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Network flexibility facilitates treatment-induced recovery in post-stroke aphasia.

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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 · 3 of them tie a paragraph to a whole file without given lines: weak matches, whose lines are not tinted
  1. [1] § Materials and methods › Temporal metrics › Community stability ↔ dFC_toolbox/other_required_code/BCT_files/community_louvain.m, lines 1 to 71 · score 0.64 · Louvain community detection, maximizing, algorithm, nodes, matrix, network · Report a mismatch
  2. [2] § Materials and methods › Temporal metrics › Community stability ↔ dFC_toolbox/other_required_code/get_transition_threshold.m, the whole file · a weak match · score 0.63 · adjacent window, inter window, apart, probability, threshold · Report a mismatch
  3. [3] § Materials and methods › Temporal metrics › Community stability ↔ dFC_toolbox/other_required_code/get_transition_threshold.m, the whole file · a weak match · score 0.56 · adjacent windows, inter window, Probability, apart, threshold · Report a mismatch
  4. [4] § Materials and methods › dFC states analysis ↔ dFC_toolbox/other_required_code/dFC_kmeans_app.m, lines 1 to 89 · score 0.55 · kmeans, elbow, PCA, reshaped, sum, clustering · Report a mismatch
  5. [5] § Materials and methods › MRI acquisition and preprocessing ↔ dFC_toolbox/other_required_code/compute_dFC.m, the whole file · a weak match · score 0.52 · functional scans, lesion mapping, TR, preprocessing, duration · Report a mismatch
  6. [6] § Results › Temporal metrics of dFC ↔ stats_revision2.Rmd, lines 327 to 357 · score 0.51 · WAB AQ, lesion volume, models, accuracy, predicting, temporal · Report a mismatch

Paper

Loaded from Europe PMC by your browser; OSCR does not store it: doi.org · Europe PMC

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The authors' code

MATLAB · 71 lines · 2.1 KB · MIT · 2 matches

  1. function [threshold,AUC,density_1] = get_transition_threshold(comm_sim,TR,plot_density)
  3. nwin = size(comm_sim,1);
  4. nsub = size(comm_sim,3);
  6. % Keep only similarity values for windows more than 30 seconds apart
  7. comm_sim_1 = zeros(nwin,nwin,nsub);
  8. for i=1:nwin
  9. for j=1:nwin
  10. if abs(j-i)<=(60/TR)
  11. comm_sim_1(i,j,:) = nan;
  12. else
  13. comm_sim_1(i,j,:) = comm_sim(i,j);
  14. end
  15. end
  16. end
  18. % Keep only similarity values for windows within 30 seconds of each other
  19. comm_sim_2 = zeros(nwin,nwin,nsub);
  20. for i=1:nwin
  21. for j=1:nwin
  22. if abs(j-i)>1 || i==j
  23. comm_sim_2(i,j,:) = nan;
  24. else
  25. comm_sim_2(i,j,:) = comm_sim(i,j);
  26. end
  27. end
  28. end
  30. pts = (1:100)/100;
  31. [density_1,x1] = ksdensity(reshape(comm_sim_1,[nsub*nwin^2 1]),pts);
  32. [density_2,x2] = ksdensity(reshape(comm_sim_2,[nsub*nwin^2 1]),pts);
  33. density_1 = smoothdata(density_1);
  34. density_2 = smoothdata(density_2);
  36. start = find(density_1 == max(density_1));
  37. stop = find(density_2 == max(density_2));
  39. abs_diff = abs(density_2(start:stop) - density_1(start:stop));
  40. stop = find(abs_diff == min(abs_diff))+start-1;
  41. threshold = pts(stop);
  43. % for i=1:numel(pts)-1
  44. % AUC = (pts(2)-pts(1))*sum(density_1(numel(pts)-i:numel(pts)));
  45. % if AUC>=0.05
  46. % stop = numel(pts)-i;
  47. % threshold = pts(stop);
  48. % break
  49. % end
  50. % end
  52. % nonzero1 = find(density_1>0.001);
  53. % threshold = pts(max(nonzero1));
  55. if plot_density
  56. figure; hold on
  57. plot(x1,density_1/100)
  58. plot(x2,density_2/100)
  59. ylim([0 1.1*max(density_1/100)])
  60. xlim([0 1])
  61. plot([threshold threshold],[0 1.1*max(density_1/100)],'k','LineStyle','--')
  62. area([1:stop]*(pts(2)-pts(1)),density_2(1:stop)/100,'EdgeColor','none','FaceColor',[0.8500, 0.3250, 0.0980],'FaceAlpha',0.5)
  63. hold off
  64. legend({'Windows >60s apart','Adjacent windows','Transition Threshold'},'Location','northeast')
  65. xlabel('Inter-window similarity')
  66. ylabel('Probability')
  67. end
  69. % AUC = (pts(2)-pts(1))*sum(density_2(1:intersection));
  71. end

