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Cortical speech envelope tracking reflects lesion-symptom profiles in post-stroke aphasia.

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  1. [1] § Continuous speech comprehension EEG paradigm › Statistical analyses › LMER of behavioural and neural data ↔ Statistical Analyses Code.zip/LME_Behavioural_and_Decoding_Reconstruction_Accuracy.R, the whole file · a weak match · score 0.81 · decoding reconstruction accuracy, Linear mixed, subjective alertness, modelled, covariates, LMER
  2. [2] § Results › Behavioural results › EEG paradigm behavioural scores ↔ Statistical Analyses Code.zip/LME_Behavioural_and_Decoding_Reconstruction_Accuracy.R, the whole file · a weak match · score 0.68 · Linear mixed, behaviour matched, emmeans, stimulus matched, interaction, models

Paper

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The authors' code

R · 89 lines · 3.9 KB · no license · 2 matches

  1. ### Linear Mixed Effects Modelling Code for Behavioural and Decoding Data ###
  2. ### Data should be organised by stimulus-matched or behaviour-matched conditions ###
  4. ###### Load required packages ######
  5. library(dplyr) # Data manipulation
  6. library(lmerTest) # Linear mixed-effects models with significance testing
  7. library(lme4) # Linear mixed-effects models
  8. library(readxl) # Reading Excel files
  9. library(emmeans) # Estimated marginal means (post hoc comparisons)
  10. library(lattice) # Visualization of random effects
  11. library(effects) # Visualization of model effects
  14. ## Load the dataset from an Excel file ##
  15. df <- read_excel("path to df.xlsx")
  17. ###### Convert predictor variables and covariates to factors ######
  18. df$Group_code <- as.factor(df$Group) # Convert Group to a categorical variable
  19. df$Experiment_version <- as.factor(df$Experiment_version) # Convert Experiment Version to a categorical variable
  20. df$Speech_condition <- as.factor(df$Speech_condition) # Convert Speech Condition to a categorical variable
  21. df$Frequency_band <- as.factor(df$Frequency_band) # Convert Frequency Band to a categorical variable
  22. df$Participant <- as.factor(df$Participant) # Convert Participant ID to a categorical variable
  25. #### Fit a Linear Mixed Effects Model for Behavioural Data #######
  26. # Model formula:
  27. # - Dependent variable: d-prime (behavioral measure)
  28. # - Fixed effects: Group, Speech Condition, Age, Hearing Threshold, Subjective Alertness
  29. # - Random effects: Participant (to account for individual differences)
  30. Behavioural_mod <- lmer(`d-prime` ~ Group * Speech_condition + Age + Hearing_Threshold_Ave + Subjective_Alertness + (1|Participant), data = df)
  32. # Display model summary including estimates, standard errors, and p-values
  33. summary(Behavioural_mod)
  35. # Compute and plot effects of fixed variables
  36. eff <- allEffects(Behavioural_mod)
  37. plot(eff)
  39. # Compute R-squared values for the model
  40. Behavioural_mod_r2_values <- r.squaredGLMM(Behavioural_mod)
  42. # Extract fixed effects coefficients
  43. Behavioural_mod_fixed_effects <- summary(Behavioural_mod)$coefficients
  45. # Extract model residuals
  46. Behavioural_mod_res <- residuals(Behavioural_mod)
  48. # Q-Q plot for checking normality of residuals
  49. qqnorm(Behavioural_mod_res, main = "Q-Q Plot of Residuals for Behavioural Model")
  50. qqline(Behavioural_mod_res, col = "red")
  52. # Shapiro-Wilk test for normality of residuals
  53. shapiro.test(Behavioural_mod_res)
  56. ###### Fit a Linear Mixed Effects Model for Decoding Reconstruction Accuracy ######
  57. # Model formula:
  58. # - Dependent variable: Decoding data (reconstruction accuracy)
  59. # - Fixed effects: Group, Speech Condition, Frequency Band, Age, Hearing, Subjective Alertness
  60. # - Random effects: Participant and EEG Version (to account for individual differences and experiment variability)
  61. Decoding_mod <- lmer(Decoding_data ~ Group * Speech_condition * Frequency_band + Age + Hearing + Subjective_Alertness + (1|Participant) + (1|EEG_version), data = df)
  63. # Display model summary including estimates, standard errors, and p-values
  64. summary(Decoding_mod)
  66. # Compute and plot effects of fixed variables
  67. eff <- allEffects(Decoding_mod)
  68. plot(eff)
  70. # Conduct pairwise comparisons for Speech Condition x Frequency Band interaction
  71. # Conduct pairwise comparisons for Group variable
  72. emmeans(Decoding_mod, pairwise ~ Group_code)
  73. emmeans(Decoding_mod, pairwise ~ Speech_condition * Frequency_band)
  75. # Compute R-squared values for the model
  76. Decoding_mod_r2_values <- r.squaredGLMM(Decoding_mod)
  78. # Extract fixed effects coefficients
  79. Decoding_mod_fixed_effects <- summary(Decoding_mod)$coefficients
  81. # Extract model residuals
  82. Decoding_mod_res <- residuals(Decoding_mod)
  84. # Q-Q plot for checking normality of residuals
  85. qqnorm(Decoding_mod_res, main = "Q-Q Plot of Residuals for Decoding Model")
  86. qqline(Decoding_mod_res, col = "red")
  88. # Shapiro-Wilk test for normality of residuals
  89. shapiro.test(Decoding_mod_res)

