},
{
"family": "Pontillo",
"given": "Giuseppe"
},
{
"family": "Santos",
"given": "Fernando A N"
},
{
"family": "Vriend",
"given": "Chris"
},
{
"family": "Prados",
"given": "Ferran"
},
{
"family": "Wink",
"given": "Alle Meije"
},
{
"family": "Bisecco",
"given": "Alvino"
},
{
"family": "Cagol",
"given": "Alessandro"
},
{
"family": "Calabrese",
"given": "Massimiliano"
},
{
"family": "Castellaro",
"given": "Marco"
},
{
"family": "Collorone",
"given": "Sara"
},
{
"family": "Cortese",
"given": "Rosa"
},
{
"family": "De Stefano",
"given": "Nicola"
},
{
"family": "Enzinger",
"given": "Christian"
},
{
"family": "Filippi",
"given": "Massimo"
},
{
"family": "Foster",
"given": "Michael A"
},
{
"family": "Gallo",
"given": "Antonio"
},
{
"family": "Gonzalez-Escamilla",
"given": "Gabriel"
},
{
"family": "Granziera",
"given": "Cristina"
},
{
"family": "Groppa",
"given": "Sergiu"
},
{
"family": "Høgestøl",
"given": "Einar A"
},
{
"family": "Llufriu",
"given": "Sara"
},
{
"family": "Martinez-Heras",
"given": "Eloy"
},
{
"family": "Solana",
"given": "Elisabeth"
},
{
"family": "Messina",
"given": "Silvia"
},
{
"family": "Moccia",
"given": "Marcello"
},
{
"family": "Nygaard",
"given": "Gro O"
},
{
"family": "Palace",
"given": "Jacqueline"
},
{
"family": "Pinter",
"given": "Daniela"
},
{
"family": "Rocca",
"given": "Mara A"
},
{
"family": "Toosy",
"given": "Ahmed"
},
{
"family": "Valsasina",
"given": "Paola"
},
{
"family": "Ciccarelli",
"given": "Olga"
},
{
"family": "Strijbis",
"given": "Eva M"
},
{
"family": "Barkhof",
"given": "Frederik"
},
{
"family": "Schoonheim",
"given": "Menno M"
},
{
"family": "Douw",
"given": "Linda"
},
{
"literal": "MAGNIMS study group"
}
],
"container-title-short": "Netw Neurosci",
"volume": "10",
"issue": "2",
"page": "400-417",
"DOI": "10.1162/netn.a.545",
"PMID": "42039096",
"PMCID": "PMC13108497",
"ISSN": "2472-1751",
"publisher": "MIT Press",
"URL": "https://doi.org/10.1162/netn.a.545",
"language": "en",
"issued": {
"date-parts": [
[
2026,
4,
22
]
]
}
}

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:10.1016/j.nicl.2026.104012 [code]
Structural-functional multilayer brain network properties and outcome of combined repetitive transcranial magnetic stimulation and psychotherapy for obsessive-compulsive disorder.
Journal: NeuroImage. Clinical
In common: NetworkX, SciPy, Matplotlib, 1 other tool, fMRI, structural MRI / diffusion, 11 references
[2] doi:10.1002/hbm.70425 [code]
Brain Age Estimation on T2-FLAIR Scans for Application to Multiple Sclerosis.
Journal: Human brain mapping
In common: SciPy, NumPy, multiple sclerosis, structural MRI / diffusion, 3 authors
[3] doi:10.1177/20552173261448282 [code]
The constrained brain in multiple sclerosis: Cognitive impairment is related to network-specific coupling of structural and functional connectivity.
Journal: Multiple sclerosis journal - experimental, translational and clinical
In common: multiple sclerosis, fMRI, structural MRI / diffusion, 6 references, author Frederik Barkhof
[4] doi:10.1038/s43856-026-01707-2 [code]
Decreased amyloid-related structure-function coupling in preclinical Alzheimer's disease.
Journal: Communications medicine
In common: SciPy, Matplotlib, NumPy, 6 references, author Frederik Barkhof
[5] doi:10.7554/elife.103097 [code]
Canonical neurodevelopmental trajectories of structural and functional manifolds.
Journal: eLife
In common: SciPy, NumPy, structural MRI / diffusion, 10 references
[6] doi:10.1093/braincomms/fcag162 [code]
Disability profiles in progressive multiple sclerosis reflect pathology distribution, independent of clinical phenotype.
Journal: Brain communications
In common: NetworkX, SciPy, NumPy, multiple sclerosis, structural MRI / diffusion, 2 references, author Frederik Barkhof
[7] doi:10.1136/jnnp-2025-335884 [code]
Diffusivity anisotropy signature of slowly expanding lesions predicts progression independent of relapse activity in multiple sclerosis.
Journal: Journal of neurology, neurosurgery, and psychiatry
In common: SciPy, Matplotlib, NumPy, multiple sclerosis, structural MRI / diffusion, 1 reference, author Eloy Martinez-Heras
[8] doi:10.1162/netn.a.547 [code]
An evaluation of the efficacy of single-echo and multi-echo fMRI denoising strategies.
Journal: Network neuroscience (Cambridge, Mass.)
In common: SciPy, Matplotlib, NumPy, fMRI, 5 references
[9] doi:10.1073/pnas.2531706123 [code]
Metabolism-weighted brain connectome reveals synaptic integration and vulnerability to neurodegeneration.
Journal: Proceedings of the National Academy of Sciences of the United States of America
In common: NetworkX, SciPy, Matplotlib, 1 other tool, 4 references
[10] doi:10.1017/s0033291726104838 [code]
Variations of structural-functional coupling in post-traumatic stress disorder are associated with underlying molecular and transcriptional features.
Journal: Psychological medicine
In common: SciPy, NumPy, structural MRI / diffusion, 6 references

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.