get_transition_threshold.m at commit 00d16a1, under MIT · at the source

Overview

Authors: Isaac Falconer1,2, Maria Varkanitsa2,3, Anne Billot4,5, Swathi Kiran2,3
ORCID iDs: Isaac Falconer, Maria Varkanitsa, Anne Billot
  1. Chobanian & Avedisian School of Medicine, Boston University, Boston, MA 02118, USA
  2. Center for Brain Recovery, Boston University, Boston, MA 02215, USA
  3. Department of Speech, Language, and Hearing Sciences, Sargent College of Health and Rehabilitation Sciences, Boston University, Boston, MA 02215, USA
  4. Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA 02115, USA
  5. Department of Psychology, Center for Brain Science, Harvard University, Cambridge, MA 02138, USA
Institutions: Boston University (United States); Harvard University (United States); Massachusetts General Hospital (United States)
Journal: Brain communications, volume 8, issue 5, article fcag279
Dates: received 3 July 2024; accepted 3 November 2025; published online 18 September 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1093/braincomms/fcag279 · PMID 42761528 · PMCID PMC13586764 · OpenAlex W7213542091
Open access: gold, a free copy (OpenAlex)
Status: code verified (how the code is found and verified)
Categories: fMRI (modality), human (organism), stroke (population)
Methods: Statistics, Smoothing, state filtering, decompositions, Machine learning, Connectivity, Graphs, fMRI & imaging
Keywords: aphasia, stroke, dynamic functional connectivity, functional MRI
Topic: Neurobiology of Language and Bilingualism (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: NIDCD NIH HHS (P50 DC012283)
Citations: not cited yet (Europe PMC); 82 references in the paper

Abstract

Predicting the recovery of post-stroke aphasia, particularly in the chronic phase, poses significant challenges. While many studies have focused on static resting state functional connectivity measures as biomarkers for language recovery, there is a significant lack of studies investigating dynamic functional connectivity, which takes into account second to minute timescale variation in connectivity and has been used extensively in other contexts. This retrospective cohort study investigates the predictive value of dynamic functional connectivity for treatment response in post-stroke aphasia. Two temporal metrics, temporal variability and community stability were explored. Temporal variability quantifies the magnitude of variation in the strength of functional connections over time, while community stability measures the mean length of time over which dynamic functional connectivity remains relatively stable. Additionally, a dynamic functional connectivity states analysis, in which time windows are clustered into states using a k-means clustering algorithm, was used to investigate time-varying network properties of dynamic functional connectivity in relation to treatment response. Baseline MRI data were collected for 30 participants with chronic post-stroke aphasia who received a semantic treatment aimed at improving word-finding. Dynamic functional connectivity was generated using a sliding-window approach and the relationships between treatment-induced improvements in naming accuracy and the respective temporal metrics and network properties were evaluated using linear mixed effects models predicting naming accuracy. Specifically, a significant interaction effect of session (i.e. time) and the respective measure was interpreted as a significant relationship between the measure and treatment response. Both temporal metrics were found to be significantly associated with naming improvements, with both higher temporal variability (i.e. magnitude of fluctuations in dynamic functional connectivity; interaction effect: β = 0.45, P = 3.6e−09, η1 = 0.072) and higher community stability (i.e. moment-to-moment stability of dynamic functional connectivity; interaction effect: β = 0.0046, P = 5.4e−06, η1 = 0.044) predicting greater treatment gains. These findings suggest that patients who benefit most from treatment have neural dynamics characterized by a large magnitude, but low frequency, of fluctuations in network synchrony. Finally, consistent with previous studies, patients who spent more time in a state characterized by higher modularity showed greater treatment response (interaction effect: β = 7.9e−05, P = 6.2e−04, η1 = 0.025). Overall, the findings of this study provide evidence for the utility of dynamic functional connectivity in studying post-stroke language recovery as a measure that captures interindividual difference in the capacity for recovery. They create a new direction for post-stroke aphasia research focusing on temporal dynamics and their relationship to long-term functional network plasticity.