LME_Behavioural_and_Decoding_Reconstruction_Accuracy.R, no license · at the source

Overview

Authors: Guangting Mai1,2, Emily Upton1, Timothy D Griffiths3,4, Alexander P Leff5,6, Jennifer T Crinion7, Gordon Mills1, Douglas Neville4, Storm Anderson4, Cathy J Price4, Ajay D Halai2, Martyn I Blairs2, Máté Aller2, Lucy J MacGregor2, Matthew H Davis2, Holly Robson1
ORCID iDs: Guangting Mai, Emily Upton, Timothy D Griffiths, Alexander P Leff, Jennifer T Crinion, Cathy J Price, Ajay D Halai, Máté Aller, Holly Robson
  1. Department of Language and Cognition, Psychology and Language Sciences, University College London, London WC1E 6AE, UK
  2. MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge CB2 7EF, UK
  3. Newcastle University Medical School, Newcastle University, Newcastle upon Tyne NE2 4HH, UK
  4. Department of Imaging Neuroscience, UCL Queen Square Institute of Neurology, University College London, London WC1E 6AE, UK
  5. Department of Translational Neuroscience and Stroke, UCL Queen Square Institute of Neurology, University College London, London WC1E 6AE, UK
  6. University College London Hospitals NHS Trust, London NW1 2BU, UK
  7. Institute of Cognitive Neuroscience, University College London, London WC1E 6AE, UK
Institutions: MRC Cognition and Brain Sciences Unit (United Kingdom); University of Cambridge (United Kingdom); University College London (United Kingdom); UCL Queen Square Institute of Neurology (United Kingdom); Newcastle University (United Kingdom); University College London Hospitals NHS Foundation Trust (United Kingdom)
Journal: Brain communications, volume 8, issue 4, article fcag261
Dates: received 31 July 2025; accepted 13 April 2026; published online 7 July 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1093/braincomms/fcag261 · PMID 42500552 · PMCID PMC13397126 · OpenAlex W4416705481
Open access: gold, a free copy (OpenAlex)
Preprint: doi:10.31234/osf.io/uqvtk_v1
Status: code verified
Categories: human (organism), stroke (population), cognitive (subfield)
Methods: Statistics, Preprocessing
Keywords: neural tracking, aphasia, speech comprehension, speech envelope, intelligibility
Topic: Neurobiology of Language and Bilingualism (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: Medical Research Council (MR/T028629/1); Medical Research Council (MC_UU_00030/6); National Institute for Health and Care Research (RP-2015-06-012); Wellcome Trust (224562/Z/21/Z); Medical Research Council Career Development Award (MR/V031481/1)
Citations: not cited yet (Europe PMC); 68 references in the paper

Abstract

Comprehending connected speech is critical for human interaction and is vulnerable in post-stroke aphasia. Understanding the neural mechanisms underlying impaired speech listening is necessary for accurate and effective assessment and treatment. Neural speech tracking methods offer a window into naturalistic speech processing and may reveal causal contributions to comprehension. EEG was recorded during story listening in 15 people with aphasia with left temporal lesions (temporal group), 14 people with aphasia with left frontal lesions (frontal group) and 15 age- and hearing-matched controls (control group). All participants listened to clear and unintelligible stories (∼12 min per condition). Controls additionally listened to low-intelligibility stories that equated comprehension success to the temporal group. Envelope tracking was measured at syllable- (theta) and multi-syllable- (delta) rates. Neural decoding and encoding analyses measured global (whole-brain) and local (sensor-wise) speech tracking, respectively. Group comparisons used linear mixed-effects regression. Linear and quadratic relationships assessed associations between neural tracking and behavioural measures of comprehension while accounting for covariates. Behavioural measures of comprehension showed the temporal group were significantly impaired compared to both frontal and control groups. Comparison of intelligible and unintelligible speech found that intelligibility affected delta but not theta tracking—suggesting that delta tracking is linked to higher-order linguistic processing and theta tracking reflects sensory responses. Theta and delta envelope decoding reflected group/comprehension status: tracking was reduced in the temporal group compared to the control group, but tracking was not significantly different when control comprehension was behaviour-matched via speech degradation. Delta encoding produced a similarly behaviour-linked pattern, but theta encoding was lesion—rather than behaviour-sensitive, in that reduced tracking was observed in temporo-parietal sensors in both aphasia groups. Theta tracking correlated with comprehension in a lesion-specific manner. Positive correlations between theta tracking and comprehension were found in the temporal group and negative correlations in the frontal and control groups. This produced a quadratic (inverted-U) relationship between theta tracking and comprehension success across all participants. The control-like, negative theta tracking–comprehension correlations in the frontal group are consistent with listening effort effects in which greater task difficulty result in greater tracking. In the temporal group, where comprehension was impaired, better envelope tracking at syllable rates may help build a stable representation of the speech stream supporting phonological and lexical analysis and comprehension. These results indicate that envelope tracking reflects a combination of lesion and symptom profiles and is a viable method for investigating the mechanisms of speech comprehension in aphasia.

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.

OSF hqs2e

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Size: 3 files
Software Heritage: not checked
Found in: “Data availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: emmeans (1 file), lme4 (1 file), lmerTest (1 file), tidyverse (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)
  • 27 September 2026: the link answers (HTTP 200)
2 files
At the source: osf.io/hqs2e/

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:

  • 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;
  • 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

Preprocessed EEG data (post ICA), restructured EEG, speech envelope and tracking results are available in the UCL data repository at https://doi.org/10.5522/04/31231285. Binary lesion maps and analysis codes are available in an Open Science Framework repository at https://osf.io/hqs2e/. Neuropsychological data are available in Supplementary Materials.

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, 15 authors, 5 keywords, 5 funders, 61 references.

Cite

This paper

Mai, G., Upton, E., Griffiths, T. D., Leff, A. P., Crinion, J. T., Mills, G., Neville, D., Anderson, S., Price, C. J., Halai, A. D., Blairs, M. I., Aller, M., MacGregor, L. J., Davis, M. H., & Robson, H. (2026). Cortical speech envelope tracking reflects lesion-symptom profiles in post-stroke aphasia. Brain communications, 8(4), fcag261. https://doi.org/10.1093/braincomms/fcag261

BibTeX

@article{mai2026cortical,
author = {Mai, Guangting and Upton, Emily and Griffiths, Timothy D and Leff, Alexander P and Crinion, Jennifer T and Mills, Gordon and Neville, Douglas and Anderson, Storm and Price, Cathy J and Halai, Ajay D and Blairs, Martyn I and Aller, Máté and MacGregor, Lucy J and Davis, Matthew H and Robson, Holly},
title = {{Cortical speech envelope tracking reflects lesion-symptom profiles in post-stroke aphasia}},
journal = {Brain communications},
year = {2026},
month = jul,
volume = {8},
number = {4},
pages = {fcag261},
publisher = {Oxford University Press},
issn = {2632-1297},
doi = {10.1093/braincomms/fcag261},
url = {https://doi.org/10.1093/braincomms/fcag261},
pmid = {42500552},
pmcid = {PMC13397126}
}

RIS

TY - JOUR
AU - Mai, Guangting
AU - Upton, Emily
AU - Griffiths, Timothy D
AU - Leff, Alexander P
AU - Crinion, Jennifer T
AU - Mills, Gordon
AU - Neville, Douglas
AU - Anderson, Storm
AU - Price, Cathy J
AU - Halai, Ajay D
AU - Blairs, Martyn I
AU - Aller, Máté
AU - MacGregor, Lucy J
AU - Davis, Matthew H
AU - Robson, Holly
TI - Cortical speech envelope tracking reflects lesion-symptom profiles in post-stroke aphasia
T2 - Brain communications
J2 - Brain Commun
PY - 2026
DA - 2026/07/07
VL - 8
IS - 4
SP - fcag261
SN - 2632-1297
PB - Oxford University Press
DO - 10.1093/braincomms/fcag261
UR - https://doi.org/10.1093/braincomms/fcag261
LA - en
ER -

CSL-JSON

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