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 6 matches between paragraphs and lines of code.

isaacfalconer/dfc_toolbox

License: MIT
State: the link answers, verified on 26 September 2026
Evidence: files inventoried
Commit: 00d16a1ad71da81ff812b37c2a3066633aae8867, 22 June 2025
Languages: MATLAB (77)
Size: 94 files, 77 scripts
Software Heritage: not archived
Found in: the text, “Dynamic functional connectivity”
Holds: README, license file
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Tools for NIfTI and ANALYZE image (MATLAB) (13 files), Statistics and Machine Learning Toolbox (3 files), Brain Connectivity Toolbox (2 files), Image Processing Toolbox (2 files)
Availability: 1 check, the latest on 26 September 2026: the link answers
  • 26 September 2026: the link answers
79 files
At the source: github.com/isaacfalconer/dfc_toolbox

OSF zja2b

License: none: the authors keep all their rights
State: the link answers, verified on 26 September 2026
Evidence: files inventoried
Languages: R (1)
Size: 12 files, 1 script
Software Heritage: not checked
Found in: “Data availability”
Holds: 1 notebook
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: broom (1 file), car (1 file), easystats (1 file), emmeans (1 file), ggplot2 (1 file), ggpubr (1 file), lme4 (1 file), lmerTest (1 file), psych (1 file), reshape2 (1 file), tidyverse (1 file), UMAP (1 file)
Availability: 1 check, the latest on 26 September 2026: the link answers (HTTP 200)
  • 26 September 2026: the link answers (HTTP 200)
1 file
At the source: osf.io/zja2b/

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:

  • 2 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 78 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

Datasets cited

Data availability

Deidentified raw imaging data, lesion information and behavioural data used in this study are available on the Open Science Framework (OSF): https://osf.io/wcuj8. Software used for image preprocessing is freely available at https://fmriprep.org/en/1.4.1/and https://github.com/neurolabusc/Clinical. Software used for additional preprocessing and generation of mean BOLD timeseries per ROI is freely available at https://web.conn-toolbox.org/. Custom MATLAB code used for estimation of dFC and subsequent analyses is available at https://github.com/isaacfalconer/dfc_toolbox. The Brain Connectivity Toolbox, which was used to compute graph measures, is freely available at http://mathworks.com/matlabcentral/fileexchange/61173-brain-connectivity-toolbox. Statistical analysis code is available at https://osf.io/zja2b/. Legal copyright restrictions prevent public archiving of the Western Aphasia Battery—Revised which can be obtained from the copyright holders in the cited references (Kertesz, 2007).

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

Versions

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Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 4 authors, 4 keywords, 1 funder, 78 references.

Cite

This paper

Falconer, I., Varkanitsa, M., Billot, A., & Kiran, S. (2026). Network flexibility facilitates treatment-induced recovery in post-stroke aphasia. Brain communications, 8(5), fcag279. https://doi.org/10.1093/braincomms/fcag279

BibTeX

@article{falconer2026network,
author = {Falconer, Isaac and Varkanitsa, Maria and Billot, Anne and Kiran, Swathi},
title = {{Network flexibility facilitates treatment-induced recovery in post-stroke aphasia}},
journal = {Brain communications},
year = {2026},
month = sep,
volume = {8},
number = {5},
pages = {fcag279},
publisher = {Oxford University Press},
issn = {2632-1297},
doi = {10.1093/braincomms/fcag279},
url = {https://doi.org/10.1093/braincomms/fcag279},
pmid = {42761528},
pmcid = {PMC13586764}
}

RIS

TY - JOUR
AU - Falconer, Isaac
AU - Varkanitsa, Maria
AU - Billot, Anne
AU - Kiran, Swathi
TI - Network flexibility facilitates treatment-induced recovery in post-stroke aphasia
T2 - Brain communications
J2 - Brain Commun
PY - 2026
DA - 2026/09/18
VL - 8
IS - 5
SP - fcag279
SN - 2632-1297
PB - Oxford University Press
DO - 10.1093/braincomms/fcag279
UR - https://doi.org/10.1093/braincomms/fcag279
LA - en
ER -

CSL-JSON

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"id": "10.1093/braincomms/fcag279",
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"container-title-short": "Brain Commun",
"volume": "8",
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"page": "fcag279",
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"PMCID": "PMC13586764",
"ISSN": "2632-1297",
"publisher": "Oxford University Press",
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